@@ -50,12 +50,17 @@
-
-
- +
- Tensors +
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -87,7 +92,7 @@
-
-
+
@@ -102,10 +107,10 @@
- +--
mnist-cnn
+mnist-cnn
Source:vignettes/examples/mnist-cnn.Rmd@@ -115,73 +120,73 @@ -+dir <- "~/Downloads/mnist" - -ds <- mnist_dataset( - dir, - download = TRUE, - transform = function(x) { - x <- x$to(dtype = torch_float())/256 - x[newaxis,..] - } -) -dl <- dataloader(ds, batch_size = 32, shuffle = TRUE) - -net <- nn_module( - "Net", - initialize = function() { - self$conv1 <- nn_conv2d(1, 32, 3, 1) - self$conv2 <- nn_conv2d(32, 64, 3, 1) - self$dropout1 <- nn_dropout2d(0.25) - self$dropout2 <- nn_dropout2d(0.5) - self$fc1 <- nn_linear(9216, 128) - self$fc2 <- nn_linear(128, 10) - }, - forward = function(x) { - x <- self$conv1(x) - x <- nnf_relu(x) - x <- self$conv2(x) - x <- nnf_relu(x) - x <- nnf_max_pool2d(x, 2) - x <- self$dropout1(x) - x <- torch_flatten(x, start_dim = 1) - x <- self$fc1(x) - x <- nnf_relu(x) - x <- self$dropout2(x) - x <- self$fc2(x) - output <- nnf_log_softmax(x, dim=1) - output - } -) - -model <- net() -optimizer <- optim_sgd(model$parameters, lr = 0.01) - -epochs <- 10 - -for (epoch in 1:10) { - - pb <- progress::progress_bar$new( - total = length(dl), - format = "[:bar] :eta Loss: :loss" - ) - l <- c() - - for (b in enumerate(dl)) { - optimizer$zero_grad() - output <- model(b[[1]]) - loss <- nnf_nll_loss(output, b[[2]]) - loss$backward() - optimizer$step() - l <- c(l, loss$item()) - pb$tick(tokens = list(loss = mean(l))) - } - - cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l))) -}dir <- "~/Downloads/mnist" + +ds <- mnist_dataset( + dir, + download = TRUE, + transform = function(x) { + x <- x$to(dtype = torch_float())/256 + x[newaxis,..] + } +) +dl <- dataloader(ds, batch_size = 32, shuffle = TRUE) + +net <- nn_module( + "Net", + initialize = function() { + self$conv1 <- nn_conv2d(1, 32, 3, 1) + self$conv2 <- nn_conv2d(32, 64, 3, 1) + self$dropout1 <- nn_dropout2d(0.25) + self$dropout2 <- nn_dropout2d(0.5) + self$fc1 <- nn_linear(9216, 128) + self$fc2 <- nn_linear(128, 10) + }, + forward = function(x) { + x <- self$conv1(x) + x <- nnf_relu(x) + x <- self$conv2(x) + x <- nnf_relu(x) + x <- nnf_max_pool2d(x, 2) + x <- self$dropout1(x) + x <- torch_flatten(x, start_dim = 1) + x <- self$fc1(x) + x <- nnf_relu(x) + x <- self$dropout2(x) + x <- self$fc2(x) + output <- nnf_log_softmax(x, dim=1) + output + } +) + +model <- net() +optimizer <- optim_sgd(model$parameters, lr = 0.01) + +epochs <- 10 + +for (epoch in 1:10) { + + pb <- progress::progress_bar$new( + total = length(dl), + format = "[:bar] :eta Loss: :loss" + ) + l <- c() + + for (b in enumerate(dl)) { + optimizer$zero_grad() + output <- model(b[[1]]) + loss <- nnf_nll_loss(output, b[[2]]) + loss$backward() + optimizer$step() + l <- c(l, loss$item()) + pb$tick(tokens = list(loss = mean(l))) + } + + cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l))) +}
+diff --git a/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000000000000000000000000000000000000..ca349fd6a570108bde9d7daace534cd651c5f042 --- /dev/null +++ b/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/examples/mnist-mlp.html b/docs/articles/examples/mnist-mlp.html index ed0ec078074d1d490ec605ffd2c9a760cca445b1..eb229a6d03c6011a468021af85e7886444ba8eb2 100644 --- a/docs/articles/examples/mnist-mlp.html +++ b/docs/articles/examples/mnist-mlp.html @@ -6,19 +6,19 @@@@ -194,7 +199,7 @@mnist-mlp • torch - - - - + + + + + - - + - +@@ -50,12 +50,17 @@-
-
- +
- Tensors +
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -87,7 +92,7 @@
-
-
+
@@ -102,10 +107,10 @@
- +--
mnist-mlp
+mnist-mlp
Source:vignettes/examples/mnist-mlp.Rmd@@ -115,61 +120,61 @@ -+dir <- "~/Downloads/mnist" - -ds <- mnist_dataset( - dir, - download = TRUE, - transform = function(x) { - x$to(dtype = torch_float())/256 - } -) -dl <- dataloader(ds, batch_size = 32, shuffle = TRUE) - -net <- nn_module( - "Net", - initialize = function() { - self$fc1 <- nn_linear(784, 128) - self$fc2 <- nn_linear(128, 10) - }, - forward = function(x) { - x %>% - torch_flatten(start_dim = 1) %>% - self$fc1() %>% - nnf_relu() %>% - self$fc2() %>% - nnf_log_softmax(dim = 1) - } -) - -model <- net() -optimizer <- optim_sgd(model$parameters, lr = 0.01) - -epochs <- 10 - -for (epoch in 1:10) { - - pb <- progress::progress_bar$new( - total = length(dl), - format = "[:bar] :eta Loss: :loss" - ) - l <- c() - - for (b in enumerate(dl)) { - optimizer$zero_grad() - output <- model(b[[1]]) - loss <- nnf_nll_loss(output, b[[2]]) - loss$backward() - optimizer$step() - l <- c(l, loss$item()) - pb$tick(tokens = list(loss = mean(l))) - } - - cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l))) -}dir <- "~/Downloads/mnist" + +ds <- mnist_dataset( + dir, + download = TRUE, + transform = function(x) { + x$to(dtype = torch_float())/256 + } +) +dl <- dataloader(ds, batch_size = 32, shuffle = TRUE) + +net <- nn_module( + "Net", + initialize = function() { + self$fc1 <- nn_linear(784, 128) + self$fc2 <- nn_linear(128, 10) + }, + forward = function(x) { + x %>% + torch_flatten(start_dim = 1) %>% + self$fc1() %>% + nnf_relu() %>% + self$fc2() %>% + nnf_log_softmax(dim = 1) + } +) + +model <- net() +optimizer <- optim_sgd(model$parameters, lr = 0.01) + +epochs <- 10 + +for (epoch in 1:10) { + + pb <- progress::progress_bar$new( + total = length(dl), + format = "[:bar] :eta Loss: :loss" + ) + l <- c() + + for (b in enumerate(dl)) { + optimizer$zero_grad() + output <- model(b[[1]]) + loss <- nnf_nll_loss(output, b[[2]]) + loss$backward() + optimizer$step() + l <- c(l, loss$item()) + pb$tick(tokens = list(loss = mean(l))) + } + + cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l))) +}
+diff --git a/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000000000000000000000000000000000000..ca349fd6a570108bde9d7daace534cd651c5f042 --- /dev/null +++ b/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/extending-autograd.html b/docs/articles/extending-autograd.html index 1abf4882f71ce57b440c9d4d98e9fd547677376c..ddb8808ea265925839088ef930de0528d45729f8 100644 --- a/docs/articles/extending-autograd.html +++ b/docs/articles/extending-autograd.html @@ -6,19 +6,19 @@@@ -182,7 +187,7 @@Extending Autograd • torch - - - - + + + + + - - + - +@@ -50,12 +50,17 @@diff --git a/docs/articles/indexing.html b/docs/articles/indexing.html index 4bb3189e2b18b2180079f3a3bbf630f4d5e90ab5..6f0e5886ffc2c3f5142d3ed3c1207352ead56f0d 100644 --- a/docs/articles/indexing.html +++ b/docs/articles/indexing.html @@ -6,19 +6,19 @@-
-
- +
- Tensors +
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -87,7 +92,7 @@
-
-
+
@@ -102,10 +107,10 @@
- +@@ -167,7 +184,7 @@diff --git a/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000000000000000000000000000000000000..ca349fd6a570108bde9d7daace534cd651c5f042 --- /dev/null +++ b/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/index.html b/docs/articles/index.html index 0a435f7380313fa08c39eb045fd255a818fcafee..468ac9d9a87c1d1c292b0ca6eecfbe202c5d9ec8 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -53,7 +57,7 @@ - +- @@ -210,7 +209,7 @@-
Extending Autograd
+Extending Autograd
Source:vignettes/extending-autograd.Rmd@@ -115,7 +120,7 @@ -+library(torch)library(torch)
Adding operations to autograd requires implementing a new
autograd_functionfor each operation. Recall thatautograd_functionss are whatautograduses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:forward()- the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., withrequires_grad=TRUE) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list ofTensors if there are multiple outputs. Also, please refer to the docs ofautograd_functionto find descriptions of useful methods that can be called only fromforward().
@@ -135,71 +140,65 @@
Examples
Below you can find code for a linear function:
-+linear <- autograd_function( - forward = function(ctx, input, weight, bias = NULL) { - ctx$save_for_backward(input = input, weight = weight, bias = bias) - output <- input$mm(weight$t()) - if (!is.null(bias)) - output <- output + bias$unsqueeze(0)$expand_as(output) - - output - }, - backward = function(ctx, grad_output) { - - s <- ctx$saved_variables - - grads <- list( - input = NULL, - weight = NULL, - bias = NULL - ) - - if (ctx$needs_input_grad$input) - grads$input <- grad_output$mm(s$weight) - - if (ctx$needs_input_grad$weight) - grads$weight <- grad_output$t()$mm(s$input) - - if (!is.null(s$bias) && ctx$needs_input_grad$bias) - grads$bias <- grad_output$sum(dim = 0) - - grads - } -)linear <- autograd_function( + forward = function(ctx, input, weight, bias = NULL) { + ctx$save_for_backward(input = input, weight = weight, bias = bias) + output <- input$mm(weight$t()) + if (!is.null(bias)) + output <- output + bias$unsqueeze(0)$expand_as(output) + + output + }, + backward = function(ctx, grad_output) { + + s <- ctx$saved_variables + + grads <- list( + input = NULL, + weight = NULL, + bias = NULL + ) + + if (ctx$needs_input_grad$input) + grads$input <- grad_output$mm(s$weight) + + if (ctx$needs_input_grad$weight) + grads$weight <- grad_output$t()$mm(s$input) + + if (!is.null(s$bias) && ctx$needs_input_grad$bias) + grads$bias <- grad_output$sum(dim = 0) + + grads + } +)
Here, we give an additional example of a function that is parametrized by non-Tensor arguments:
--mul_constant <- autograd_function( - forward = function(ctx, tensor, constant) { - ctx$save_for_backward(constant = constant) - tensor * constant - }, - backward = function(ctx, grad_output) { - v <- ctx$saved_variables - list( - tensor = grad_output * v$constant - ) - } -)+x <- torch_tensor(1, requires_grad = TRUE) -o <- mul_constant(x, 2) -o$backward() -x$grad -#> torch_tensor -#> 2 -#> [ CPUFloatType{1} ]+mul_constant <- autograd_function( + forward = function(ctx, tensor, constant) { + ctx$save_for_backward(constant = constant) + tensor * constant + }, + backward = function(ctx, grad_output) { + v <- ctx$saved_variables + list( + tensor = grad_output * v$constant + ) + } +)
x <- torch_tensor(1, requires_grad = TRUE) +o <- mul_constant(x, 2) +o$backward() +x$grad +#> torch_tensor +#> 2 +#> [ CPUFloatType{1} ]
@@ -86,12 +90,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -123,7 +132,7 @@
-
-
+
@@ -148,14 +157,22 @@
All vignettes
--
-
- mnist-cnn -
- mnist-mlp -
- Extending Autograd -
- Indexing tensors -
- Creating tensors -
- Using autograd -
-
+
- mnist-cnn +
- +
- mnist-mlp +
- +
- Extending Autograd +
- +
- Indexing tensors +
- +
- Loading data +
- +
- Creating tensors +
- +
- Using autograd +
- +
Indexing tensors • torch - - - - + + + + + - - + - +@@ -50,12 +50,17 @@-
-
- +
- Tensors +
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -87,7 +92,7 @@
-
-
+
@@ -102,10 +107,10 @@
- +-diff --git a/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000000000000000000000000000000000000..ca349fd6a570108bde9d7daace534cd651c5f042 --- /dev/null +++ b/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/loading-data.html b/docs/articles/loading-data.html new file mode 100644 index 0000000000000000000000000000000000000000..3654c05851645b8c3b0ddcbdb854f386fb19f09a --- /dev/null +++ b/docs/articles/loading-data.html @@ -0,0 +1,335 @@ + + + + + + + +
Indexing tensors
+Indexing tensors
Source:vignettes/indexing.Rmd@@ -115,188 +120,177 @@ -+library(torch)library(torch)
In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.
Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the
numpyindexing article available here.Single element indexing
-Single element indexing for a 1-D tensors is what one expects. It is 1-based, and, unlike R, accepts negative indices for indexing from the end of the array (In R, negative indexes are used to remove elements).
-+x <- torch_tensor(1:10) -x[1] -#> torch_tensor -#> 1 -#> [ CPUIntType{} ] -x[-1] -#> torch_tensor -#> 10 -#> [ CPUIntType{} ]Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)
+x <- torch_tensor(1:10) +x[1] +#> torch_tensor +#> 1 +#> [ CPUIntType{} ] +x[-1] +#> torch_tensor +#> 10 +#> [ CPUIntType{} ]
You can also subset matrices and higher dimensions arrays using the same syntax:
--x <- x$reshape(shape = c(2,5)) -x -#> torch_tensor -#> 1 2 3 4 5 -#> 6 7 8 9 10 -#> [ CPUIntType{2,5} ] -x[1,3] -#> torch_tensor -#> 3 -#> [ CPUIntType{} ] -x[1,-1] -#> torch_tensor -#> 5 -#> [ CPUIntType{} ]Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike R, that would flatten the array. For example:
- ++x <- x$reshape(shape = c(2,5)) +x +#> torch_tensor +#> 1 2 3 4 5 +#> 6 7 8 9 10 +#> [ CPUIntType{2,5} ] +x[1,3] +#> torch_tensor +#> 3 +#> [ CPUIntType{} ] +x[1,-1] +#> torch_tensor +#> 5 +#> [ CPUIntType{} ]
Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:
+x[1] +#> Error: incorrect number of dimensions
Slicing and striding
-It is possible to slice and stride arrays to extract arrays of the same number of dimensions, but of different sizes than the original. A few examples illustrates best:
--x <- torch_tensor(1:10) -x -#> torch_tensor -#> 1 -#> 2 -#> 3 -#> 4 -#> 5 -#> 6 -#> 7 -#> 8 -#> 9 -#> 10 -#> [ CPUIntType{10} ] -x[2:5] -#> torch_tensor -#> 2 -#> 3 -#> 4 -#> 5 -#> [ CPUIntType{4} ] -x[1:(-7)] -#> torch_tensor -#> 1 -#> 2 -#> 3 -#> [ CPUIntType{3} ]You can also use the
- +1:10:2syntax which means 1 to 10 by 2. For example:It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:
++x <- torch_tensor(1:10) +x +#> torch_tensor +#> 1 +#> 2 +#> 3 +#> 4 +#> 5 +#> 6 +#> 7 +#> 8 +#> 9 +#> 10 +#> [ CPUIntType{10} ] +x[2:5] +#> torch_tensor +#> 2 +#> 3 +#> 4 +#> 5 +#> [ CPUIntType{4} ] +x[1:(-7)] +#> torch_tensor +#> 1 +#> 2 +#> 3 +#> [ CPUIntType{3} ]
You can also use the
+1:10:2syntax which means: In the range from 1 to 10, take every second item. For example:x[1:5:2] +#> torch_tensor +#> 1 +#> 3 +#> 5 +#> [ CPUIntType{3} ]
Another special syntax is the
- +N, meaning the size of the specified dimension.x[5:N] +#> torch_tensor +#> 5 +#> 6 +#> 7 +#> 8 +#> 9 +#> 10 +#> [ CPUIntType{6} ]
-Getting the complete dimension
-Like in R, you can skip indexing dimension by leaving an empty index for that dimension.
-Considering a matrix:
-+x <- torch_randn(2, 3) -x -#> torch_tensor -#> 0.1022 0.5167 -0.7508 -#> 1.1705 -0.2243 0.3953 -#> [ CPUFloatType{2,3} ]Like in R, you can take all elements in a dimension by leaving an index empty.
+Consider a matrix:
+x <- torch_randn(2, 3) +x +#> torch_tensor +#> -0.3880 -0.1607 -0.5363 +#> -0.2395 0.2673 -1.7572 +#> [ CPUFloatType{2,3} ]
The following syntax will give you the first row:
- +x[1,] +#> torch_tensor +#> -0.3880 +#> -0.1607 +#> -0.5363 +#> [ CPUFloatType{3} ]
And this would give you the first 2 columns:
- +x[,1:2] +#> torch_tensor +#> -0.3880 -0.1607 +#> -0.2395 0.2673 +#> [ CPUFloatType{2,2} ]
+--Droping dimensions
+Dropping dimensionsBy default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:
-+x <- torch_randn(2, 3) -x[1,]$shape -#> [1] 3x <- torch_randn(2, 3) +x[1,]$shape +#> [1] 3
You can optionally use the
- +drop = FALSEargument to avoid dropping the dimension.x[1,,drop = FALSE]$shape +#> [1] 1 3
Adding a new dimension
-It’s possible to add a new dimension to a tensor using an index like syntax:
-+x <- torch_tensor(c(10)) -x$shape -#> [1] 1 -x[, newaxis]$shape -#> [1] 1 1 -x[, newaxis, newaxis]$shape -#> [1] 1 1 1It’s possible to add a new dimension to a tensor using index-like syntax:
+x <- torch_tensor(c(10)) +x$shape +#> [1] 1 +x[, newaxis]$shape +#> [1] 1 1 +x[, newaxis, newaxis]$shape +#> [1] 1 1 1
You can also use
- +NULLinstead ofnewaxis:x[,NULL]$shape +#> [1] 1 1
Dealing with variable number of indices
-The index syntax is very powerful but limiting when dealing with a variable number of indices. For example, if you want to write a function that can handle arguments with various numbers of dimensions without having to write special case code for each number of possible dimensions, how can that be done? If one supplies to the index a tuple, the tuple will be interpreted as a list of indices. For example:
- -If you want to always subset the first or last dimension you can use the
-..:-z[1,..] -#> torch_tensor -#> 1 2 3 4 5 -#> 6 7 8 9 10 -#> 11 12 13 14 15 -#> 16 17 18 19 20 -#> 21 22 23 24 25 -#> [ CPUIntType{5,5} ] -z[..,1] -#> torch_tensor -#> 1 6 11 16 21 -#> 26 31 36 41 46 -#> 51 56 61 66 71 -#> 76 81 86 91 96 -#> 101 106 111 116 121 -#> [ CPUIntType{5,5} ]You can also use the unsplice operator here, for example:
-+i <- c(5,5,5) -z[!!!i] -#> torch_tensor -#> 125 -#> [ CPUIntType{} ]Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use
+..:+z <- torch_tensor(1:125)$reshape(c(5,5,5)) +z[1,..] +#> torch_tensor +#> 1 2 3 4 5 +#> 6 7 8 9 10 +#> 11 12 13 14 15 +#> 16 17 18 19 20 +#> 21 22 23 24 25 +#> [ CPUIntType{5,5} ] +z[..,1] +#> torch_tensor +#> 1 6 11 16 21 +#> 26 31 36 41 46 +#> 51 56 61 66 71 +#> 76 81 86 91 96 +#> 101 106 111 116 121 +#> [ CPUIntType{5,5} ]
If an index is stored in a variable, it can be convenient to use the unsplice operator, like so:
+i <- c(5,5,5) +z[!!!i] +#> torch_tensor +#> 125 +#> [ CPUIntType{} ]
More complex indexing should make use of
torch_selectortorch_slice.+@@ -307,7 +301,7 @@- -Loading data • torch + + + + + + + + + + +++ + + + + + diff --git a/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000000000000000000000000000000000000..ca349fd6a570108bde9d7daace534cd651c5f042 --- /dev/null +++ b/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/tensor-creation.html b/docs/articles/tensor-creation.html index f1e5578e232829ed93ae3fd24f211c3844003ac1..8c1bfa3d188402fdd7bf13f478fbcb7c42fea48d 100644 --- a/docs/articles/tensor-creation.html +++ b/docs/articles/tensor-creation.html @@ -6,19 +6,19 @@++ + + + +++ ++ + + torch + 0.0.1.9000 + ++ +++ +-
+
- + + + + + +
-
+
+ Articles
+
+
+
+
-
+
- Tensors +
- + Creating tensors + +
- + Indexing + +
- Datasets +
- + Loading Data + +
- Autograd +
- + Using autograd + +
- + Extending autograd + +
+ - + + Examples + + + + + +
- + Reference + +
-
+
- + + + + + +
++ + + + ++ + + + ++ ++library(torch)
+++Datasets and data loaders
+Central to data ingestion and preprocessing are datasets and data loaders.
+
+torchcomes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g.,mnist_dataset()), which can be iterated over by means ofdataloaders:+# ... +ds <- mnist_dataset( + dir, + download = TRUE, + transform = function(x) { + x <- x$to(dtype = torch_float())/256 + x[newaxis,..] + } +) + +dl <- dataloader(ds, batch_size = 32, shuffle = TRUE) + +for (b in enumerate(dl)) { + # ...Cf.
+vignettes/examples/mnist-cnn.Rfor a complete example.What if you want to train on a different dataset? In these cases, you subclass
+Dataset, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement.getitem(), and say what should be returned when the data loader is asking for the next batch.In
+.getitem(), you can implement whatever preprocessing you require. Additionally, you should implement.length(), so users can find out how many items there are in the dataset.While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own
+datasetto train on Allison Horst's penguins.+++A custom dataset
++library(palmerpenguins) +library(magrittr) + +penguins +#> # A tibble: 344 x 7 +#> species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g +#> <fct> <fct> <dbl> <dbl> <int> <int> +#> 1 Adelie Torge… 39.1 18.7 181 3750 +#> 2 Adelie Torge… 39.5 17.4 186 3800 +#> 3 Adelie Torge… 40.3 18 195 3250 +#> 4 Adelie Torge… NA NA NA NA +#> 5 Adelie Torge… 36.7 19.3 193 3450 +#> 6 Adelie Torge… 39.3 20.6 190 3650 +#> 7 Adelie Torge… 38.9 17.8 181 3625 +#> 8 Adelie Torge… 39.2 19.6 195 4675 +#> 9 Adelie Torge… 34.1 18.1 193 3475 +#> 10 Adelie Torge… 42 20.2 190 4250 +#> # … with 334 more rows, and 1 more variable: sex <fct>
+Datasets are R6 classes created using thedataset()constructor. You can pass anameand various member functions. Among those should beinitialize(), to create instance variables,.getitem(), to indicate how the data should be returned, and.length(), to say how many items we have.In addition, any number of helper functions can be defined.
+Here, we assume the
+penguinshave already been loaded, and all preprocessing consists in removing lines withNAvalues, transformingfactors to numbers starting from 0, and converting from R data types totorchtensors.In
+.getitem, we essentially decide how this data is going to be used: All variables besidesspeciesgo intox, the predictor, andspecieswill constitutey, the target. Predictor and target are returned in a list, to be accessed asbatch[[1]]andbatch[[2]]during training.+penguins_dataset <- dataset( + + name = "penguins_dataset", + + initialize = function() { + self$data <- self$prepare_penguin_data() + }, + + .getitem = function(index) { + + # tbd replace following https://github.com/mlverse/torch/issues/135 + x <- self$data[index, 2:N] + y <- self$data[index, 1]$to(torch_long()) + + list(x, y) + }, + + .length = function() { + self$data$size()[[1]] + }, + + prepare_penguin_data = function() { + + input <- na.omit(penguins) + # conveniently, the categorical data are already factors + input$species <- as.numeric(input$species) - 1 + input$island <- as.numeric(input$island) - 1 + input$sex <- as.numeric(input$sex) - 1 + + input <- as.matrix(input) + torch_tensor(input) + } +)
Let’s create the dataset , query for it’s length, and look at its first item:
++tuxes <- penguins_dataset() +tuxes$.length() +#> [1] 333 +tuxes$.getitem(1) +#> [[1]] +#> torch_tensor +#> 2.0000 +#> 39.1000 +#> 18.7000 +#> 181.0000 +#> 3750.0000 +#> 1.0000 +#> [ CPUFloatType{6} ] +#> +#> [[2]] +#> torch_tensor +#> 0 +#> [ CPULongType{} ]
To be able to iterate over
+tuxes, we need a data loader (we override the default batch size of1):+dl <-tuxes %>% dataloader(batch_size = 8)
Calling
+.length()on a data loader (as opposed to a dataset) will return the number ofbatcheswe have:+dl$.length() +#> [1] 42
And we can create an iterator to inspect the first batch:
++iter <- dl$.iter() +b <- iter$.next() +b +#> [[1]] +#> torch_tensor +#> 2.0000 39.1000 18.7000 181.0000 3750.0000 1.0000 +#> 2.0000 39.5000 17.4000 186.0000 3800.0000 0.0000 +#> 2.0000 40.3000 18.0000 195.0000 3250.0000 0.0000 +#> 2.0000 36.7000 19.3000 193.0000 3450.0000 0.0000 +#> 2.0000 39.3000 20.6000 190.0000 3650.0000 1.0000 +#> 2.0000 38.9000 17.8000 181.0000 3625.0000 0.0000 +#> 2.0000 39.2000 19.6000 195.0000 4675.0000 1.0000 +#> 2.0000 41.1000 17.6000 182.0000 3200.0000 0.0000 +#> [ CPUFloatType{8,6} ] +#> +#> [[2]] +#> torch_tensor +#> 0 +#> 0 +#> 0 +#> 0 +#> 0 +#> 0 +#> 0 +#> 0 +#> [ CPULongType{8} ]
To train a network, we can use
+enumerateto iterate over batches.+++Training with data loaders
+Our example network is very simple. (In reality, we would want to treat
+islandas the categorical variable it is, and either one-hot-encode or embed it.)+net <- nn_module( + "PenguinNet", + initialize = function() { + self$fc1 <- nn_linear(6, 32) + self$fc2 <- nn_linear(32, 3) + }, + forward = function(x) { + x %>% + self$fc1() %>% + nnf_relu() %>% + self$fc2() %>% + nnf_log_softmax(dim = 1) + } +) + +model <- net()
We still need an optimizer:
++optimizer <- optim_sgd(model$parameters, lr = 0.01)
And we’re ready to train:
++for (epoch in 1:10) { + + l <- c() + + for (b in enumerate(dl)) { + optimizer$zero_grad() + output <- model(b[[1]]) + loss <- nnf_nll_loss(output, b[[2]]) + loss$backward() + optimizer$step() + l <- c(l, loss$item()) + } + + cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l))) +} +#> Loss at epoch 1: 15131.174536 +#> Loss at epoch 2: 1.096939 +#> Loss at epoch 3: 1.089476 +#> Loss at epoch 4: 1.083687 +#> Loss at epoch 5: 1.079173 +#> Loss at epoch 6: 1.075634 +#> Loss at epoch 7: 1.072847 +#> Loss at epoch 8: 1.070639 +#> Loss at epoch 9: 1.068882 +#> Loss at epoch 10: 1.067477
+ + ++ +Creating tensors • torch - - - - + + + + + - - + - +@@ -50,12 +50,17 @@-
-
- +
- Tensors +
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -87,7 +92,7 @@
-
-
+
@@ -102,10 +107,10 @@
- +-diff --git a/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000000000000000000000000000000000000..ca349fd6a570108bde9d7daace534cd651c5f042 --- /dev/null +++ b/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/using-autograd.html b/docs/articles/using-autograd.html index bcbdb802ad89119bb69e5e380973f07d1d8716dd..1e9c881322094b59537a08c3cde1203e33cc087d 100644 --- a/docs/articles/using-autograd.html +++ b/docs/articles/using-autograd.html @@ -6,19 +6,19 @@
Creating tensors
+Creating tensors
Source:vignettes/tensor-creation.Rmd@@ -115,78 +120,80 @@ --library(torch)In this article we describe all the possibilities available to create
+torchtensors in R.+library(torch)
In this article we describe various ways of creating
torchtensors in R.From R objects
-You can create tensors from R objects using the
+torch_tensorfunction. Thetorch_tensorfunction takes R a vector, matrix or array and creates atorch_tensorwith the data.You can create tensors from R objects using the
torch_tensorfunction. Thetorch_tensorfunction takes an R vector, matrix or array and creates an equivalenttorch_tensor.You can see a few examples below:
--torch_tensor(c(1,2,3)) -#> torch_tensor -#> 1 -#> 2 -#> 3 -#> [ CPUFloatType{3} ] -torch_tensor(matrix(1:10, ncol = 5, nrow = 2)) -#> torch_tensor -#> 1 3 5 7 9 -#> 2 4 6 8 10 -#> [ CPUIntType{2,5} ] -torch_tensor(array(runif(12), dim = c(2, 2, 3))) -#> torch_tensor -#> (1,.,.) = -#> 0.7745 0.5400 0.8119 -#> 0.4902 0.1951 0.8365 -#> -#> (2,.,.) = -#> 0.4471 0.0986 0.9734 -#> 0.1990 0.2491 0.6471 -#> [ CPUFloatType{2,2,3} ]Note that by default we will create tensors in the
+cpudevice and that we will convert their numeric to the corresponding torchdtype.+torch_tensor(c(1,2,3)) +#> torch_tensor +#> 1 +#> 2 +#> 3 +#> [ CPUFloatType{3} ] + +# conform to row-major indexing used in torch +torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE)) +#> torch_tensor +#> 1 2 3 4 5 +#> 6 7 8 9 10 +#> [ CPUIntType{2,5} ] +torch_tensor(array(runif(12), dim = c(2, 2, 3))) +#> torch_tensor +#> (1,.,.) = +#> 0.2386 0.2433 0.5574 +#> 0.6517 0.7180 0.2243 +#> +#> (2,.,.) = +#> 0.6333 0.6377 0.7834 +#> 0.7099 0.6501 0.2625 +#> [ CPUFloatType{2,2,3} ]
By default, we will create tensors in the
cpudevice, converting their R datatype to the corresponding torchdtype.
-Note currently, only numeric and boolean types are supported.
You can always modify the dtype and the device when converting an R object to a torch tensor. For example:
-+torch_tensor(1, dtype = torch_long()) -#> torch_tensor -#> 1 -#> [ CPULongType{1} ] -torch_tensor(1, device = torch_device("cpu"), dtype = torch_float64()) -#> torch_tensor -#> 1 -#> [ CPUDoubleType{1} ]You can always modify
+dtypeanddevicewhen converting an R object to a torch tensor. For example:torch_tensor(1, dtype = torch_long()) +#> torch_tensor +#> 1 +#> [ CPULongType{1} ] +torch_tensor(1, device = "cpu", dtype = torch_float64()) +#> torch_tensor +#> 1 +#> [ CPUDoubleType{1} ]
Other options available when creating a tensor are:
-
requires_grad: boolean indicating if you wantautogradto record operations on them for automatic differentiation. -
-
pin_memmory: – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors.
+
pin_memory: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.
-Using creation functions
-You can also use the
-torch_*functionslisted below to create torch tensors using some algorithm.For example, the
-torch_randnfunction will create tensors using the normal distribution with mean equals to 0 and standard deviation equals 1. You can use the...to pass the size of the dimensions, ie, the code below will create a normally distributed tensor with shape 5x3.-x <- torch_randn(5, 3) -x -#> torch_tensor -#> 1.3062 0.3865 0.5905 -#> 0.2367 -0.1128 -0.0492 -#> 0.6891 1.3700 -0.2468 -#> -0.7459 0.0574 -0.9745 -#> 0.6810 0.2206 0.7651 -#> [ CPUFloatType{5,3} ]Another example is the
-torch_onesfunction, which creates a tensor filled with ones.-x <- torch_ones(2, 4, dtype = torch_int64(), device = "cpu") -x -#> torch_tensor -#> 1 1 1 1 -#> 1 1 1 1 -#> [ CPULongType{2,4} ]Here is the full list of functions that can be used to create tensors in torch:
+You can also use the
+torch_*functions listed below to create torch tensors using some algorithm.For example, the
+torch_randnfunction will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the...argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.+x <- torch_randn(5, 3) +x +#> torch_tensor +#> -0.8401 -1.7399 -0.6563 +#> 0.7008 -1.0224 -1.3219 +#> 0.6671 -0.7899 0.3788 +#> 0.1183 0.5231 -1.8783 +#> -0.2334 -0.2402 0.3168 +#> [ CPUFloatType{5,3} ]
Another example is
+torch_ones, which creates a tensor filled with ones.+x <- torch_ones(2, 4, dtype = torch_int64(), device = "cpu") +x +#> torch_tensor +#> 1 1 1 1 +#> 1 1 1 1 +#> [ CPULongType{2,4} ]
Here is the full list of functions that can be used to bulk-create tensors in torch:
-
torch_arange: Returns a tensor with a sequence of integers,
@@ -214,38 +221,31 @@
torch_zeros: Returns a tensor filled with all zeros.+- @@ -256,7 +256,7 @@-Convertion
-Once a tensor exists you can convert it between dtypes and move to different devices with to
-tomethod. For example:-x <- torch_tensor(1) -y <- x$to(dtype = torch_int32()) -x -#> torch_tensor -#> 1 -#> [ CPUFloatType{1} ] -y -#> torch_tensor -#> 1 -#> [ CPUIntType{1} ]You can also copy a tensor the GPU using:
-+Conversion +x <- torch_tensor(1) -y <- x$to(torch_device("cuda"))Once a tensor exists you can convert between
+dtypes and move to a different device withtomethod. For example:+x <- torch_tensor(1) +y <- x$to(dtype = torch_int32()) +x +#> torch_tensor +#> 1 +#> [ CPUFloatType{1} ] +y +#> torch_tensor +#> 1 +#> [ CPUIntType{1} ]
You can also copy a tensor to the GPU using:
+x <- torch_tensor(1) +y <- x$cuda())
Using autograd • torch - - - - + + + + + - - + - +@@ -50,12 +50,17 @@-
-
- +
- Tensors +
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -87,7 +92,7 @@
-
-
+
@@ -102,10 +107,10 @@
- +diff --git a/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000000000000000000000000000000000000..ca349fd6a570108bde9d7daace534cd651c5f042 --- /dev/null +++ b/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/authors.html b/docs/authors.html index 8d0b2677a6a7a1feb66ea29b18fc542cedb6b9fb..bdee20dfde45e546d873a287052305ec4f999576 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -53,7 +57,7 @@ - +-- @@ -306,7 +305,7 @@
Using autograd
+Using autograd
Source:vignettes/using-autograd.Rmd@@ -115,7 +120,7 @@ -+library(torch)library(torch)
So far, all we’ve been using from torch is tensors, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.
Before we see that in action, let’s get some more background.
@@ -123,158 +128,158 @@ Automatic differentiation with autogradTorch uses a module called autograd to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and propagating gradients back through the network. This is a form of reverse mode automatic differentiation.
As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with
-requires_grad = TRUE. E.g.+x <- torch_ones(2,2, requires_grad = TRUE)x <- torch_ones(2,2, requires_grad = TRUE)
To be clear, this is a tensor with respect to which gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data 1. If we now perform some operation on that tensor, assigning the result to
-y+y <- x$mean()y <- x$mean()
we find that
- +ynow has a non-emptygrad_fnthat tells torch how to compute the gradient ofywith respect tox:y$grad_fn +#> MeanBackward0
Actual computation of gradients is triggered by calling
- +backward()on the output tensor.y$backward()
That executed,
- +xnow has a non-empty fieldgradthat stores the gradient ofywith respect tox:x$grad +#> torch_tensor +#> 0.2500 0.2500 +#> 0.2500 0.2500 +#> [ CPUFloatType{2,2} ]
With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.
Here is a slightly more complex example. We call
-retain_grad()onyandzjust for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.+x1 <- torch_ones(2,2, requires_grad = TRUE) -x2 <- torch_tensor(1.1, requires_grad = TRUE) -y <- x1 * (x2 + 2) -y$retain_grad() -z <- y$pow(2) * 3 -z$retain_grad() -out <- z$mean()x1 <- torch_ones(2,2, requires_grad = TRUE) +x2 <- torch_tensor(1.1, requires_grad = TRUE) +y <- x1 * (x2 + 2) +y$retain_grad() +z <- y$pow(2) * 3 +z$retain_grad() +out <- z$mean()
Starting from
-out$grad_fn, we can follow the graph all back to the leaf nodes:+# how to compute the gradient for mean, the last operation executed -out$grad_fn -#> MeanBackward0 -# how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3 -out$grad_fn$next_functions -#> [[1]] -#> MulBackward1 -# how to compute the gradient for pow in z = y.pow(2) * 3 -out$grad_fn$next_functions[[1]]$next_functions -#> [[1]] -#> PowBackward0 -# how to compute the gradient for the multiplication in y = x * (x + 2) -out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions -#> [[1]] -#> MulBackward0 -# how to compute the gradient for the two branches of y = x * (x + 2), -# where the left branch is a leaf node (AccumulateGrad for x1) -out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions -#> [[1]] -#> torch::autograd::AccumulateGrad -#> [[2]] -#> AddBackward1 -# here we arrive at the other leaf node (AccumulateGrad for x2) -out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions -#> [[1]] -#> torch::autograd::AccumulateGrad# how to compute the gradient for mean, the last operation executed +out$grad_fn +#> MeanBackward0 +# how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3 +out$grad_fn$next_functions +#> [[1]] +#> MulBackward1 +# how to compute the gradient for pow in z = y.pow(2) * 3 +out$grad_fn$next_functions[[1]]$next_functions +#> [[1]] +#> PowBackward0 +# how to compute the gradient for the multiplication in y = x * (x + 2) +out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions +#> [[1]] +#> MulBackward0 +# how to compute the gradient for the two branches of y = x * (x + 2), +# where the left branch is a leaf node (AccumulateGrad for x1) +out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions +#> [[1]] +#> torch::autograd::AccumulateGrad +#> [[2]] +#> AddBackward1 +# here we arrive at the other leaf node (AccumulateGrad for x2) +out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions +#> [[1]] +#> torch::autograd::AccumulateGrad
After calling
-out$backward(), all tensors in the graph will have their respective gradients created. Without our calls toretain_gradabove,z$gradandy$gradwould be empty:+out$backward() -z$grad -#> torch_tensor -#> 0.2500 0.2500 -#> 0.2500 0.2500 -#> [ CPUFloatType{2,2} ] -y$grad -#> torch_tensor -#> 4.6500 4.6500 -#> 4.6500 4.6500 -#> [ CPUFloatType{2,2} ] -x2$grad -#> torch_tensor -#> 18.6000 -#> [ CPUFloatType{1} ] -x1$grad -#> torch_tensor -#> 14.4150 14.4150 -#> 14.4150 14.4150 -#> [ CPUFloatType{2,2} ]out$backward() +z$grad +#> torch_tensor +#> 0.2500 0.2500 +#> 0.2500 0.2500 +#> [ CPUFloatType{2,2} ] +y$grad +#> torch_tensor +#> 4.6500 4.6500 +#> 4.6500 4.6500 +#> [ CPUFloatType{2,2} ] +x2$grad +#> torch_tensor +#> 18.6000 +#> [ CPUFloatType{1} ] +x1$grad +#> torch_tensor +#> 14.4150 14.4150 +#> 14.4150 14.4150 +#> [ CPUFloatType{2,2} ]
Thus acquainted with autograd, we’re ready to modify our example.
The simple network, now using autograd
For a single new line calling
-loss$backward(), now a number of lines (that did manual backprop) are gone:+### generate training data ----------------------------------------------------- -# input dimensionality (number of input features) -d_in <- 3 -# output dimensionality (number of predicted features) -d_out <- 1 -# number of observations in training set -n <- 100 -# create random data -x <- torch_randn(n, d_in) -y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n) -y <- y$unsqueeze(dim = 1) -### initialize weights --------------------------------------------------------- -# dimensionality of hidden layer -d_hidden <- 32 -# weights connecting input to hidden layer -w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE) -# weights connecting hidden to output layer -w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE) -# hidden layer bias -b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE) -# output layer bias -b2 <- torch_zeros(1, d_out,requires_grad = TRUE) -### network parameters --------------------------------------------------------- -learning_rate <- 1e-4 -### training loop -------------------------------------------------------------- -for (t in 1:200) { - - ### -------- Forward pass -------- - y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2) - ### -------- compute loss -------- - loss <- (y_pred - y)$pow(2)$mean() - if (t %% 10 == 0) cat(t, as_array(loss), "\n") - ### -------- Backpropagation -------- - # compute the gradient of loss with respect to all tensors with requires_grad = True. - loss$backward() - - ### -------- Update weights -------- - - # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation - with_no_grad({ - - w1$sub_(learning_rate * w1$grad) - w2$sub_(learning_rate * w2$grad) - b1$sub_(learning_rate * b1$grad) - b2$sub_(learning_rate * b2$grad) - - # Zero the gradients after every pass, because they'd accumulate otherwise - w1$grad$zero_() - w2$grad$zero_() - b1$grad$zero_() - b2$grad$zero_() - - }) - -} -#> 10 56.10756 -#> 20 50.51255 -#> 30 45.60201 -#> 40 41.28008 -#> 50 37.46877 -#> 60 34.10844 -#> 70 31.12374 -#> 80 28.47405 -#> 90 26.11216 -#> 100 24.00321 -#> 110 22.12399 -#> 120 20.43928 -#> 130 18.92206 -#> 140 17.55327 -#> 150 16.31589 -#> 160 15.19459 -#> 170 14.17743 -#> 180 13.25329 -#> 190 12.41257 -#> 200 11.64563### generate training data ----------------------------------------------------- +# input dimensionality (number of input features) +d_in <- 3 +# output dimensionality (number of predicted features) +d_out <- 1 +# number of observations in training set +n <- 100 +# create random data +x <- torch_randn(n, d_in) +y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n) +y <- y$unsqueeze(dim = 1) +### initialize weights --------------------------------------------------------- +# dimensionality of hidden layer +d_hidden <- 32 +# weights connecting input to hidden layer +w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE) +# weights connecting hidden to output layer +w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE) +# hidden layer bias +b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE) +# output layer bias +b2 <- torch_zeros(1, d_out,requires_grad = TRUE) +### network parameters --------------------------------------------------------- +learning_rate <- 1e-4 +### training loop -------------------------------------------------------------- +for (t in 1:200) { + + ### -------- Forward pass -------- + y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2) + ### -------- compute loss -------- + loss <- (y_pred - y)$pow(2)$mean() + if (t %% 10 == 0) cat(t, as_array(loss), "\n") + ### -------- Backpropagation -------- + # compute the gradient of loss with respect to all tensors with requires_grad = True. + loss$backward() + + ### -------- Update weights -------- + + # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation + with_no_grad({ + + w1$sub_(learning_rate * w1$grad) + w2$sub_(learning_rate * w2$grad) + b1$sub_(learning_rate * b1$grad) + b2$sub_(learning_rate * b2$grad) + + # Zero the gradients after every pass, because they'd accumulate otherwise + w1$grad$zero_() + w2$grad$zero_() + b1$grad$zero_() + b2$grad$zero_() + + }) + +} +#> 10 40.47609 +#> 20 37.57487 +#> 30 34.97118 +#> 40 32.6471 +#> 50 30.5681 +#> 60 28.69917 +#> 70 27.01172 +#> 80 25.48003 +#> 90 24.0868 +#> 100 22.81834 +#> 110 21.65865 +#> 120 20.59585 +#> 130 19.6186 +#> 140 18.71652 +#> 150 17.88323 +#> 160 17.11178 +#> 170 16.39673 +#> 180 15.73189 +#> 190 15.11256 +#> 200 14.53398
We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.
@@ -285,17 +290,11 @@@@ -86,12 +90,17 @@diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css new file mode 100644 index 0000000000000000000000000000000000000000..5a859415c1f7eacfd94920968bc910e2f1f1427e --- /dev/null +++ b/docs/bootstrap-toc.css @@ -0,0 +1,60 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ + +/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ + +/* All levels of nav */ +nav[data-toggle='toc'] .nav > li > a { + display: block; + padding: 4px 20px; + font-size: 13px; + font-weight: 500; + color: #767676; +} +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 19px; + color: #563d7c; + text-decoration: none; + background-color: transparent; + border-left: 1px solid #563d7c; +} +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 18px; + font-weight: bold; + color: #563d7c; + background-color: transparent; + border-left: 2px solid #563d7c; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} +nav[data-toggle='toc'] .nav .nav > li > a { + padding-top: 1px; + padding-bottom: 1px; + padding-left: 30px; + font-size: 12px; + font-weight: normal; +} +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 29px; +} +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 28px; + font-weight: 500; +} + +/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ +nav[data-toggle='toc'] .nav > .active > ul { + display: block; +} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js new file mode 100644 index 0000000000000000000000000000000000000000..1cdd573b20f53b3ebe31c021e154c4338ca456af --- /dev/null +++ b/docs/bootstrap-toc.js @@ -0,0 +1,159 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ +(function() { + 'use strict'; + + window.Toc = { + helpers: { + // return all matching elements in the set, or their descendants + findOrFilter: function($el, selector) { + // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ + // http://stackoverflow.com/a/12731439/358804 + var $descendants = $el.find(selector); + return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); + }, + + generateUniqueIdBase: function(el) { + var text = $(el).text(); + var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); + return anchor || el.tagName.toLowerCase(); + }, + + generateUniqueId: function(el) { + var anchorBase = this.generateUniqueIdBase(el); + for (var i = 0; ; i++) { + var anchor = anchorBase; + if (i > 0) { + // add suffix + anchor += '-' + i; + } + // check if ID already exists + if (!document.getElementById(anchor)) { + return anchor; + } + } + }, + + generateAnchor: function(el) { + if (el.id) { + return el.id; + } else { + var anchor = this.generateUniqueId(el); + el.id = anchor; + return anchor; + } + }, + + createNavList: function() { + return $('-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -123,7 +132,7 @@
- - + @@ -179,7 +188,7 @@
`, then `
`, etc.) that has more than one element. Defaults to 1 (for `
`). + getTopLevel: function($scope) { + for (var i = 1; i <= 6; i++) { + var $headings = this.findOrFilter($scope, 'h' + i); + if ($headings.length > 1) { + return i; + } + } + + return 1; + }, + + // returns the elements for the top level, and the next below it + getHeadings: function($scope, topLevel) { + var topSelector = 'h' + topLevel; + + var secondaryLevel = topLevel + 1; + var secondarySelector = 'h' + secondaryLevel; + + return this.findOrFilter($scope, topSelector + ',' + secondarySelector); + }, + + getNavLevel: function(el) { + return parseInt(el.tagName.charAt(1), 10); + }, + + populateNav: function($topContext, topLevel, $headings) { + var $context = $topContext; + var $prevNav; + + var helpers = this; + $headings.each(function(i, el) { + var $newNav = helpers.generateNavItem(el); + var navLevel = helpers.getNavLevel(el); + + // determine the proper $context + if (navLevel === topLevel) { + // use top level + $context = $topContext; + } else if ($prevNav && $context === $topContext) { + // create a new level of the tree and switch to it + $context = helpers.createChildNavList($prevNav); + } // else use the current $context + + $context.append($newNav); + + $prevNav = $newNav; + }); + }, + + parseOps: function(arg) { + var opts; + if (arg.jquery) { + opts = { + $nav: arg + }; + } else { + opts = arg; + } + opts.$scope = opts.$scope || $(document.body); + return opts; + } + }, + + // accepts a jQuery object, or an options object + init: function(opts) { + opts = this.helpers.parseOps(opts); + + // ensure that the data attribute is in place for styling + opts.$nav.attr('data-toggle', 'toc'); + + var $topContext = this.helpers.createChildNavList(opts.$nav); + var topLevel = this.helpers.getTopLevel(opts.$scope); + var $headings = this.helpers.getHeadings(opts.$scope, topLevel); + this.helpers.populateNav($topContext, topLevel, $headings); + } + }; + + $(function() { + $('nav[data-toggle="toc"]').each(function(i, el) { + var $nav = $(el); + Toc.init($nav); + }); + }); +})(); diff --git a/docs/index.html b/docs/index.html index 4c867e0eee483e9962e3482b13e3d23858922ea1..2e7d5de689ed22fd86e53d1224ed620ac1b22616 100644 --- a/docs/index.html +++ b/docs/index.html @@ -6,20 +6,20 @@
Tensors and Neural Networks with GPU Acceleration • torch - - - - + + + + + - - +diff --git a/docs/pkgdown.css b/docs/pkgdown.css index 9145958100221f2aa0772a573f7847ce6e60ad5b..c01e5923be6ff1edbccf20f93be6bdf8dbd67bb3 100644 --- a/docs/pkgdown.css +++ b/docs/pkgdown.css @@ -17,6 +17,10 @@ html, body { height: 100%; } +body { + position: relative; +} + body > .container { display: flex; height: 100%; @@ -67,6 +71,10 @@ summary { margin-top: calc(-60px + 1em); } +dd { + margin-left: 3em; +} + /* Section anchors ---------------------------------*/ a.anchor { @@ -100,29 +108,132 @@ a.anchor { margin-top: -40px; } +/* Navbar submenu --------------------------*/ + +.dropdown-submenu { + position: relative; +} + +.dropdown-submenu>.dropdown-menu { + top: 0; + left: 100%; + margin-top: -6px; + margin-left: -1px; + border-radius: 0 6px 6px 6px; +} + +.dropdown-submenu:hover>.dropdown-menu { + display: block; +} + +.dropdown-submenu>a:after { + display: block; + content: " "; + float: right; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; + border-width: 5px 0 5px 5px; + border-left-color: #cccccc; + margin-top: 5px; + margin-right: -10px; +} + +.dropdown-submenu:hover>a:after { + border-left-color: #ffffff; +} + +.dropdown-submenu.pull-left { + float: none; +} + +.dropdown-submenu.pull-left>.dropdown-menu { + left: -100%; + margin-left: 10px; + border-radius: 6px 0 6px 6px; +} + /* Sidebar --------------------------*/ -#sidebar { +#pkgdown-sidebar { margin-top: 30px; position: -webkit-sticky; position: sticky; top: 70px; } -#sidebar h2 { + +#pkgdown-sidebar h2 { font-size: 1.5em; margin-top: 1em; } -#sidebar h2:first-child { +#pkgdown-sidebar h2:first-child { margin-top: 0; } -#sidebar .list-unstyled li { +#pkgdown-sidebar .list-unstyled li { margin-bottom: 0.5em; } +/* bootstrap-toc tweaks ------------------------------------------------------*/ + +/* All levels of nav */ + +nav[data-toggle='toc'] .nav > li > a { + padding: 4px 20px 4px 6px; + font-size: 1.5rem; + font-weight: 400; + color: inherit; +} + +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 5px; + color: inherit; + border-left: 1px solid #878787; +} + +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 5px; + font-size: 1.5rem; + font-weight: 400; + color: inherit; + border-left: 2px solid #878787; +} + +/* Nav: second level (shown on .active) */ + +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} + +nav[data-toggle='toc'] .nav .nav > li > a { + padding-left: 16px; + font-size: 1.35rem; +} + +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 15px; +} + +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 15px; + font-weight: 500; + font-size: 1.35rem; +} + +/* orcid ------------------------------------------------------------------- */ + .orcid { - height: 16px; + font-size: 16px; + color: #A6CE39; /* margins are required by official ORCID trademark and display guidelines */ margin-left:4px; margin-right:4px; diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 087a76227251d084449b3512dfe9ff20b680c371..7e7048faebb92b85ed06afddd1a8a4581241d6a4 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -9,11 +9,6 @@ $('body').css('padding-top', $('.navbar').height() + 10); }); - $('body').scrollspy({ - target: '#sidebar', - offset: 60 - }); - $('[data-toggle="tooltip"]').tooltip(); var cur_path = paths(location.pathname); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 4dacb3b9101fd57d85d989f35e2e3488dff7b8ec..9df148b2a6f4d51666c49920a8025e2db3b2c779 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,11 +1,13 @@ -pandoc: 2.9.2.1 -pkgdown: 1.4.1.9000 -pkgdown_sha: d01672efa3baa80fd7ab238b7143080b0daf49a4 +pandoc: 2.7.3 +pkgdown: 1.5.1 +pkgdown_sha: ~ articles: mnist-cnn: examples/mnist-cnn.html mnist-mlp: examples/mnist-mlp.html extending-autograd: extending-autograd.html indexing: indexing.html + loading-data: loading-data.html tensor-creation: tensor-creation.html using-autograd: using-autograd.html +last_built: 2020-07-02T19:07Z diff --git a/docs/reference/AutogradContext.html b/docs/reference/AutogradContext.html index c22c7bc2e048520c50f8e8c13a7d6e652e522fdb..e56b0f8b6e6b3e52cf631f10e23d3b24d8b35547 100644 --- a/docs/reference/AutogradContext.html +++ b/docs/reference/AutogradContext.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ Class representing the context." /> - +@@ -245,7 +256,7 @@@@ -51,12 +51,17 @@--
-
- +
- Tensors +
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -88,7 +93,7 @@
-
-
+
@@ -109,91 +114,91 @@
-
+
Installation
Run:
-+remotes::install_github("mlverse/torch")remotes::install_github("mlverse/torch")
At the first package load additional software will be installed.
Example
Currently this package is only a prrof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.
-+library(torch) -x <- array(runif(8), dim = c(2, 2, 2)) -y <- torch_tensor(x, dtype = torch_float64()) -y -#> torch_tensor -#> (1,.,.) = -#> 0.8687 0.0157 -#> 0.4237 0.8971 -#> -#> (2,.,.) = -#> 0.4021 0.5509 -#> 0.3374 0.9034 -#> [ CPUDoubleType{2,2,2} ] -identical(x, as_array(y)) -#> [1] TRUElibrary(torch) +x <- array(runif(8), dim = c(2, 2, 2)) +y <- torch_tensor(x, dtype = torch_float64()) +y +#> torch_tensor +#> (1,.,.) = +#> 0.8687 0.0157 +#> 0.4237 0.8971 +#> +#> (2,.,.) = +#> 0.4021 0.5509 +#> 0.3374 0.9034 +#> [ CPUDoubleType{2,2,2} ] +identical(x, as_array(y)) +#> [1] TRUE
Simple Autograd Example
In the following snippet we let torch, using the autograd feature, calculate the derivatives:
-+x <- torch_tensor(1, requires_grad = TRUE) -w <- torch_tensor(2, requires_grad = TRUE) -b <- torch_tensor(3, requires_grad = TRUE) -y <- w * x + b -y$backward() -x$grad -#> torch_tensor -#> 2 -#> [ CPUFloatType{1} ] -w$grad -#> torch_tensor -#> 1 -#> [ CPUFloatType{1} ] -b$grad -#> torch_tensor -#> 1 -#> [ CPUFloatType{1} ]x <- torch_tensor(1, requires_grad = TRUE) +w <- torch_tensor(2, requires_grad = TRUE) +b <- torch_tensor(3, requires_grad = TRUE) +y <- w * x + b +y$backward() +x$grad +#> torch_tensor +#> 2 +#> [ CPUFloatType{1} ] +w$grad +#> torch_tensor +#> 1 +#> [ CPUFloatType{1} ] +b$grad +#> torch_tensor +#> 1 +#> [ CPUFloatType{1} ]
Linear Regression
In the following example we are going to fit a linear regression from scratch using torch’s Autograd.
Note all methods that end with
-_(eg.sub_), will modify the tensors in place.+x <- torch_randn(100, 2) -y <- 0.1 + 0.5*x[,1] - 0.7*x[,2] - -w <- torch_randn(2, 1, requires_grad = TRUE) -b <- torch_zeros(1, requires_grad = TRUE) - -lr <- 0.5 -for (i in 1:100) { - y_hat <- torch_mm(x, w) + b - loss <- torch_mean((y - y_hat$squeeze(1))^2) - - loss$backward() - - with_no_grad({ - w$sub_(w$grad*lr) - b$sub_(b$grad*lr) - - w$grad$zero_() - b$grad$zero_() - }) -} -print(w) -#> torch_tensor -#> 0.5000 -#> -0.7000 -#> [ CPUFloatType{2,1} ] -print(b) -#> torch_tensor -#> 0.01 * -#> 10.0000 -#> [ CPUFloatType{1} ]x <- torch_randn(100, 2) +y <- 0.1 + 0.5*x[,1] - 0.7*x[,2] + +w <- torch_randn(2, 1, requires_grad = TRUE) +b <- torch_zeros(1, requires_grad = TRUE) + +lr <- 0.5 +for (i in 1:100) { + y_hat <- torch_mm(x, w) + b + loss <- torch_mean((y - y_hat$squeeze(1))^2) + + loss$backward() + + with_no_grad({ + w$sub_(w$grad*lr) + b$sub_(b$grad*lr) + + w$grad$zero_() + b$grad$zero_() + }) +} +print(w) +#> torch_tensor +#> 0.5000 +#> -0.7000 +#> [ CPUFloatType{2,1} ] +print(b) +#> torch_tensor +#> 0.01 * +#> 10.0000 +#> [ CPUFloatType{1} ]
@@ -204,11 +209,11 @@
++-Links
-
-
- Browse source code at
https://github.com/mlverse/torch + - Browse source code at
https://github.com/mlverse/torch/ - Report a bug at
https://github.com/mlverse/torch/issues
@@ -236,7 +241,13 @@
++Dev status
+-
+
-
+
@@ -89,12 +92,17 @@ Class representing the context." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ Class representing the context." />
-
-
+
@@ -158,13 +166,13 @@ Class representing the context." />
Public fields
--
+
ptr(Dev related) pointer to the context c++ object.
needs_input_gradboolean listing arguments of
forwardand whether they require_grad.saved_variableslist of objects that were saved for backward via
save_for_backward.
@@ -185,15 +193,15 @@ Class representing the context." />
ptrpointer to the c++ object
envenvironment that encloses both forward and backward
@@ -212,10 +220,10 @@ method.
Before returning them to the user, a check is made to ensure they weren’t used
in any in-place operation that modified their content.
...any kind of R object that will be saved for the backward pass. It's common to pass named arguments.
@@ -230,10 +238,10 @@ of backward computation. You still need to accept a gradient for each output
in ...non-differentiable outputs.
...tensors that are modified in-place.
deepWhether to make a deep clone.
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -132,7 +140,7 @@ tensors)." />
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -130,7 +138,7 @@ Function object, and passing returned gradients on to next Function's." />
- - + @@ -203,13 +211,10 @@ See AutogradContext for more information abou } )
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ the outputs. If an output doesn’t require_grad, then the gradient can be None)
-
-
+
@@ -230,14 +238,10 @@ will still be computed, and will be accumulated into their
.gradat #> - Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -165,12 +173,10 @@
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -157,11 +165,10 @@
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -157,11 +165,10 @@
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -157,11 +165,10 @@
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ single- or multi-process iterators over the dataset." />
-
-
+
@@ -238,12 +246,10 @@ input, after seeding and before data loading. (default:
NULL) - @@ -254,7 +260,7 @@ input, after seeding and before data loading. (default:NULL) diff --git a/docs/reference/dataloader_make_iter.html b/docs/reference/dataloader_make_iter.html index 5bd8a337a7be8d86a76010c7d8e34746c147bbcf..4ea3e4cbf1dbb854ad7b56f9330fc1aaeec635e1 100644 --- a/docs/reference/dataloader_make_iter.html +++ b/docs/reference/dataloader_make_iter.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/dataloader_next.html b/docs/reference/dataloader_next.html index b5bb4f19abe8d2c7487950e208a60b7b92def2ac..9151559319671838197fb5a43a643cc1fe9b53e4 100644 --- a/docs/reference/dataloader_next.html +++ b/docs/reference/dataloader_next.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -181,7 +187,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -165,12 +173,10 @@
diff --git a/docs/reference/dataset.html b/docs/reference/dataset.html index 528680df112726f3e7353d695543f092eeea800a..7ea6332439ed17f28c1c3ab9e2e2234645ea0558 100644 --- a/docs/reference/dataset.html +++ b/docs/reference/dataset.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -42,7 +46,6 @@ data sample for a given key. Subclasses could also optionally overwrite lenght, which is expected to return the size of the dataset by many ~torch.utils.data.Sampler implementations and the default options of ~torch.utils.data.DataLoader." /> - @@ -60,7 +63,7 @@ of ~torch.utils.data.DataLoader." /> - +@@ -88,12 +91,17 @@- @@ -181,7 +187,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -165,12 +173,10 @@
diff --git a/docs/reference/default_dtype.html b/docs/reference/default_dtype.html index c518c34f4fd45d79c32f574e761f6170c57a2666..ee57f42cb50f7205efdc59676f41557796b34be5 100644 --- a/docs/reference/default_dtype.html +++ b/docs/reference/default_dtype.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -93,12 +96,17 @@ of ~torch.utils.data.DataLoader." />- @@ -202,7 +207,7 @@ dataset with non-integral indices/keys, a custom sampler must be provided.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -130,7 +138,7 @@ of ~torch.utils.data.DataLoader." />
- - + @@ -185,13 +193,10 @@ sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.
diff --git a/docs/reference/enumerate.dataloader.html b/docs/reference/enumerate.dataloader.html index 26dd738c67890cc59be988fad4be10a685f4beee..b90f930016859ffb1562041c20d8d818b9420a2e 100644 --- a/docs/reference/enumerate.dataloader.html +++ b/docs/reference/enumerate.dataloader.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -184,7 +190,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -168,12 +176,10 @@
diff --git a/docs/reference/enumerate.html b/docs/reference/enumerate.html index b183c9705c28e1fbe092ade1f54603f746980429..a92f64fd61ab878c71269a06049bd8c25204f50d 100644 --- a/docs/reference/enumerate.html +++ b/docs/reference/enumerate.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -186,7 +192,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -170,12 +178,10 @@
diff --git a/docs/reference/index.html b/docs/reference/index.html index c1c41912f0b9cb6b1ed3b986edb3aabeca8372dd..b5c78e0afd081302505ae059dd0fabd689610850 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -53,7 +57,7 @@ - +@@ -88,12 +91,17 @@- @@ -181,7 +187,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -165,12 +173,10 @@
@@ -86,12 +90,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -123,7 +132,7 @@
-
-
+
@@ -159,6 +168,11 @@
+
+
+
+
+
@@ -282,6 +296,12 @@ single- or multi-process iterators over the dataset. BatchNorm1D module
+ + + +BatchNorm2D
+ ConvTranpose3D module
+ + + +CrossEntropyLoss module
+ -
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -190,13 +198,10 @@ to avoid loading dependencies automatically. These environment variables are mea cases and troubleshootinng only.
diff --git a/docs/reference/mnist_dataset.html b/docs/reference/mnist_dataset.html index c312fd844338268742461edd656c244f54290c38..4d738c6f53b11df2d8342599e0217afac0af58ae 100644 --- a/docs/reference/mnist_dataset.html +++ b/docs/reference/mnist_dataset.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -181,7 +187,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -165,12 +173,10 @@
diff --git a/docs/reference/nn_adaptive_log_softmax_with_loss.html b/docs/reference/nn_adaptive_log_softmax_with_loss.html index ea8437b4790e1fc66d178b0cb990593f256ff452..456ea86e983381a2d1ab157291563c65d99db52e 100644 --- a/docs/reference/nn_adaptive_log_softmax_with_loss.html +++ b/docs/reference/nn_adaptive_log_softmax_with_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Mousta - +@@ -88,12 +91,17 @@- @@ -203,7 +209,7 @@ target and transforms it.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -187,12 +195,10 @@ target and transforms it.
diff --git a/docs/reference/nn_batch_norm1d.html b/docs/reference/nn_batch_norm1d.html index 4dd2285b5a43923f8b9a174539fcffe648c26fa0..921adc688244dccca091a1ef659a336942543db1 100644 --- a/docs/reference/nn_batch_norm1d.html +++ b/docs/reference/nn_batch_norm1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov - +@@ -89,12 +92,17 @@ Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Mousta- @@ -275,7 +276,7 @@ label should be represented by the index-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Mousta
-
-
+
@@ -254,17 +262,10 @@ label should be represented by the index
n_classes - 1.
n_classes - 1.- @@ -258,7 +260,7 @@ on@@ -90,12 +93,17 @@ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov
-
-
+
@@ -238,16 +246,10 @@ on
(N, L)slices, it's common terminology to call this Temporal Bat input <- torch_randn(20, 100) output <- m(input)
(N, L)slices, it's common terminology to call this Temporal Bat diff --git a/docs/reference/nn_batch_norm2d.html b/docs/reference/nn_batch_norm2d.html new file mode 100644 index 0000000000000000000000000000000000000000..55c31f096ac339e8d2ad01cdbc5b5b66b0cbbf6d --- /dev/null +++ b/docs/reference/nn_batch_norm2d.html @@ -0,0 +1,274 @@ + + + + + + + + +BatchNorm2D — nn_batch_norm2d • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +++ + + + + + + + diff --git a/docs/reference/nn_bce_loss.html b/docs/reference/nn_bce_loss.html index 68fe32d5e89e53c288e76cf699682bf125516100..4bb253a330de6f071163090101f31a285e5b320a 100644 --- a/docs/reference/nn_bce_loss.html +++ b/docs/reference/nn_bce_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ between the target and the output:" /> - ++ + +++ + + ++++ + + torch + 0.0.1.9000 + ++ +++-
+
- + + + + + +
-
+
+ Articles
+
+
+
+
-
+
- Tensors +
- + Creating tensors + +
- + Indexing + +
- Datasets +
- + Loading Data + +
- Autograd +
- + Using autograd + +
- + Extending autograd + +
+ - + + Examples + + + + + +
- + Reference + +
-
+
- + + + + + +
++ + + ++ + ++++ +Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs +additional channel dimension) as described in the paper +Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
+nn_batch_norm2d(...)
+ +Arguments
++
+ ++ +num_features +\(C\) from an expected input of size +\((N, C, H, W)\)
+ +eps +a value added to the denominator for numerical stability. +Default: 1e-5
+ +momentum +the value used for the running_mean and running_var +computation. Can be set to
Nonefor cumulative moving average +(i.e. simple average). Default: 0.1+ +affine +a boolean value that when set to
TRUE, this module has +learnable affine parameters. Default:TRUE+ +track_running_stats +a boolean value that when set to
TRUE, this +module tracks the running mean and variance, and when set toFALSE, +this module does not track such statistics and uses batch statistics instead +in both training and eval modes if the running mean and variance areNone. +Default:TRUEDetails
+ +$$ + y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta +$$
+The mean and standard-deviation are calculated per-dimension over +the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors +of size
+C(whereCis the input size). By default, the elements of \(\gamma\) are set +to 1 and the elements of \(\beta\) are set to 0. The standard-deviation is calculated +via the biased estimator, equivalent totorch_var(input, unbiased=FALSE). +Also by default, during training this layer keeps running estimates of its +computed mean and variance, which are then used for normalization during +evaluation. The running estimates are kept with a defaultmomentum+of 0.1.If
+track_running_statsis set toFALSE, this layer then does not +keep running estimates, and batch statistics are instead used during +evaluation time as well.Note
+ +This
+momentumargument is different from one used in optimizer +classes and the conventional notion of momentum. Mathematically, the +update rule for running statistics here is +\(\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t\), +where \(\hat{x}\) is the estimated statistic and \(x_t\) is the +new observed value. +Because the Batch Normalization is done over theCdimension, computing statistics +on(N, H, W)slices, it's common terminology to call this Spatial Batch Normalization.Shape
+ + + +-
+
Input: \((N, C, H, W)\)
+Output: \((N, C, H, W)\) (same shape as input)
+
Examples
+
+# With Learnable Parameters +m <- nn_batch_norm2d(100) +# Without Learnable Parameters +m <- nn_batch_norm2d(100, affine=FALSE) +input <- torch_randn(20, 100, 35, 45) +output <- m(input)+ ++- @@ -246,7 +249,7 @@ shape as input. diff --git a/docs/reference/nn_bilinear.html b/docs/reference/nn_bilinear.html index 8dd5ef380d57ddbea7746006cac51dfc1accf7d8..6f344c7d9ad4b0edb959a1fd8bdf6bac911e9346 100644 --- a/docs/reference/nn_bilinear.html +++ b/docs/reference/nn_bilinear.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ - +@@ -89,12 +92,17 @@ between the target and the output:" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ between the target and the output:" />
- - + @@ -227,15 +235,10 @@ shape as input. output <- loss(m(input), target) output$backward()
- @@ -234,7 +237,7 @@ If@@ -89,12 +92,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@
#> [1] 128 50biasisTRUE, the values are initialized from diff --git a/docs/reference/nn_celu.html b/docs/reference/nn_celu.html index a018ce32798988fcb578929785cfccc9d0587c25..199874ea0ce221c6ed7eae6c87361e1e31dfedb7 100644 --- a/docs/reference/nn_celu.html +++ b/docs/reference/nn_celu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -209,7 +212,7 @@ dimensions diff --git a/docs/reference/nn_conv1d.html b/docs/reference/nn_conv1d.html index 0b300db834f079c876b758a1de3059920b492208..c556198424e8226e16418797a7ebea1763af5445 100644 --- a/docs/reference/nn_conv1d.html +++ b/docs/reference/nn_conv1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ planes. In the simplest case, the output value of the layer with input size \((N, C_{\text{in}}, L)\) and output \((N, C_{\text{out}}, L_{\text{out}})\) can be precisely described as:" /> - @@ -59,7 +62,7 @@ precisely described as:" /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -190,15 +198,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -310,7 +311,7 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where diff --git a/docs/reference/nn_conv2d.html b/docs/reference/nn_conv2d.html index a8ccf4c512918324c74feeadeb769d14b2e47a20..3b6c3238fb8ce0390a1cc9096a61fb4fe7c653dc 100644 --- a/docs/reference/nn_conv2d.html +++ b/docs/reference/nn_conv2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -92,12 +95,17 @@ precisely described as:" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -129,7 +137,7 @@ precisely described as:" />
- - + @@ -289,17 +297,10 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where input <- torch_randn(20, 16, 50) output <- m(input)
- @@ -326,7 +327,7 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where diff --git a/docs/reference/nn_conv3d.html b/docs/reference/nn_conv3d.html index 9357f918236c916e3233570eec71be7492875c05..261c94acc8ad04e42b4ad8db6926c3b29bd6d0ed 100644 --- a/docs/reference/nn_conv3d.html +++ b/docs/reference/nn_conv3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ planes. In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\) and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:" /> - @@ -58,7 +61,7 @@ and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely describe - +@@ -89,12 +92,17 @@ planes." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
- - + @@ -305,17 +313,10 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where input <- torch_randn(20, 16, 50, 100) output <- m(input)
- @@ -317,7 +318,7 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where diff --git a/docs/reference/nn_conv_transpose1d.html b/docs/reference/nn_conv_transpose1d.html index d097d8d4048ff518e8070ff553827e7aa968078c..fc0617e6a8d6d1947615e9ff12ab8b39bab777fc 100644 --- a/docs/reference/nn_conv_transpose1d.html +++ b/docs/reference/nn_conv_transpose1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ composed of several input planes." /> - +@@ -91,12 +94,17 @@ and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely describe-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely describe
- - + @@ -296,17 +304,10 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where input <- torch_randn(20, 16, 10, 50, 100) output <- m(input)
- @@ -304,7 +305,7 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where diff --git a/docs/reference/nn_conv_transpose2d.html b/docs/reference/nn_conv_transpose2d.html index 089f1d92edd3374f0311d6dad7f262a7de4d5739..e3f7a5b866ed8b3a461e65abdf406eacd9090301 100644 --- a/docs/reference/nn_conv_transpose2d.html +++ b/docs/reference/nn_conv_transpose2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ composed of several input planes." /> - +@@ -89,12 +92,17 @@ composed of several input planes." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ composed of several input planes." />
- - + @@ -283,17 +291,10 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where input <- torch_randn(10, 32, 2) output <- m(input)
- @@ -324,7 +325,7 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where diff --git a/docs/reference/nn_conv_transpose3d.html b/docs/reference/nn_conv_transpose3d.html index 5c9c9617876e1062b9556ab3b196062304e875e3..6d76cc202190145ce27e5ebb317e22226f8d5337 100644 --- a/docs/reference/nn_conv_transpose3d.html +++ b/docs/reference/nn_conv_transpose3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -89,12 +92,17 @@ composed of several input planes." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ composed of several input planes." />
- - + @@ -303,17 +311,10 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where output$size()
#> [1] 1 16 12 12- @@ -320,7 +321,7 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where diff --git a/docs/reference/nn_cross_entropy_loss.html b/docs/reference/nn_cross_entropy_loss.html new file mode 100644 index 0000000000000000000000000000000000000000..fabdee4efd50acd0682cc6720599f18bec40ff87 --- /dev/null +++ b/docs/reference/nn_cross_entropy_loss.html @@ -0,0 +1,271 @@ + + + + + + + + +@@ -89,12 +92,17 @@ planes." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
- - + @@ -299,17 +307,10 @@ sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where input <- torch_randn(20, 16, 10, 50, 100) output <- m(input)
CrossEntropyLoss module — nn_cross_entropy_loss • torch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +++ + + + + + + + diff --git a/docs/reference/nn_dropout.html b/docs/reference/nn_dropout.html index 08079892372f02466bb6d83f00decc9824cd2ef1..354d65798228cad97eb5a117f51c9410f4e19394 100644 --- a/docs/reference/nn_dropout.html +++ b/docs/reference/nn_dropout.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ tensor with probability p using samples from a Bernoulli distribution. Each channel will be zeroed out independently on every forward call." /> - @@ -58,7 +61,7 @@ call." /> - ++ + +++ + + ++++ + + torch + 0.0.1.9000 + ++ +++-
+
- + + + + + +
-
+
+ Articles
+
+
+
+
-
+
- Tensors +
- + Creating tensors + +
- + Indexing + +
- Datasets +
- + Loading Data + +
- Autograd +
- + Using autograd + +
- + Extending autograd + +
+ - + + Examples + + + + + +
- + Reference + +
-
+
- + + + + + +
++ + + ++ + ++++ +This criterion combines nn_log_softmax and nn_nll_loss in one single class. +It is useful when training a classification problem with
+Cclasses.nn_cross_entropy_loss(...)
+ +Arguments
++
+ ++ +weight +(Tensor, optional): a manual rescaling weight given to each class. +If given, has to be a Tensor of size
C+ +ignore_index +(int, optional): Specifies a target value that is ignored +and does not contribute to the input gradient. When
size_averageis +TRUE, the loss is averaged over non-ignored targets.+ +reduction +(string, optional): Specifies the reduction to apply to the output: +
'none'|'mean'|'sum'.'none': no reduction will be applied, +'mean': the sum of the output will be divided by the number of +elements in the output,'sum': the output will be summed. Note:size_average+andreduceare in the process of being deprecated, and in the meantime, +specifying either of those two args will overridereduction. Default:'mean'Details
+ +If provided, the optional argument
+weightshould be a 1DTensor+assigning weight to each of the classes.This is particularly useful when you have an unbalanced training set. +The
+inputis expected to contain raw, unnormalized scores for each class. +inputhas to be a Tensor of size either \((minibatch, C)\) or +\((minibatch, C, d_1, d_2, ..., d_K)\) +with \(K \geq 1\) for theK-dimensional case (described later).This criterion expects a class index in the range \([0, C-1]\) as the +
+targetfor each value of a 1D tensor of sizeminibatch; ifignore_index+is specified, this criterion also accepts this class index (this index may not +necessarily be in the class range).The loss can be described as: +$$ + \text{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right) += -x[class] + \log\left(\sum_j \exp(x[j])\right) +$$ +or in the case of the
+weightargument being specified: +$$ + \text{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right) +$$The losses are averaged across observations for each minibatch. +Can also be used for higher dimension inputs, such as 2D images, by providing +an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\), +where \(K\) is the number of dimensions, and a target of appropriate shape +(see below).
+Shape
+ + + +-
+
Input: \((N, C)\) where
C = number of classes, or +\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\) +in the case ofK-dimensional loss.
+Target: \((N)\) where each value is \(0 \leq \text{targets}[i] \leq C-1\), or +\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of +K-dimensional loss.
+Output: scalar. +If
reductionis'none', then the same size as the target: +\((N)\), or +\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case +of K-dimensional loss.
+
Examples
+
+loss <- nn_cross_entropy_loss() +input <- torch_randn(3, 5, requires_grad=TRUE) +target <- torch_randint(low = 0, high = 5, size = 3, dtype = torch_long()) +output <- loss(input, target) +output$backward()+ ++- @@ -215,7 +218,7 @@ identity function. diff --git a/docs/reference/nn_dropout2d.html b/docs/reference/nn_dropout2d.html index 89c29ef3a85a8d279c2ca35f944957756d111db9..4281e487ee442a916df093b93798309b93091db0 100644 --- a/docs/reference/nn_dropout2d.html +++ b/docs/reference/nn_dropout2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ batched input is a 2D tensor \(\text{input}[i, j]\))." /> - +@@ -91,12 +94,17 @@ call." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ call." />
- - + @@ -196,15 +204,10 @@ identity function. input <- torch_randn(20, 16) output <- m(input)
- @@ -219,7 +222,7 @@ feature maps and should be used instead. diff --git a/docs/reference/nn_dropout3d.html b/docs/reference/nn_dropout3d.html index 67216c68c8fda2f59a613dc4a199928cb1fd407f..ca5b9796458af9ca7b68f9075c6d839b59db3ae3 100644 --- a/docs/reference/nn_dropout3d.html +++ b/docs/reference/nn_dropout3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ batched input is a 3D tensor \(\text{input}[i, j]\))." /> - +@@ -90,12 +93,17 @@ batched input is a 2D tensor \(\text{input}[i, j]\))." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ batched input is a 2D tensor \(\text{input}[i, j]\))." />
- - + @@ -200,15 +208,10 @@ feature maps and should be used instead. input <- torch_randn(20, 16, 32, 32) output <- m(input)
- @@ -219,7 +222,7 @@ feature maps and should be used instead. diff --git a/docs/reference/nn_elu.html b/docs/reference/nn_elu.html index 49e2ead2dd1e3ccdae61f42c4d2fe162bb9ded84..cdf8a35c11ebbb5b6091ebc6a3fde0b78186e8bf 100644 --- a/docs/reference/nn_elu.html +++ b/docs/reference/nn_elu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ batched input is a 3D tensor \(\text{input}[i, j]\))." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ batched input is a 3D tensor \(\text{input}[i, j]\))." />
- - + @@ -200,15 +208,10 @@ feature maps and should be used instead. input <- torch_randn(20, 16, 4, 32, 32) output <- m(input)
- @@ -207,7 +210,7 @@ dimensions diff --git a/docs/reference/nn_embedding.html b/docs/reference/nn_embedding.html index 552ebdbb1d9eb9a3ee733451c2354ea830c02679..5e656f63d15f69ba077260bcf747cb54f46c3536 100644 --- a/docs/reference/nn_embedding.html +++ b/docs/reference/nn_embedding.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings." /> - @@ -58,7 +61,7 @@ word embeddings." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -188,15 +196,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -280,7 +282,7 @@ initialized from \(\mathcal{N}(0, 1)\) diff --git a/docs/reference/nn_gelu.html b/docs/reference/nn_gelu.html index a3291aff4ad315ca6c582dab919f35bc1eb385e7..4415c4e74b4871e045d565cc994bdea643a78dfa 100644 --- a/docs/reference/nn_gelu.html +++ b/docs/reference/nn_gelu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ $$\text{GELU}(x) = x * \Phi(x)$$" /> - +@@ -91,12 +94,17 @@ word embeddings." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ word embeddings." />
#> torch_tensor #> (1,.,.) = -#> 0.4220 -0.6692 0.1791 -#> 0.3226 0.5255 -0.6208 -#> -1.2119 0.8679 -1.1647 -#> 0.8176 2.1419 1.3021 +#> -1.7249 0.6086 -0.6237 +#> 0.4060 -0.6416 0.9324 +#> 0.7504 -1.0183 -0.4136 +#> -2.0975 -1.9158 -0.3238 #> #> (2,.,.) = -#> -1.2119 0.8679 -1.1647 -#> 1.7851 0.8554 -0.1514 -#> 0.3226 0.5255 -0.6208 -#> -1.6437 0.8686 1.6509 +#> 0.7504 -1.0183 -0.4136 +#> -1.1737 1.1999 1.6121 +#> 0.4060 -0.6416 0.9324 +#> -0.3202 -0.0498 -0.0549 #> [ CPUFloatType{2,4,3} ]# example with padding_idx embedding <- nn_embedding(10, 3, padding_idx=0) input <- torch_tensor(matrix(c(0,2,0,5), nrow = 1), dtype = torch_long()) embedding(input)#> torch_tensor #> (1,.,.) = #> 0.0000 0.0000 0.0000 -#> 1.9475 -0.1121 1.6192 +#> -0.8975 0.7640 -0.2097 #> 0.0000 0.0000 0.0000 -#> -0.3052 0.2334 -0.2075 +#> 1.6086 -2.3784 -1.1710 #> [ CPUFloatType{1,4,3} ]- @@ -194,7 +198,7 @@ dimensions diff --git a/docs/reference/nn_glu.html b/docs/reference/nn_glu.html index 25535b9944d0e2b3953e24a5a2efefebe356e522..7cdc8976b24e0180591f7bc5ce18cf48f1d8072c 100644 --- a/docs/reference/nn_glu.html +++ b/docs/reference/nn_glu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ of the input matrices and \(b\) is the second half." /> - +@@ -89,12 +92,17 @@ $$\text{GELU}(x) = x * \Phi(x)$$" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ $$\text{GELU}(x) = x * \Phi(x)$$" />
- - + @@ -176,14 +184,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -201,7 +205,7 @@ dimensions diff --git a/docs/reference/nn_hardshrink.html b/docs/reference/nn_hardshrink.html index 83df8d6172ae5ba3fe56cd24dc02c07e061836e1..912436af17d9a89475d38f2d85f839a23c8f94f4 100644 --- a/docs/reference/nn_hardshrink.html +++ b/docs/reference/nn_hardshrink.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ of the input matrices and \(b\) is the second half." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ of the input matrices and \(b\) is the second half." />
- - + @@ -183,14 +191,10 @@ dimensions input <- torch_randn(4, 2) output <- m(input)
- @@ -208,7 +211,7 @@ dimensions diff --git a/docs/reference/nn_hardsigmoid.html b/docs/reference/nn_hardsigmoid.html index 3abce0b85438410a44b5ff6ce3f4a7c19658e226..b7b40d84a37224c209984fdf84ed4597fcb63fee 100644 --- a/docs/reference/nn_hardsigmoid.html +++ b/docs/reference/nn_hardsigmoid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -189,15 +197,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -198,7 +202,7 @@ dimensions diff --git a/docs/reference/nn_hardswish.html b/docs/reference/nn_hardswish.html index 9a0bb0c25957bf011f3076d6152de2a695c7a9d2..d9061361945ea9a6503889ce16d1a2425e610c08 100644 --- a/docs/reference/nn_hardswish.html +++ b/docs/reference/nn_hardswish.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -45,7 +49,6 @@ x & \text{if~} x \ge +3, \\ x \cdot (x + 3) /6 & \text{otherwise} \end{cases} $$" /> - @@ -63,7 +66,7 @@ $$" /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -180,14 +188,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -206,7 +211,7 @@ dimensions diff --git a/docs/reference/nn_hardtanh.html b/docs/reference/nn_hardtanh.html index aa7032eef2eee8e8b6f0d14e602119e8c48cc697..c4b7bf9757e8b9874fe3455183cb9445f16cdac8 100644 --- a/docs/reference/nn_hardtanh.html +++ b/docs/reference/nn_hardtanh.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ HardTanh is defined as:" /> - +@@ -96,12 +99,17 @@ $$" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -133,7 +141,7 @@ $$" />
- - + @@ -189,13 +197,10 @@ dimensions output <- m(input) }
- @@ -219,7 +222,7 @@ dimensions diff --git a/docs/reference/nn_identity.html b/docs/reference/nn_identity.html index 764ba796fea66591a471b672eeb7de5d86b32510..7c182a677d645c17a6bb5de55642651b2bf7c56f 100644 --- a/docs/reference/nn_identity.html +++ b/docs/reference/nn_identity.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ HardTanh is defined as:" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ HardTanh is defined as:" />
- - + @@ -200,15 +208,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -188,7 +193,7 @@ diff --git a/docs/reference/nn_init_calculate_gain.html b/docs/reference/nn_init_calculate_gain.html index 52e86ad53dd6077f0788b8037194ea4e76617516..2a0e75ad1cca1bf73914fe840e88e9a37c4bcd60 100644 --- a/docs/reference/nn_init_calculate_gain.html +++ b/docs/reference/nn_init_calculate_gain.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
#> [1] 128 20diff --git a/docs/reference/nn_init_constant_.html b/docs/reference/nn_init_constant_.html index 90dbfabafec7b672ddd87f572f2b38094637aa12..370db96f37d78a8985d7a609247a64d0c0c20454 100644 --- a/docs/reference/nn_init_constant_.html +++ b/docs/reference/nn_init_constant_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -185,7 +191,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -169,12 +177,10 @@
- @@ -194,7 +199,7 @@ diff --git a/docs/reference/nn_init_dirac_.html b/docs/reference/nn_init_dirac_.html index 46e9b2c4d1586241a14e63678b9b78cc2e96f9d8..54bc47e02c9dc7bbb49b889583846143665b6509 100644 --- a/docs/reference/nn_init_dirac_.html +++ b/docs/reference/nn_init_dirac_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ delta function. Preserves the identity of the inputs in Convolutional layers, where as many input channels are preserved as possible. In case of groups>1, each group of channels preserves identity." /> - @@ -58,7 +61,7 @@ of groups>1, each group of channels preserves identity." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -177,13 +185,10 @@ #> [ CPUFloatType{3,5} ]
- @@ -197,7 +202,7 @@ of groups>1, each group of channels preserves identity. diff --git a/docs/reference/nn_init_eye_.html b/docs/reference/nn_init_eye_.html index a460cae14d3826821df7a7d43e6069c9473554cd..f9207bf9606e83f7dd6d67b03b1887502eb1ee52 100644 --- a/docs/reference/nn_init_eye_.html +++ b/docs/reference/nn_init_eye_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ many inputs are preserved as possible." /> - +@@ -91,12 +94,17 @@ of groups>1, each group of channels preserves identity." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ of groups>1, each group of channels preserves identity." />
- - + @@ -180,13 +188,10 @@ of groups>1, each group of channels preserves identity. nn_init_dirac_(w) }
- @@ -194,7 +199,7 @@ many inputs are preserved as possible. diff --git a/docs/reference/nn_init_kaiming_normal_.html b/docs/reference/nn_init_kaiming_normal_.html index d1a0982d6da500c7bad845dd7b1e54926e4136e9..9937f3703c1995e4f6e1accdcdb709c5a5f6c9b0 100644 --- a/docs/reference/nn_init_kaiming_normal_.html +++ b/docs/reference/nn_init_kaiming_normal_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ normal distribution." /> - +@@ -90,12 +93,17 @@ many inputs are preserved as possible." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ many inputs are preserved as possible." />
- - + @@ -177,13 +185,10 @@ many inputs are preserved as possible. #> [ CPUFloatType{3,5} ]
diff --git a/docs/reference/nn_init_kaiming_uniform_.html b/docs/reference/nn_init_kaiming_uniform_.html index c39bc8115a9aa07c725a010c8165fa896fe35d18..3f5c9aaff50b1a3e70c413074873492b0af27a81 100644 --- a/docs/reference/nn_init_kaiming_uniform_.html +++ b/docs/reference/nn_init_kaiming_uniform_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ uniform distribution." /> - +@@ -90,12 +93,17 @@ normal distribution." />- @@ -215,7 +220,7 @@ or 'leaky_relu' (default).-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ normal distribution." />
-
-
+
@@ -192,19 +200,16 @@ or 'leaky_relu' (default).
Examples
#> torch_tensor -#> -0.7415 -0.4026 -0.1830 0.9564 0.5827 -#> 0.8907 -1.0440 0.0456 -0.1924 1.1919 -#> -1.1077 -0.2889 0.0341 0.3169 -0.3735 +#> -0.1709 0.2737 -0.6741 0.4601 -0.6038 +#> -0.8405 0.3481 -0.8425 0.4830 0.6868 +#> 0.4648 -0.5415 -0.3472 0.1678 -0.2254 #> [ CPUFloatType{3,5} ]
diff --git a/docs/reference/nn_init_normal_.html b/docs/reference/nn_init_normal_.html index e4577cc8e741aab0e054578a7b047c2a8813a1a2..172975f4178ee3c3be8b4a5af2c635fbeb0e0dff 100644 --- a/docs/reference/nn_init_normal_.html +++ b/docs/reference/nn_init_normal_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ uniform distribution." />- @@ -215,7 +220,7 @@ or 'leaky_relu' (default).-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ uniform distribution." />
-
-
+
@@ -192,19 +200,16 @@ or 'leaky_relu' (default).
Examples
#> torch_tensor -#> 0.3884 -0.2300 -0.0174 -0.0099 -0.1727 -#> -0.8038 -0.2143 0.0366 0.2000 0.8205 -#> -0.1976 -0.0535 0.2054 -0.6540 -0.3100 +#> -0.7346 0.7460 -0.1933 -0.2699 0.8112 +#> -0.7825 -0.7528 0.5129 0.1513 0.4588 +#> 0.7606 -0.4530 -0.7420 1.0928 0.1926 #> [ CPUFloatType{3,5} ]
diff --git a/docs/reference/nn_init_ones_.html b/docs/reference/nn_init_ones_.html index 35cab69c54a01bbf1fa7df2fa6774f18a056d8bc..54efa69723d0a21c79cb2f992848c38ec330c1de 100644 --- a/docs/reference/nn_init_ones_.html +++ b/docs/reference/nn_init_ones_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -198,7 +203,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -175,19 +183,16 @@
Examples
#> torch_tensor -#> 0.2024 0.8415 1.1169 -0.2307 0.6890 -#> -1.6605 -0.2450 -0.0345 0.5755 0.4283 -#> 1.9908 -0.2990 -0.4083 -1.5996 0.5604 +#> -0.0164 -0.5420 0.7708 0.0254 1.1605 +#> -1.2175 -0.6695 0.2084 1.0712 0.8271 +#> 1.0186 0.4521 -0.8803 -0.2194 -0.8884 #> [ CPUFloatType{3,5} ]
- @@ -190,7 +195,7 @@ diff --git a/docs/reference/nn_init_orthogonal_.html b/docs/reference/nn_init_orthogonal_.html index d72b4cf432e2eb75ed57cd74a8df9fa489defb62..2fdd575aaf211a37c03fb654380d49a9d4599416 100644 --- a/docs/reference/nn_init_orthogonal_.html +++ b/docs/reference/nn_init_orthogonal_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have at least 2 dimensions, and for tensors with more than 2 dimensions the trailing dimensions are flattened." /> - @@ -58,7 +61,7 @@ trailing dimensions are flattened." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -173,13 +181,10 @@ #> [ CPUFloatType{3,5} ]
diff --git a/docs/reference/nn_init_sparse_.html b/docs/reference/nn_init_sparse_.html index 09533806a4544cf7575bda51dc8da78ae9c13910..0bbfd81095663e6600169a8a5cb9307d8ba29884 100644 --- a/docs/reference/nn_init_sparse_.html +++ b/docs/reference/nn_init_sparse_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ as described in Deep learning via Hessian-free optimization - Martens, J. (2010) - +@@ -91,12 +94,17 @@ trailing dimensions are flattened." />- @@ -200,7 +205,7 @@ trailing dimensions are flattened.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ trailing dimensions are flattened." />
-
-
+
@@ -177,19 +185,16 @@ trailing dimensions are flattened.
Examples
#> torch_tensor -#> 0.1595 0.1070 0.5508 -0.1093 0.8049 -#> -0.5126 -0.4252 -0.3083 0.5180 0.4394 -#> 0.3874 0.4503 0.0228 0.8030 -0.0432 +#> 0.0669 0.0610 0.7511 0.6239 0.1958 +#> 0.4993 -0.0467 -0.2887 0.5007 -0.6437 +#> 0.4964 -0.1280 -0.3966 0.2067 0.7330 #> [ CPUFloatType{3,5} ]
- @@ -200,7 +205,7 @@ the non-zero values diff --git a/docs/reference/nn_init_trunc_normal_.html b/docs/reference/nn_init_trunc_normal_.html index 1a94224286b20a53c938eb7e2f33b90f7758b103..0fa360baf78963d8c869085c5378cb1c9a43aebf 100644 --- a/docs/reference/nn_init_trunc_normal_.html +++ b/docs/reference/nn_init_trunc_normal_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ normal distribution." /> - +@@ -90,12 +93,17 @@ as described in Deep learning via Hessian-free optimization - Martens, J. (2010)-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ as described in Deep learning via Hessian-free optimization - Martens, J. (2010)
- - + @@ -183,13 +191,10 @@ the non-zero values nn_init_sparse_(w, sparsity = 0.1) }
- @@ -208,7 +213,7 @@ normal distribution. diff --git a/docs/reference/nn_init_uniform_.html b/docs/reference/nn_init_uniform_.html index 7fbfe1ee56e544f0365e0f3ad9fdb0088c626e78..80631ac63eb9fff633116f345a4922fec5e4d3c2 100644 --- a/docs/reference/nn_init_uniform_.html +++ b/docs/reference/nn_init_uniform_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ normal distribution." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ normal distribution." />
- - + @@ -191,13 +199,10 @@ normal distribution. #> [ CPUFloatType{3,5} ]
diff --git a/docs/reference/nn_init_xavier_normal_.html b/docs/reference/nn_init_xavier_normal_.html index 330bab94a98a985ebdfdc001cf8b94e4aa785269..7ca9e15842f0a0ac8c4f8db8866ec1cf2939aa64 100644 --- a/docs/reference/nn_init_xavier_normal_.html +++ b/docs/reference/nn_init_xavier_normal_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ distribution." /> - +@@ -88,12 +91,17 @@- @@ -198,7 +203,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -175,19 +183,16 @@
Examples
#> torch_tensor -#> 0.7553 0.1845 0.5545 0.5227 0.8495 -#> 0.2232 0.6142 0.8447 0.9005 0.2004 -#> 0.6416 0.1704 0.3309 0.4614 0.0487 +#> 0.9593 0.7709 0.5674 0.4726 0.4224 +#> 0.1374 0.0369 0.9586 0.8060 0.9205 +#> 0.1720 0.8994 0.0537 0.6152 0.9574 #> [ CPUFloatType{3,5} ]
diff --git a/docs/reference/nn_init_xavier_uniform_.html b/docs/reference/nn_init_xavier_uniform_.html index 11556ffa901e8eb384eb06b5e1474c909c2a8492..6bff577e5e0052688d12fb61539b726833d92a94 100644 --- a/docs/reference/nn_init_xavier_uniform_.html +++ b/docs/reference/nn_init_xavier_uniform_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ distribution." /> - +@@ -90,12 +93,17 @@ distribution." />- @@ -198,7 +203,7 @@ distribution.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ distribution." />
-
-
+
@@ -175,19 +183,16 @@ distribution.
Examples
#> torch_tensor -#> 1.3691 -0.1195 2.8073 0.7087 -2.5727 -#> 0.6295 0.2508 1.3190 -0.7838 1.2254 -#> 0.0110 -1.1451 -1.1653 2.2463 -1.0186 +#> -1.2843 -1.5138 -1.4775 2.0179 -3.6371 +#> -1.8362 0.5337 1.7215 -0.0508 1.6074 +#> -0.4646 -3.0213 -0.5919 -0.7111 0.5664 #> [ CPUFloatType{3,5} ]
diff --git a/docs/reference/nn_init_zeros_.html b/docs/reference/nn_init_zeros_.html index 8657315b2e105f59dfd13fe17a18365034155edf..57f79bdea37f71ac3d69f453d93eaf33bf2a6f86 100644 --- a/docs/reference/nn_init_zeros_.html +++ b/docs/reference/nn_init_zeros_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ distribution." />- @@ -198,7 +203,7 @@ distribution.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ distribution." />
-
-
+
@@ -175,19 +183,16 @@ distribution.
Examples
#> torch_tensor -#> -0.9740 -1.7832 -1.1882 -2.4257 -0.7565 -#> 2.3570 -2.3040 -0.1676 3.0898 3.1603 -#> 1.7190 -1.1704 0.0970 -1.2508 2.3888 +#> -0.3007 -2.8792 1.7356 0.6091 -2.1586 +#> -2.3940 -0.2137 2.7815 0.3577 0.6719 +#> -2.3765 2.5391 -1.5260 2.7369 3.1903 #> [ CPUFloatType{3,5} ]
- @@ -190,7 +195,7 @@ diff --git a/docs/reference/nn_leaky_relu.html b/docs/reference/nn_leaky_relu.html index df3bafa9062153acdc28891e94ce59f3578c3f96..5c8b82fe34a16bb5c7c111e238d2957f1766133e 100644 --- a/docs/reference/nn_leaky_relu.html +++ b/docs/reference/nn_leaky_relu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -173,13 +181,10 @@ #> [ CPUFloatType{3,5} ]
- @@ -215,7 +218,7 @@ dimensions diff --git a/docs/reference/nn_linear.html b/docs/reference/nn_linear.html index f949be7978d054a14020e36451b88e73f056f647..4734565647c67f483ebcd329078bc7892b6e3e33 100644 --- a/docs/reference/nn_linear.html +++ b/docs/reference/nn_linear.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -196,15 +204,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -225,7 +228,7 @@ If@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
#> [1] 128 30biasisTRUE, the values are initialized from diff --git a/docs/reference/nn_log_sigmoid.html b/docs/reference/nn_log_sigmoid.html index 585d2b08f8894c63fa10689b3044e69462598b63..a41c69ee0ca546d69f07070ab88678b4884fb87b 100644 --- a/docs/reference/nn_log_sigmoid.html +++ b/docs/reference/nn_log_sigmoid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ $$ \text{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right) $$" /> - @@ -58,7 +61,7 @@ $$ - +- @@ -194,7 +199,7 @@ dimensions diff --git a/docs/reference/nn_log_softmax.html b/docs/reference/nn_log_softmax.html index f7a46218b6045fa694d3c34fa3e27252edaee18a..382ce2ed16112a1b6404b1202641bdb46e20766d 100644 --- a/docs/reference/nn_log_softmax.html +++ b/docs/reference/nn_log_softmax.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ input Tensor. The LogSoftmax formulation can be simplified as:" /> - +@@ -91,12 +94,17 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ $$
- - + @@ -177,13 +185,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -210,7 +212,7 @@ dimensions diff --git a/docs/reference/nn_max_pool1d.html b/docs/reference/nn_max_pool1d.html index 4f6a012f2c1d45c7769682045f04ea0a0997f7bc..a22779d1ad2cbc329f31695baa2868ceb10e41ee 100644 --- a/docs/reference/nn_max_pool1d.html +++ b/docs/reference/nn_max_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -89,12 +92,17 @@ input Tensor. The LogSoftmax formulation can be simplified as:" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ input Tensor. The LogSoftmax formulation can be simplified as:" />
- - + @@ -190,16 +198,10 @@ dimensions input <- torch_randn(2, 3) output <- m(input)
- @@ -235,7 +239,7 @@ has a nice visualization of what@@ -89,12 +92,17 @@ planes." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
-
-
+
@@ -217,14 +225,10 @@ has a nice visualization of what
dilationdoes. input <- torch_randn(20, 16, 50) output <- m(input)
dilationdoes. diff --git a/docs/reference/nn_max_pool2d.html b/docs/reference/nn_max_pool2d.html index f02d2c5b2af690633c3c53f4742f5a57ae8358b1..1622806273aae815a55bcf0a963c8733232b4eac 100644 --- a/docs/reference/nn_max_pool2d.html +++ b/docs/reference/nn_max_pool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +- @@ -250,7 +253,7 @@ $$ diff --git a/docs/reference/nn_module.html b/docs/reference/nn_module.html index 707ba7b0399850ee390ae5f25b15dd180c67f251..6df5e23a82211eccae253f95ae2ca404737d3248 100644 --- a/docs/reference/nn_module.html +++ b/docs/reference/nn_module.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ planes." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
- - + @@ -231,15 +239,10 @@ $$ input <- torch_randn(20, 16, 50, 32) output <- m(input)
- @@ -209,7 +213,7 @@ structure. You can assign the submodules as regular attributes. diff --git a/docs/reference/nn_module_list.html b/docs/reference/nn_module_list.html index 3011dfcdc099bbf341c1685385020f36d4f3ebcb..8affee0881407709396397d8f2179acad0ae3585 100644 --- a/docs/reference/nn_module_list.html +++ b/docs/reference/nn_module_list.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ nn_module methods." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -191,14 +199,10 @@ structure. You can assign the submodules as regular attributes. } )
- @@ -198,7 +203,7 @@ modules it contains are properly registered, and will be visible by all diff --git a/docs/reference/nn_multihead_attention.html b/docs/reference/nn_multihead_attention.html index 5d456bd1e41c890d562baf72e43fc6ca5418e1ad..1b12c1d30f19baceedf39f1f69ec7b84f17b93db 100644 --- a/docs/reference/nn_multihead_attention.html +++ b/docs/reference/nn_multihead_attention.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ See reference: Attention Is All You Need" /> - +@@ -90,12 +93,17 @@ nn_module methods." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ nn_module methods." />
- - + @@ -181,13 +189,10 @@ modules it contains are properly registered, and will be visible by all } )
- @@ -264,7 +267,7 @@ L is the target sequence length, S is the source sequence length. diff --git a/docs/reference/nn_prelu.html b/docs/reference/nn_prelu.html index 6dd23d656e92b811ca3cf22ef826421e9b74e966..989464704d8e9d0a5e0df0ad7c636e733d519267 100644 --- a/docs/reference/nn_prelu.html +++ b/docs/reference/nn_prelu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -48,7 +52,6 @@ x, & \text{ if } x \geq 0 \\ ax, & \text{ otherwise } \end{cases} $$" /> - @@ -66,7 +69,7 @@ $$" /> - +@@ -90,12 +93,17 @@ See reference: Attention Is All You Need" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ See reference: Attention Is All You Need" />
- - + @@ -245,15 +253,10 @@ L is the target sequence length, S is the source sequence length. attn_output_weights <- out[[2]] }
- @@ -246,7 +247,7 @@ dimensions diff --git a/docs/reference/nn_relu.html b/docs/reference/nn_relu.html index 91f09e7c0a29d477c73635155d5be050491448aa..ba667a6821d554b768121fe46873734491ba619b 100644 --- a/docs/reference/nn_relu.html +++ b/docs/reference/nn_relu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ $$\text{ReLU}(x) = (x)^+ = \max(0, x)$$" /> - +@@ -99,12 +102,17 @@ $$" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -136,7 +144,7 @@ $$" />
- - + @@ -225,17 +233,10 @@ dimensions input <- torch_randn(2) output <- m(input)
diff --git a/docs/reference/nn_relu6.html b/docs/reference/nn_relu6.html index c65269c2ee838b7bf3d5008102a46fd834265782..b475a7e20e366c8f1864daa09456e40a4f62ed2a 100644 --- a/docs/reference/nn_relu6.html +++ b/docs/reference/nn_relu6.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ $$\text{ReLU}(x) = (x)^+ = \max(0, x)$$" />- @@ -203,7 +207,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ $$\text{ReLU}(x) = (x)^+ = \max(0, x)$$" />
- - + @@ -180,19 +188,15 @@ dimensions
#> torch_tensor +#> 1.2679 #> 0.0000 -#> 1.0205 #> [ CPUFloatType{2} ]- @@ -203,7 +206,7 @@ dimensions diff --git a/docs/reference/nn_rnn.html b/docs/reference/nn_rnn.html index d6096ecf0a939c8a73473e479b659901efddf738..81f7e7487aa9f3b9db4e028bc007e78398b451f8 100644 --- a/docs/reference/nn_rnn.html +++ b/docs/reference/nn_rnn.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ to an input sequence." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,15 +192,10 @@ dimensions input <- torch_randn(2) output <- m(input)
-@@ -89,12 +92,17 @@ to an input sequence." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ to an input sequence." />
- - + @@ -294,115 +302,106 @@ where \(k = \frac{1}{\text{hidden\_size}}\) rnn(input, h0)
#> [[1]] #> torch_tensor #> (1,.,.) = -#> Columns 1 to 9 0.4276 0.0772 -0.5042 -0.6518 0.1811 -0.8639 0.2111 0.4346 -0.8817 -#> -0.3943 0.1186 0.2027 -0.0821 0.2938 0.0389 -0.3633 -0.3283 -0.7592 -#> -0.1428 -0.5500 0.5352 0.4765 0.1696 0.7090 0.5303 -0.6220 -0.2569 +#> Columns 1 to 9 -0.7707 -0.2127 0.0833 -0.5020 0.7743 0.9150 0.0502 -0.3490 0.9177 +#> -0.7058 -0.3404 0.9008 0.5612 0.1238 0.1118 0.2086 0.3772 -0.2031 +#> -0.3612 -0.1235 -0.7028 0.4200 0.0411 -0.2072 0.5555 0.9468 -0.0299 #> -#> Columns 10 to 18 -0.6619 -0.5207 0.8410 -0.3293 0.7054 0.0577 -0.8078 -0.5257 0.6800 -#> -0.3951 -0.6624 0.6272 -0.0957 0.3011 -0.6020 0.5136 -0.5943 0.4131 -#> 0.6771 0.9127 -0.3022 0.0709 -0.4073 -0.3627 -0.7434 0.4078 0.6302 +#> Columns 10 to 18 0.1455 0.6700 0.8668 0.7365 0.5706 0.5940 0.5845 -0.3978 -0.3471 +#> 0.0042 -0.0642 0.0454 -0.2421 -0.6776 0.0689 -0.4781 0.5221 -0.8138 +#> -0.7804 0.6880 -0.6256 0.5869 -0.5678 -0.0954 0.0748 -0.7484 0.2925 #> -#> Columns 19 to 20 -0.4580 -0.0341 -#> -0.6723 -0.3000 -#> 0.6360 -0.3711 +#> Columns 19 to 20 0.2962 -0.4581 +#> 0.8697 0.9076 +#> 0.5304 0.0117 #> #> (2,.,.) = -#> Columns 1 to 9 -0.1384 -0.0091 -0.0056 0.3984 0.5643 -0.0460 0.8278 0.3454 -0.5000 -#> 0.2556 -0.4035 -0.7108 0.3854 -0.3852 0.1034 0.5604 0.2520 0.1212 -#> 0.3114 -0.4072 0.4441 0.6002 -0.2457 -0.1299 0.1112 0.3806 -0.3545 +#> Columns 1 to 9 0.3931 0.3354 -0.6032 0.5553 -0.0911 -0.0173 -0.4029 0.1835 0.4575 +#> -0.7199 -0.4652 0.2671 -0.4557 0.0219 -0.0998 -0.2311 -0.6716 0.0719 +#> -0.6873 -0.4190 0.2820 0.4307 -0.3875 -0.0353 0.1192 0.1583 0.0578 #> -#> Columns 10 to 18 -0.2446 0.0692 0.4671 -0.5671 0.4301 0.1657 0.6823 -0.6196 0.3762 -#> -0.7497 0.0340 -0.3474 -0.6738 -0.0242 0.1581 0.5449 -0.4903 -0.3489 -#> 0.1728 0.2955 0.3845 -0.4589 0.2551 0.4480 -0.4291 -0.4480 0.5182 +#> Columns 10 to 18 0.0573 -0.3042 -0.2850 0.2343 0.1400 -0.2909 0.7082 0.3566 -0.0579 +#> 0.5340 -0.3909 -0.1861 -0.2526 0.1529 0.1250 -0.0907 0.2993 0.2565 +#> 0.2468 -0.6759 -0.1813 0.1958 -0.1157 0.7653 0.0484 -0.4860 0.5066 #> -#> Columns 19 to 20 0.0809 -0.4582 -#> -0.2338 -0.5071 -#> -0.1378 -0.0010 +#> Columns 19 to 20 -0.0106 0.5368 +#> 0.6679 -0.0620 +#> 0.8322 0.0632 #> #> (3,.,.) = -#> Columns 1 to 9 -0.3947 -0.4012 -0.3611 0.3505 0.0165 0.1398 -0.0617 0.0763 0.1940 -#> 0.3219 0.4130 0.6590 -0.1642 -0.0500 0.5086 -0.3445 -0.0939 -0.2414 -#> 0.3590 -0.1237 0.0934 -0.2235 -0.2663 0.2981 0.0762 -0.0298 0.4023 +#> Columns 1 to 9 -0.2025 -0.6474 0.4180 -0.3793 -0.0460 0.2731 0.0927 -0.4733 -0.0691 +#> -0.4081 -0.5279 -0.0483 -0.2613 -0.1064 -0.2496 0.1590 -0.2841 0.4583 +#> -0.5098 0.0026 -0.3198 0.6048 -0.1103 0.2459 -0.0079 -0.2687 0.2318 #> -#> Columns 10 to 18 0.0585 0.5780 -0.4226 -0.5474 -0.1996 -0.0245 0.2509 -0.2825 0.0077 -#> 0.5736 0.1683 0.3064 -0.3716 0.3403 -0.0382 0.5370 -0.4994 0.6753 -#> -0.3686 0.2479 -0.0329 -0.6391 -0.5372 -0.0355 -0.4228 0.1528 0.2182 +#> Columns 10 to 18 0.0581 0.4398 -0.3201 0.3175 -0.0661 0.4878 -0.0683 -0.0018 0.1940 +#> 0.0484 -0.1143 0.0090 -0.1148 0.0090 0.2331 0.1753 0.0266 0.1241 +#> 0.4600 -0.2172 -0.1378 -0.4170 -0.1131 0.6376 0.5091 -0.1358 -0.2057 #> -#> Columns 19 to 20 -0.0757 -0.2216 -#> -0.5597 0.6125 -#> -0.1468 -0.1929 +#> Columns 19 to 20 0.3009 -0.2259 +#> -0.0075 0.0121 +#> 0.5144 0.1673 #> #> (4,.,.) = -#> Columns 1 to 9 -0.0795 0.1925 0.5405 0.0138 -0.4712 -0.0736 -0.4220 0.3339 -0.1666 -#> -0.0071 -0.2551 -0.1282 -0.0769 0.1891 0.4082 0.4852 -0.0548 -0.7814 -#> 0.4382 0.2154 0.3009 0.3081 -0.6001 0.3156 -0.0491 0.0601 0.3104 +#> Columns 1 to 9 -0.4531 -0.2168 -0.5408 -0.0071 -0.2930 0.0422 -0.3546 -0.4449 0.3840 +#> -0.3371 -0.4406 -0.0102 -0.1182 -0.3625 0.1465 -0.3650 0.0263 0.5210 +#> -0.3134 -0.1051 -0.2616 0.0008 0.1564 0.3192 -0.0420 -0.0784 0.3283 #> -#> Columns 10 to 18 0.8121 0.2617 0.6845 -0.3926 0.5111 0.3392 0.1697 -0.6972 0.5728 -#> 0.1224 -0.1796 0.4606 -0.2305 0.0887 0.6502 -0.1052 -0.7079 0.3113 -#> -0.4494 0.2919 0.1243 -0.6592 -0.4256 0.2197 0.0171 -0.0662 -0.2708 +#> Columns 10 to 18 0.3663 0.1935 -0.2922 -0.4084 0.1909 0.2754 0.4684 -0.0819 0.2299 +#> 0.1765 -0.1040 -0.4538 0.2761 0.1063 0.2704 0.2483 -0.0832 -0.2099 +#> 0.0862 0.0529 -0.1402 -0.1801 -0.1077 0.4180 0.2165 0.1804 0.0430 #> -#> Columns 19 to 20 -0.6355 0.4968 -#> 0.2371 -0.2860 -#> -0.3225 -0.0697 +#> Columns 19 to 20 -0.1043 -0.1730 +#> 0.2977 0.5321 +#> 0.4780 0.0898 #> #> (5,.,.) = -#> Columns 1 to 9 0.0960 -0.3353 -0.4172 -0.5399 -0.2198 0.1494 0.0701 -0.1455 0.1152 -#> 0.0498 -0.3871 0.1187 0.3674 -0.0330 -0.1328 0.3940 -0.0463 -0.2081 -#> 0.3725 0.2881 0.6440 0.0512 -0.2188 0.4613 0.2503 -0.2195 -0.5595 +#> Columns 1 to 9 -0.1420 -0.4948 -0.0771 -0.4554 -0.1340 0.0152 -0.2039 0.1756 0.5272 +#> 0.0046 -0.2156 -0.1057 0.0029 -0.1536 0.0371 -0.0947 -0.3541 0.3468 +#> -0.3493 -0.0818 0.0744 0.2137 0.3389 -0.0741 0.0560 0.0755 0.3899 #> -#> Columns 10 to 18 0.0065 -0.1936 0.1489 -0.3465 -0.4079 0.2003 -0.4970 -0.1207 -0.1250 -#> 0.2579 0.1203 0.1182 -0.4260 0.4201 -0.3926 0.0258 -0.2187 -0.0547 -#> 0.1541 0.3313 0.5169 -0.1832 -0.0369 0.5818 0.1595 -0.6282 0.5641 +#> Columns 10 to 18 -0.1118 0.1548 -0.1897 0.4725 0.3748 0.1778 -0.0191 -0.2158 0.0108 +#> 0.1313 0.4106 -0.4301 -0.4310 0.4229 0.5693 0.2579 -0.1599 0.3064 +#> 0.2120 -0.2889 0.5255 0.1630 -0.2614 0.4556 -0.1420 0.3162 -0.0570 #> -#> Columns 19 to 20 -0.3074 0.1372 -#> -0.1573 -0.3119 -#> -0.3600 0.3056 +#> Columns 19 to 20 0.0304 -0.1577 +#> 0.2358 -0.2714 +#> 0.2031 0.0557 #> [ CPUFloatType{5,3,20} ] #> #> [[2]] #> torch_tensor #> (1,.,.) = -#> Columns 1 to 9 -0.0959 0.0263 -0.4122 -0.0120 -0.2258 -0.5127 0.5458 0.1145 -0.0379 -#> -0.0000 0.1686 -0.2831 0.0969 -0.7582 -0.0024 0.4333 -0.6053 0.0907 -#> 0.6877 0.3841 0.1194 -0.3983 0.5711 0.4428 -0.4015 0.2521 0.4396 +#> Columns 1 to 9 -0.6077 0.0478 0.0164 -0.2950 -0.2971 -0.4781 0.0169 0.2555 0.0133 +#> -0.4616 0.1612 0.5310 0.1255 -0.2302 0.0283 0.8916 0.6087 -0.3284 +#> -0.2969 -0.3002 -0.5179 0.6761 -0.2708 -0.5304 0.6671 -0.4906 0.0421 #> -#> Columns 10 to 18 -0.2907 -0.4035 -0.6876 -0.2241 0.4380 0.1664 -0.0698 -0.3933 -0.4121 -#> -0.3388 -0.2028 -0.5774 -0.1317 -0.3398 -0.3893 0.5495 -0.4529 -0.0072 -#> 0.1676 0.2725 0.8899 0.6866 0.7163 -0.4537 -0.2172 0.1430 -0.0643 +#> Columns 10 to 18 0.6736 0.2246 0.1710 0.3099 -0.1167 0.0170 0.5478 -0.2716 0.2036 +#> 0.6366 -0.3136 0.6336 -0.4472 -0.4417 0.4317 0.2124 0.4570 0.1472 +#> -0.4469 -0.3307 -0.1321 -0.3429 0.3623 -0.3800 0.4302 0.1987 -0.2188 #> -#> Columns 19 to 20 -0.6915 -0.2926 -#> -0.1140 -0.0890 -#> 0.2944 0.4723 +#> Columns 19 to 20 0.0917 0.2792 +#> -0.0328 0.1105 +#> 0.0621 -0.1483 #> #> (2,.,.) = -#> Columns 1 to 9 0.0960 -0.3353 -0.4172 -0.5399 -0.2198 0.1494 0.0701 -0.1455 0.1152 -#> 0.0498 -0.3871 0.1187 0.3674 -0.0330 -0.1328 0.3940 -0.0463 -0.2081 -#> 0.3725 0.2881 0.6440 0.0512 -0.2188 0.4613 0.2503 -0.2195 -0.5595 +#> Columns 1 to 9 -0.1420 -0.4948 -0.0771 -0.4554 -0.1340 0.0152 -0.2039 0.1756 0.5272 +#> 0.0046 -0.2156 -0.1057 0.0029 -0.1536 0.0371 -0.0947 -0.3541 0.3468 +#> -0.3493 -0.0818 0.0744 0.2137 0.3389 -0.0741 0.0560 0.0755 0.3899 #> -#> Columns 10 to 18 0.0065 -0.1936 0.1489 -0.3465 -0.4079 0.2003 -0.4970 -0.1207 -0.1250 -#> 0.2579 0.1203 0.1182 -0.4260 0.4201 -0.3926 0.0258 -0.2187 -0.0547 -#> 0.1541 0.3313 0.5169 -0.1832 -0.0369 0.5818 0.1595 -0.6282 0.5641 +#> Columns 10 to 18 -0.1118 0.1548 -0.1897 0.4725 0.3748 0.1778 -0.0191 -0.2158 0.0108 +#> 0.1313 0.4106 -0.4301 -0.4310 0.4229 0.5693 0.2579 -0.1599 0.3064 +#> 0.2120 -0.2889 0.5255 0.1630 -0.2614 0.4556 -0.1420 0.3162 -0.0570 #> -#> Columns 19 to 20 -0.3074 0.1372 -#> -0.1573 -0.3119 -#> -0.3600 0.3056 +#> Columns 19 to 20 0.0304 -0.1577 +#> 0.2358 -0.2714 +#> 0.2031 0.0557 #> [ CPUFloatType{2,3,20} ] #>-@@ -413,7 +412,7 @@ where \(k = \frac{1}{\text{hidden\_size}}\) diff --git a/docs/reference/nn_rrelu.html b/docs/reference/nn_rrelu.html index 1fd40b5e82b53c2404dc3010fecfbcfb6a483593..64123be81b43b0fe719569bf656213ef0fdec63e 100644 --- a/docs/reference/nn_rrelu.html +++ b/docs/reference/nn_rrelu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ as described in the paper:" /> - +Contents
- - ++diff --git a/docs/reference/nn_selu.html b/docs/reference/nn_selu.html index ef3b34a696182ec3794c197d10d6719c0f72a2c5..28bbe9babcb96fad9d5f910de9caf3535ee00b4a 100644 --- a/docs/reference/nn_selu.html +++ b/docs/reference/nn_selu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ as described in the paper:" />- @@ -225,7 +227,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ as described in the paper:" />
- - + @@ -200,21 +208,15 @@ dimensions
#> torch_tensor -#> 0.01 * -#> -8.0921 -#> -31.8194 +#> 0.9540 +#> 0.2055 #> [ CPUFloatType{2} ]- @@ -207,7 +210,7 @@ dimensions diff --git a/docs/reference/nn_sequential.html b/docs/reference/nn_sequential.html index 647f3c9a9cd9c88b6ddf54ec68ae5b791cbe154c..b1329becc60453e879a3b5b26a310d23de40c51d 100644 --- a/docs/reference/nn_sequential.html +++ b/docs/reference/nn_sequential.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ See examples." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -188,15 +196,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -200,7 +205,7 @@ See examples. diff --git a/docs/reference/nn_sigmoid.html b/docs/reference/nn_sigmoid.html index d08af85f0c2eda0cc16d9e1613209a01312a4bc1..9f525657a4b0f805bff9c4dbd14531f3f099a237 100644 --- a/docs/reference/nn_sigmoid.html +++ b/docs/reference/nn_sigmoid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ See examples." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ See examples." />
- - + @@ -183,13 +191,10 @@ See examples. input <- torch_randn(32, 1, 28, 28) output <- model(input)
- @@ -194,7 +198,7 @@ dimensions diff --git a/docs/reference/nn_softmax.html b/docs/reference/nn_softmax.html index e86cecf8eb2b678b8642e972ccc4a27be29e7214..5939ddf36b366050c43cfd8a2b0ea25123b14ec7 100644 --- a/docs/reference/nn_softmax.html +++ b/docs/reference/nn_softmax.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ rescaling them so that the elements of the n-dimensional output Tensor lie in the range 0,1 and sum to 1. Softmax is defined as:" /> - @@ -58,7 +61,7 @@ Softmax is defined as:" /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,14 +184,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -224,7 +225,7 @@ dimensions diff --git a/docs/reference/nn_softmax2d.html b/docs/reference/nn_softmax2d.html index bab80541bb31696217602197dfdc775b16e0e5c1..b1eb619deed0361f53c5abd396f8284e8bb7da30 100644 --- a/docs/reference/nn_softmax2d.html +++ b/docs/reference/nn_softmax2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ apply Softmax to each location \((Channels, h_i, w_j)\)" /> - +@@ -91,12 +94,17 @@ Softmax is defined as:" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ Softmax is defined as:" />
- - + @@ -203,17 +211,10 @@ dimensions input <- torch_randn(2, 3) output <- m(input)
- @@ -196,7 +200,7 @@ values in the range 0, 1 diff --git a/docs/reference/nn_softmin.html b/docs/reference/nn_softmin.html index 1ce710ac9cf340465a2a24fc101d8911ca3d1692..7f9dfada859375e27f99cff12f01ae00bd0b77be 100644 --- a/docs/reference/nn_softmin.html +++ b/docs/reference/nn_softmin.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0, 1] and sum to 1. Softmin is defined as:" /> - @@ -58,7 +61,7 @@ Softmin is defined as:" /> - +@@ -90,12 +93,17 @@ apply Softmax to each location \((Channels, h_i, w_j)\)" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ apply Softmax to each location \((Channels, h_i, w_j)\)" />
- - + @@ -178,14 +186,10 @@ values in the range 0, 1 input <- torch_randn(2, 3, 12, 13) output <- m(input)
- @@ -215,7 +217,7 @@ dimensions diff --git a/docs/reference/nn_softplus.html b/docs/reference/nn_softplus.html index 57ff99ec8536ddf31afedf92e007304a991edab6..69ad7fb5f63ee99e0d988c8a5c72c1930a652e07 100644 --- a/docs/reference/nn_softplus.html +++ b/docs/reference/nn_softplus.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ $$ \text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) $$" /> - @@ -58,7 +61,7 @@ $$" /> - +@@ -91,12 +94,17 @@ Softmin is defined as:" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ Softmin is defined as:" />
- - + @@ -195,16 +203,10 @@ dimensions input <- torch_randn(2, 2) output <- m(input)
- @@ -214,7 +217,7 @@ dimensions diff --git a/docs/reference/nn_softshrink.html b/docs/reference/nn_softshrink.html index d2b095a3684585ba57a67f98c5c03ca718ceed61..e47dc21a3003994181b2a6bb939e1c95f52ef227 100644 --- a/docs/reference/nn_softshrink.html +++ b/docs/reference/nn_softshrink.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -91,12 +94,17 @@ $$" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ $$" />
- - + @@ -195,15 +203,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -207,7 +210,7 @@ dimensions diff --git a/docs/reference/nn_softsign.html b/docs/reference/nn_softsign.html index 0e4433aca552b3fd92c28039ae956260eccddbe4..dd18a649b93c7559132e054d8f5716214ec0d864 100644 --- a/docs/reference/nn_softsign.html +++ b/docs/reference/nn_softsign.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ $$ \text{SoftSign}(x) = \frac{x}{ 1 + |x|} $$" /> - @@ -58,7 +61,7 @@ $$" /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -188,15 +196,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -194,7 +199,7 @@ dimensions diff --git a/docs/reference/nn_tanh.html b/docs/reference/nn_tanh.html index 87c179c20d9909faf69bc72b378a8c81206695e4..83b2e69859475ac07708a425330c7e27fe05373c 100644 --- a/docs/reference/nn_tanh.html +++ b/docs/reference/nn_tanh.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -91,12 +94,17 @@ $$" />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ $$" />
- - + @@ -177,13 +185,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -194,7 +198,7 @@ dimensions diff --git a/docs/reference/nn_tanhshrink.html b/docs/reference/nn_tanhshrink.html index b674a511a526a1098187b98034fba3b8eff180e5..8ceaf9719baf646fddc7b2513daaeebdb8f3e5bd 100644 --- a/docs/reference/nn_tanhshrink.html +++ b/docs/reference/nn_tanhshrink.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,14 +184,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -194,7 +198,7 @@ dimensions diff --git a/docs/reference/nn_threshold.html b/docs/reference/nn_threshold.html index 805087ca7dc621f92322e69ecb2246f84d757f8f..8ad7e62b273aa819d3a0dbd828d706996d0a6b6e 100644 --- a/docs/reference/nn_threshold.html +++ b/docs/reference/nn_threshold.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,14 +184,10 @@ dimensions input <- torch_randn(2) output <- m(input)
- @@ -216,7 +219,7 @@ dimensions diff --git a/docs/reference/nn_utils_rnn_pack_padded_sequence.html b/docs/reference/nn_utils_rnn_pack_padded_sequence.html index 2d8b5ca18e6188fd16cd3f48bd0c37e1b676d66a..67d32c086f2b0774ddc41f9c3489ced9e3a19359 100644 --- a/docs/reference/nn_utils_rnn_pack_padded_sequence.html +++ b/docs/reference/nn_utils_rnn_pack_padded_sequence.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ longest sequence (equal to lengths[1]), B is the batch size, and * is any number of dimensions (including 0). If batch_first is TRUE, B x T x * input is expected." /> - @@ -58,7 +61,7 @@ TRUE, B x T x * input is expected." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -197,15 +205,10 @@ dimensions input <- torch_randn(2) output <- m(input)
diff --git a/docs/reference/nn_utils_rnn_pack_sequence.html b/docs/reference/nn_utils_rnn_pack_sequence.html index 4746f9a58d1f84792746f4b344b0503e9e2edfa1..56653e0ae04c536885bd3b46796116a17d65001c 100644 --- a/docs/reference/nn_utils_rnn_pack_sequence.html +++ b/docs/reference/nn_utils_rnn_pack_sequence.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ including zero." /> - +@@ -91,12 +94,17 @@ TRUE, B x T x * input is expected." />- @@ -225,7 +228,7 @@ a-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ TRUE, B x T x * input is expected." />
-
-
+
@@ -206,15 +214,10 @@ them to compute the loss directly. A Tensor can be retrieved from
a
PackedSequenceobject by accessing its.dataattribute.
PackedSequenceobject by accessing its.dataattribu- @@ -208,7 +211,7 @@ is@@ -90,12 +93,17 @@ including zero." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ including zero." />
TRUE, the sequences should be sorted in the order of decreasing diff --git a/docs/reference/nn_utils_rnn_pad_packed_sequence.html b/docs/reference/nn_utils_rnn_pad_packed_sequence.html index 94da2929d3b889278903626d36a70392c8bad86e..5e981770c9168a3ac0b7ed477d9d598a094db1aa 100644 --- a/docs/reference/nn_utils_rnn_pad_packed_sequence.html +++ b/docs/reference/nn_utils_rnn_pad_packed_sequence.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -250,7 +252,7 @@ the data will be transposed into@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -230,16 +238,10 @@ the data will be transposed into
B x T x *format. #>
B x T x *format. diff --git a/docs/reference/nn_utils_rnn_pad_sequence.html b/docs/reference/nn_utils_rnn_pad_sequence.html index a9c7e7330b3852ea6b27c7c1c9560a455ec7b0a5..eb84dbbfde645ab41cb7aca0561d081938929b36 100644 --- a/docs/reference/nn_utils_rnn_pad_sequence.html +++ b/docs/reference/nn_utils_rnn_pad_sequence.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ and pads them to equal length. For example, if the input is list of sequences with size L x * and if batch_first is False, and T x B x * otherwise." /> - @@ -58,7 +61,7 @@ otherwise." /> - +- @@ -221,7 +223,7 @@ trailing dimensions and type of all the Tensors in sequences are same. diff --git a/docs/reference/nnf_adaptive_avg_pool1d.html b/docs/reference/nnf_adaptive_avg_pool1d.html index 98920d8010db90aff8326a7865f4f3d7155f6b4c..06a92f0fb3575d122fa148d1f1ccce8a9525be24 100644 --- a/docs/reference/nnf_adaptive_avg_pool1d.html +++ b/docs/reference/nnf_adaptive_avg_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ several input planes." /> - +@@ -91,12 +94,17 @@ otherwise." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ otherwise." />
#> [1] 25 3 300diff --git a/docs/reference/nnf_adaptive_avg_pool2d.html b/docs/reference/nnf_adaptive_avg_pool2d.html index da62c5d0c66ecc20f682397cafff8677a849806f..27ad35dd4dc79d075fa1f45978bcb7f3ae3cf6ca 100644 --- a/docs/reference/nnf_adaptive_avg_pool2d.html +++ b/docs/reference/nnf_adaptive_avg_pool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ several input planes." /> - +@@ -89,12 +92,17 @@ several input planes." />- @@ -187,7 +193,7 @@ several input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ several input planes." />
- - + @@ -171,12 +179,10 @@ several input planes.
diff --git a/docs/reference/nnf_adaptive_avg_pool3d.html b/docs/reference/nnf_adaptive_avg_pool3d.html index 9ef7c8f2281c7429f4250ea16d1114465de3da31..8d9fd7db66ac7f0020e4c8a8c06023f7dbc1fe69 100644 --- a/docs/reference/nnf_adaptive_avg_pool3d.html +++ b/docs/reference/nnf_adaptive_avg_pool3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ several input planes." /> - +@@ -89,12 +92,17 @@ several input planes." />- @@ -187,7 +193,7 @@ several input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ several input planes." />
- - + @@ -171,12 +179,10 @@ several input planes.
diff --git a/docs/reference/nnf_adaptive_max_pool1d.html b/docs/reference/nnf_adaptive_max_pool1d.html index 789ae160742dbe00995caa1b65514b1fb3986c95..095bfe943be87744277ee5ab3e2464802287eb93 100644 --- a/docs/reference/nnf_adaptive_max_pool1d.html +++ b/docs/reference/nnf_adaptive_max_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ several input planes." /> - +@@ -89,12 +92,17 @@ several input planes." />- @@ -187,7 +193,7 @@ several input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ several input planes." />
- - + @@ -171,12 +179,10 @@ several input planes.
diff --git a/docs/reference/nnf_adaptive_max_pool2d.html b/docs/reference/nnf_adaptive_max_pool2d.html index fcc399ef3745d83f279861d2eee28dfd5245ea5e..d442661f3a14a92cf71c693a8d816e5640ec0c3f 100644 --- a/docs/reference/nnf_adaptive_max_pool2d.html +++ b/docs/reference/nnf_adaptive_max_pool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ several input planes." /> - +@@ -89,12 +92,17 @@ several input planes." />- @@ -191,7 +197,7 @@ several input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ several input planes." />
- - + @@ -175,12 +183,10 @@ several input planes.
diff --git a/docs/reference/nnf_adaptive_max_pool3d.html b/docs/reference/nnf_adaptive_max_pool3d.html index 2785f66058fc822e1173f9d18a1e4e73cc1091d1..937ed9706ad72cbdac70b0bb98a91c8684a60f20 100644 --- a/docs/reference/nnf_adaptive_max_pool3d.html +++ b/docs/reference/nnf_adaptive_max_pool3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ several input planes." /> - +@@ -89,12 +92,17 @@ several input planes." />- @@ -191,7 +197,7 @@ several input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ several input planes." />
- - + @@ -175,12 +183,10 @@ several input planes.
diff --git a/docs/reference/nnf_affine_grid.html b/docs/reference/nnf_affine_grid.html index dfa474bf5228b2213d1617433dc01a488b843250..e1e59eb455825cd98819e99ed161de4bbe032d27 100644 --- a/docs/reference/nnf_affine_grid.html +++ b/docs/reference/nnf_affine_grid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ affine matrices theta." /> - +@@ -89,12 +92,17 @@ several input planes." />- @@ -191,7 +197,7 @@ several input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ several input planes." />
- - + @@ -175,12 +183,10 @@ several input planes.
diff --git a/docs/reference/nnf_alpha_dropout.html b/docs/reference/nnf_alpha_dropout.html index f02471d4bf4e8766aafc61f1957fa0133da4e002..1bd3bc8236e736635626cb9b9eadf36df7d682ed 100644 --- a/docs/reference/nnf_alpha_dropout.html +++ b/docs/reference/nnf_alpha_dropout.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ affine matrices theta." />- @@ -206,7 +211,7 @@ to build-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ affine matrices theta." />
-
-
+
@@ -189,13 +197,10 @@ this option. Default:
Falseto buildSpatial Transformer Networks_ .
Spatial Transformer Networks_ .diff --git a/docs/reference/nnf_avg_pool1d.html b/docs/reference/nnf_avg_pool1d.html index 44b0569264f300014710e9b046c7fd8a93557f65..aa7e5e5ebf1374351c7267353352803929b662a1 100644 --- a/docs/reference/nnf_avg_pool1d.html +++ b/docs/reference/nnf_avg_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ input planes." /> - +@@ -88,12 +91,17 @@- @@ -194,7 +200,7 @@ Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -178,12 +186,10 @@ Default:
FALSE
FALSEdiff --git a/docs/reference/nnf_avg_pool2d.html b/docs/reference/nnf_avg_pool2d.html index 78c574dda006953b131c1ed2a26207d3090ee23d..3cba82471e52f77aa2ac8e272cd491871fe54029 100644 --- a/docs/reference/nnf_avg_pool2d.html +++ b/docs/reference/nnf_avg_pool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ input planes." /> - +@@ -89,12 +92,17 @@ input planes." />- @@ -215,7 +221,7 @@ averaging calculation. Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ input planes." />
-
-
+
@@ -199,12 +207,10 @@ averaging calculation. Default:
TRUE
TRUEdiff --git a/docs/reference/nnf_avg_pool3d.html b/docs/reference/nnf_avg_pool3d.html index 1e200f912f731f3554498c220c666153e55e57b5..3b5c9db3c9b114392625710945d49f548ae2eb78 100644 --- a/docs/reference/nnf_avg_pool3d.html +++ b/docs/reference/nnf_avg_pool3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ size \(sT * sH * sW\) steps. The number of output features is equal to - +@@ -90,12 +93,17 @@ input planes." />- @@ -223,7 +229,7 @@ size of the pooling region will be used. Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ input planes." />
-
-
+
@@ -207,12 +215,10 @@ size of the pooling region will be used. Default:
NULL
NULLdiff --git a/docs/reference/nnf_batch_norm.html b/docs/reference/nnf_batch_norm.html index 2217d4618db9169497b0999140a67fb63ec0b9b1..2010a9e8d78cb039b5448d5afc4abac6f02bbaee 100644 --- a/docs/reference/nnf_batch_norm.html +++ b/docs/reference/nnf_batch_norm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ size \(sT * sH * sW\) steps. The number of output features is equal to- @@ -223,7 +229,7 @@ size of the pooling region will be used. Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ size \(sT * sH * sW\) steps. The number of output features is equal to
-
-
+
@@ -207,12 +215,10 @@ size of the pooling region will be used. Default:
NULL
NULLdiff --git a/docs/reference/nnf_bilinear.html b/docs/reference/nnf_bilinear.html index d39fecaa00ae548a9c9283e1820d7b8d1380b8b3..f7a0ac0b06d32b91b3281c12f9bf24ecd34668de 100644 --- a/docs/reference/nnf_bilinear.html +++ b/docs/reference/nnf_bilinear.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ - +@@ -88,12 +91,17 @@- @@ -219,7 +225,7 @@ Can be set to None for cumulative moving average (i.e. simple average). Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -203,12 +211,10 @@ Can be set to None for cumulative moving average (i.e. simple average). Default:
diff --git a/docs/reference/nnf_binary_cross_entropy.html b/docs/reference/nnf_binary_cross_entropy.html index 0269bc7b2b93be646db5d22545cb6966ae6ba696..acdfed0288d7110696ee57e00c99b6f897e501ba 100644 --- a/docs/reference/nnf_binary_cross_entropy.html +++ b/docs/reference/nnf_binary_cross_entropy.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ between the target and the output." /> - +@@ -89,12 +92,17 @@- @@ -203,7 +209,7 @@ and all but the last dimension are the same shape as the input.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@
- - + @@ -187,12 +195,10 @@ and all but the last dimension are the same shape as the input.
diff --git a/docs/reference/nnf_binary_cross_entropy_with_logits.html b/docs/reference/nnf_binary_cross_entropy_with_logits.html index 5974920b3e4b20fe8db574350bbd38f802108238..aec0cc570fd372d166ab17ad947ac2a6f822e1b1 100644 --- a/docs/reference/nnf_binary_cross_entropy_with_logits.html +++ b/docs/reference/nnf_binary_cross_entropy_with_logits.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ logits." /> - +@@ -89,12 +92,17 @@ between the target and the output." />- @@ -199,7 +205,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ between the target and the output." />
- - + @@ -183,12 +191,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_celu.html b/docs/reference/nnf_celu.html index b897d2fcae338208670ae5cadf2abd9cee831b57..05da6e31687e9356d90a34ab4108212ab7194f33 100644 --- a/docs/reference/nnf_celu.html +++ b/docs/reference/nnf_celu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ logits." />- @@ -211,7 +217,7 @@ Must be a vector with length equal to the number of classes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ logits." />
- - + @@ -195,12 +203,10 @@ Must be a vector with length equal to the number of classes.
diff --git a/docs/reference/nnf_conv1d.html b/docs/reference/nnf_conv1d.html index 9a147530bd5dd3fb7d72dbb81de5dd5c070c9f93..ee18aaf6f19a9794158d11e1500354ff1c5d9d34 100644 --- a/docs/reference/nnf_conv1d.html +++ b/docs/reference/nnf_conv1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -88,12 +91,17 @@- @@ -192,7 +198,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,12 +184,10 @@ dimensions
diff --git a/docs/reference/nnf_conv2d.html b/docs/reference/nnf_conv2d.html index de4ea4d65ea111562935a72841a975a3b2b109e6..70114b845db333770a99b73f5b93b459ea5018fd 100644 --- a/docs/reference/nnf_conv2d.html +++ b/docs/reference/nnf_conv2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -89,12 +92,17 @@ planes." />- @@ -219,7 +225,7 @@ the number of groups. Default: 1-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
- - + @@ -203,12 +211,10 @@ the number of groups. Default: 1
diff --git a/docs/reference/nnf_conv3d.html b/docs/reference/nnf_conv3d.html index 26aa5d0fa5bc342a2c6e9ab10e3fed645ad8e8ed..4fb8675babd4adacf2bab45b53ad5332c13f8257 100644 --- a/docs/reference/nnf_conv3d.html +++ b/docs/reference/nnf_conv3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -89,12 +92,17 @@ planes." />- @@ -219,7 +225,7 @@ number of groups. Default: 1-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
- - + @@ -203,12 +211,10 @@ number of groups. Default: 1
diff --git a/docs/reference/nnf_conv_tbc.html b/docs/reference/nnf_conv_tbc.html index de69c683ac111aaf73cd2e1adb13582713b13fdd..5267c0c0ce3983558bd52e1e38a5bb8c30483136 100644 --- a/docs/reference/nnf_conv_tbc.html +++ b/docs/reference/nnf_conv_tbc.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC." /> - +@@ -89,12 +92,17 @@ planes." />- @@ -219,7 +225,7 @@ the number of groups. Default: 1-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
- - + @@ -203,12 +211,10 @@ the number of groups. Default: 1
diff --git a/docs/reference/nnf_conv_transpose1d.html b/docs/reference/nnf_conv_transpose1d.html index e9f5cf9ea6d9b74ebd1661af1d7c0defd14c49b3..54e7178c961a031b67b759c1ffebf70ee78b9506 100644 --- a/docs/reference/nnf_conv_transpose1d.html +++ b/docs/reference/nnf_conv_transpose1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ composed of several input planes, sometimes also called "deconvolution" - +@@ -89,12 +92,17 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC." />- @@ -197,7 +203,7 @@ batch \times \text{in\_channels})\)-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC." />
- - + @@ -181,12 +189,10 @@ batch \times \text{in\_channels})\)
diff --git a/docs/reference/nnf_conv_transpose2d.html b/docs/reference/nnf_conv_transpose2d.html index 6ee3e32b2a960962b2471bf02628061ed7b7fa48..e3c2977bd2b85de114663e05ba62060feab85766 100644 --- a/docs/reference/nnf_conv_transpose2d.html +++ b/docs/reference/nnf_conv_transpose2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ composed of several input planes, sometimes also called "deconvolution" - +@@ -89,12 +92,17 @@ composed of several input planes, sometimes also called "deconvolution"- @@ -220,7 +226,7 @@ a one-element tuple-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ composed of several input planes, sometimes also called "deconvolution"
-
-
+
@@ -204,12 +212,10 @@ a one-element tuple
(dW,). Default: 1
(dW,). Default: 1diff --git a/docs/reference/nnf_conv_transpose3d.html b/docs/reference/nnf_conv_transpose3d.html index 07187716cf8f4ae6bc2169bf3630368083bd6fa4..4c5ade49f4f581adecd862d1dd73503ff6a88e6d 100644 --- a/docs/reference/nnf_conv_transpose3d.html +++ b/docs/reference/nnf_conv_transpose3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ composed of several input planes, sometimes also called "deconvolution" - +@@ -89,12 +92,17 @@ composed of several input planes, sometimes also called "deconvolution"- @@ -220,7 +226,7 @@ a tuple-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ composed of several input planes, sometimes also called "deconvolution"
-
-
+
@@ -204,12 +212,10 @@ a tuple
(dH, dW). Default: 1
(dH, dW). Default: 1diff --git a/docs/reference/nnf_cosine_embedding_loss.html b/docs/reference/nnf_cosine_embedding_loss.html index af38ff8c4adc5cda9cd0cbf53beb9a21628bd759..4194dfb85eda52d19ea4b7b1793a907945ed4d75 100644 --- a/docs/reference/nnf_cosine_embedding_loss.html +++ b/docs/reference/nnf_cosine_embedding_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ Tensor label y with values 1 or -1. This is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning." /> - @@ -58,7 +61,7 @@ for learning nonlinear embeddings or semi-supervised learning." /> - +@@ -89,12 +92,17 @@ composed of several input planes, sometimes also called "deconvolution"- @@ -220,7 +226,7 @@ a tuple-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ composed of several input planes, sometimes also called "deconvolution"
-
-
+
@@ -204,12 +212,10 @@ a tuple
(dT, dH, dW). Default: 1
(dT, dH, dW). Default: 1diff --git a/docs/reference/nnf_cosine_similarity.html b/docs/reference/nnf_cosine_similarity.html index 5483192d2442ae011fb13c6464147af525bcd298..aa30cb7f51aa1fc25f5c58338d1b99950a8aeb40 100644 --- a/docs/reference/nnf_cosine_similarity.html +++ b/docs/reference/nnf_cosine_similarity.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -91,12 +94,17 @@ for learning nonlinear embeddings or semi-supervised learning." />- @@ -213,7 +219,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ for learning nonlinear embeddings or semi-supervised learning." />
- - + @@ -197,12 +205,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_cross_entropy.html b/docs/reference/nnf_cross_entropy.html index f80c69beb501ad6f634ad179dc1cc853cfe12c7b..2dfa00f581e4c3e29d957cf7b65e80e83d73fa5f 100644 --- a/docs/reference/nnf_cross_entropy.html +++ b/docs/reference/nnf_cross_entropy.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ function." /> - +@@ -88,12 +91,17 @@- @@ -200,7 +205,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -183,13 +191,10 @@ Default: 1e-8 $$
diff --git a/docs/reference/nnf_ctc_loss.html b/docs/reference/nnf_ctc_loss.html index 9816fd7bbadf841cadcb97aa5e12c3a07fa25c20..10f09358ba292f91d94b31247a787c62c5e22dae 100644 --- a/docs/reference/nnf_ctc_loss.html +++ b/docs/reference/nnf_ctc_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ function." />- @@ -213,7 +219,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ function." />
- - + @@ -197,12 +205,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_dropout.html b/docs/reference/nnf_dropout.html index b3579a91d2a4f7aeaf927f7c249ac4e53b192b05..e58dd6b73f898ea11030c3dbeb8c8215beb09c39 100644 --- a/docs/reference/nnf_dropout.html +++ b/docs/reference/nnf_dropout.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ distribution." /> - +@@ -88,12 +91,17 @@- @@ -221,7 +227,7 @@ inputs are too short to be aligned to the targets.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -205,12 +213,10 @@ inputs are too short to be aligned to the targets.
diff --git a/docs/reference/nnf_dropout2d.html b/docs/reference/nnf_dropout2d.html index a3dcfd28a05fb274b128df9f32bcda7372eef450..9347f06330e66270e188639944986d912fca7662 100644 --- a/docs/reference/nnf_dropout2d.html +++ b/docs/reference/nnf_dropout2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ e.g., the \(j\)-th channel of the \(i\)-th sample in the batched input is a 2D tensor \(input[i, j]\)) of the input tensor). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution." /> - @@ -59,7 +62,7 @@ probability p using samples from a Bernoulli distribution." /> - +@@ -90,12 +93,17 @@ distribution." />- @@ -198,7 +204,7 @@ Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ distribution." />
-
-
+
@@ -182,12 +190,10 @@ Default:
FALSE
FALSEdiff --git a/docs/reference/nnf_dropout3d.html b/docs/reference/nnf_dropout3d.html index eda0daf4c0ce5da064049851a063599fe3911832..b86ac8964f610544fb38728aafe880cee2b395b2 100644 --- a/docs/reference/nnf_dropout3d.html +++ b/docs/reference/nnf_dropout3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ e.g., the \(j\)-th channel of the \(i\)-th sample in the batched input is a 3D tensor \(input[i, j]\)) of the input tensor). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution." /> - @@ -59,7 +62,7 @@ probability p using samples from a Bernoulli distribution." /> - +@@ -92,12 +95,17 @@ probability p using samples from a Bernoulli distribution." />- @@ -202,7 +208,7 @@ Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -129,7 +137,7 @@ probability p using samples from a Bernoulli distribution." />
-
-
+
@@ -186,12 +194,10 @@ Default:
FALSE
FALSEdiff --git a/docs/reference/nnf_elu.html b/docs/reference/nnf_elu.html index be292a1e97f59cdb2e49021177086f7333293303..b4c3b141d8196f2aa0867e325c401c82e45b94f6 100644 --- a/docs/reference/nnf_elu.html +++ b/docs/reference/nnf_elu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." /> - +@@ -92,12 +95,17 @@ probability p using samples from a Bernoulli distribution." />- @@ -202,7 +208,7 @@ Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -129,7 +137,7 @@ probability p using samples from a Bernoulli distribution." />
-
-
+
@@ -186,12 +194,10 @@ Default:
FALSE
FALSEdiff --git a/docs/reference/nnf_embedding.html b/docs/reference/nnf_embedding.html index 5190e472ee7cfcbcfd72af8bbfd8be0b722837ae..1c283b1d4d5a5c752cde977d7ab7939fe0bbb7b0 100644 --- a/docs/reference/nnf_embedding.html +++ b/docs/reference/nnf_embedding.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." />- @@ -203,7 +208,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." />
-
-
+
@@ -181,18 +189,15 @@ dimensions
#> torch_tensor -#> -0.3243 0.4303 -#> 0.4869 0.3897 +#> 1.1223 -0.7585 +#> -0.1866 -0.7298 #> [ CPUFloatType{2,2} ]#> [1] TRUE
diff --git a/docs/reference/nnf_embedding_bag.html b/docs/reference/nnf_embedding_bag.html index 03d05d6adef87f49684e4e8b7be1d840175b35eb..a6021bcb20430585e86163aa435afff12ae3543d 100644 --- a/docs/reference/nnf_embedding_bag.html +++ b/docs/reference/nnf_embedding_bag.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ intermediate embeddings." /> - +@@ -88,12 +91,17 @@- @@ -227,7 +232,7 @@ and the output is the corresponding word embeddings.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -210,13 +218,10 @@ The input to the module is a list of indices, and the embedding matrix, and the output is the corresponding word embeddings.
diff --git a/docs/reference/nnf_fold.html b/docs/reference/nnf_fold.html index 2f0eb6a1092867cc0c15ed6f822f62269e45e3e9..67dd78f8acfa13588b56c37a3ccfe823e83ff2fc 100644 --- a/docs/reference/nnf_fold.html +++ b/docs/reference/nnf_fold.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ tensor." /> - +@@ -89,12 +92,17 @@ intermediate embeddings." />- @@ -243,7 +249,7 @@ equal to the number of bags + 1.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ intermediate embeddings." />
- - + @@ -227,12 +235,10 @@ equal to the number of bags + 1.
diff --git a/docs/reference/nnf_fractional_max_pool2d.html b/docs/reference/nnf_fractional_max_pool2d.html index d554005f6b76da074a64cf689eb66ef8624badff..0a96dfc48e441231f1ec1e64f46e4fb1df65b8b7 100644 --- a/docs/reference/nnf_fractional_max_pool2d.html +++ b/docs/reference/nnf_fractional_max_pool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ tensor." />- @@ -222,7 +227,7 @@ supported.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ tensor." />
- - + @@ -205,13 +213,10 @@ Default: 1 supported.
diff --git a/docs/reference/nnf_fractional_max_pool3d.html b/docs/reference/nnf_fractional_max_pool3d.html index cd4f1f84430df568b21f882b9cfd24779eadd6d1..ed16224702d801c444b83e00de8d8a3ead2ee119 100644 --- a/docs/reference/nnf_fractional_max_pool3d.html +++ b/docs/reference/nnf_fractional_max_pool3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -219,7 +224,7 @@ The number of output features is equal to the number of input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -202,13 +210,10 @@ step size determined by the target output size. The number of output features is equal to the number of input planes.
diff --git a/docs/reference/nnf_gelu.html b/docs/reference/nnf_gelu.html index 2126f9737b136b3363bb8c6cf240e8b1b8f7c4ed..5c514ce91ebcea4f9c9787b9e3dce834a38d9a44 100644 --- a/docs/reference/nnf_gelu.html +++ b/docs/reference/nnf_gelu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -215,7 +220,7 @@ The number of output features is equal to the number of input planes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -198,13 +206,10 @@ step size determined by the target output size. The number of output features is equal to the number of input planes.
diff --git a/docs/reference/nnf_glu.html b/docs/reference/nnf_glu.html index 702fedd220a989b2ae755f5c0ecb68ae6723f48e..97c3a514ed87a76965fe7c3fad4c4d691b1fb93b 100644 --- a/docs/reference/nnf_glu.html +++ b/docs/reference/nnf_glu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -193,7 +198,7 @@ Gaussian Distribution.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,13 +184,10 @@ Gaussian Distribution.
diff --git a/docs/reference/nnf_grid_sample.html b/docs/reference/nnf_grid_sample.html index 0326076972a2290300bec9da66024220150f578b..2c899a12c880f151f9dce7d3fc6b058b655ba28c 100644 --- a/docs/reference/nnf_grid_sample.html +++ b/docs/reference/nnf_grid_sample.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ output using input values and pixel locations from grid." /> - +@@ -88,12 +91,17 @@- @@ -193,7 +198,7 @@ between matrices.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,13 +184,10 @@ between matrices.
diff --git a/docs/reference/nnf_group_norm.html b/docs/reference/nnf_group_norm.html index 81db2c236e8931390521984aca77bc7751e9495c..66da3387b23861ec18a0e3c3c7f7d11786490b81 100644 --- a/docs/reference/nnf_group_norm.html +++ b/docs/reference/nnf_group_norm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ output using input values and pixel locations from grid." />- @@ -255,7 +259,7 @@ to build-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ output using input values and pixel locations from grid." />
-
-
+
@@ -237,14 +245,10 @@ and becomes
x' = 1.5, then reflects by border1and be to buildSpatial Transformer Networks_ .
Spatial Transformer Networks_ .diff --git a/docs/reference/nnf_gumbel_softmax.html b/docs/reference/nnf_gumbel_softmax.html index b5109b9fd9375f76b9a6eaa9e198c1f612344158..5bfc8e2cdf884da6b7317d7e9f4e49348a0f0509 100644 --- a/docs/reference/nnf_gumbel_softmax.html +++ b/docs/reference/nnf_gumbel_softmax.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ optionally discretizes." /> - +@@ -88,12 +91,17 @@- @@ -197,7 +203,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -181,12 +189,10 @@
diff --git a/docs/reference/nnf_hardshrink.html b/docs/reference/nnf_hardshrink.html index 6d5191c1fed507da2c9d1fdc4267e56f6d7d47c9..daa7853320d4a04b7af94d200f1c4b08aca8aee6 100644 --- a/docs/reference/nnf_hardshrink.html +++ b/docs/reference/nnf_hardshrink.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ optionally discretizes." />- @@ -195,7 +201,7 @@ optionally discretizes.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ optionally discretizes." />
- - + @@ -179,12 +187,10 @@ optionally discretizes.
diff --git a/docs/reference/nnf_hardsigmoid.html b/docs/reference/nnf_hardsigmoid.html index 30b001db98cba5bb85027d30e898e7ba0438d54c..28448a9dca77c378cf80af1b211216b1947ccaee 100644 --- a/docs/reference/nnf_hardsigmoid.html +++ b/docs/reference/nnf_hardsigmoid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -186,7 +192,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -170,12 +178,10 @@ dimensions
diff --git a/docs/reference/nnf_hardswish.html b/docs/reference/nnf_hardswish.html index b1ab9fb8932ee2dcbe46c32f72bd7a0a5f24aeed..78a9e8e71ff8e1d5b7a76acbb4cb15952312ae57 100644 --- a/docs/reference/nnf_hardswish.html +++ b/docs/reference/nnf_hardswish.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ Searching for MobileNetV3." /> - +@@ -88,12 +91,17 @@- @@ -186,7 +192,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -170,12 +178,10 @@ dimensions
diff --git a/docs/reference/nnf_hardtanh.html b/docs/reference/nnf_hardtanh.html index 74882732026ced7dbd2c541bb24f577068a0ce23..622504f61207086e368a3be72cb4365eb8727c85 100644 --- a/docs/reference/nnf_hardtanh.html +++ b/docs/reference/nnf_hardtanh.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ Searching for MobileNetV3." />- @@ -198,7 +203,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ Searching for MobileNetV3." />
- - + @@ -181,13 +189,10 @@ x \cdot (x + 3) /6 & \text{otherwise} $$
diff --git a/docs/reference/nnf_hinge_embedding_loss.html b/docs/reference/nnf_hinge_embedding_loss.html index 60487ce3e8b41647927af5271341d11112903a0d..a816cc8c8b694cf6073fb1645888d9de30d63bb0 100644 --- a/docs/reference/nnf_hinge_embedding_loss.html +++ b/docs/reference/nnf_hinge_embedding_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ This is usually used for measuring whether two inputs are similar or dissimilar, e.g. using the L1 pairwise distance as xx , and is typically used for learning nonlinear embeddings or semi-supervised learning." /> - @@ -58,7 +61,7 @@ embeddings or semi-supervised learning." /> - +@@ -88,12 +91,17 @@- @@ -196,7 +202,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -180,12 +188,10 @@ dimensions
diff --git a/docs/reference/nnf_instance_norm.html b/docs/reference/nnf_instance_norm.html index eb1da4c2cfc92a38531695a8559e8114a3184987..42179c37625dd7c80b7024e09f0342c93f2e9b0f 100644 --- a/docs/reference/nnf_instance_norm.html +++ b/docs/reference/nnf_instance_norm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ batch." /> - +@@ -91,12 +94,17 @@ embeddings or semi-supervised learning." />- @@ -202,7 +208,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ embeddings or semi-supervised learning." />
- - + @@ -186,12 +194,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_interpolate.html b/docs/reference/nnf_interpolate.html index 26bf2ac56fa911a172e6e070a1e0ba69bcb5cd6a..b6ae689c3b2b4ba3d86532935796d552a38bc263 100644 --- a/docs/reference/nnf_interpolate.html +++ b/docs/reference/nnf_interpolate.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ scale_factor" /> - +@@ -89,12 +92,17 @@ batch." />- @@ -220,7 +226,7 @@ batch.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ batch." />
- - + @@ -204,12 +212,10 @@ batch.
diff --git a/docs/reference/nnf_kl_div.html b/docs/reference/nnf_kl_div.html index 7fbfb335b1dd0edc47fdc771ebce23b0c712de25..879a846a33547def5cde2f5a4facbe4f880f1396 100644 --- a/docs/reference/nnf_kl_div.html +++ b/docs/reference/nnf_kl_div.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ scale_factor" />- @@ -240,7 +245,7 @@ expected inputs are 3-D, 4-D or 5-D in shape.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ scale_factor" />
-
-
+
@@ -223,13 +231,10 @@ expected inputs are 3-D, 4-D or 5-D in shape.
bilinear,bicubic(4D-only),trilinear(5D-only),area
diff --git a/docs/reference/nnf_l1_loss.html b/docs/reference/nnf_l1_loss.html index a4d7dafaf9db44aeb52dc3e51917a35bc3701933..b23bdbeb1f5dfb70ef453c2e095c6af4b02829bf 100644 --- a/docs/reference/nnf_l1_loss.html +++ b/docs/reference/nnf_l1_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -192,7 +198,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,12 +184,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_layer_norm.html b/docs/reference/nnf_layer_norm.html index 48e36b8b81764f907c3768406dccead27b85c1a4..ac5e290c9ae9d67e24eceef30491eebaab797cc4 100644 --- a/docs/reference/nnf_layer_norm.html +++ b/docs/reference/nnf_layer_norm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -192,7 +198,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,12 +184,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_leaky_relu.html b/docs/reference/nnf_leaky_relu.html index c6b71283444cbeb12a00099d8c2677edd0010895..17c5bf15f68c96de30a6c168bb6cc1ce1d597b9a 100644 --- a/docs/reference/nnf_leaky_relu.html +++ b/docs/reference/nnf_leaky_relu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ - +@@ -88,12 +91,17 @@- @@ -205,7 +211,7 @@ over the last dimension which is expected to be of that specific size.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -189,12 +197,10 @@ over the last dimension which is expected to be of that specific size.
diff --git a/docs/reference/nnf_linear.html b/docs/reference/nnf_linear.html index 8e19c281ef461bc99171c3e2f760bd54ed57b5b4..742a2a1a950dd5620d14f293aabd48065ed47a0a 100644 --- a/docs/reference/nnf_linear.html +++ b/docs/reference/nnf_linear.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@- @@ -192,7 +198,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@
- - + @@ -176,12 +184,10 @@ dimensions
diff --git a/docs/reference/nnf_local_response_norm.html b/docs/reference/nnf_local_response_norm.html index 8aea475aacc8b7eaa3af5a1c0d465188f3d315af..b529dea174668fc54fc957c7327335f458221812 100644 --- a/docs/reference/nnf_local_response_norm.html +++ b/docs/reference/nnf_local_response_norm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ Applies normalization across channels." /> - +@@ -88,12 +91,17 @@- @@ -190,7 +196,7 @@ additional dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -174,12 +182,10 @@ additional dimensions
diff --git a/docs/reference/nnf_log_softmax.html b/docs/reference/nnf_log_softmax.html index b8f2d933ad74e9263fa722f539ba1bc4474298e3..ca503c4ced03fbe381ea44a7bb467b1f5747c301 100644 --- a/docs/reference/nnf_log_softmax.html +++ b/docs/reference/nnf_log_softmax.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ Applies normalization across channels." />- @@ -201,7 +207,7 @@ Applies normalization across channels.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ Applies normalization across channels." />
- - + @@ -185,12 +193,10 @@ Applies normalization across channels.
diff --git a/docs/reference/tensor_dataset.html b/docs/reference/tensor_dataset.html index ffaaa61e049a5534e819269c570af0be4bcfe1a5..775bbe28f49e36c5e77af7226a8dd9bd531494e7 100644 --- a/docs/reference/tensor_dataset.html +++ b/docs/reference/tensor_dataset.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -198,7 +203,7 @@ uses an alternative formulation to compute the output and gradient correctly. diff --git a/docs/reference/nnf_logsigmoid.html b/docs/reference/nnf_logsigmoid.html index d726a3c575d5845086f8a8b7e9ce852229b789a0..e22f71f9044ccfabf56ef83829ae513f3669f17d 100644 --- a/docs/reference/nnf_logsigmoid.html +++ b/docs/reference/nnf_logsigmoid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -181,13 +189,10 @@ operations separately is slower, and numerically unstable. This function uses an alternative formulation to compute the output and gradient correctly.
diff --git a/docs/reference/nnf_lp_pool1d.html b/docs/reference/nnf_lp_pool1d.html index 0db808d261ef5c29b6ae800b9a0912b73d8b5fbf..0d336cffbedadc48e49e4ae1e9bbd83308b9daf9 100644 --- a/docs/reference/nnf_lp_pool1d.html +++ b/docs/reference/nnf_lp_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ zero, the gradient is set to zero as well." /> - +@@ -88,12 +91,17 @@- @@ -182,7 +188,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,12 +174,10 @@ dimensions
diff --git a/docs/reference/nnf_lp_pool2d.html b/docs/reference/nnf_lp_pool2d.html index 808b9fad9e02e484248c644f9ae7a2e97c2556c2..f42b8cc8c5bae0057e21ef1e3bd33be7ce88ea53 100644 --- a/docs/reference/nnf_lp_pool2d.html +++ b/docs/reference/nnf_lp_pool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ zero, the gradient is set to zero as well." /> - +@@ -90,12 +93,17 @@ zero, the gradient is set to zero as well." />- @@ -202,7 +208,7 @@ proportional to the avg pooling)-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ zero, the gradient is set to zero as well." />
- - + @@ -186,12 +194,10 @@ proportional to the avg pooling)
diff --git a/docs/reference/nnf_margin_ranking_loss.html b/docs/reference/nnf_margin_ranking_loss.html index fa0ebc686ed4f7e87bb94f403f300f7b3da910b7..64592707665111bc843e4e822e924968b2b02793 100644 --- a/docs/reference/nnf_margin_ranking_loss.html +++ b/docs/reference/nnf_margin_ranking_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)." /> - +@@ -90,12 +93,17 @@ zero, the gradient is set to zero as well." />- @@ -202,7 +208,7 @@ proportional to the avg pooling)-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ zero, the gradient is set to zero as well." />
- - + @@ -186,12 +194,10 @@ proportional to the avg pooling)
diff --git a/docs/reference/nnf_max_pool1d.html b/docs/reference/nnf_max_pool1d.html index 5db9bfc87f92b4608a52e97afe42521c42af37e7..ffdb93f9ce8e12bea751f0ac164714633a67c768 100644 --- a/docs/reference/nnf_max_pool1d.html +++ b/docs/reference/nnf_max_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -89,12 +92,17 @@ mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)." />- @@ -202,7 +208,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)." />
- - + @@ -186,12 +194,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_max_pool2d.html b/docs/reference/nnf_max_pool2d.html index aff0991c2ee2b4662493241615e4c5cfe1bcf92e..56dc4399c794170a5557f491bddde4ef150e52f6 100644 --- a/docs/reference/nnf_max_pool2d.html +++ b/docs/reference/nnf_max_pool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -89,12 +92,17 @@ planes." />- @@ -215,7 +221,7 @@ output shape. Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
-
-
+
@@ -199,12 +207,10 @@ output shape. Default:
FALSE
FALSEdiff --git a/docs/reference/nnf_max_pool3d.html b/docs/reference/nnf_max_pool3d.html index 407a4bbc1fc6ffedf238878af9f1da8e327ad925..ada5bfcaf6dbfcb06e4745bd5f057740b4114c8f 100644 --- a/docs/reference/nnf_max_pool3d.html +++ b/docs/reference/nnf_max_pool3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ planes." /> - +@@ -89,12 +92,17 @@ planes." />- @@ -215,7 +221,7 @@ to compute the output shape. Default:-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
-
-
+
@@ -199,12 +207,10 @@ to compute the output shape. Default:
FALSE
FALSEdiff --git a/docs/reference/nnf_max_unpool1d.html b/docs/reference/nnf_max_unpool1d.html index 6ca7f54c243ed783894852f3bd65b961c049e309..1ede4d0e46644e1d41156bfc190432da59192a2c 100644 --- a/docs/reference/nnf_max_unpool1d.html +++ b/docs/reference/nnf_max_unpool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ planes." />- @@ -215,7 +221,7 @@ to compute the output shape-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ planes." />
- - + @@ -199,12 +207,10 @@ to compute the output shape
diff --git a/docs/reference/nnf_max_unpool2d.html b/docs/reference/nnf_max_unpool2d.html index ccca8e7d2fc54fe1b1826ad89e0bc4a55533dc56..540bbd496f01c88d8f91c0b12c67a0f5603f2eb2 100644 --- a/docs/reference/nnf_max_unpool2d.html +++ b/docs/reference/nnf_max_unpool2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -208,7 +214,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -192,12 +200,10 @@
diff --git a/docs/reference/nnf_max_unpool3d.html b/docs/reference/nnf_max_unpool3d.html index b5fef3ed56ec2c1280b8f547cef8212e47dc1821..2f4f22e6e1eb8ed3c86b19484a48a9a99708b85f 100644 --- a/docs/reference/nnf_max_unpool3d.html +++ b/docs/reference/nnf_max_unpool3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -208,7 +214,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -192,12 +200,10 @@
diff --git a/docs/reference/nnf_mse_loss.html b/docs/reference/nnf_mse_loss.html index 1993230774154628fcd1af5722aa94aa7557c15b..12b6e0182c8ab0d9916da476344383cf80c024d4 100644 --- a/docs/reference/nnf_mse_loss.html +++ b/docs/reference/nnf_mse_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -208,7 +214,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -192,12 +200,10 @@
diff --git a/docs/reference/nnf_multi_head_attention_forward.html b/docs/reference/nnf_multi_head_attention_forward.html index 4eb459e8d05d4db02fb968f9bd0c4140f93abecc..c8523f853027b5d9f926cfe7d4d453ea125542a8 100644 --- a/docs/reference/nnf_multi_head_attention_forward.html +++ b/docs/reference/nnf_multi_head_attention_forward.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ subspaces. See reference: Attention Is All You Need" /> - +@@ -88,12 +91,17 @@- @@ -192,7 +198,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,12 +184,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_multi_margin_loss.html b/docs/reference/nnf_multi_margin_loss.html index 17855826245eda9e85b6bddb4d7d85a32f973117..8ddd646129d1c1713d2955603403a578e2954181 100644 --- a/docs/reference/nnf_multi_margin_loss.html +++ b/docs/reference/nnf_multi_margin_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ - +@@ -89,12 +92,17 @@ subspaces. See reference: Attention Is All You Need" />- @@ -272,7 +278,7 @@ which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ subspaces. See reference: Attention Is All You Need" />
- - + @@ -256,12 +264,10 @@ which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.
diff --git a/docs/reference/nnf_multilabel_margin_loss.html b/docs/reference/nnf_multilabel_margin_loss.html index fe015c753ffdde3569f49722a6eef2e16cbe12b4..ea6a71e1f0c3f5c8dc0ca27bda81e9829487404f 100644 --- a/docs/reference/nnf_multilabel_margin_loss.html +++ b/docs/reference/nnf_multilabel_margin_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ is a 2D Tensor of target class indices)." /> - +@@ -90,12 +93,17 @@- @@ -216,7 +222,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@
- - + @@ -200,12 +208,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_multilabel_soft_margin_loss.html b/docs/reference/nnf_multilabel_soft_margin_loss.html index 2f9268a71d15565474e70a62cda2d1669b08e1b0..1370e1001e7d8e9a8f88cd4d08131f54832078a9 100644 --- a/docs/reference/nnf_multilabel_soft_margin_loss.html +++ b/docs/reference/nnf_multilabel_soft_margin_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ max-entropy, between input x and target y of size (N, C)." /> - +@@ -90,12 +93,17 @@ is a 2D Tensor of target class indices)." />- @@ -196,7 +202,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ is a 2D Tensor of target class indices)." />
- - + @@ -180,12 +188,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_nll_loss.html b/docs/reference/nnf_nll_loss.html index b4f0373fccd8ad2ca9d9b7ed1948a7c39f313cb3..c11ba06940cd3eacd96096d3b14ab7d0c75e99d6 100644 --- a/docs/reference/nnf_nll_loss.html +++ b/docs/reference/nnf_nll_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ max-entropy, between input x and target y of size (N, C)." />- @@ -194,7 +200,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ max-entropy, between input x and target y of size (N, C)." />
- - + @@ -178,12 +186,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_normalize.html b/docs/reference/nnf_normalize.html index e1a22165a38ffde69f1668660ab5cfe7d9d68236..807d647652de573e0541f0dc2ec3d142bc806a02 100644 --- a/docs/reference/nnf_normalize.html +++ b/docs/reference/nnf_normalize.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -211,7 +217,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -195,12 +203,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_one_hot.html b/docs/reference/nnf_one_hot.html index 183f6ff7f045ea90ee69df62b08d3df4c2809ef5..648cd0d36930b0710caf16b409017840051eb316 100644 --- a/docs/reference/nnf_one_hot.html +++ b/docs/reference/nnf_one_hot.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ of shape (*, num_classes) that have zeros everywhere except where the index of last dimension matches the corresponding value of the input tensor, in which case it will be 1." /> - @@ -58,7 +61,7 @@ in which case it will be 1." /> - +@@ -88,12 +91,17 @@- @@ -207,7 +212,7 @@ dimension \(1\) for normalization.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -190,13 +198,10 @@ $$ dimension \(1\) for normalization.
diff --git a/docs/reference/nnf_pad.html b/docs/reference/nnf_pad.html index 1037d5a670fdfb9ff8ae52a429b14028c50b4e73..081493970a00ccde33806069b08853ea04c1e5d7 100644 --- a/docs/reference/nnf_pad.html +++ b/docs/reference/nnf_pad.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -91,12 +94,17 @@ in which case it will be 1." />- @@ -197,7 +202,7 @@ the input tensor.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ in which case it will be 1." />
-
-
+
@@ -180,13 +188,10 @@ the input tensor.
One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot
diff --git a/docs/reference/nnf_pairwise_distance.html b/docs/reference/nnf_pairwise_distance.html index b2be77aa5bf7b5bd537bbb4e72ca801863436088..460a8da19a01a5f305146bcc86767a534e6a5285 100644 --- a/docs/reference/nnf_pairwise_distance.html +++ b/docs/reference/nnf_pairwise_distance.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -226,7 +230,7 @@ dimensions of 4D input tensor, or the last dimension of 3D input tensor.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -208,14 +216,10 @@ tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of dimensions of 4D input tensor, or the last dimension of 3D input tensor.
diff --git a/docs/reference/nnf_pdist.html b/docs/reference/nnf_pdist.html index bd621cb9903ccd8f63327daaccdc2c5338ad86d6..90066dc5f9595df13f4cc68a4a34fc2fa7aad9e2 100644 --- a/docs/reference/nnf_pdist.html +++ b/docs/reference/nnf_pdist.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ This is identical to the upper triangular portion, excluding the diagonal, of torch_norm(input[:, None] - input, dim=2, p=p). This function will be faster if the rows are contiguous." /> - @@ -58,7 +61,7 @@ if the rows are contiguous." /> - +@@ -88,12 +91,17 @@- @@ -198,7 +204,7 @@ Default: 1e-8-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -182,12 +190,10 @@ Default: 1e-8
diff --git a/docs/reference/nnf_pixel_shuffle.html b/docs/reference/nnf_pixel_shuffle.html index 0e4c3a40d35ec9546a1bca1f3be32d959629cbbe..b827dea8495fe57d2732c0e33ef069c4162d23a6 100644 --- a/docs/reference/nnf_pixel_shuffle.html +++ b/docs/reference/nnf_pixel_shuffle.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ tensor of shape \((*, C, H \times r, W \times r)\)." /> - +@@ -91,12 +94,17 @@ if the rows are contiguous." />- @@ -197,7 +202,7 @@ if the rows are contiguous.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ if the rows are contiguous." />
- - + @@ -180,13 +188,10 @@ if the rows are contiguous. \(\frac{1}{2} N (N - 1)\).
diff --git a/docs/reference/nnf_poisson_nll_loss.html b/docs/reference/nnf_poisson_nll_loss.html index f27f96b2fd5fb232142ec4c97784c5017f1e8ff3..aff756c5ac84708d7589c96bc7cbdbeb2d941b10 100644 --- a/docs/reference/nnf_poisson_nll_loss.html +++ b/docs/reference/nnf_poisson_nll_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ tensor of shape \((*, C, H \times r, W \times r)\)." />- @@ -187,7 +193,7 @@ tensor of shape \((*, C, H \times r, W \times r)\).-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ tensor of shape \((*, C, H \times r, W \times r)\)." />
- - + @@ -171,12 +179,10 @@ tensor of shape \((*, C, H \times r, W \times r)\).
diff --git a/docs/reference/nnf_prelu.html b/docs/reference/nnf_prelu.html index d2860d146133e4f7297be044a8a091c3c6911d6e..0bcf71e7faea97d96fc8a5d947dcd136b9951304 100644 --- a/docs/reference/nnf_prelu.html +++ b/docs/reference/nnf_prelu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ where weight is a learnable parameter." /> - +@@ -88,12 +91,17 @@- @@ -215,7 +221,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -199,12 +207,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_relu.html b/docs/reference/nnf_relu.html index b90fc10c85f0133e79cfecd6e0aa1449a6b539de..85fe5a165415cf5059614cda51bb9fa92d852389 100644 --- a/docs/reference/nnf_relu.html +++ b/docs/reference/nnf_relu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ where weight is a learnable parameter." />- @@ -190,7 +196,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ where weight is a learnable parameter." />
- - + @@ -174,12 +182,10 @@ dimensions
diff --git a/docs/reference/nnf_relu6.html b/docs/reference/nnf_relu6.html index 7bb14058d91fb697564045e1ba120a5e4cc69ddb..3d119937292b9fa06569f92ae9ddac70dc19a034 100644 --- a/docs/reference/nnf_relu6.html +++ b/docs/reference/nnf_relu6.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -188,7 +194,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -172,12 +180,10 @@ dimensions
diff --git a/docs/reference/nnf_rrelu.html b/docs/reference/nnf_rrelu.html index 9c382eea7f440565a6f2e60131b782b37246a09c..11596e37bffbd7356166717c48a1171b54d7f1ec 100644 --- a/docs/reference/nnf_rrelu.html +++ b/docs/reference/nnf_rrelu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -186,7 +192,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -170,12 +178,10 @@ dimensions
diff --git a/docs/reference/nnf_selu.html b/docs/reference/nnf_selu.html index 126d9982b4f7f9481c44e1bd62d6ca65ecb8c0b4..458c9939fc952a7d561c39f80dc205e2514b970f 100644 --- a/docs/reference/nnf_selu.html +++ b/docs/reference/nnf_selu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ $$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$, with \(\alpha=1.6732632423543772848170429916717\) and \(scale=1.0507009873554804934193349852946\)." /> - @@ -58,7 +61,7 @@ with \(\alpha=1.6732632423543772848170429916717\) and - +@@ -88,12 +91,17 @@- @@ -200,7 +206,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,12 +192,10 @@ dimensions
diff --git a/docs/reference/nnf_smooth_l1_loss.html b/docs/reference/nnf_smooth_l1_loss.html index 02447550de2c2893e97583525b53c5a12ed87808..c82543798adae4fb5884925d17f3af87124ddda8 100644 --- a/docs/reference/nnf_smooth_l1_loss.html +++ b/docs/reference/nnf_smooth_l1_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ element-wise error falls below 1 and an L1 term otherwise." /> - +@@ -91,12 +94,17 @@ with \(\alpha=1.6732632423543772848170429916717\) and- @@ -203,7 +208,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -128,7 +136,7 @@ with \(\alpha=1.6732632423543772848170429916717\) and
-
-
+
@@ -181,18 +189,15 @@ dimensions
#> torch_tensor -#> 0.1501 0.5228 -#> 2.5860 -0.5925 +#> -0.9556 0.6446 +#> -1.2354 0.1074 #> [ CPUFloatType{2,2} ]#> [1] TRUE
diff --git a/docs/reference/nnf_soft_margin_loss.html b/docs/reference/nnf_soft_margin_loss.html index bbf39037b3539c79099c9ed7251a3130cde6933d..0b0801d00eb5366f5b877eead348f1189929cf4b 100644 --- a/docs/reference/nnf_soft_margin_loss.html +++ b/docs/reference/nnf_soft_margin_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ between input tensor x and target tensor y (containing 1 or -1)." /> - +@@ -89,12 +92,17 @@ element-wise error falls below 1 and an L1 term otherwise." />- @@ -194,7 +200,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ element-wise error falls below 1 and an L1 term otherwise." />
- - + @@ -178,12 +186,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_softmax.html b/docs/reference/nnf_softmax.html index 0b54b21a0dd73b02090f56b117fdffd0a90841c8..d8b484bb0bee53062878fa82170e702b11f5e9c8 100644 --- a/docs/reference/nnf_softmax.html +++ b/docs/reference/nnf_softmax.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ between input tensor x and target tensor y (containing 1 or -1)." />- @@ -194,7 +200,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ between input tensor x and target tensor y (containing 1 or -1)." />
- - + @@ -178,12 +186,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/nnf_softmin.html b/docs/reference/nnf_softmin.html index 456ccb6860389a5a54d301202043af6fe8439db1..589326cb06912a06e439029a5f644e554591dbb4 100644 --- a/docs/reference/nnf_softmin.html +++ b/docs/reference/nnf_softmin.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -197,7 +202,7 @@ lie in the range-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,13 +188,10 @@ Default: NULL.
lie in the range
[0, 1]and sum to 1.
[0, 1]and sum to 1.diff --git a/docs/reference/nnf_softplus.html b/docs/reference/nnf_softplus.html index f600e792522817419db9c19c8b5f888f183364ef..b773cbbca074dc44b4122e01c50de0a91d35ba78 100644 --- a/docs/reference/nnf_softplus.html +++ b/docs/reference/nnf_softplus.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -197,7 +202,7 @@ This is useful for preventing data type overflows. Default: NULL.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,13 +188,10 @@ This is useful for preventing data type overflows. Default: NULL.
See nnf_softmax definition for mathematical formula.
diff --git a/docs/reference/nnf_softshrink.html b/docs/reference/nnf_softshrink.html index 998ade4d5230c70a23a5e402ac35d5db2d05a9b8..0ded98af6b2d30dc6bc60aefab23c125806dd5ce 100644 --- a/docs/reference/nnf_softshrink.html +++ b/docs/reference/nnf_softshrink.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -195,7 +200,7 @@ when \(input * \beta > threshold\).-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -178,13 +186,10 @@ dimensions when \(input * \beta > threshold\).
diff --git a/docs/reference/nnf_softsign.html b/docs/reference/nnf_softsign.html index 5ce92f031cf921451b8aa4b96b7a6c98b09659b0..e3e4a9827771192e912cfec3297cb52450f4eb64 100644 --- a/docs/reference/nnf_softsign.html +++ b/docs/reference/nnf_softsign.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -187,7 +193,7 @@ formulation. Default: 0.5-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -171,12 +179,10 @@ formulation. Default: 0.5
diff --git a/docs/reference/nnf_tanhshrink.html b/docs/reference/nnf_tanhshrink.html index 07471ba25d8576b6b07ff80643e729c2e1e64057..892454e54c79d901e0368171ba93dbaffed31c2c 100644 --- a/docs/reference/nnf_tanhshrink.html +++ b/docs/reference/nnf_tanhshrink.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -182,7 +188,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,12 +174,10 @@ dimensions
diff --git a/docs/reference/nnf_threshold.html b/docs/reference/nnf_threshold.html index c2e959b23e835c341ba32badf0bdea03404f8232..54d71611d005f2337dd72f004aac5bf2365a4fe2 100644 --- a/docs/reference/nnf_threshold.html +++ b/docs/reference/nnf_threshold.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -182,7 +188,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,12 +174,10 @@ dimensions
diff --git a/docs/reference/nnf_triplet_margin_loss.html b/docs/reference/nnf_triplet_margin_loss.html index 195c2bfacd0fe60da2f464e652272f933b2f1dc2..1804eb945a54e5cdbb7c4dc1fc768020c5e193ca 100644 --- a/docs/reference/nnf_triplet_margin_loss.html +++ b/docs/reference/nnf_triplet_margin_loss.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ x2 , x3 and a margin with a value greater than 0 . This is used for measuring a relative similarity between samples. A triplet is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be (N, D)." /> - @@ -59,7 +62,7 @@ input tensors should be (N, D)." /> - +@@ -88,12 +91,17 @@- @@ -196,7 +202,7 @@ dimensions-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -180,12 +188,10 @@ dimensions
diff --git a/docs/reference/nnf_unfold.html b/docs/reference/nnf_unfold.html index d8cb183617a384bed74fc514d25d4a8e61be9354..c5693c9edced521242543a2feacd1dea2ccdb47a 100644 --- a/docs/reference/nnf_unfold.html +++ b/docs/reference/nnf_unfold.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -92,12 +95,17 @@ input tensors should be (N, D)." />- @@ -231,7 +237,7 @@ the sum of the output will be divided by the number of elements in the output,-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -129,7 +137,7 @@ input tensors should be (N, D)." />
- - + @@ -215,12 +223,10 @@ the sum of the output will be divided by the number of elements in the output,
diff --git a/docs/reference/optim_required.html b/docs/reference/optim_required.html index 51e0eee6c12ff099bd67bc4f4541ee26b754edf7..999f2b63a84ce8621406a936d17fd07ae8b0cfeb 100644 --- a/docs/reference/optim_required.html +++ b/docs/reference/optim_required.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -214,7 +219,7 @@ to the tensor, please clone it first.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -197,13 +205,10 @@ are vectorized) may result in incorrect behavior. If you need to write to the tensor, please clone it first.
diff --git a/docs/reference/optim_sgd.html b/docs/reference/optim_sgd.html index 6fe0f74f79524c4963e12b029ff72a720806b032..57cc5c20018b8b31cdb854abfc27f0bebfc0606f 100644 --- a/docs/reference/optim_sgd.html +++ b/docs/reference/optim_sgd.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ On the importance of initialization and momentum in deep learning." /> - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -157,11 +165,10 @@
-@@ -172,7 +179,7 @@Contents
--
-
+- @@ -247,7 +251,7 @@ The Nesterov version is analogously modified.@@ -90,12 +93,17 @@ On the importance of initialization and momentum in deep learning." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ On the importance of initialization and momentum in deep learning." />
diff --git a/docs/reference/torch_abs.html b/docs/reference/torch_abs.html index 2c3c47559950acee8ca4ff791179a545305b070d..81b33bfad5aa69f15c1b145bf51192b693d67233 100644 --- a/docs/reference/torch_abs.html +++ b/docs/reference/torch_abs.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -181,7 +187,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -165,12 +173,10 @@
- @@ -202,7 +206,7 @@ $$ diff --git a/docs/reference/torch_acos.html b/docs/reference/torch_acos.html index 6f046e6ce3f1aee6cab43041c1bad72ba4fb4736..3325cec719d684ca757e8e248fcaf0098c589045 100644 --- a/docs/reference/torch_acos.html +++ b/docs/reference/torch_acos.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,14 +192,10 @@ $$ #> 3 #> [ CPUFloatType{3} ]
diff --git a/docs/reference/torch_adaptive_avg_pool1d.html b/docs/reference/torch_adaptive_avg_pool1d.html index 4dc0503edb3a5e5ea502b3347574734d072a425b..86c64917c394e0c27efd527efcfac6cfb2682653 100644 --- a/docs/reference/torch_adaptive_avg_pool1d.html +++ b/docs/reference/torch_adaptive_avg_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -209,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,25 +188,21 @@ $$
#> torch_tensor -#> -0.7438 -#> -0.2643 -#> 0.5109 -#> -0.7602 +#> -0.9445 +#> -0.5799 +#> 0.8095 +#> 0.5103 #> [ CPUFloatType{4} ]torch_acos(a)#> torch_tensor -#> 2.4095 -#> 1.8383 -#> 1.0346 -#> 2.4343 +#> 2.8070 +#> 2.1894 +#> 0.6275 +#> 1.0352 #> [ CPUFloatType{4} ]
- @@ -194,7 +198,7 @@ several input planes. diff --git a/docs/reference/torch_add.html b/docs/reference/torch_add.html index 9d77dcc1164cd5d3c7a3ce34957db9d193787b25..8e2f299cecfc2b6d66c0100d49b70dbd7d8cb3ac 100644 --- a/docs/reference/torch_add.html +++ b/docs/reference/torch_add.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -176,14 +184,10 @@ several input planes.
diff --git a/docs/reference/torch_addbmm.html b/docs/reference/torch_addbmm.html index 52758d9f39647d9dbbf170a01aef698517ecd3f5..3393155ab1d0040ff56efbe9be0e5d8a2a938362 100644 --- a/docs/reference/torch_addbmm.html +++ b/docs/reference/torch_addbmm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -206,45 +214,40 @@ a real number, otherwise it should be an integer.
#> torch_tensor -#> -2.3453 -#> 0.4201 -#> 0.8056 -#> 0.4007 +#> -0.4889 +#> 0.3144 +#> 0.3134 +#> -0.2945 #> [ CPUFloatType{4} ]torch_add(a, 20)#> torch_tensor -#> 17.6547 -#> 20.4201 -#> 20.8056 -#> 20.4007 +#> 19.5111 +#> 20.3144 +#> 20.3134 +#> 19.7055 #> [ CPUFloatType{4} ]#> torch_tensor -#> -1.7433 -#> 0.4334 -#> -0.0335 -#> -0.8046 +#> -2.1583 +#> -0.4015 +#> -0.6636 +#> -0.5354 #> [ CPUFloatType{4} ]#> torch_tensor -#> 1.9842 -#> -1.2331 -#> 1.2785 -#> -0.7455 +#> -0.3925 +#> -0.2561 +#> 0.6745 +#> 1.2762 #> [ CPUFloatType{4,1} ]torch_add(a, b)#> torch_tensor -#> 0.2410 2.4176 1.9507 1.1796 -#> -2.9764 -0.7998 -1.2667 -2.0377 -#> -0.4647 1.7119 1.2450 0.4739 -#> -2.4887 -0.3121 -0.7790 -1.5500 +#> -2.5507 -0.7940 -1.0561 -0.9278 +#> -2.4144 -0.6576 -0.9197 -0.7915 +#> -1.4838 0.2730 0.0108 0.1391 +#> -0.8821 0.8746 0.6125 0.7408 #> [ CPUFloatType{4,4} ]
-@@ -255,7 +258,7 @@ a real number, otherwise it should be an integer.Contents
- - ++-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -210,19 +218,15 @@ must be real numbers, otherwise they should be integers. batch1 = torch_randn(c(10, 3, 4)) batch2 = torch_randn(c(10, 4, 5)) torch_addbmm(M, batch1, batch2)
#> torch_tensor -#> -0.3003 0.3048 -4.4671 6.7209 -0.5864 -#> 3.4396 13.3720 -6.1987 3.9558 10.6695 -#> 10.3270 0.2771 -7.4006 2.8166 3.6295 +#> 0.0056 -0.6895 0.8576 9.0844 -8.8459 +#> 1.4513 -18.8992 -9.9800 -3.6803 -8.3003 +#> -6.2237 4.0048 7.9344 4.5626 -2.1477 #> [ CPUFloatType{3,5} ]-@@ -233,7 +237,7 @@ must be real numbers, otherwise they should be integers. diff --git a/docs/reference/torch_addcdiv.html b/docs/reference/torch_addcdiv.html index 4615310f3d2638d1e5d2550e0c2e01f17bdb6dec..8b1df2ced758b2b2f06d1c98fb699d84510be426 100644 --- a/docs/reference/torch_addcdiv.html +++ b/docs/reference/torch_addcdiv.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -213,20 +221,15 @@ a real number, otherwise an integer. t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcdiv(t, t1, t2, 0.1)
#> torch_tensor -#> 0.3149 0.9439 -0.1786 -#> 0.6064 1.1837 -0.2312 -#> -0.0519 0.6420 -0.1125 +#> 2.1235 0.1496 2.2654 +#> 2.2252 0.3993 1.0274 +#> 2.0223 -0.0987 3.4963 #> [ CPUFloatType{3,3} ]-@@ -237,7 +240,7 @@ a real number, otherwise an integer. diff --git a/docs/reference/torch_addcmul.html b/docs/reference/torch_addcmul.html index 922ad0a732c5d9c949a2d33e616b78619561970c..77bed181d899001c7e6d28f3473e38caebebbb5a 100644 --- a/docs/reference/torch_addcmul.html +++ b/docs/reference/torch_addcmul.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor -
- Warning -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -200,19 +208,15 @@ a real number, otherwise an integer. t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcmul(t, t1, t2, 0.1)
#> torch_tensor -#> -1.5059 -0.8138 -1.9018 -#> -1.4378 -0.8953 -1.8804 -#> -1.5422 -0.7704 -1.9132 +#> -0.0426 0.8101 0.1256 +#> 0.1604 0.7367 0.0433 +#> 0.2020 0.7217 0.0264 #> [ CPUFloatType{3,3} ]-@@ -223,7 +227,7 @@ a real number, otherwise an integer. diff --git a/docs/reference/torch_addmm.html b/docs/reference/torch_addmm.html index 8651fa25c4b1fb86774dc0c0389d73bdbc83ad60..4be17f3d81bd8da7bb12b48b7fc21d28d6fd6c27 100644 --- a/docs/reference/torch_addmm.html +++ b/docs/reference/torch_addmm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -207,18 +215,14 @@ For inputs of type
FloatTensororDoubleTensor, argume mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_addmm(M, mat1, mat2)
#> torch_tensor -#> 0.1004 -0.2141 -0.2335 -#> -1.1116 0.7334 -5.7537 +#> -1.5406 -2.6374 2.8996 +#> 1.5895 2.1413 -6.3147 #> [ CPUFloatType{2,3} ]-@@ -229,7 +233,7 @@ For inputs of typeContents
- - ++FloatTensororDoubleTensor, argume diff --git a/docs/reference/torch_addmv.html b/docs/reference/torch_addmv.html index a6ad17d3b7829ac02fa1dd400cb4c8daec768e7e..10e1b8e35436d2688bcefede507390ba80ec9e82 100644 --- a/docs/reference/torch_addmv.html +++ b/docs/reference/torch_addmv.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -208,18 +216,14 @@ For inputs of type
FloatTensororDoubleTensor, argume mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_addmv(M, mat, vec)
#> torch_tensor -#> 2.2386 -#> 0.2180 +#> 0.1951 +#> 1.9495 #> [ CPUFloatType{2} ]-@@ -230,7 +234,7 @@ For inputs of typeContents
- - ++FloatTensororDoubleTensor, argume diff --git a/docs/reference/torch_addr.html b/docs/reference/torch_addr.html index e6881db3aa974ff2388c5644711538d3b097c2e1..29948f4ecd31ddd15bdf17fae9d0aff927a43713 100644 --- a/docs/reference/torch_addr.html +++ b/docs/reference/torch_addr.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -214,14 +222,10 @@ broadcastable with a matrix of size #> 3 6 #> [ CPUFloatType{3,2} ]
-@@ -232,7 +236,7 @@ broadcastable with a matrix of size diff --git a/docs/reference/torch_allclose.html b/docs/reference/torch_allclose.html index f826ccd44699f49a2f0efc97522806a3999dfe15..c10e7328d0f61e3dc8db8480412961c7a3b967c7 100644 --- a/docs/reference/torch_allclose.html +++ b/docs/reference/torch_allclose.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++diff --git a/docs/reference/torch_angle.html b/docs/reference/torch_angle.html index 16c2a4f25556735ac27c22c9ef8d642b37d012d4..9293e2ca218e3c95596b069f0ab5e8d3c8c5e97a 100644 --- a/docs/reference/torch_angle.html +++ b/docs/reference/torch_angle.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -194,14 +202,10 @@ elementwise, for all elements of
inputandother. The#> [1] FALSE#> [1] FALSE#> [1] FALSE#> [1] TRUE
-@@ -212,7 +216,7 @@ elementwise, for all elements ofContents
- - ++inputandother. The- @@ -199,7 +203,7 @@ $$ diff --git a/docs/reference/torch_arange.html b/docs/reference/torch_arange.html index 430a4978a9bb741ecda2bc9a13eba8eb322eb6c0..daa184ea77074b4c734cd00678bd71715d831716 100644 --- a/docs/reference/torch_arange.html +++ b/docs/reference/torch_arange.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -223,14 +231,10 @@ $$ #> 2.0000 #> [ CPUFloatType{3} ]
-@@ -241,7 +245,7 @@ $$ diff --git a/docs/reference/torch_argmax.html b/docs/reference/torch_argmax.html index 50970c918d1a8dad6719cd7cd75c47e6858860be..26257e93503a8a12594b9c5e9c7925734b9d6266 100644 --- a/docs/reference/torch_argmax.html +++ b/docs/reference/torch_argmax.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -198,26 +206,21 @@ documentation for the exact semantics of this method. a = torch_randn(c(4, 4)) a
#> torch_tensor -#> -0.4246 0.5570 -0.2311 -1.8952 -#> 0.2595 0.6579 -1.1486 -0.8021 -#> -1.1541 0.7955 -0.9770 -2.2117 -#> 0.1137 -0.3595 -0.1008 0.4997 +#> -1.1678 -0.8749 -0.3145 -0.7441 +#> 1.2422 -0.6964 0.4481 -0.8855 +#> 1.1389 -0.3338 0.4285 -1.9451 +#> -0.2891 1.6210 1.3582 -0.7004 #> [ CPUFloatType{4,4} ]torch_argmax(a, dim=1)#> torch_tensor +#> 2 +#> 0 +#> 0 #> 1 -#> 1 -#> 1 -#> 3 #> [ CPULongType{4} ]-@@ -228,7 +231,7 @@ documentation for the exact semantics of this method. diff --git a/docs/reference/torch_argmin.html b/docs/reference/torch_argmin.html index 2b498e58dba843937c25ee03316090bee4cbddfa..2f10b0b314278dd2903a83d6e21f50624d40515b 100644 --- a/docs/reference/torch_argmin.html +++ b/docs/reference/torch_argmin.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- argmax(input) -> LongTensor -
- argmax(input, dim, keepdim=False) -> LongTensor -
- Examples -
+diff --git a/docs/reference/torch_argsort.html b/docs/reference/torch_argsort.html index d0f0df4e005c768de00b1607f183ded935b23c9c..ddcb0f426309d2c7118a34e58b38bceea5705bbb 100644 --- a/docs/reference/torch_argsort.html +++ b/docs/reference/torch_argsort.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -191,36 +199,31 @@ documentation for the exact semantics of this method.
#> torch_tensor -#> -0.4118 -0.9672 0.2185 1.0924 -#> 1.7189 0.6154 -1.4004 0.2964 -#> 0.5216 0.9087 0.1407 -1.2497 -#> 1.0930 -0.3835 -0.6061 1.3624 +#> -1.4540 1.0559 -0.2123 -0.0959 +#> -1.6404 -1.4737 -0.9287 -1.2562 +#> 1.0944 -1.5114 0.7530 1.7703 +#> 1.1729 -0.0781 -0.5965 -1.8268 #> [ CPUFloatType{4,4} ]torch_argmin(a)#> torch_tensor -#> 6 +#> 15 #> [ CPULongType{} ]#> torch_tensor -#> 1.2451 -1.4834 -2.0925 -0.1798 -#> 0.5640 -0.9888 -0.4802 -0.8833 -#> -0.0349 -0.3207 -0.4660 0.9148 -#> 1.8179 0.7134 -0.2468 0.9256 +#> -0.7465 1.5828 0.3605 -0.2214 +#> 0.7565 -0.9244 -1.4485 0.7458 +#> 0.6008 0.4704 0.8442 -0.2170 +#> -1.9292 -2.1756 -0.2528 -0.4802 #> [ CPUFloatType{4,4} ]torch_argmin(a, dim=1)#> torch_tensor +#> 0 #> 2 +#> 3 #> 1 -#> 2 -#> 2 #> [ CPULongType{4} ]
-@@ -231,7 +234,7 @@ documentation for the exact semantics of this method.Contents
--
-
- Arguments -
- argmin(input) -> LongTensor -
- argmin(input, dim, keepdim=False, out=None) -> LongTensor -
- Examples -
+diff --git a/docs/reference/torch_as_strided.html b/docs/reference/torch_as_strided.html index aee9939de9a37547d01ff03bee646919fca6263e..269bf6154366ea44364f4c0a15db9d1f2e4ded3c 100644 --- a/docs/reference/torch_as_strided.html +++ b/docs/reference/torch_as_strided.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -184,25 +192,21 @@ for the exact semantics of this method.
#> torch_tensor -#> 0.5328 0.6698 0.9724 0.4631 -#> 0.9124 1.2066 0.7613 -0.4394 -#> 0.2213 -0.6208 -0.5236 -1.3487 -#> 0.3425 0.8520 -1.4772 0.2025 +#> -0.0103 -0.5220 1.8508 0.3191 +#> 0.6556 -1.5240 0.8632 0.0098 +#> 0.1511 -1.5785 -0.2832 0.3058 +#> 0.4103 0.7241 -0.4341 0.6786 #> [ CPUFloatType{4,4} ]torch_argsort(a, dim=1)#> torch_tensor -#> 3 0 1 2 -#> 3 2 0 1 -#> 3 1 2 0 -#> 2 3 0 1 +#> 1 0 3 2 +#> 1 3 0 2 +#> 1 2 0 3 +#> 2 0 3 1 #> [ CPULongType{4,4} ]
-@@ -213,7 +217,7 @@ for the exact semantics of this method.Contents
- - ++diff --git a/docs/reference/torch_asin.html b/docs/reference/torch_asin.html index 20aefea9daef3dc3fafffee5809d0c6d792bdfac..7cf9debddbb28ae0c41f836d80cd0993a85b4c9e 100644 --- a/docs/reference/torch_asin.html +++ b/docs/reference/torch_asin.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -199,28 +207,24 @@ advisable to use.
#> torch_tensor -#> 0.5570 1.6374 -0.2422 -#> -1.0533 0.9608 -0.5974 -#> 1.3392 -0.2947 -0.3964 +#> -0.0168 -0.7891 -0.6764 +#> -0.2499 -0.0602 0.5284 +#> -1.6220 -1.8723 -0.4804 #> [ CPUFloatType{3,3} ]#> torch_tensor -#> 0.5570 -0.2422 -#> 1.6374 -1.0533 +#> 0.01 * +#> -1.6798 -67.6353 +#> -78.9127 -24.9900 #> [ CPUFloatType{2,2} ]#> torch_tensor -#> 1.6374 -1.0533 -#> -0.2422 0.9608 +#> -0.7891 -0.2499 +#> -0.6764 -0.0602 #> [ CPUFloatType{2,2} ]
-@@ -231,7 +235,7 @@ advisable to use.Contents
- - ++diff --git a/docs/reference/torch_atan.html b/docs/reference/torch_atan.html index 6f0315790c7157fc59cbc3a6383881a68f9b84e0..edd9b49c99a785eb40e75cfddaa061b1487d01d4 100644 --- a/docs/reference/torch_atan.html +++ b/docs/reference/torch_atan.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -209,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,25 +188,21 @@ $$
#> torch_tensor -#> 0.4744 -#> 0.8110 -#> 0.6712 -#> -0.1170 +#> -0.2815 +#> 2.2511 +#> -0.3555 +#> 1.0426 #> [ CPUFloatType{4} ]torch_asin(a)#> torch_tensor -#> 0.4942 -#> 0.9459 -#> 0.7358 -#> -0.1173 +#> -0.2854 +#> nan +#> -0.3634 +#> nan #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_atan2.html b/docs/reference/torch_atan2.html index 77ae4a81320105c26663204887f3739fc1b16233..4eaf901bcb5d4f5302a34a36222122acca13b11f 100644 --- a/docs/reference/torch_atan2.html +++ b/docs/reference/torch_atan2.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -211,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,27 +188,21 @@ $$
#> torch_tensor -#> 0.01 * -#> 2.8009 -#> -63.2785 -#> -180.7373 -#> -3.8285 +#> -2.0543 +#> 0.6212 +#> -0.6091 +#> 0.2427 #> [ CPUFloatType{4} ]torch_atan(a)#> torch_tensor -#> 0.01 * -#> 2.8002 -#> -56.4178 -#> -106.5431 -#> -3.8266 +#> -1.1178 +#> 0.5558 +#> -0.5471 +#> 0.2381 #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_avg_pool1d.html b/docs/reference/torch_avg_pool1d.html index 01965ef6654e9c16e370d27febfc5dc7f9660322..bffe0b39240e03c11667d91060fe3a8dce2c8cbe 100644 --- a/docs/reference/torch_avg_pool1d.html +++ b/docs/reference/torch_avg_pool1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -217,7 +221,7 @@ broadcastable .-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -188,25 +196,21 @@ broadcastable .
#> torch_tensor -#> 1.5345 -#> -1.0349 -#> -1.2481 -#> -0.2392 +#> 0.0939 +#> 0.2847 +#> 0.2652 +#> 0.0718 #> [ CPUFloatType{4} ]#> torch_tensor -#> 1.4160 -#> -2.1925 -#> -0.8276 -#> -0.2070 +#> 0.5921 +#> 0.3646 +#> 1.8194 +#> 1.2931 #> [ CPUFloatType{4} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -196,14 +204,10 @@ input planes.
-@@ -214,7 +218,7 @@ input planes. diff --git a/docs/reference/torch_baddbmm.html b/docs/reference/torch_baddbmm.html index e4372f28ce0c90c05fe8701fb92505e20f38ab2f..1397366825fface03c6115bbc0bb019f81fc8bf3 100644 --- a/docs/reference/torch_baddbmm.html +++ b/docs/reference/torch_baddbmm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -211,64 +219,60 @@ For inputs of type
FloatTensororDoubleTensor, argume batch2 = torch_randn(c(10, 4, 5)) torch_baddbmm(M, batch1, batch2)
#> torch_tensor #> (1,.,.) = -#> -2.9869 -1.3917 4.8121 2.6801 0.4970 -#> -0.6722 -0.5343 -0.2736 -0.2910 -0.8027 -#> 1.2152 -0.8970 0.3727 1.5642 -1.8818 +#> -3.1362 -2.4402 1.9198 -2.7523 2.1046 +#> 0.1492 -0.1034 -1.1902 2.2334 -0.8871 +#> 2.0365 -1.3281 0.1097 2.0461 -0.5725 #> #> (2,.,.) = -#> -1.0864 -2.3874 1.1161 -1.0352 -2.5379 -#> -2.6038 -0.6353 -0.3067 -2.1740 -3.0761 -#> -0.7750 0.0849 -1.5978 0.0331 -4.5138 +#> 0.8292 1.8242 -0.7493 0.7543 -0.4375 +#> 1.5200 -3.2145 0.2280 0.8277 0.1510 +#> -2.9473 5.1171 -1.3905 1.5434 -3.8350 #> #> (3,.,.) = -#> -1.3408 0.3945 0.0186 0.8016 0.3782 -#> 0.3144 -0.1460 1.8359 -0.4717 -0.4261 -#> -0.2644 0.9224 -0.9514 -0.3230 -0.8804 +#> 0.0799 -4.3140 -0.6557 -4.0161 1.6501 +#> 0.2608 -2.2498 -1.7169 -5.5390 5.5681 +#> -2.5244 3.6268 1.9341 5.9345 -5.5884 #> #> (4,.,.) = -#> -1.7710 1.8200 5.1139 1.7719 1.3042 -#> 0.4179 -2.9976 5.5907 1.9686 2.0681 -#> -1.5007 -0.8825 1.3306 3.2263 5.9130 +#> 3.4253 -3.8292 5.0747 1.9894 1.7453 +#> 1.2957 0.1318 -2.6503 -0.8512 3.0607 +#> 2.1913 -3.4281 -0.1110 4.5760 1.2138 #> #> (5,.,.) = -#> 1.0443 -1.3458 -3.3142 -1.3143 -0.2952 -#> 2.4082 0.8633 1.5211 -1.4663 2.7027 -#> 4.9612 -2.7759 -2.9186 -2.9989 -4.1435 +#> -2.0102 0.2638 0.2315 2.2472 -1.2765 +#> 1.1315 -1.4136 0.6377 0.9502 0.0074 +#> 1.5379 0.6689 0.7699 0.1216 1.4433 #> #> (6,.,.) = -#> 1.2320 1.6265 4.0314 0.9003 0.1198 -#> -2.2102 -2.8019 1.2727 0.1213 1.4512 -#> -1.0200 -3.7673 3.5301 2.1980 2.0768 +#> 0.2184 0.3440 1.8239 -0.7699 1.6485 +#> -1.3275 0.0661 0.1862 -1.5553 -0.5945 +#> 1.5134 0.0898 2.5102 0.1612 -0.4426 #> #> (7,.,.) = -#> -0.2709 -2.0239 0.4376 -0.1861 -0.4127 -#> 6.5082 4.0861 -4.5176 -0.1955 -4.9896 -#> 0.9454 2.4810 2.2381 0.0161 2.0335 +#> -1.0019 -0.1102 1.2641 3.3594 1.1865 +#> 1.7645 2.6655 0.4440 1.3305 0.6995 +#> 0.1302 1.9521 -2.1035 0.9167 -1.8378 #> #> (8,.,.) = -#> -1.3979 -0.3542 0.8288 -2.0490 -0.5423 -#> 0.2708 -1.0947 -1.0527 0.0600 -0.0195 -#> 2.0560 -0.0066 0.2028 2.9960 -2.6488 +#> 1.6157 -1.9324 -2.6435 -1.3508 2.3126 +#> 2.4947 -0.4956 -1.0130 0.0370 -1.4539 +#> 1.2888 0.5589 -1.8420 -1.1686 -1.1956 #> #> (9,.,.) = -#> 2.2763 -5.0346 -2.6252 0.4741 2.1598 -#> -2.9263 3.2645 1.7366 -2.8737 -2.1571 -#> -4.0837 1.2854 3.9757 -0.0473 -0.0701 +#> -2.6521 2.0132 0.3045 -0.9801 1.1129 +#> 2.6551 -4.0959 -0.5808 4.5503 -0.6912 +#> -2.2121 -2.6277 -1.5587 -0.0660 -2.5625 #> #> (10,.,.) = -#> -2.9929 5.9263 0.1980 3.0945 -3.0164 -#> -0.5926 -0.4319 -1.6611 -4.0885 0.1696 -#> 1.2437 -1.7912 -1.7166 0.2167 1.6076 +#> -0.6664 -0.4538 0.5439 1.2781 1.0205 +#> -1.1803 -2.1973 -1.4437 -1.2907 -2.2796 +#> 1.1009 3.0711 4.7308 -2.7528 0.1991 #> [ CPUFloatType{10,3,5} ]-@@ -279,7 +283,7 @@ For inputs of typeContents
- - ++FloatTensororDoubleTensor, argume diff --git a/docs/reference/torch_bartlett_window.html b/docs/reference/torch_bartlett_window.html index cbc7ca2bde654519002bd79496e51416d56dedd0..ec7ca13206a3818efca63daa24c9914af547317f 100644 --- a/docs/reference/torch_bartlett_window.html +++ b/docs/reference/torch_bartlett_window.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -215,15 +223,10 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have
-@@ -234,7 +237,7 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have diff --git a/docs/reference/torch_bernoulli.html b/docs/reference/torch_bernoulli.html index 4194a770122fa67192e968fc1daee26b5bfd3074..c6aae4735d7e5f58b729e732ca11bbb73c538e8c 100644 --- a/docs/reference/torch_bernoulli.html +++ b/docs/reference/torch_bernoulli.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_bincount.html b/docs/reference/torch_bincount.html index f0d0dc5ff176c5cae27ffae5102fbdbb5ef4f041..b48554250f274b906c9eaf36ce37ee571de8bb03 100644 --- a/docs/reference/torch_bincount.html +++ b/docs/reference/torch_bincount.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -195,13 +203,13 @@ point
dtype.#> torch_tensor -#> 0.1124 0.8943 0.2515 -#> 0.3284 0.5211 0.7126 -#> 0.6336 0.6298 0.1427 +#> 0.4168 0.9872 0.7867 +#> 0.6836 0.2282 0.3014 +#> 0.6749 0.1681 0.8949 #> [ CPUFloatType{3,3} ]torch_bernoulli(a)#> torch_tensor #> 0 1 1 -#> 0 1 0 -#> 1 1 0 +#> 0 0 0 +#> 0 0 1 #> [ CPUFloatType{3,3} ]#> torch_tensor #> 1 1 1 @@ -214,14 +222,10 @@ pointdtype. #> 0 0 0 #> [ CPUFloatType{3,3} ]
-@@ -232,7 +236,7 @@ pointContents
- - ++dtype.- @@ -239,7 +243,7 @@ tensor of size 0. If@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -190,11 +198,11 @@ tensor of size 0. If
minlengthis specified, the number of bins is input = torch_randint(0, 8, list(5), dtype=torch_int64()) weights = torch_linspace(0, 1, steps=5) input
#> torch_tensor -#> 1 -#> 7 #> 3 -#> 1 #> 5 +#> 7 +#> 1 +#> 6 #> [ CPULongType{5} ]weights#> torch_tensor #> 0.0000 #> 0.2500 @@ -205,30 +213,26 @@ tensor of size 0. Ifminlengthis specified, the number of bins is #> 0.0000 #> 0.7500 #> 0.0000 -#> 0.5000 #> 0.0000 -#> 1.0000 #> 0.0000 #> 0.2500 +#> 1.0000 +#> 0.5000 #> [ CPUFloatType{8} ]input$bincount(weights)#> torch_tensor #> 0.0000 #> 0.7500 #> 0.0000 -#> 0.5000 #> 0.0000 -#> 1.0000 #> 0.0000 #> 0.2500 +#> 1.0000 +#> 0.5000 #> [ CPUFloatType{8} ]minlengthis specified, the number of bins is diff --git a/docs/reference/torch_bitwise_and.html b/docs/reference/torch_bitwise_and.html index 1d528ad3fb97ea408723c45ec487afc33e831b6c..de75e422dc26b56c9d048126e3aa091190ff7922 100644 --- a/docs/reference/torch_bitwise_and.html +++ b/docs/reference/torch_bitwise_and.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -201,7 +205,7 @@ integral or Boolean types. For bool tensors, it computes the logical AND. diff --git a/docs/reference/torch_bitwise_not.html b/docs/reference/torch_bitwise_not.html index 2c32edd2c8b23b37c5e6f0d1dafe2d762b19af26..da6181a403a1c536e89bf3e426ed7a2326cb1d97 100644 --- a/docs/reference/torch_bitwise_not.html +++ b/docs/reference/torch_bitwise_not.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -183,14 +191,10 @@ integral or Boolean types. For bool tensors, it computes the logical AND.
- @@ -197,7 +201,7 @@ integral or Boolean types. For bool tensors, it computes the logical NOT. diff --git a/docs/reference/torch_bitwise_or.html b/docs/reference/torch_bitwise_or.html index 228a4b575eea1da5a9087c3104fb4880d774f0bd..460b3f2a4b1fdb218797c2d321472c256e3365b0 100644 --- a/docs/reference/torch_bitwise_or.html +++ b/docs/reference/torch_bitwise_or.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -179,14 +187,10 @@ integral or Boolean types. For bool tensors, it computes the logical NOT.
- @@ -201,7 +205,7 @@ integral or Boolean types. For bool tensors, it computes the logical OR. diff --git a/docs/reference/torch_bitwise_xor.html b/docs/reference/torch_bitwise_xor.html index 8b0dd7571d7e344c2c17b38c617798e042d9a042..b012c293630db5d7380a20a04db8d0992cf81bde 100644 --- a/docs/reference/torch_bitwise_xor.html +++ b/docs/reference/torch_bitwise_xor.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -183,14 +191,10 @@ integral or Boolean types. For bool tensors, it computes the logical OR.
- @@ -201,7 +205,7 @@ integral or Boolean types. For bool tensors, it computes the logical XOR. diff --git a/docs/reference/torch_blackman_window.html b/docs/reference/torch_blackman_window.html index a4a91c3e0428eeed683762bee3760e951ee08981..fb9e2649b898d47d6f6f2dc5421af7c2d61b20c9 100644 --- a/docs/reference/torch_blackman_window.html +++ b/docs/reference/torch_blackman_window.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -183,14 +191,10 @@ integral or Boolean types. For bool tensors, it computes the logical XOR.
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -212,15 +220,10 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have
-@@ -231,7 +234,7 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have diff --git a/docs/reference/torch_bmm.html b/docs/reference/torch_bmm.html index c388720810e7933aaa9f1fd59f7c432614ddece4..91763f5bb5e1237b4af315adc0e577f9ea13321a 100644 --- a/docs/reference/torch_bmm.html +++ b/docs/reference/torch_bmm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+- @@ -266,7 +269,7 @@ $$ diff --git a/docs/reference/torch_broadcast_tensors.html b/docs/reference/torch_broadcast_tensors.html index 36fdc091debf3c2eaf7fb147bb522d6ce6bf2c2c..24d1e5f60adc35fc06623dfaa4c763b0c8b73a26 100644 --- a/docs/reference/torch_broadcast_tensors.html +++ b/docs/reference/torch_broadcast_tensors.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -197,65 +205,60 @@ $$ res = torch_bmm(input, mat2) res
#> torch_tensor #> (1,.,.) = -#> -0.4810 -3.8862 -0.6360 4.7232 3.4954 -#> -0.1906 -2.9850 -2.3733 3.4274 0.9894 -#> 1.3230 -0.0257 -3.4623 -0.3454 1.8202 +#> 1.1745 1.9056 -0.9863 -2.3934 -1.3069 +#> 1.3692 0.3791 -0.0341 -0.2138 1.0240 +#> 2.0640 1.6461 -1.3174 -0.6458 1.1465 #> #> (2,.,.) = -#> 1.3136 1.2998 0.1779 1.7448 2.9434 -#> 1.4301 2.4857 -1.0745 2.4742 3.4763 -#> 0.1725 2.4205 -2.6732 2.0590 3.4500 +#> -1.4357 -1.6141 -0.5452 0.4601 -0.9672 +#> -2.1103 0.4079 -0.5910 -0.9853 -1.3962 +#> -3.0251 -1.8102 -0.1124 -0.3983 0.0524 #> #> (3,.,.) = -#> -1.1241 0.6023 -1.5348 -2.1619 -2.8092 -#> 0.5506 -0.3041 -0.1659 0.9251 2.2883 -#> -0.3035 -3.2013 -0.4295 2.2507 3.9028 +#> 0.0590 -3.0489 -1.6948 -1.6117 -2.8706 +#> 2.5704 -1.8781 1.7294 -0.6041 0.9296 +#> -4.3975 -0.0287 -2.2884 -4.4353 -0.5809 #> #> (4,.,.) = -#> 0.1065 1.4173 1.1992 -0.0503 -0.1201 -#> -0.1524 -0.9645 -6.1269 -1.2613 3.2294 -#> 0.2087 0.9056 -0.1299 -3.4663 -2.7709 +#> -1.9596 -0.3429 2.3779 0.2105 3.2258 +#> -1.0426 -0.7531 0.4453 -1.0624 12.3185 +#> 0.7665 -1.6109 0.2223 -0.6109 0.1197 #> #> (5,.,.) = -#> -0.4655 -1.1896 0.0018 -0.0180 0.5612 -#> -1.2008 2.5886 0.4804 -1.6062 1.8784 -#> 0.0711 2.3897 0.7693 -0.7885 1.1344 +#> -1.5448 -1.1669 0.8417 -0.3208 0.3590 +#> 0.5674 1.3536 -0.9211 -2.3607 -0.1648 +#> 0.1414 -0.8032 -0.0501 2.5116 -0.2105 #> #> (6,.,.) = -#> -1.0970 1.3536 1.0811 -1.9103 2.6837 -#> 2.7371 0.5648 -0.9161 -4.1339 3.9011 -#> -0.7459 2.3405 -0.5041 -0.7489 2.8337 +#> -0.6188 -0.3471 -0.0005 0.2015 1.3495 +#> 0.8262 -1.8843 -0.7714 -2.0338 0.1879 +#> 0.8399 0.5305 -1.2765 -0.1254 -1.1123 #> #> (7,.,.) = -#> -1.5387 0.2769 -1.6734 -0.6808 0.6839 -#> -2.4320 6.8926 2.8228 0.5861 1.0656 -#> 4.7034 -5.1779 0.4174 0.1485 -2.7007 +#> -0.2501 -2.8244 -1.4297 0.0888 -2.9237 +#> -1.1889 0.1134 -0.0686 0.1014 0.7825 +#> 0.1186 1.1271 0.0892 1.0449 0.6209 #> #> (8,.,.) = -#> -0.1557 -0.3498 -1.7397 -3.6324 2.9935 -#> -1.2862 -0.5034 -1.7978 -2.2431 1.1642 -#> 7.4131 -0.8078 0.5200 -0.4595 0.2365 +#> -1.0997 -2.1891 -1.8006 -3.0030 2.7693 +#> 1.4913 0.3336 0.2121 0.0021 -4.9589 +#> 0.0988 0.3188 -0.0340 1.2144 -0.0815 #> #> (9,.,.) = -#> -3.3582 -1.0544 1.6696 0.9335 -0.8285 -#> -0.5341 -0.6120 -0.1374 0.7783 1.8157 -#> 4.8516 -0.1463 0.0206 6.9989 -0.5377 +#> 1.4224 -0.2146 -0.7632 -1.9639 2.1865 +#> -2.4053 0.2016 2.2731 3.3917 0.3603 +#> -2.7371 0.2854 2.9043 3.4133 2.6291 #> #> (10,.,.) = -#> 0.0379 0.7356 0.2464 -1.9521 -2.7243 -#> 0.4943 1.2687 1.0980 -3.8939 -2.3259 -#> 0.4141 -3.4522 -3.7885 3.2081 -0.2202 +#> -1.1746 -1.4925 -2.7750 1.3687 -1.1787 +#> -0.0823 -0.5667 -0.7986 0.8820 -0.4830 +#> 2.1016 0.6070 1.3933 -1.8003 -0.4758 #> [ CPUFloatType{10,3,5} ]- @@ -197,7 +201,7 @@ diff --git a/docs/reference/torch_can_cast.html b/docs/reference/torch_can_cast.html index 8d6bb92809b4c2a865bb5cb2fa80c2e3c578bdc8..062b3b2e861daad52a1ded3305e94c476a7ad9d9 100644 --- a/docs/reference/torch_can_cast.html +++ b/docs/reference/torch_can_cast.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -179,14 +187,10 @@ #> 0 1 2 #> [ CPUFloatType{2,3} ]
diff --git a/docs/reference/torch_cartesian_prod.html b/docs/reference/torch_cartesian_prod.html index 33fcb70d7e02d8bbd60d322ff7a62d3180e714b4..d9f066767e401498389929063f73efc8bfb8069e 100644 --- a/docs/reference/torch_cartesian_prod.html +++ b/docs/reference/torch_cartesian_prod.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -196,7 +200,7 @@ described in the type promotion documentation .-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -178,14 +186,10 @@ described in the type promotion documentation .
torch_can_cast(torch_double(), torch_float())#> [1] TRUEtorch_can_cast(torch_float(), torch_int())#> [1] FALSE
- @@ -203,7 +207,7 @@ python's@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -185,14 +193,10 @@ python's
itertools.product. #> 3 5 #> [ CPUFloatType{6,2} ]
itertools.product. diff --git a/docs/reference/torch_cat.html b/docs/reference/torch_cat.html index c58d9c03a04af765f4f8101febb3c4b465eec8ee..57c3b2a482428bc2ec6541d156d3eba8613d887c 100644 --- a/docs/reference/torch_cat.html +++ b/docs/reference/torch_cat.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_cdist.html b/docs/reference/torch_cdist.html index deed46dd68d9ad21c790a3b63c64ad7f11894564..f87d3aa6cf1419ed4d9bd4d3d846651184672a54 100644 --- a/docs/reference/torch_cdist.html +++ b/docs/reference/torch_cdist.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -218,7 +222,7 @@ and-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -186,28 +194,24 @@ and
torch_chunk.#> torch_tensor -#> -0.1967 -1.2040 0.5930 -#> 0.0165 -0.6426 -0.6616 +#> -1.3799 -1.2977 0.4490 +#> -0.6698 1.4223 0.4787 #> [ CPUFloatType{2,3} ]#> torch_tensor -#> -0.1967 -1.2040 0.5930 -#> 0.0165 -0.6426 -0.6616 -#> -0.1967 -1.2040 0.5930 -#> 0.0165 -0.6426 -0.6616 -#> -0.1967 -1.2040 0.5930 -#> 0.0165 -0.6426 -0.6616 +#> -1.3799 -1.2977 0.4490 +#> -0.6698 1.4223 0.4787 +#> -1.3799 -1.2977 0.4490 +#> -0.6698 1.4223 0.4787 +#> -1.3799 -1.2977 0.4490 +#> -0.6698 1.4223 0.4787 #> [ CPUFloatType{6,3} ]#> torch_tensor -#> -0.1967 -1.2040 0.5930 -0.1967 -1.2040 0.5930 -0.1967 -1.2040 0.5930 -#> 0.0165 -0.6426 -0.6616 0.0165 -0.6426 -0.6616 0.0165 -0.6426 -0.6616 +#> -1.3799 -1.2977 0.4490 -1.3799 -1.2977 0.4490 -1.3799 -1.2977 0.4490 +#> -0.6698 1.4223 0.4787 -0.6698 1.4223 0.4787 -0.6698 1.4223 0.4787 #> [ CPUFloatType{2,9} ]
torch_chunk.- @@ -204,7 +208,7 @@ diff --git a/docs/reference/torch_ceil.html b/docs/reference/torch_ceil.html index 98715c717da19f9dc46d7b32582e2e499fe056be..d1262d5b747ff585e096a751408dedd1b2d4b7ff 100644 --- a/docs/reference/torch_ceil.html +++ b/docs/reference/torch_ceil.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -186,14 +194,10 @@
diff --git a/docs/reference/torch_celu_.html b/docs/reference/torch_celu_.html index 5ef75d3733a770edcdfea957590f3dd653bcb1c4..57dcb0d05695b26ca40a9ff1b71c230988519c07 100644 --- a/docs/reference/torch_celu_.html +++ b/docs/reference/torch_celu_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -210,7 +214,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,25 +189,21 @@ $$
#> torch_tensor -#> -0.3008 -#> -0.2338 -#> -0.3937 -#> -0.5329 +#> -0.9848 +#> 0.0983 +#> 1.1650 +#> -0.7938 #> [ CPUFloatType{4} ]torch_ceil(a)#> torch_tensor #> -0 -#> -0 -#> -0 +#> 1 +#> 2 #> -0 #> [ CPUFloatType{4} ]
- @@ -183,7 +188,7 @@ diff --git a/docs/reference/torch_chain_matmul.html b/docs/reference/torch_chain_matmul.html index 563cf83a3fc9ce7394381c54527efba5ceea48d4..b30701ee0088bfb042a278afb6696f628e0ebb5d 100644 --- a/docs/reference/torch_chain_matmul.html +++ b/docs/reference/torch_chain_matmul.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,13 +174,10 @@
- @@ -203,7 +207,7 @@ If \(N\) is 1, then this is a no-op - the original matrix is returned as is. diff --git a/docs/reference/torch_cholesky.html b/docs/reference/torch_cholesky.html index 7fdc6795f2ff9135d17cdad6a2c0a474feb65a42..7995e111973b9caab8f91c3fb967529318c9442d 100644 --- a/docs/reference/torch_cholesky.html +++ b/docs/reference/torch_cholesky.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -180,19 +188,15 @@ If \(N\) is 1, then this is a no-op - the original matrix is returned as is. c = torch_randn(c(5, 6)) d = torch_randn(c(6, 7)) torch_chain_matmul(list(a, b, c, d))
#> torch_tensor -#> 30.2029 -4.2118 7.0224 -8.0350 8.0048 20.4973 18.7344 -#> 37.8088 11.9236 3.3578 -10.8967 -1.3074 25.0973 19.3134 -#> 14.0909 0.1985 2.1048 -3.6515 2.5577 8.6584 8.2248 +#> 2.0943 3.8123 -13.6250 -14.2998 1.6971 -0.6454 -4.7941 +#> -10.2585 -2.7219 16.5745 9.0715 -3.8223 -2.2886 3.3800 +#> 16.9294 5.3907 -30.6877 -17.4281 7.7560 4.3074 -7.2602 #> [ CPUFloatType{3,7} ]- @@ -234,7 +238,7 @@ matrices. diff --git a/docs/reference/torch_cholesky_inverse.html b/docs/reference/torch_cholesky_inverse.html index f76e2f8b71273ae0fde211bfa21fc2210b6c5a8d..4f68c9fdafae04599c912a2ddf6d74b371f38386 100644 --- a/docs/reference/torch_cholesky_inverse.html +++ b/docs/reference/torch_cholesky_inverse.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
#> torch_tensor -#> 7.1574 -0.3896 4.2226 -#> -0.3896 1.6170 1.7057 -#> 4.2226 1.7057 8.0317 +#> 15.1174 -0.8492 0.5825 +#> -0.8492 0.9786 -1.6969 +#> 0.5825 -1.6969 3.4253 #> [ CPUFloatType{3,3} ]l#> torch_tensor -#> 2.6753 0.0000 0.0000 -#> -0.1456 1.2632 0.0000 -#> 1.5783 1.5322 1.7868 +#> 3.8881 0.0000 0.0000 +#> -0.2184 0.9649 0.0000 +#> 0.1498 -1.7248 0.6541 #> [ CPUFloatType{3,3} ]#> torch_tensor -#> 7.1574 -0.3896 4.2226 -#> -0.3896 1.6170 1.7057 -#> 4.2226 1.7057 8.0317 +#> 15.1174 -0.8492 0.5825 +#> -0.8492 0.9786 -1.6969 +#> 0.5825 -1.6969 3.4253 #> [ CPUFloatType{3,3} ]a = torch_randn(c(3, 2, 2)) if (FALSE) { a = torch_matmul(a, a$transpose(-1, -2)) + 1e-03 # make symmetric positive-definite @@ -216,14 +224,10 @@ matrices. torch_max(torch_abs(z - a)) # Max non-zero }-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -200,14 +208,10 @@ $$ a$inverse() }
-@@ -218,7 +222,7 @@ $$ diff --git a/docs/reference/torch_cholesky_solve.html b/docs/reference/torch_cholesky_solve.html index 8a2ccfc29c9311c574fd1c1e94a394d119394218..e7f26c5f15558c6a421dd8707053d500eac0586b 100644 --- a/docs/reference/torch_cholesky_solve.html +++ b/docs/reference/torch_cholesky_solve.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -201,32 +209,28 @@ batched outputs
ca = torch_mm(a, a$t()) # make symmetric positive definite u = torch_cholesky(a) a
#> torch_tensor -#> 3.8415 -0.2191 2.7920 -#> -0.2191 4.5465 6.0028 -#> 2.7920 6.0028 10.7656 +#> 3.1556 -0.7314 -0.1760 +#> -0.7314 0.1828 0.1168 +#> -0.1760 0.1168 0.6770 #> [ CPUFloatType{3,3} ]#> torch_tensor -#> 1.2246 -0.2244 -#> 0.5929 -1.0500 -#> 1.1082 0.4790 +#> 1.2306 1.3854 +#> 0.3226 1.2456 +#> 1.7452 -1.8279 #> [ CPUFloatType{3,2} ]torch_cholesky_solve(b, u)#> torch_tensor -#> 1.8448 -4.7115 -#> 2.7106 -8.0742 -#> -1.8869 5.7685 +#> 20.0912 86.3579 +#> 86.7275 381.8104 +#> -7.1625 -46.1242 #> [ CPUFloatType{3,2} ]#> torch_tensor -#> 1.8448 -4.7115 -#> 2.7106 -8.0742 -#> -1.8869 5.7685 +#> 20.0911 86.3577 +#> 86.7273 381.8092 +#> -7.1625 -46.1241 #> [ CPUFloatType{3,2} ]-@@ -237,7 +241,7 @@ batched outputsContents
- - ++cdiff --git a/docs/reference/torch_chunk.html b/docs/reference/torch_chunk.html index 1b79680aebca9b9a220b2ccc79ea3eae2e19d51e..9dcc3ba7c838451c6268bbcc578dfec56d7b0d17 100644 --- a/docs/reference/torch_chunk.html +++ b/docs/reference/torch_chunk.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -203,7 +207,7 @@ the input tensor. diff --git a/docs/reference/torch_clamp.html b/docs/reference/torch_clamp.html index d4013ccda926977c9f97b66429a9f7ef47b46be1..b41ae432e39ce1be48f9a70aeed47ce135471dd6 100644 --- a/docs/reference/torch_clamp.html +++ b/docs/reference/torch_clamp.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -185,14 +193,10 @@ the input tensor.
diff --git a/docs/reference/torch_combinations.html b/docs/reference/torch_combinations.html index c95040cd4f01d31f2a53d35d05ddcb8c19d61abc..ec1833c5af99c99329365f5ec87e3d4c40ed525f 100644 --- a/docs/reference/torch_combinations.html +++ b/docs/reference/torch_combinations.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -215,53 +223,47 @@ should be a real number, otherwise it should be an integer.
#> torch_tensor -#> 0.3933 -#> -0.7728 -#> -0.0798 -#> 0.2504 +#> -0.7587 +#> 0.0699 +#> 0.2056 +#> 0.2476 #> [ CPUFloatType{4} ]torch_clamp(a, min=-0.5, max=0.5)#> torch_tensor -#> 0.3933 #> -0.5000 -#> -0.0798 -#> 0.2504 +#> 0.0699 +#> 0.2056 +#> 0.2476 #> [ CPUFloatType{4} ]#> torch_tensor -#> -1.2919 -#> -1.0849 -#> 0.9610 -#> 1.9679 +#> 0.2372 +#> 0.6398 +#> 1.2345 +#> 0.2035 #> [ CPUFloatType{4} ]torch_clamp(a, min=0.5)#> torch_tensor #> 0.5000 +#> 0.6398 +#> 1.2345 #> 0.5000 -#> 0.9610 -#> 1.9679 #> [ CPUFloatType{4} ]#> torch_tensor -#> -1.1275 -#> -1.1873 -#> -0.2130 -#> 1.2523 +#> -0.8008 +#> -0.0063 +#> -1.1391 +#> 0.3050 #> [ CPUFloatType{4} ]torch_clamp(a, max=0.5)#> torch_tensor -#> -1.1275 -#> -1.1873 -#> -0.2130 -#> 0.5000 +#> -0.8008 +#> -0.0063 +#> -1.1391 +#> 0.3050 #> [ CPUFloatType{4} ]
-@@ -272,7 +274,7 @@ should be a real number, otherwise it should be an integer.Contents
--
-
- Arguments -
- clamp(input, min, max, out=None) -> Tensor -
- clamp(input, *, min, out=None) -> Tensor -
- clamp(input, *, max, out=None) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -198,14 +206,10 @@ python's
itertools.combinationswhenwith_replacement#> 3 3 #> [ CPUFloatType{6,2} ]
-@@ -216,7 +220,7 @@ python'sContents
- - ++itertools.combinationswhenwith_replacementdiff --git a/docs/reference/torch_conj.html b/docs/reference/torch_conj.html index 0bb9f4822fd2fdd846cd23b4a0091fcf4a248c9a..9d369cb984703c553ce7a51f5ad542cf488cc00e 100644 --- a/docs/reference/torch_conj.html +++ b/docs/reference/torch_conj.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -199,7 +203,7 @@ $$ diff --git a/docs/reference/torch_conv1d.html b/docs/reference/torch_conv1d.html index 9f0791172bc2f955ee1f35eceb3df73d9b8be18e..c07dcfd2df6dcac3cb1ab6a5f04c557524386a86 100644 --- a/docs/reference/torch_conv1d.html +++ b/docs/reference/torch_conv1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -201,14 +209,10 @@ planes.
-@@ -219,7 +223,7 @@ planes. diff --git a/docs/reference/torch_conv2d.html b/docs/reference/torch_conv2d.html index f0a8bd0a85dac8149ae1539d4255fe3013bbe09e..136303dfcf8fda03430fa77a0bf6160d5d7d5ada 100644 --- a/docs/reference/torch_conv2d.html +++ b/docs/reference/torch_conv2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -201,14 +209,10 @@ planes.
-@@ -219,7 +223,7 @@ planes. diff --git a/docs/reference/torch_conv3d.html b/docs/reference/torch_conv3d.html index ba8604a2fbbe0a0c8fa9194f495da87108695d0d..c36d810b9f18f75a191a4cacf35efbfa09e3a39a 100644 --- a/docs/reference/torch_conv3d.html +++ b/docs/reference/torch_conv3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -201,14 +209,10 @@ planes.
-@@ -219,7 +223,7 @@ planes. diff --git a/docs/reference/torch_conv_tbc.html b/docs/reference/torch_conv_tbc.html index 1e4a94eae201c2cc874c8a114943b1b6480cf496..78d97e08e248c8de0c66c616afdbe566ce139c92 100644 --- a/docs/reference/torch_conv_tbc.html +++ b/docs/reference/torch_conv_tbc.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor -
- Examples -
+- @@ -205,7 +209,7 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC. diff --git a/docs/reference/torch_conv_transpose1d.html b/docs/reference/torch_conv_transpose1d.html index 8344f9e142015d2c5624e29cb1c2b12457b8732c..f89bbfce6a9464c0b6771613e2dd43ba779fe079 100644 --- a/docs/reference/torch_conv_transpose1d.html +++ b/docs/reference/torch_conv_transpose1d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -187,14 +195,10 @@ Input and output dimensions are (Time, Batch, Channels) - hence TBC.
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -205,14 +213,10 @@ composed of several input planes, sometimes also called "deconvolution".
-@@ -223,7 +227,7 @@ composed of several input planes, sometimes also called "deconvolution". diff --git a/docs/reference/torch_conv_transpose2d.html b/docs/reference/torch_conv_transpose2d.html index 1d1f65d2975cb5b790c462917e49dda0aef9b812..03ad04e52ee4092132421a760eae125d964b3fe7 100644 --- a/docs/reference/torch_conv_transpose2d.html +++ b/docs/reference/torch_conv_transpose2d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -205,14 +213,10 @@ composed of several input planes, sometimes also called "deconvolution".
-@@ -223,7 +227,7 @@ composed of several input planes, sometimes also called "deconvolution". diff --git a/docs/reference/torch_conv_transpose3d.html b/docs/reference/torch_conv_transpose3d.html index 8ae839489e527bdab7d1c7b2fc3fee692d8fff08..7a6144f255d39258ce9bcbdbd89ab2d7f00e8944 100644 --- a/docs/reference/torch_conv_transpose3d.html +++ b/docs/reference/torch_conv_transpose3d.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -205,14 +213,10 @@ composed of several input planes, sometimes also called "deconvolution"
-@@ -223,7 +227,7 @@ composed of several input planes, sometimes also called "deconvolution" diff --git a/docs/reference/torch_cos.html b/docs/reference/torch_cos.html index 9c1054c5f1eba21a07443a6e383253882090b81e..2165566029a8754f7f5771fecbd0a27fea3b059c 100644 --- a/docs/reference/torch_cos.html +++ b/docs/reference/torch_cos.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_cosh.html b/docs/reference/torch_cosh.html index 1c9f5a6548924989203c827c0129d44c274f7a87..4694fa690c1b406a18cf7977c10b5edb13804b08 100644 --- a/docs/reference/torch_cosh.html +++ b/docs/reference/torch_cosh.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -209,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,25 +188,21 @@ $$
#> torch_tensor -#> -1.0549 -#> 1.2990 -#> -0.9629 -#> -0.7385 +#> 0.1767 +#> -0.7347 +#> 1.1696 +#> -0.4113 #> [ CPUFloatType{4} ]torch_cos(a)#> torch_tensor -#> 0.4933 -#> 0.2685 -#> 0.5712 -#> 0.7395 +#> 0.9844 +#> 0.7421 +#> 0.3905 +#> 0.9166 #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_cosine_similarity.html b/docs/reference/torch_cosine_similarity.html index 6bbb9d4341104910ae8a1a8d0bbcb735838b060c..580b30bbcf56ac7a432f815a52f92032f9f15568 100644 --- a/docs/reference/torch_cosine_similarity.html +++ b/docs/reference/torch_cosine_similarity.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -210,7 +214,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,25 +189,21 @@ $$
#> torch_tensor -#> 0.1397 -#> 0.3260 -#> 0.9304 -#> -0.3613 +#> 0.1583 +#> 1.2100 +#> -0.3203 +#> 1.2066 #> [ CPUFloatType{4} ]torch_cosh(a)#> torch_tensor -#> 1.0098 -#> 1.0536 -#> 1.4650 -#> 1.0660 +#> 1.0126 +#> 1.8258 +#> 1.0517 +#> 1.8206 #> [ CPUFloatType{4} ]
- @@ -310,7 +314,7 @@ $$ diff --git a/docs/reference/torch_cross.html b/docs/reference/torch_cross.html index 880b038cbb791a5dc2281ef8db17185a90afcadf..88ae8248048049b1638b9a2ee1f7abb37e7de33c 100644 --- a/docs/reference/torch_cross.html +++ b/docs/reference/torch_cross.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -190,116 +198,112 @@ $$ input2 = torch_randn(c(100, 128)) output = torch_cosine_similarity(input1, input2) output
#> torch_tensor -#> -0.1569 -#> 0.1006 -#> 0.0153 +#> 0.0263 +#> -0.0763 #> 0.0789 -#> 0.1126 -#> 0.0797 -#> 0.0259 -#> -0.0052 -#> 0.1218 -#> 0.0131 -#> -0.0693 -#> -0.1339 -#> -0.0372 -#> -0.0876 -#> 0.1678 -#> -0.0871 -#> -0.0124 -#> 0.0135 -#> 0.0111 -#> -0.0506 -#> 0.0127 -#> -0.0708 -#> 0.0137 -#> 0.0140 -#> 0.0780 -#> 0.1152 -#> -0.0042 -#> -0.0594 -#> 0.0262 -#> -0.2342 -#> 0.1262 -#> -0.0988 -#> -0.0387 -#> 0.0286 -#> 0.0143 -#> 0.0216 -#> 0.1066 -#> -0.0184 -#> 0.0295 -#> -0.0732 -#> -0.1372 -#> -0.0231 -#> -0.0352 -#> -0.0515 -#> 0.2235 -#> -0.1587 -#> -0.1000 -#> -0.0332 -#> -0.0116 -#> 0.1577 -#> -0.1124 -#> -0.0848 -#> 0.2118 -#> 0.1197 -#> 0.0648 -#> 0.1678 -#> -0.0088 -#> 0.2057 -#> -0.0278 -#> -0.0856 -#> -0.0495 -#> 0.0248 -#> 0.0910 -#> 0.0317 -#> 0.0760 -#> -0.1908 +#> 0.0770 +#> -0.0968 +#> -0.0367 +#> -0.0316 +#> -0.0499 +#> -0.1581 +#> 0.0673 +#> -0.0110 +#> -0.0916 +#> 0.0374 +#> -0.0762 +#> -0.0754 +#> -0.2115 +#> 0.0153 +#> -0.1287 +#> -0.0884 +#> -0.0211 +#> -0.0918 +#> -0.0012 +#> 0.1748 +#> 0.0642 +#> 0.0451 +#> -0.0335 +#> -0.1944 +#> -0.1517 +#> 0.0920 +#> -0.1079 +#> -0.2428 +#> -0.0909 +#> 0.0691 +#> -0.1527 +#> 0.0953 +#> 0.0458 +#> 0.0122 +#> 0.0996 +#> 0.0538 +#> -0.1366 +#> -0.1217 +#> -0.0312 +#> 0.0610 +#> -0.0650 +#> -0.0338 +#> 0.1201 +#> -0.1051 +#> -0.0883 +#> 0.0415 +#> -0.0773 +#> 0.0222 +#> -0.0438 +#> 0.0102 +#> -0.0022 +#> -0.0893 +#> 0.0891 +#> 0.0776 +#> -0.1275 +#> 0.0418 +#> -0.0716 +#> -0.1250 +#> 0.0285 +#> 0.0687 +#> 0.1884 +#> -0.0453 +#> -0.0510 +#> -0.1294 +#> -0.1340 +#> 0.0145 +#> 0.0501 +#> 0.1669 +#> 0.0125 +#> 0.0337 +#> 0.1500 +#> -0.1540 +#> 0.1909 +#> 0.0979 +#> -0.0350 +#> -0.0622 +#> 0.0173 +#> -0.0972 +#> -0.0858 +#> 0.0274 #> -0.0196 -#> 0.1030 -#> 0.0001 -#> -0.1256 -#> -0.1372 -#> 0.0761 -#> 0.1186 -#> -0.0794 -#> -0.0374 -#> -0.0475 -#> 0.1376 -#> -0.0452 -#> -0.1993 -#> 0.0900 -#> -0.0780 -#> -0.0765 -#> 0.0552 -#> 0.0656 -#> 0.1303 -#> 0.0220 -#> -0.1305 -#> -0.0221 -#> -0.0103 -#> 0.0126 -#> 0.0111 -#> -0.0132 -#> 0.0390 -#> 0.0566 -#> -0.1246 -#> -0.1158 -#> -0.1314 -#> 0.1597 -#> 0.0721 -#> 0.1145 +#> -0.2183 +#> 0.0174 +#> -0.0238 +#> -0.0479 +#> -0.0530 +#> 0.0421 +#> -0.0530 +#> -0.1692 +#> 0.0488 +#> -0.0140 +#> -0.1420 +#> -0.0361 +#> 0.1230 +#> 0.0648 +#> 0.0388 +#> -0.0385 #> [ CPUFloatType{100} ]diff --git a/docs/reference/torch_cummax.html b/docs/reference/torch_cummax.html index 799801bc6b9ad3babb96448c9bb161673e13945f..5b966934fa2fff58e93dbcebbba2b08d927fc54e 100644 --- a/docs/reference/torch_cummax.html +++ b/docs/reference/torch_cummax.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -230,7 +234,7 @@ size 3.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -190,36 +198,32 @@ size 3.
#> torch_tensor -#> 0.6848 -0.7350 0.2157 -#> 1.7849 0.3456 1.3741 -#> -1.0606 -0.0076 0.0373 -#> 1.9250 -0.4869 0.9716 +#> 1.2848 0.9117 1.2809 +#> -0.9080 -0.1721 0.9073 +#> -1.2477 0.1759 -0.4336 +#> -1.1736 -0.9302 0.2414 #> [ CPUFloatType{4,3} ]#> torch_tensor -#> 0.5104 0.9913 -0.2464 -#> -1.5599 0.6199 -1.7663 -#> -0.2542 0.7731 1.6322 -#> -0.7990 0.1296 0.8166 +#> -0.0837 -0.7765 0.2939 +#> -0.6446 0.4275 -0.6441 +#> -1.0360 1.9134 2.0891 +#> 0.9578 -0.5048 1.1494 #> [ CPUFloatType{4,3} ]torch_cross(a, b, dim=1)#> torch_tensor -#> -0.0328 0.2788 1.0540 -#> -1.4622 1.0093 1.6456 -#> -0.0412 1.7217 -0.8219 -#> -0.5235 -2.3482 -0.1395 +#> 1.2626 -0.4848 -0.9213 +#> -0.2771 -1.1698 -0.4992 +#> 1.1972 3.0558 -2.2051 +#> -0.9472 1.5802 1.4835 #> [ CPUFloatType{4,3} ]torch_cross(a, b)#> torch_tensor -#> -0.0328 0.2788 1.0540 -#> -1.4622 1.0093 1.6456 -#> -0.0412 1.7217 -0.8219 -#> -0.5235 -2.3482 -0.1395 +#> 1.2626 -0.4848 -0.9213 +#> -0.2771 -1.1698 -0.4992 +#> 1.1972 3.0558 -2.2051 +#> -0.9472 1.5802 1.4835 #> [ CPUFloatType{4,3} ]
diff --git a/docs/reference/torch_cummin.html b/docs/reference/torch_cummin.html index 4a37a071582f3497f3c242320fbd1e102917a292..de46de4f680b7ce685633e65a556c1f9c2b14c46 100644 --- a/docs/reference/torch_cummin.html +++ b/docs/reference/torch_cummin.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -243,7 +247,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -186,53 +194,49 @@ $$
#> torch_tensor -#> 0.2880 -#> 1.4154 -#> 0.0357 -#> -0.3602 -#> -0.1543 -#> 0.3235 -#> -0.5091 -#> 0.0494 -#> -1.4993 -#> 0.8103 +#> 0.3751 +#> -0.6010 +#> 1.7446 +#> -0.1877 +#> -0.1277 +#> -0.0612 +#> -1.8206 +#> 0.6495 +#> 0.8354 +#> 0.2696 #> [ CPUFloatType{10} ]torch_cummax(a, dim=0)#> [[1]] #> torch_tensor -#> 0.2880 -#> 1.4154 -#> 1.4154 -#> 1.4154 -#> 1.4154 -#> 1.4154 -#> 1.4154 -#> 1.4154 -#> 1.4154 -#> 1.4154 +#> 0.3751 +#> 0.3751 +#> 1.7446 +#> 1.7446 +#> 1.7446 +#> 1.7446 +#> 1.7446 +#> 1.7446 +#> 1.7446 +#> 1.7446 #> [ CPUFloatType{10} ] #> #> [[2]] #> torch_tensor #> 0 -#> 1 -#> 1 -#> 1 -#> 1 -#> 1 -#> 1 -#> 1 -#> 1 -#> 1 +#> 0 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 #> [ CPULongType{10} ] #>
diff --git a/docs/reference/torch_cumprod.html b/docs/reference/torch_cumprod.html index ad84833e02e5d34c04659dcf866009ca55e3e1c1..331aa522323a3997c303152a9142f8b81000092b 100644 --- a/docs/reference/torch_cumprod.html +++ b/docs/reference/torch_cumprod.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -243,7 +247,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -186,53 +194,49 @@ $$
#> torch_tensor -#> 0.1476 -#> -0.5527 -#> 0.7756 -#> -0.8587 -#> -0.6745 -#> 1.6640 -#> -1.5817 -#> -1.7267 -#> -0.9755 -#> -1.5346 +#> 0.4807 +#> -0.1043 +#> -1.5956 +#> 0.7636 +#> 0.0498 +#> 0.0839 +#> 0.1117 +#> 0.4206 +#> -0.7038 +#> -0.6216 #> [ CPUFloatType{10} ]torch_cummin(a, dim=0)#> [[1]] #> torch_tensor -#> 0.1476 -#> -0.5527 -#> -0.5527 -#> -0.8587 -#> -0.8587 -#> -0.8587 -#> -1.5817 -#> -1.7267 -#> -1.7267 -#> -1.7267 +#> 0.4807 +#> -0.1043 +#> -1.5956 +#> -1.5956 +#> -1.5956 +#> -1.5956 +#> -1.5956 +#> -1.5956 +#> -1.5956 +#> -1.5956 #> [ CPUFloatType{10} ] #> #> [[2]] #> torch_tensor #> 0 #> 1 -#> 1 -#> 3 -#> 3 -#> 3 -#> 6 -#> 7 -#> 7 -#> 7 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 +#> 2 #> [ CPULongType{10} ] #>
diff --git a/docs/reference/torch_cumsum.html b/docs/reference/torch_cumsum.html index 5760bd44938baecf34a8e085fc89812a6e57fcf7..395c06f60198e63e1790b85310d7deb0fe566346 100644 --- a/docs/reference/torch_cumsum.html +++ b/docs/reference/torch_cumsum.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -232,7 +236,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -191,37 +199,33 @@ $$
#> torch_tensor -#> 0.3944 -#> 1.6777 -#> 0.1170 -#> -0.0161 -#> 1.5475 -#> 0.6160 -#> -0.7775 -#> -1.4953 -#> 0.8252 -#> -0.1637 +#> 1.6953 +#> 0.6814 +#> -1.9031 +#> 0.6438 +#> -0.4781 +#> 0.9717 +#> 1.5482 +#> -0.7728 +#> 0.0737 +#> 0.8900 #> [ CPUFloatType{10} ]torch_cumprod(a, dim=0)#> torch_tensor -#> 0.3944 -#> 0.6616 -#> 0.0774 -#> -0.0012 -#> -0.0019 -#> -0.0012 -#> 0.0009 -#> -0.0014 -#> -0.0011 -#> 0.0002 +#> 1.6953 +#> 1.1552 +#> -2.1985 +#> -1.4154 +#> 0.6767 +#> 0.6576 +#> 1.0181 +#> -0.7868 +#> -0.0580 +#> -0.0516 #> [ CPUFloatType{10} ]
diff --git a/docs/reference/torch_det.html b/docs/reference/torch_det.html index 792ade51eb8e4bcd282ad4d60597d3140bca0dcb..4dd34a9eff1d08d16e67a18cf7c349e3bbb394fb 100644 --- a/docs/reference/torch_det.html +++ b/docs/reference/torch_det.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -232,7 +236,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -191,37 +199,33 @@ $$
#> torch_tensor -#> -0.8416 -#> 0.4333 -#> -0.4679 -#> -0.8919 -#> -0.3301 -#> -0.0339 -#> -0.4897 -#> -1.1311 -#> -1.4731 -#> -0.9011 +#> -2.0091 +#> -0.1505 +#> 1.4729 +#> 0.3228 +#> 0.4651 +#> 0.4226 +#> -1.0641 +#> -1.8574 +#> -0.0408 +#> 0.5819 #> [ CPUFloatType{10} ]torch_cumsum(a, dim=0)#> torch_tensor -#> -0.8416 -#> -0.4083 -#> -0.8762 -#> -1.7681 -#> -2.0983 -#> -2.1322 -#> -2.6219 -#> -3.7529 -#> -5.2261 -#> -6.1271 +#> -2.0091 +#> -2.1596 +#> -0.6867 +#> -0.3640 +#> 0.1011 +#> 0.5237 +#> -0.5404 +#> -2.3978 +#> -2.4386 +#> -1.8567 #> [ CPUFloatType{10} ]
diff --git a/docs/reference/torch_device.html b/docs/reference/torch_device.html index 036f2c96efd378f7fdb5b66e8f391d41507b2db0..ef9fcbaf33ab3df68d6afb10986f5834be9ecb4d 100644 --- a/docs/reference/torch_device.html +++ b/docs/reference/torch_device.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ is or will be allocated." /> - +@@ -88,12 +91,17 @@- @@ -222,7 +224,7 @@ unstable in when `input` doesn't have distinct singular values. See-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -182,36 +190,30 @@ unstable in when `input` doesn't have distinct singular values. See
#> torch_tensor -#> 0.480488 +#> -6.17158 #> [ CPUFloatType{} ]#> torch_tensor #> (1,.,.) = -#> -0.5255 -0.1535 -#> -0.6931 -0.6311 +#> 1.0234 -0.0210 +#> -0.6296 0.7397 #> #> (2,.,.) = -#> 0.01 * -#> -7.1440 -49.5890 -#> -128.0330 -154.2893 +#> 0.4909 2.2873 +#> 0.2083 -1.2617 #> #> (3,.,.) = -#> 0.1935 -0.6411 -#> 1.2664 1.1412 +#> -0.9767 -0.8264 +#> 2.2081 0.1595 #> [ CPUFloatType{3,2,2} ]A$det()#> torch_tensor -#> 0.2252 -#> -0.5247 -#> 1.0327 +#> 0.7437 +#> -1.0958 +#> 1.6691 #> [ CPUFloatType{3} ]
- @@ -200,7 +205,7 @@ with device@@ -89,12 +92,17 @@ is or will be allocated." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ is or will be allocated." />
-
-
+
@@ -183,13 +191,10 @@ with device
'cuda'is equivalent to'cuda:X'where X i torch_device("cuda", 0)
#> torch_device(type='cuda', index=0)torch_device("cpu", 0)#> torch_device(type='cpu', index=0)'cuda'is equivalent to'cuda:X'where X i diff --git a/docs/reference/torch_diag.html b/docs/reference/torch_diag.html index 82efe08328a42026568709fc207dab7e8637185a..c46385641328a46c463aef7e673781722f68e274 100644 --- a/docs/reference/torch_diag.html +++ b/docs/reference/torch_diag.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -211,7 +215,7 @@ the diagonal elements of@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -193,14 +201,10 @@ the diagonal elements of
input.
input. diff --git a/docs/reference/torch_diag_embed.html b/docs/reference/torch_diag_embed.html index ec6f4bc3bf9ce24734043f9caf5c9858521d6e0b..4147b1aab169eddd5b95e93bdb6b8338c77a72eb 100644 --- a/docs/reference/torch_diag_embed.html +++ b/docs/reference/torch_diag_embed.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -204,40 +212,36 @@ need to be explicitly specified. a = torch_randn(c(2, 3)) torch_diag_embed(a)
#> torch_tensor #> (1,.,.) = -#> 1.2480 0.0000 0.0000 -#> 0.0000 1.6831 0.0000 -#> 0.0000 0.0000 0.0076 +#> 0.8700 0.0000 0.0000 +#> 0.0000 2.4435 0.0000 +#> 0.0000 0.0000 1.3747 #> #> (2,.,.) = -#> 0.9348 0.0000 0.0000 -#> 0.0000 -1.1223 0.0000 -#> 0.0000 0.0000 0.7119 +#> 0.2728 0.0000 0.0000 +#> 0.0000 -0.7072 0.0000 +#> 0.0000 0.0000 -0.1288 #> [ CPUFloatType{2,3,3} ]torch_diag_embed(a, offset=1, dim1=0, dim2=2)#> torch_tensor #> (1,.,.) = -#> 0.0000 1.2480 0.0000 0.0000 -#> 0.0000 0.9348 0.0000 0.0000 +#> 0.0000 0.8700 0.0000 0.0000 +#> 0.0000 0.2728 0.0000 0.0000 #> #> (2,.,.) = -#> 0.0000 0.0000 1.6831 0.0000 -#> 0.0000 0.0000 -1.1223 0.0000 +#> 0.0000 0.0000 2.4435 0.0000 +#> 0.0000 0.0000 -0.7072 0.0000 #> #> (3,.,.) = -#> 0.0000 0.0000 0.0000 0.0076 -#> 0.0000 0.0000 0.0000 0.7119 +#> 0.0000 0.0000 0.0000 1.3747 +#> 0.0000 0.0000 0.0000 -0.1288 #> #> (4,.,.) = #> 0 0 0 0 #> 0 0 0 0 #> [ CPUFloatType{4,2,4} ]-@@ -248,7 +252,7 @@ need to be explicitly specified. diff --git a/docs/reference/torch_diagflat.html b/docs/reference/torch_diagflat.html index 2aa4069373b52a1f9e162d4a40a05e2772cca14d..ed1f512f61f47161c1692cef9a4d36cfb613ff3b 100644 --- a/docs/reference/torch_diagflat.html +++ b/docs/reference/torch_diagflat.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++- @@ -207,7 +211,7 @@ with the elements of@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -189,14 +197,10 @@ with the elements of
inputas the diagonal.
inputas the diagonal. diff --git a/docs/reference/torch_diagonal.html b/docs/reference/torch_diagonal.html index d8c73d4391765fe1b96e69ca9e985ef3417fa62f..994c95ff2744d21bdfd21200c06e03e80af535f5 100644 --- a/docs/reference/torch_diagonal.html +++ b/docs/reference/torch_diagonal.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -216,7 +220,7 @@ dimensions, so those need to be explicitly specified. diff --git a/docs/reference/torch_digamma.html b/docs/reference/torch_digamma.html index f7567952d76df197a3a1cbe7d0864d834feed99e..0a76d83e7f8a6f00fd621637b9e6f9c04ea745b5 100644 --- a/docs/reference/torch_digamma.html +++ b/docs/reference/torch_digamma.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -198,14 +206,10 @@ dimensions, so those need to be explicitly specified.
- @@ -198,7 +202,7 @@ $$ diff --git a/docs/reference/torch_dist.html b/docs/reference/torch_dist.html index 3cf2a001f76c97042a28cba0e4b22072ef92c182..d9162c277262eebc3f19de8f21d65eca37cb46df 100644 --- a/docs/reference/torch_dist.html +++ b/docs/reference/torch_dist.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -180,14 +188,10 @@ $$ #> -1.9635 #> [ CPUFloatType{2} ]
diff --git a/docs/reference/torch_div.html b/docs/reference/torch_div.html index aa04536c7ec6588eee34cddfa1463634fe296bac..5c00d1e4764e88fb380a5c7e0b66c09df0ab8c2e 100644 --- a/docs/reference/torch_div.html +++ b/docs/reference/torch_div.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -221,7 +225,7 @@ broadcastable .-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -183,34 +191,30 @@ broadcastable .
#> torch_tensor -#> -0.5662 -#> -1.7307 -#> 0.5544 -#> 1.0005 +#> 1.1977 +#> 2.2095 +#> 1.7161 +#> 1.4106 #> [ CPUFloatType{4} ]#> torch_tensor -#> 0.6658 -#> -1.3955 -#> -0.6781 -#> -2.0262 +#> -0.3824 +#> -1.0210 +#> 0.8483 +#> -0.2487 #> [ CPUFloatType{4} ]torch_dist(x, y, 3.5)#> torch_tensor -#> 3.09935 +#> 3.39426 #> [ CPUFloatType{} ]torch_dist(x, y, 3)#> torch_tensor -#> 3.15842 +#> 3.50012 #> [ CPUFloatType{} ]torch_dist(x, y, 0)#> torch_tensor #> 4 #> [ CPUFloatType{} ]torch_dist(x, y, 1)#> torch_tensor -#> 5.82649 +#> 7.33765 #> [ CPUFloatType{} ]
diff --git a/docs/reference/torch_dot.html b/docs/reference/torch_dot.html index bc1cdbf5379f242c1e4e67888565c6df31f33256..033cfd03daa339f006fbb004bde9a2fe48dcfb0e 100644 --- a/docs/reference/torch_dot.html +++ b/docs/reference/torch_dot.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -260,7 +263,7 @@ by zero leads to undefined behavior.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -209,47 +217,42 @@ by zero leads to undefined behavior.
#> torch_tensor -#> 0.0009 -#> 0.3340 -#> 0.6953 -#> -1.1893 -#> 0.6504 +#> -0.4509 +#> -1.4741 +#> 1.4410 +#> -1.7545 +#> 0.7006 #> [ CPUFloatType{5} ]torch_div(a, 0.5)#> torch_tensor -#> 0.0018 -#> 0.6679 -#> 1.3906 -#> -2.3786 -#> 1.3009 +#> -0.9018 +#> -2.9482 +#> 2.8819 +#> -3.5089 +#> 1.4013 #> [ CPUFloatType{5} ]#> torch_tensor -#> -0.6645 0.9020 0.8946 0.5315 -#> -0.0686 0.0120 0.8446 0.4115 -#> 0.4494 0.8383 1.3197 -0.0228 -#> 0.1216 1.2859 -1.7075 -0.2060 +#> 2.0242 0.0625 1.1603 1.3928 +#> 0.4491 0.0361 0.4277 1.9981 +#> -0.5807 -0.4782 -0.2684 -1.0164 +#> -0.6413 0.6253 -0.9699 -0.3891 #> [ CPUFloatType{4,4} ]#> torch_tensor -#> 0.6574 -#> 1.3830 -#> 2.6405 -#> 1.7615 +#> -0.9792 +#> -0.4541 +#> 3.7302 +#> -1.7271 #> [ CPUFloatType{4} ]torch_div(a, b)#> torch_tensor -#> -1.0107 0.6522 0.3388 0.3018 -#> -0.1044 0.0087 0.3199 0.2336 -#> 0.6836 0.6061 0.4998 -0.0130 -#> 0.1849 0.9298 -0.6466 -0.1170 +#> -2.0671 -0.1377 0.3111 -0.8064 +#> -0.4586 -0.0795 0.1147 -1.1570 +#> 0.5930 1.0529 -0.0720 0.5885 +#> 0.6549 -1.3770 -0.2600 0.2253 #> [ CPUFloatType{4,4} ]
- @@ -188,7 +192,7 @@ diff --git a/docs/reference/torch_eig.html b/docs/reference/torch_eig.html index c05dac719bfb825ab55375c44d4f8d2e89bb3ba7..67f598ec3ab790b03725bb77b49a33a75aad369a 100644 --- a/docs/reference/torch_eig.html +++ b/docs/reference/torch_eig.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -170,14 +178,10 @@ #> 7 #> [ CPUFloatType{} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -189,15 +197,10 @@ for [`torch_symeig`]
-@@ -208,7 +211,7 @@ for [`torch_symeig`] diff --git a/docs/reference/torch_einsum.html b/docs/reference/torch_einsum.html index 2b30cad817a534c46b6c9d6b60ef1f9a381d4bc3..af41655f140c4a4c185c189d6d867c7ef95fa26a 100644 --- a/docs/reference/torch_einsum.html +++ b/docs/reference/torch_einsum.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++- @@ -197,7 +201,7 @@ Einstein summation convention. diff --git a/docs/reference/torch_empty.html b/docs/reference/torch_empty.html index 173e5e030c1b002c53ec6aed2e8a90bd0714820a..e168562996051417583dcacc77bb063a6fdc6096 100644 --- a/docs/reference/torch_empty.html +++ b/docs/reference/torch_empty.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -179,14 +187,10 @@ Einstein summation convention.
diff --git a/docs/reference/torch_empty_like.html b/docs/reference/torch_empty_like.html index ce76c722cad571b17b287e82b71d66e415021924..65bdf64e6271cee2a4e2a216eff7a8dd1743069b 100644 --- a/docs/reference/torch_empty_like.html +++ b/docs/reference/torch_empty_like.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -201,18 +209,15 @@ defined by the variable argument
size.Examples
#> torch_tensor -#> 0.0000e+00 2.5244e-29 2.3802e+07 -#> 8.5920e+09 8.4078e-45 4.9045e-44 +#> 1e-43 * +#> 0.5745 1.2892 1.6115 +#> 0.5885 0.8548 1.2892 #> [ CPUFloatType{2,3} ]
-@@ -223,7 +228,7 @@ defined by the variable argumentContents
--
-
- Arguments -
- empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor -
- Examples -
+size.diff --git a/docs/reference/torch_empty_strided.html b/docs/reference/torch_empty_strided.html index 937c47a3b2d448500bc9c6e3f1541930a999047f..f768c7fcee50d8d74b97e246dc4ea46a3c20d835 100644 --- a/docs/reference/torch_empty_strided.html +++ b/docs/reference/torch_empty_strided.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -216,7 +220,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -194,18 +202,14 @@
Examples
#> torch_tensor -#> 1.1529e+18 1.1529e+18 1.4021e+14 -#> 1.4021e+14 0.0000e+00 0.0000e+00 +#> 8.5899e+09 0.0000e+00 0.0000e+00 +#> 0.0000e+00 8.5899e+09 1.3700e+02 #> [ CPULongType{2,3} ]
diff --git a/docs/reference/torch_eq.html b/docs/reference/torch_eq.html index 93a10dea674fdd654b100c25f552487210e1acd3..021865b599b7480a36eefe10f98d69cfcffbafc3 100644 --- a/docs/reference/torch_eq.html +++ b/docs/reference/torch_eq.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -231,7 +234,7 @@ the tensors, please clone them first.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -208,19 +216,14 @@ the tensors, please clone them first.
#> torch_tensor -#> 1.8389e+25 3.8318e+30 1.7700e+31 -#> 9.1503e+05 2.6777e+11 1.7225e+22 +#> 4.4726e+21 2.7945e+20 7.0688e+28 +#> 8.4843e+26 2.1723e+29 5.5596e+31 #> [ CPUFloatType{2,3} ]a$stride(0)#> [1] 1a$size(0)#> [1] 2
- @@ -206,7 +210,7 @@ broadcastable with the first argument. diff --git a/docs/reference/torch_equal.html b/docs/reference/torch_equal.html index 4555398a88ec4636e0f3723964245aadc0b996bc..282265ed6a5bd71adc645e06168c17a16530992d 100644 --- a/docs/reference/torch_equal.html +++ b/docs/reference/torch_equal.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -188,14 +196,10 @@ broadcastable with the first argument. #> 1 #> [ CPUBoolType{4} ]
diff --git a/docs/reference/torch_erf.html b/docs/reference/torch_erf.html index 6b7bb711067246359fba0cb841219df0b42ffa6b..1a6a82eb54a5b1b27c7acc44e760a9a35403065d 100644 --- a/docs/reference/torch_erf.html +++ b/docs/reference/torch_erf.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -182,7 +187,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -165,13 +173,10 @@
#> [1] TRUE
- @@ -202,7 +206,7 @@ $$ diff --git a/docs/reference/torch_erfc.html b/docs/reference/torch_erfc.html index 4b07e6945d89517d73b1e43d099b405a5c5f816f..93727425675196828011621283365f261d029a8a 100644 --- a/docs/reference/torch_erfc.html +++ b/docs/reference/torch_erfc.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,14 +192,10 @@ $$ #> 1.0000 #> [ CPUFloatType{3} ]
- @@ -203,7 +207,7 @@ $$ diff --git a/docs/reference/torch_erfinv.html b/docs/reference/torch_erfinv.html index 496a4c0bc4f0319f85463222a7918f4520694dad..d08fd3acbf14c29e5d3cd3297ba8636291b016fa 100644 --- a/docs/reference/torch_erfinv.html +++ b/docs/reference/torch_erfinv.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -185,14 +193,10 @@ $$ #> 2.8026e-45 #> [ CPUFloatType{3} ]
- @@ -203,7 +207,7 @@ $$ diff --git a/docs/reference/torch_exp.html b/docs/reference/torch_exp.html index b958e7f70eb7334b93ff8008b30a16d45996693f..2ef4476951e22de5f0fbe47a011d8c05af267b26 100644 --- a/docs/reference/torch_exp.html +++ b/docs/reference/torch_exp.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -185,14 +193,10 @@ $$ #> -inf #> [ CPUFloatType{3} ]
- @@ -202,7 +206,7 @@ $$ diff --git a/docs/reference/torch_expm1.html b/docs/reference/torch_expm1.html index b278bbf8e9ebfcae35cf73153ddc4af004cadd3d..5cf1dbb27e5d46728365f96e166517f01ec531cf 100644 --- a/docs/reference/torch_expm1.html +++ b/docs/reference/torch_expm1.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,14 +192,10 @@ $$ #> 2 #> [ CPUFloatType{2} ]
- @@ -202,7 +206,7 @@ $$ diff --git a/docs/reference/torch_eye.html b/docs/reference/torch_eye.html index 62826ad58aacbfe7bf0b04cba0d68ad931dc1617..ce753482271c6cacf43d41a9511e857da3a6fa96 100644 --- a/docs/reference/torch_eye.html +++ b/docs/reference/torch_eye.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,14 +192,10 @@ $$ #> 1 #> [ CPUFloatType{2} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -201,14 +209,10 @@ #> 0 0 1 #> [ CPUFloatType{3,3} ]
-@@ -219,7 +223,7 @@ diff --git a/docs/reference/torch_fft.html b/docs/reference/torch_fft.html index 408fae4069eda7ba37de0fbc2a196a0c73c584b2..0ee34295ea618623c64e81ed873aa617b16eab24 100644 --- a/docs/reference/torch_fft.html +++ b/docs/reference/torch_fft.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -217,375 +225,369 @@ shape of
input. x = torch_randn(c(4, 3, 2)) torch_fft(x, 2)
#> torch_tensor #> (1,.,.) = -#> 3.1269 0.9525 -#> -2.5551 -9.2671 -#> -0.3517 -3.9285 +#> 0.6538 2.3902 +#> -0.0407 -2.1009 +#> -1.7075 0.2743 #> #> (2,.,.) = -#> 1.3474 -1.2334 -#> -4.5162 4.7339 -#> 0.2207 -1.8316 +#> -7.3254 -4.1155 +#> -3.4979 4.6414 +#> -1.9202 6.4543 #> #> (3,.,.) = -#> 4.1118 2.3770 -#> 4.5216 0.1284 -#> 2.5540 -10.7896 +#> 0.7929 2.8143 +#> -1.5744 -1.1399 +#> 1.8256 0.9958 #> #> (4,.,.) = -#> 2.4745 5.5189 -#> 4.1149 2.1041 -#> -2.7162 -7.8699 +#> -1.1778 4.2228 +#> -1.2220 -6.5910 +#> -4.3523 -0.8053 #> [ CPUFloatType{4,3,2} ]# batched 1D FFT torch_fft(x, 1)#> torch_tensor #> (1,.,.) = -#> 2.7652 1.9038 -#> 0.3913 -0.5752 -#> -0.0733 -6.1049 +#> -1.7641 1.3280 +#> -1.5838 -1.2976 +#> -1.5386 1.7298 #> #> (2,.,.) = -#> 1.4418 -0.6379 -#> -2.4266 -4.5066 -#> -2.2360 2.4495 +#> 2.0498 -1.6429 +#> -2.4247 -0.8092 +#> -2.6982 0.4276 #> #> (3,.,.) = -#> 0.8542 -0.2390 -#> 0.5920 -3.9942 -#> 1.1744 -1.2541 +#> 2.4875 1.2743 +#> 0.7762 -0.3228 +#> 1.5976 -1.0947 #> #> (4,.,.) = -#> -1.9343 -0.0744 -#> -1.1117 -0.1911 -#> 0.7832 0.9810 +#> -2.1193 1.4309 +#> 3.1915 0.3287 +#> 0.9316 -0.7884 #> [ CPUFloatType{4,3,2} ]#> torch_tensor #> (1,1,1,.,.) = -#> -3.0607 -2.0808 -#> -0.7614 11.3993 -#> -9.7078 -1.1342 -#> 5.3198 -1.8183 -#> -2.2561 1.1860 +#> -5.9427 2.3830 +#> -0.9876 3.9555 +#> -7.3313 3.1490 +#> 4.2801 -8.8746 +#> -7.3879 5.3391 #> #> (2,1,1,.,.) = -#> 5.9581 5.6764 -#> -0.7409 5.7791 -#> -2.2354 -3.3075 -#> 1.5357 -5.2179 -#> -0.4934 -10.6985 +#> 4.5545 3.1398 +#> 6.6397 12.7704 +#> -11.8139 -3.3971 +#> -6.0994 3.0087 +#> 0.8275 1.3478 #> #> (3,1,1,.,.) = -#> 5.6713 -1.4706 -#> 7.7647 0.3945 -#> 1.7536 -2.3804 -#> -2.7693 -1.4015 -#> 7.3998 7.8926 +#> -0.1075 2.4478 +#> -6.8575 1.1242 +#> -6.0275 3.7266 +#> 4.3421 11.8535 +#> -1.6037 7.5695 #> #> (1,2,1,.,.) = -#> -7.3846 1.0945 -#> 2.6440 -0.0749 -#> 0.8468 0.6714 -#> 2.8560 -0.5236 -#> 4.6443 -11.6895 +#> 2.8736 -2.1080 +#> 1.0969 -7.3034 +#> 9.1364 -0.4367 +#> 2.2691 -3.3444 +#> -4.3820 -4.2208 #> #> (2,2,1,.,.) = -#> -2.6286 4.1849 -#> 0.0714 9.0369 -#> 2.2322 3.0162 -#> -6.9693 3.6747 -#> 2.1045 5.4243 +#> -0.9118 4.7446 +#> 1.1206 7.9196 +#> 1.6641 0.7576 +#> 3.0925 1.1970 +#> 8.5249 -1.4439 #> #> (3,2,1,.,.) = -#> 12.0462 -0.1237 -#> 6.1222 4.7002 -#> 2.4700 9.5251 -#> -2.4001 6.1526 -#> 6.2922 -4.2304 +#> 4.8518 0.1233 +#> -6.2846 7.4662 +#> -1.7859 6.2408 +#> 1.5846 -0.8276 +#> 1.2933 -3.6956 #> #> (1,3,1,.,.) = -#> 7.1441 1.4981 -#> -4.1623 -0.3252 -#> 2.9155 -1.5450 -#> 7.9392 -1.9687 -#> -2.3094 0.0935 +#> -2.0138 -1.6578 +#> -4.5817 -4.3853 +#> -4.8263 -1.1006 +#> 1.6396 -3.9959 +#> 0.3164 18.2475 #> #> (2,3,1,.,.) = -#> -0.9630 -6.0835 -#> 0.4546 -6.4689 -#> -0.0282 -9.4388 -#> -7.4903 -1.5077 -#> 1.7163 -6.8045 +#> 7.8291 2.7374 +#> -5.7064 -9.9621 +#> -2.8155 -0.1657 +#> -3.9620 12.4611 +#> 3.4435 3.2326 #> #> (3,3,1,.,.) = -#> -8.3626 2.2797 -#> 3.1811 -3.3831 -#> 4.2462 2.6255 -#> -3.0474 6.3158 -#> 6.0139 3.5483 +#> 4.4183 3.6809 +#> 3.2348 -0.1091 +#> 6.8238 -4.1220 +#> -4.5586 -7.4389 +#> 5.1245 -2.3675 #> #> (1,1,2,.,.) = -#> 1.6137 2.7773 -#> 0.8053 2.4957 -#> 2.0098 4.0531 -#> 7.1536 -4.0740 -#> 0.5047 -3.4478 +#> 9.7920 0.6806 +#> -0.3190 -3.7368 +#> 0.7023 -0.9383 +#> 4.7008 -1.5883 +#> -3.9566 3.6726 #> #> (2,1,2,.,.) = -#> 6.9573 15.8894 -#> -2.4017 -1.8492 -#> 6.6191 -0.4522 -#> 0.2472 0.4819 -#> 2.7057 4.7820 +#> -3.0512 1.9221 +#> -0.2157 0.0452 +#> -2.9183 -4.3607 +#> 8.7492 7.3246 +#> 1.5165 9.6445 #> #> (3,1,2,.,.) = -#> -5.5708 -0.4651 -#> -3.3221 -7.2540 -#> 4.7071 8.4420 -#> -1.0274 5.4142 -#> -6.8729 -0.6304 +#> -0.0073 -0.8282 +#> -0.6035 -5.8420 +#> 12.4597 -2.4005 +#> 2.2515 11.3698 +#> 4.6597 4.6143 #> #> (1,2,2,.,.) = -#> 1.1419 4.0883 -#> -4.2120 4.7751 -#> -0.8735 -2.5643 -#> -2.3289 0.6712 -#> -3.3304 4.8199 +#> -1.2314 -3.1506 +#> 1.3068 1.5538 +#> 8.0836 -3.6069 +#> 0.0689 7.7218 +#> 1.6400 7.3772 #> #> (2,2,2,.,.) = -#> -0.5676 9.9439 -#> -3.4647 4.7481 -#> -1.3951 0.8998 -#> 5.0574 -4.5266 -#> -6.7789 -1.1194 +#> 7.2325 -3.6465 +#> 6.0541 -4.9124 +#> -2.4519 2.3710 +#> -9.0196 8.2937 +#> -6.5627 -7.3668 #> #> (3,2,2,.,.) = -#> 3.8036 6.1886 -#> -7.1878 3.4328 -#> 1.1591 3.7105 -#> -7.5438 0.0551 -#> 8.2602 9.0398 +#> 4.8670 9.0995 +#> 0.0583 4.9745 +#> 3.3052 -2.3065 +#> -1.0784 -3.5113 +#> -1.3564 -2.9838 #> #> (1,3,2,.,.) = -#> -5.2171 -13.2233 -#> -2.0567 -1.1374 -#> 4.2039 -1.5208 -#> 1.9455 -4.7267 -#> 9.0767 -4.1536 +#> 0.1958 -0.7882 +#> -2.9442 -4.7447 +#> -0.6995 -5.9500 +#> -4.5564 -0.7496 +#> 5.6657 -3.0886 #> #> (2,3,2,.,.) = -#> 3.7466 -4.5032 -#> 1.4704 -2.0007 -#> -1.9988 -0.1092 -#> -4.4596 -0.3324 -#> 2.1152 -13.2775 +#> 7.0723 -4.2332 +#> 7.6992 -0.9180 +#> 2.7281 -8.0427 +#> 5.9747 1.6752 +#> -2.7002 -0.1596 #> #> (3,3,2,.,.) = -#> -3.3043 0.5113 -#> -2.8777 4.2746 -#> -3.3955 1.7092 -#> 9.2973 10.1919 -#> -1.5666 2.8480 +#> 5.7039 1.1480 +#> 2.7226 3.4380 +#> -4.1206 0.7478 +#> -1.0942 -2.8951 +#> -1.4779 1.0268 #> #> (1,1,3,.,.) = -#> 0.3320 4.6616 -#> 0.8167 2.9600 -#> 0.4455 4.4140 -#> -1.3307 10.8842 -#> -9.4580 1.1702 +#> 7.1011 -0.2380 +#> -3.5223 3.5401 +#> 5.7742 1.4789 +#> -4.6115 2.6932 +#> -2.7191 -4.2168 #> #> (2,1,3,.,.) = -#> -1.8528 7.6680 -#> 8.2839 14.2152 -#> 2.9796 -3.5969 -#> -1.7270 1.0632 -#> 0.1975 -0.5567 +#> -5.7986 6.4509 +#> -5.6827 -6.2617 +#> -4.3300 2.0698 +#> 6.8867 -2.4145 +#> 9.1154 3.2265 #> #> (3,1,3,.,.) = -#> -0.7397 2.4980 -#> -2.3425 1.0752 -#> -1.2656 -9.2947 -#> -7.2362 -3.9018 -#> 5.9827 2.7699 +#> 2.4795 1.2525 +#> 5.3633 -4.1375 +#> -2.7429 -0.0403 +#> 1.9457 1.0570 +#> 0.7156 -1.8286 #> #> (1,2,3,.,.) = -#> -15.5498 1.0546 -#> 1.3051 0.4347 -#> -1.4093 7.5153 -#> -6.1192 4.7335 -#> -4.9058 -4.5730 +#> -0.0121 -3.2482 +#> -2.3066 3.7388 +#> -8.0017 3.0797 +#> 6.7179 4.0203 +#> -2.2814 -5.3970 #> #> (2,2,3,.,.) = -#> -3.6604 1.9234 -#> -0.8458 -0.4348 -#> -1.8828 3.6361 -#> 1.8287 -1.8895 -#> 2.6902 -5.2685 +#> 0.6086 -5.4349 +#> 3.6174 2.9092 +#> -7.8689 -13.5817 +#> -6.2091 -1.2536 +#> -0.3103 7.7553 #> #> (3,2,3,.,.) = -#> -1.3802 -0.7002 -#> 2.3379 2.1387 -#> 0.3050 -2.9317 -#> 8.0050 -0.7583 -#> -5.9590 -1.9482 +#> -10.8803 3.3297 +#> -6.5093 -1.5097 +#> -8.7847 -4.2053 +#> -11.8536 -7.2926 +#> -7.0811 -8.0697 #> #> (1,3,3,.,.) = -#> 6.9329 6.3405 -#> 3.6646 -13.5643 -#> -2.9506 5.9330 -#> -2.8361 -7.1243 -#> 0.7209 -3.3815 +#> -2.9286 6.8577 +#> 3.9069 0.3475 +#> 0.2377 -3.9633 +#> 7.2230 9.0634 +#> 6.9915 5.3864 #> #> (2,3,3,.,.) = -#> 1.5262 2.7090 -#> 5.2327 2.3287 -#> 6.2155 -2.0571 -#> -4.2310 -4.1960 -#> 3.1807 2.8638 +#> -0.5471 3.3024 +#> 5.6635 0.4806 +#> 9.6620 8.0607 +#> -1.2398 1.4398 +#> 1.1879 4.9579 #> #> (3,3,3,.,.) = -#> 0.5015 2.4013 -#> 9.8059 -7.5275 -#> -0.3134 3.5004 -#> 2.5308 1.6895 -#> 7.3772 -7.0387 +#> 7.0490 9.4731 +#> -2.1840 7.1841 +#> 1.6218 0.4988 +#> 12.1390 10.0518 +#> -10.0381 3.5385 #> #> (1,1,4,.,.) = -#> -13.6532 4.4051 -#> -10.4147 5.0181 -#> -1.1286 1.7529 -#> 2.8230 -5.6151 -#> -7.9838 -2.6413 +#> -9.0473 -4.1510 +#> 4.4224 -11.9244 +#> 0.2593 5.7600 +#> 5.8278 2.6686 +#> -3.3019 -3.9117 #> #> (2,1,4,.,.) = -#> 4.0084 7.7560 -#> -0.2307 -0.0883 -#> 4.3662 10.6252 -#> -2.3291 -0.1440 -#> 1.5278 0.1987 +#> 0.3849 3.7750 +#> 4.9458 -3.1538 +#> 7.8825 -1.8965 +#> 0.3016 -0.8280 +#> -1.3189 -2.3694 #> #> (3,1,4,.,.) = -#> -9.1532 -6.4304 -#> 6.9663 -1.3172 -#> -3.9041 -5.8065 -#> 0.3933 -2.6468 -#> 8.6516 4.8018 +#> -2.7014 -5.1757 +#> 3.8298 -5.0397 +#> -2.6360 -0.4463 +#> -4.8332 3.6021 +#> 3.3230 -1.7663 #> #> (1,2,4,.,.) = -#> -0.3304 5.5103 -#> -10.1824 6.2636 -#> 2.9708 -8.4041 -#> -10.9114 -7.5795 -#> -2.4482 -5.0504 +#> 8.1613 -2.2573 +#> -3.2551 1.3833 +#> 4.3634 6.3142 +#> 0.5248 -7.8280 +#> 0.8889 -3.6975 #> #> (2,2,4,.,.) = -#> -8.0483 0.7491 -#> -5.7506 -2.1713 -#> 2.0948 6.2371 -#> -4.2344 -0.3634 -#> -5.2435 -3.3073 +#> 2.2364 3.2553 +#> 3.8263 3.7596 +#> -3.3828 1.5186 +#> 0.7146 -5.7372 +#> -3.8133 -0.6495 #> #> (3,2,4,.,.) = -#> 4.8102 5.9549 -#> 0.1113 4.5301 -#> 7.1013 5.4995 -#> -4.5926 -3.2111 -#> 6.9338 6.1466 +#> -10.9589 5.8050 +#> -5.3726 5.7243 +#> 4.5528 -6.0136 +#> 7.4999 -0.2582 +#> 3.3877 6.1032 #> #> (1,3,4,.,.) = -#> 6.1493 4.0264 -#> 1.4464 13.3106 -#> 1.6339 6.9375 -#> 2.8700 6.0982 -#> -1.5679 1.1448 +#> 0.7270 6.1670 +#> -6.5006 -5.9085 +#> 2.4775 1.7599 +#> -3.6453 4.0545 +#> 5.4839 1.2570 #> #> (2,3,4,.,.) = -#> 2.6702 3.4808 -#> 6.4623 4.0797 -#> 1.3476 4.3666 -#> -3.1097 -6.0254 -#> 0.4799 -3.6648 +#> -6.8501 -5.9425 +#> 1.7361 1.2303 +#> -0.0090 2.3497 +#> -1.8686 -0.6607 +#> 2.4513 -1.8840 #> #> (3,3,4,.,.) = -#> 3.7527 5.6061 -#> -2.6108 2.9753 -#> -4.2929 -6.6872 -#> 3.3525 -9.4171 -#> -5.5593 -3.8154 +#> 5.9438 -1.2280 +#> -8.8390 9.0097 +#> 2.8616 -1.3395 +#> -9.2470 0.0511 +#> -0.1519 -1.3970 #> #> (1,1,5,.,.) = -#> -6.2914 -3.7980 -#> -0.1560 2.0582 -#> -1.9810 0.4032 -#> 2.8557 3.1879 -#> -7.8761 5.1946 +#> -3.3137 1.7789 +#> 2.6371 2.7102 +#> 4.4069 -4.4288 +#> -3.4736 1.9758 +#> -4.3432 -4.9600 #> #> (2,1,5,.,.) = -#> -1.4126 1.8956 -#> 6.4759 -3.8589 -#> 7.1188 -1.0978 -#> -5.6880 1.9322 -#> -6.6137 -5.2791 +#> -2.1086 -0.6690 +#> -3.5679 -1.3710 +#> -3.9918 -4.4313 +#> 3.3377 -1.4201 +#> 4.3771 2.7254 #> #> (3,1,5,.,.) = -#> 0.3488 -3.0268 -#> 2.4352 -3.3522 -#> 9.2019 -6.0556 -#> -2.2342 -1.8149 -#> 2.2566 -1.4601 +#> -9.5478 4.2810 +#> -5.3256 -5.4435 +#> 1.0563 -2.9631 +#> 3.6469 -2.4466 +#> 2.9408 6.8321 #> #> (1,2,5,.,.) = -#> -2.9594 -1.2969 -#> 2.0627 -1.3316 -#> 3.2959 2.1453 -#> 4.5162 -6.4966 -#> -7.8497 5.9245 +#> -3.1676 4.9672 +#> 5.1714 2.4288 +#> 0.3678 5.9836 +#> -1.7141 3.7235 +#> -5.0729 -2.1338 #> #> (2,2,5,.,.) = -#> -6.4284 7.8053 -#> 1.0767 -1.3863 -#> 8.8502 8.3183 -#> 0.7857 1.9681 -#> -3.2123 -1.1060 +#> 3.2250 3.2594 +#> 1.1617 -3.0566 +#> -1.8365 -0.7719 +#> -0.5639 -2.4706 +#> 5.6257 9.9585 #> #> (3,2,5,.,.) = -#> -3.9549 -5.9318 -#> 1.7523 2.4051 -#> 3.3194 5.8822 -#> 0.0958 5.6401 -#> -0.5973 1.8942 +#> -0.1548 -9.0428 +#> 0.6164 -2.1338 +#> -2.3196 1.0116 +#> 6.2667 -8.7648 +#> -6.9029 -2.2244 #> #> (1,3,5,.,.) = -#> 0.6352 1.7775 -#> -1.8487 -0.7606 -#> 4.7322 8.5498 -#> -0.2256 0.7480 -#> -8.2643 -1.3865 +#> 6.4957 -2.0517 +#> 0.6094 -3.5123 +#> -4.1952 -8.5566 +#> 4.5907 -4.7177 +#> -7.8233 -2.9789 #> #> (2,3,5,.,.) = -#> 1.4332 0.5647 -#> 3.8933 7.6698 -#> -9.0813 0.8803 -#> -0.8624 1.6519 -#> 3.8879 2.2301 +#> 1.4311 -0.1023 +#> -1.5866 3.9124 +#> 3.7206 -2.0941 +#> -13.1653 5.1716 +#> -0.5996 7.7850 #> #> (3,3,5,.,.) = -#> -6.1772 -0.0762 -#> 4.7082 2.9675 -#> 3.5757 -5.9739 -#> -0.3279 1.5277 -#> -4.3354 -0.5859 +#> 0.8066 -1.9938 +#> -10.3948 0.4750 +#> -6.6004 6.0039 +#> 0.0750 6.6125 +#> -1.6083 -0.2164 #> [ CPUFloatType{3,3,5,5,2} ]-@@ -596,7 +598,7 @@ shape ofContents
- - ++input. diff --git a/docs/reference/torch_flatten.html b/docs/reference/torch_flatten.html index 15e6f3f2cc4ce69ad3e70c9e8bc17ca35de3bbfe..b1bfbe10f8dc995cd6547d8e2d67256687255a2a 100644 --- a/docs/reference/torch_flatten.html +++ b/docs/reference/torch_flatten.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -205,7 +209,7 @@ diff --git a/docs/reference/torch_flip.html b/docs/reference/torch_flip.html index 83d4b6055118ba55c5498729c9bdee6be954ad86..ec020bedf8e27febb0e970a019fb199dee0ecdf7 100644 --- a/docs/reference/torch_flip.html +++ b/docs/reference/torch_flip.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -187,14 +195,10 @@ #> 1 2 #> [ CPUFloatType{1,2} ]
- @@ -212,7 +216,7 @@ diff --git a/docs/reference/torch_floor.html b/docs/reference/torch_floor.html index 3c2587e39f58d05a2794a2571dc56d70091f800d..0fcc221d84f910acd9339c052c1361bcf69e6e77 100644 --- a/docs/reference/torch_floor.html +++ b/docs/reference/torch_floor.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -194,14 +202,10 @@ #> 0 1 #> [ CPUFloatType{2,2,2} ]
diff --git a/docs/reference/torch_floor_divide.html b/docs/reference/torch_floor_divide.html index d3b58dcdd3526e87173bfdaa22ce3a20d78fcbd4..b64def16c5d31e54537337b8ec1cdf83bcbeed9c 100644 --- a/docs/reference/torch_floor_divide.html +++ b/docs/reference/torch_floor_divide.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -210,7 +214,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,25 +189,21 @@ $$
#> torch_tensor -#> 0.4768 -#> 0.7149 -#> 1.7838 -#> -2.1206 +#> 0.9479 +#> 0.9924 +#> 0.6519 +#> 1.1174 #> [ CPUFloatType{4} ]torch_floor(a)#> torch_tensor #> 0 #> 0 +#> 0 #> 1 -#> -3 #> [ CPUFloatType{4} ]
- @@ -207,7 +211,7 @@ $$ diff --git a/docs/reference/torch_fmod.html b/docs/reference/torch_fmod.html index 705cbb7734078db4b2223edccdc3db30af39cbc6..0484905e53a301b9adfaec6ec6620ad952c38c35 100644 --- a/docs/reference/torch_fmod.html +++ b/docs/reference/torch_fmod.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -189,14 +197,10 @@ $$ #> 2 #> [ CPUFloatType{2} ]
- @@ -216,7 +220,7 @@ numbers. The remainder has the same sign as the dividend@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -198,14 +206,10 @@ numbers. The remainder has the same sign as the dividend
input. #> 0.5000 #> [ CPUFloatType{5} ]
input. diff --git a/docs/reference/torch_frac.html b/docs/reference/torch_frac.html index 92f9afc33a0af37c91ff9932c46c183f7d5f97a4..e9f4ad721bc5e20bae888cee5b151dca226beb38 100644 --- a/docs/reference/torch_frac.html +++ b/docs/reference/torch_frac.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -189,7 +194,7 @@ $$ diff --git a/docs/reference/torch_full.html b/docs/reference/torch_full.html index 5d13313d66b0a52f5545bafdc9c015c5b6763973..4b33a92b5992d41b686bd5f3ae8ad03bf4896983 100644 --- a/docs/reference/torch_full.html +++ b/docs/reference/torch_full.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -172,13 +180,10 @@ $$ #> -0.2000 #> [ CPUFloatType{3} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -209,15 +217,10 @@ and an integral
fill_valuewill return a tensor of torch.long dtype #> 3.1416 3.1416 3.1416 #> [ CPUFloatType{2,3} ]
-@@ -228,7 +231,7 @@ and an integralContents
--
-
- Arguments -
- full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Warning -
- Examples -
+fill_valuewill return a tensor of torch.long dtype diff --git a/docs/reference/torch_full_like.html b/docs/reference/torch_full_like.html index ad09cfb789d22fe15275fa34845293ae16ca7eb6..dbfd4d16b1d6aca970a2be38bd36cb5b53a4381b 100644 --- a/docs/reference/torch_full_like.html +++ b/docs/reference/torch_full_like.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -201,14 +209,10 @@
-@@ -219,7 +223,7 @@ diff --git a/docs/reference/torch_gather.html b/docs/reference/torch_gather.html index 351dfd6a39f8e53ea485e3e50a1db8c938cc3905..3f1043c663e68511cd9bcba3265725de3b5f9b8d 100644 --- a/docs/reference/torch_gather.html +++ b/docs/reference/torch_gather.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -184,10 +192,9 @@
Gathers values along an axis specified by
-dim.For a 3-D tensor the output is specified by::
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 -out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 -out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2 -
+For a 3-D tensor the output is specified by::
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 +out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 +out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
If
#> 4 3 #> [ CPUFloatType{2,2} ]inputis an n-dimensional tensor with size \((x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})\) @@ -203,14 +210,10 @@ andoutwill have the same size asindex.
-@@ -221,7 +224,7 @@ andContents
- - ++outwill have the same size asindex. diff --git a/docs/reference/torch_ge.html b/docs/reference/torch_ge.html index 323e8c4f2beaf865f4f05626bde2a3847e3b353f..f68bde28244131e545cc14502d1611b40a0ff13e 100644 --- a/docs/reference/torch_ge.html +++ b/docs/reference/torch_ge.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -204,7 +208,7 @@ broadcastable with the first argument. diff --git a/docs/reference/torch_generator.html b/docs/reference/torch_generator.html index 2b20614889451029a15777ebb91d80e139db43c8..58e620106c8173e900f6186feb368d21677d7111 100644 --- a/docs/reference/torch_generator.html +++ b/docs/reference/torch_generator.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ In-place random sampling functions." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -186,14 +194,10 @@ broadcastable with the first argument. #> 0 1 #> [ CPUBoolType{2,2} ]
diff --git a/docs/reference/torch_geqrf.html b/docs/reference/torch_geqrf.html index abd74629930561a43151f594987c0fb8cb4e5b88..772064e6d6f932e37313adc83bf534f796aba581 100644 --- a/docs/reference/torch_geqrf.html +++ b/docs/reference/torch_geqrf.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ In-place random sampling functions." />- @@ -195,7 +195,7 @@ In-place random sampling functions.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ In-place random sampling functions." />
-
-
+
@@ -164,27 +172,19 @@ In-place random sampling functions.
# Via string generator <- torch_generator() -generator$current_seed()#> Loading required package: bit64#> Loading required package: bit#> Attaching package bit#> package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)#> creators: bit bitwhich#> coercion: as.logical as.integer as.bit as.bitwhich which#> operator: ! & | xor != ==#> querying: print length any all min max range sum summary#> bit access: length<- [ [<- [[ [[<-#> for more help type ?bit#> -#> Attaching package: ‘bit’#> The following object is masked from ‘package:base’: -#> -#> xor#> Attaching package bit64#> package:bit64 (c) 2011-2012 Jens Oehlschlaegel#> creators: integer64 seq :#> coercion: as.integer64 as.vector as.logical as.integer as.double as.character as.bin#> logical operator: ! & | xor != == < <= >= >#> arithmetic operator: + - * / %/% %% ^#> math: sign abs sqrt log log2 log10#> math: floor ceiling trunc round#> querying: is.integer64 is.vector [is.atomic} [length] format print str#> values: is.na is.nan is.finite is.infinite#> aggregation: any all min max range sum prod#> cumulation: diff cummin cummax cumsum cumprod#> access: length<- [ [<- [[ [[<-#> combine: c rep cbind rbind as.data.frame#> WARNING don't use as subscripts#> WARNING semantics differ from integer#> for more help type ?bit64#> -#> Attaching package: ‘bit64’#> The following object is masked from ‘package:bit’: -#> -#> still.identical#> The following objects are masked from ‘package:base’: -#> -#> :, %in%, is.double, match, order, rank#> integer64 -#> [1] 67280421310721generator$set_current_seed(1234567L) -generator$current_seed()#> integer64 -#> [1] 1234567+generator$current_seed()#> Loading required package: bit64#> Warning: there is no package called ‘bit64’#> Warning: bit64 is required to correctly show the seed.#> [1] 3.324094e-310 +#> attr(,"class") +#> [1] "integer64"generator$set_current_seed(1234567L) +generator$current_seed()#> Loading required package: bit64#> Warning: there is no package called ‘bit64’#> Warning: bit64 is required to correctly show the seed.#> [1] 6.099571e-318 +#> attr(,"class") +#> [1] "integer64"
- @@ -203,7 +207,7 @@ which produces a sequence of 'elementary reflectors'. diff --git a/docs/reference/torch_ger.html b/docs/reference/torch_ger.html index a58376a080f47e9e02801683959b89bae42d609e..13500eecaa47dabf0362f73744bc4ee83d8ba4a0 100644 --- a/docs/reference/torch_ger.html +++ b/docs/reference/torch_ger.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -185,14 +193,10 @@ which produces a sequence of 'elementary reflectors'.
- @@ -212,7 +215,7 @@ size \(m\), then@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -193,15 +201,10 @@ size \(m\), then
outmust be a matrix of size \((n \times m)\). #> 4 8 12 #> [ CPUFloatType{4,3} ]
outmust be a matrix of size \((n \times m)\). diff --git a/docs/reference/torch_gt.html b/docs/reference/torch_gt.html index f4b1902b103a4dc93e001783aca22f2e94c657df..041d937605da0ad6ac91d8da882d0bbc3e9111a5 100644 --- a/docs/reference/torch_gt.html +++ b/docs/reference/torch_gt.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -204,7 +208,7 @@ broadcastable with the first argument. diff --git a/docs/reference/torch_hamming_window.html b/docs/reference/torch_hamming_window.html index 21186fb115782a1306e7080d04b6b009ec774ad9..d8ae462404eed056e47bc4a01acf9dd23a1a4126 100644 --- a/docs/reference/torch_hamming_window.html +++ b/docs/reference/torch_hamming_window.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -186,14 +194,10 @@ broadcastable with the first argument. #> 0 0 #> [ CPUBoolType{2,2} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -223,15 +231,10 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have
-@@ -242,7 +245,7 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have diff --git a/docs/reference/torch_hann_window.html b/docs/reference/torch_hann_window.html index d8563394fd875264ba3d0006c6f082796a8acffa..d0f7d91a2e3725fd234678f60c22609897a2484b 100644 --- a/docs/reference/torch_hann_window.html +++ b/docs/reference/torch_hann_window.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -213,15 +221,10 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have
-@@ -232,7 +235,7 @@ above formula is in fact \(\text{window\_length} + 1\). Also, we always have diff --git a/docs/reference/torch_histc.html b/docs/reference/torch_histc.html index 118e06158d4316fe7ca0e7b6761f06fe61ef5aa6..88c95ac2083f580075c968c7506fcbebd3225935 100644 --- a/docs/reference/torch_histc.html +++ b/docs/reference/torch_histc.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -197,14 +205,10 @@ maximum values of the data are used. #> 0 #> [ CPUFloatType{4} ]
-@@ -215,7 +219,7 @@ maximum values of the data are used. diff --git a/docs/reference/torch_ifft.html b/docs/reference/torch_ifft.html index 25d1448f29b282e8ddedbe0bb856182defca6d83..1ae3cfc73d60af4ba051efde2b5c8bd5631318cb 100644 --- a/docs/reference/torch_ifft.html +++ b/docs/reference/torch_ifft.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -215,47 +223,41 @@ shape of
input. x = torch_randn(c(3, 3, 2)) x
#> torch_tensor #> (1,.,.) = -#> 1.2565 0.9511 -#> -1.4309 -0.8159 -#> -0.2985 -0.1503 +#> 0.4053 -1.0535 +#> 1.2809 -0.6868 +#> -0.7918 -0.5001 #> #> (2,.,.) = -#> 1.0511 -0.4074 -#> -0.0719 -0.5882 -#> 1.7008 -1.3123 +#> -0.3554 -0.0106 +#> -0.5705 -1.7366 +#> -0.1588 -0.3846 #> #> (3,.,.) = -#> -1.2550 -0.2420 -#> 1.3266 1.0617 -#> 0.3093 -0.7691 +#> 0.2100 0.4636 +#> -0.3310 -1.0296 +#> 0.5527 0.4706 #> [ CPUFloatType{3,3,2} ]#> torch_tensor #> (1,.,.) = -#> 1.2565 0.9511 -#> -1.4309 -0.8159 -#> -0.2985 -0.1503 +#> 0.4053 -1.0535 +#> 1.2809 -0.6868 +#> -0.7918 -0.5001 #> #> (2,.,.) = -#> 1.0511 -0.4074 -#> -0.0719 -0.5882 -#> 1.7008 -1.3123 +#> -0.3554 -0.0106 +#> -0.5705 -1.7366 +#> -0.1588 -0.3846 #> #> (3,.,.) = -#> -1.2550 -0.2420 -#> 1.3266 1.0617 -#> 0.3093 -0.7691 +#> 0.2100 0.4636 +#> -0.3310 -1.0296 +#> 0.5527 0.4706 #> [ CPUFloatType{3,3,2} ]-@@ -266,7 +268,7 @@ shape ofContents
- - ++input. diff --git a/docs/reference/torch_imag.html b/docs/reference/torch_imag.html index e3db9bf51e70a9f25464018e122b17e506ed373f..7516ed80813f4f912607ea5bf8a365b3ceed4ee2 100644 --- a/docs/reference/torch_imag.html +++ b/docs/reference/torch_imag.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -205,7 +208,7 @@ $$ diff --git a/docs/reference/torch_index_select.html b/docs/reference/torch_index_select.html index 7b4da396b7b80bae5d8e44b53664bab81487ca41..ee7f5dc6ee190a4f01e2e997e83d42be96efc478 100644 --- a/docs/reference/torch_index_select.html +++ b/docs/reference/torch_index_select.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
diff --git a/docs/reference/torch_inverse.html b/docs/reference/torch_inverse.html index e12180efcba3f92562f7959dd4899554011fb7d2..7dd0210c7ed5f301037163e891f464df2cb56764 100644 --- a/docs/reference/torch_inverse.html +++ b/docs/reference/torch_inverse.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -195,28 +203,23 @@ of
index; other dimensions have the same size as in the original te#> torch_tensor -#> 0.7457 0.0331 1.1083 -0.6772 -#> 1.2947 1.2644 0.4920 1.0830 -#> -0.3448 -0.2078 2.0594 0.4653 +#> -2.3646 -0.0278 -1.0814 -1.9364 +#> 1.6946 -0.4611 -1.9319 -1.8129 +#> 1.9425 -0.5303 -0.5905 -1.3148 #> [ CPUFloatType{3,4} ]#> torch_tensor -#> 0.7457 0.0331 1.1083 -0.6772 -#> -0.3448 -0.2078 2.0594 0.4653 +#> -2.3646 -0.0278 -1.0814 -1.9364 +#> 1.9425 -0.5303 -0.5905 -1.3148 #> [ CPUFloatType{2,4} ]torch_index_select(x, 1, indices)#> torch_tensor -#> 0.7457 1.1083 -#> 1.2947 0.4920 -#> -0.3448 2.0594 +#> -2.3646 -1.0814 +#> 1.6946 -1.9319 +#> 1.9425 -0.5905 #> [ CPUFloatType{3,2} ]
-@@ -227,7 +230,7 @@ ofContents
- - ++index; other dimensions have the same size as in the original te- @@ -215,7 +218,7 @@ individual inverses. diff --git a/docs/reference/torch_irfft.html b/docs/reference/torch_irfft.html index 8feee6dfc5254f6bcebc84b6551e1166331b4063..695ebd0ff9a68a9bb41722c53c81166ef0482d05 100644 --- a/docs/reference/torch_irfft.html +++ b/docs/reference/torch_irfft.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -239,63 +247,57 @@ certainly fail the check. x = torch_randn(c(4, 4)) torch_rfft(x, 2, onesided=TRUE)
#> torch_tensor #> (1,.,.) = -#> -0.7584 0.0000 -#> -0.2138 -2.2106 -#> 3.8990 0.0000 +#> 3.9155 0.0000 +#> -4.1027 -2.3817 +#> 3.4753 0.0000 #> #> (2,.,.) = -#> -2.7608 -4.3736 -#> 4.5284 4.0627 -#> -6.3501 0.4960 +#> -0.5477 -1.9754 +#> 1.4978 3.3253 +#> 3.7515 0.2343 #> #> (3,.,.) = -#> 2.6138 0.0000 -#> -0.1434 -0.4128 -#> 0.3747 0.0000 +#> -0.0131 0.0000 +#> -3.0062 -1.6157 +#> 1.9030 0.0000 #> #> (4,.,.) = -#> -2.7608 4.3736 -#> 0.4554 3.6659 -#> -6.3501 -0.4960 +#> -0.5477 1.9754 +#> -0.1102 1.9626 +#> 3.7515 -0.2343 #> [ CPUFloatType{4,3,2} ]#> torch_tensor #> (1,.,.) = -#> -0.6195 0.0000 -#> 0.3333 0.9091 -#> -4.1023 1.9537 +#> 4.1861 0.0000 +#> -1.1518 -0.4106 +#> 1.5567 -1.2641 #> #> (2,.,.) = -#> 0.2853 -0.4759 -#> -3.0050 -5.5547 -#> 1.2021 -1.2450 +#> -6.7795 -4.7280 +#> -1.5206 0.6219 +#> 0.8539 -1.1360 #> #> (3,.,.) = -#> -0.9047 0.0000 -#> -5.7936 2.0794 -#> -2.8287 -4.5172 +#> -0.2864 0.0000 +#> 1.5039 4.3041 +#> -6.2167 0.7949 #> #> (4,.,.) = -#> 0.2853 0.4759 -#> -2.7122 -0.4550 -#> 1.8644 -2.0110 +#> -6.7795 4.7280 +#> -4.0121 -3.7228 +#> -0.6624 -2.6865 #> [ CPUFloatType{4,3,2} ]y = torch_rfft(x, 2, onesided=TRUE) torch_irfft(y, 2, onesided=TRUE, signal_sizes=c(4,5)) # recover x#> torch_tensor -#> -1.5519 0.5489 0.3613 1.1131 -0.7098 -#> 0.9806 0.3715 -0.2710 -1.5479 0.7760 -#> -1.0788 -0.1845 -0.8975 0.0286 1.6085 -#> 0.0186 -0.2964 0.5979 -0.8621 0.3753 +#> -1.4479 -0.1047 -0.6567 0.2528 -0.4584 +#> 0.6187 0.2200 1.5341 1.3760 -0.2667 +#> 0.9763 0.5850 0.1547 0.3391 2.3096 +#> 0.8521 -0.0558 -0.0142 -0.1812 -1.8468 #> [ CPUFloatType{4,5} ]-@@ -306,7 +308,7 @@ certainly fail the check. diff --git a/docs/reference/torch_is_complex.html b/docs/reference/torch_is_complex.html index 31efdafe007b4f74847993ef9ccfad4096096280..daae118b863362b85639111cedac53cd833ec7d8 100644 --- a/docs/reference/torch_is_complex.html +++ b/docs/reference/torch_is_complex.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor -
- Warning -
- Examples -
+- @@ -193,7 +197,7 @@ one of@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -175,14 +183,10 @@ one of
torch_complex64, andtorch.complex128.
torch_complex64, andtorch.complex128. diff --git a/docs/reference/torch_is_floating_point.html b/docs/reference/torch_is_floating_point.html index de9428cda710c03cb7ad899203b3b1bbc1fe8f37..abb040d71fb4896c2a3790af8a640a2b63165f4c 100644 --- a/docs/reference/torch_is_floating_point.html +++ b/docs/reference/torch_is_floating_point.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -193,7 +197,7 @@ one of@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -175,14 +183,10 @@ one of
torch_float64,torch.float32andtorch.fl
torch_float64,torch.float32andtorch.fl diff --git a/docs/reference/torch_isfinite.html b/docs/reference/torch_isfinite.html index 405ccf587b0356ba82b8504a70aa715d8ea278d4..e09c4c9406a46c57c4ce55af8f6fd204bea7a0c7 100644 --- a/docs/reference/torch_isfinite.html +++ b/docs/reference/torch_isfinite.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -197,7 +201,7 @@ diff --git a/docs/reference/torch_isinf.html b/docs/reference/torch_isinf.html index 9ee9866bab58d95e25a914c3647d3b4ac26fc278..45f0c55977ac3400c329c92722cfd5fa28950e2b 100644 --- a/docs/reference/torch_isinf.html +++ b/docs/reference/torch_isinf.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -179,14 +187,10 @@ #> 0 #> [ CPUBoolType{5} ]
- @@ -197,7 +201,7 @@ diff --git a/docs/reference/torch_isnan.html b/docs/reference/torch_isnan.html index bcffe1384faafa7646b9e44101844db802131b87..ba8bd30ac37e5029d22aaec83d79999285f3c5bf 100644 --- a/docs/reference/torch_isnan.html +++ b/docs/reference/torch_isnan.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -179,14 +187,10 @@ #> 0 #> [ CPUBoolType{5} ]
- @@ -195,7 +199,7 @@ diff --git a/docs/reference/torch_kthvalue.html b/docs/reference/torch_kthvalue.html index bee385baa45ce51eb64ebf9a2d2c3f78904848b9..3e853bb2d508e56975b3392749a2952eb525a040 100644 --- a/docs/reference/torch_kthvalue.html +++ b/docs/reference/torch_kthvalue.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -177,14 +185,10 @@ #> 0 #> [ CPUBoolType{3} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -226,14 +234,10 @@ they are of size 1. Otherwise,
dimis squeezed #> [ CPULongType{1,3} ] #>
-@@ -244,7 +248,7 @@ they are of size 1. Otherwise,Contents
--
-
- Arguments -
- kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor) -
- Examples -
+dimis squeezed diff --git a/docs/reference/torch_le.html b/docs/reference/torch_le.html index 6c34cef04c4d32d74d64c5052a52dea4cdcf9053..5d8f98397879a99e607af81f746a021916bb2391 100644 --- a/docs/reference/torch_le.html +++ b/docs/reference/torch_le.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -204,7 +208,7 @@ broadcastable with the first argument. diff --git a/docs/reference/torch_lerp.html b/docs/reference/torch_lerp.html index c00854dea8ca5528088b9b0642ac42440021795c..11fec065a9a5488bbcf3a7e9540a448b85e81688 100644 --- a/docs/reference/torch_lerp.html +++ b/docs/reference/torch_lerp.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -186,14 +194,10 @@ broadcastable with the first argument. #> 1 1 #> [ CPUBoolType{2,2} ]
- @@ -232,7 +236,7 @@ the shapes of@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -214,14 +222,10 @@ the shapes of
weight,start, andendmust #> 7.0000 #> [ CPUFloatType{4} ]
weight,start, andendmust diff --git a/docs/reference/torch_lgamma.html b/docs/reference/torch_lgamma.html index 6d1ba41355c6e0abbd68601e811585fce6d67a23..64f3ab51f81a198190d61d6afd05dcb612ca1438 100644 --- a/docs/reference/torch_lgamma.html +++ b/docs/reference/torch_lgamma.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -203,7 +207,7 @@ $$ diff --git a/docs/reference/torch_linspace.html b/docs/reference/torch_linspace.html index 5ebb2768ae014af5168b5736b81396fec990e9ca..3bc6df976d6e27923b40c3bf186dfb2b237966fd 100644 --- a/docs/reference/torch_linspace.html +++ b/docs/reference/torch_linspace.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -185,14 +193,10 @@ $$ #> -0.1208 #> [ CPUFloatType{3} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -223,14 +231,10 @@ equally spaced points between
startandend. #> -10 #> [ CPUFloatType{1} ]
-@@ -241,7 +245,7 @@ equally spaced points betweenContents
--
-
- Arguments -
- linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+startandend. diff --git a/docs/reference/torch_load.html b/docs/reference/torch_load.html index c6cbdd0befbf5e39a45dbec1c7bafad2857af84d..93d4bebaf1fa6cddd9b6965e8aa0f0643630aff2 100644 --- a/docs/reference/torch_load.html +++ b/docs/reference/torch_load.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -186,7 +191,7 @@ diff --git a/docs/reference/torch_log.html b/docs/reference/torch_log.html index 028719215bc5357eecd5d7927924209035afe7cb..cda7629fd2c41c3d6028bf59aa6fb6de54c7b598 100644 --- a/docs/reference/torch_log.html +++ b/docs/reference/torch_log.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -169,13 +177,10 @@
torch_save()
diff --git a/docs/reference/torch_log10.html b/docs/reference/torch_log10.html index aad84fd2e1115eb4ba336ab4a98e8dd56b3444a9..70af74515072a162774a0c637c11dc374e794d57 100644 --- a/docs/reference/torch_log10.html +++ b/docs/reference/torch_log10.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -212,7 +216,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,27 +189,23 @@ $$
#> torch_tensor -#> 1.6536 -#> -1.7079 -#> -1.4212 -#> -0.9809 -#> -1.0529 +#> -0.9985 +#> -0.8507 +#> -1.7436 +#> -0.6526 +#> 0.4230 #> [ CPUFloatType{5} ]torch_log(a)#> torch_tensor -#> 0.5030 #> nan #> nan #> nan #> nan +#> -0.8603 #> [ CPUFloatType{5} ]
diff --git a/docs/reference/torch_log1p.html b/docs/reference/torch_log1p.html index 75c1aeb60b3d3fbd0a47713be64891b4dfc857da..8c65056de3beeb38a43bd508b2dfb0c5cedfbbaf 100644 --- a/docs/reference/torch_log1p.html +++ b/docs/reference/torch_log1p.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -212,7 +216,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,27 +189,23 @@ $$
#> torch_tensor -#> 0.3298 -#> 0.2551 -#> 0.3903 -#> 0.5454 -#> 0.4660 +#> 0.6243 +#> 0.7191 +#> 0.6296 +#> 0.4557 +#> 0.6912 #> [ CPUFloatType{5} ]torch_log10(a)#> torch_tensor -#> -0.4817 -#> -0.5932 -#> -0.4086 -#> -0.2633 -#> -0.3316 +#> -0.2046 +#> -0.1432 +#> -0.2010 +#> -0.3413 +#> -0.1604 #> [ CPUFloatType{5} ]
diff --git a/docs/reference/torch_log2.html b/docs/reference/torch_log2.html index 7bab4f6e3e23f098575340bc72fd7d6797f109fa..30ee017138e84604956320ec3101b2b983b8c9fb 100644 --- a/docs/reference/torch_log2.html +++ b/docs/reference/torch_log2.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -216,7 +219,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -184,28 +192,23 @@ $$
#> torch_tensor -#> 2.7846 -#> 0.8044 -#> 0.4010 -#> -0.2055 -#> -1.4104 +#> -0.9215 +#> -1.9462 +#> -0.8953 +#> -0.0292 +#> -0.5215 #> [ CPUFloatType{5} ]torch_log1p(a)#> torch_tensor -#> 1.3309 -#> 0.5902 -#> 0.3372 -#> -0.2301 +#> -2.5442 #> nan +#> -2.2568 +#> -0.0296 +#> -0.7371 #> [ CPUFloatType{5} ]
diff --git a/docs/reference/torch_logdet.html b/docs/reference/torch_logdet.html index 0307660a53eccb3a253a4e31f9b32c99ccf03af9..edbd42c9e5d55777cc0e5fc3b510704a106745de 100644 --- a/docs/reference/torch_logdet.html +++ b/docs/reference/torch_logdet.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -212,7 +216,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,27 +189,23 @@ $$
#> torch_tensor -#> 0.8896 -#> 0.4131 -#> 0.9534 -#> 0.3417 -#> 0.0101 +#> 0.4172 +#> 0.9821 +#> 0.3948 +#> 0.7847 +#> 0.2493 #> [ CPUFloatType{5} ]torch_log2(a)#> torch_tensor -#> -0.1688 -#> -1.2754 -#> -0.0688 -#> -1.5492 -#> -6.6226 +#> -1.2613 +#> -0.0261 +#> -1.3410 +#> -0.3497 +#> -2.0041 #> [ CPUFloatType{5} ]
diff --git a/docs/reference/torch_logical_and.html b/docs/reference/torch_logical_and.html index 46c0ed3582ac991263e587590e2d2f9372232d41..47ffa31b0202faad102d661bf15da0a3c4c4f4b8 100644 --- a/docs/reference/torch_logical_and.html +++ b/docs/reference/torch_logical_and.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -218,7 +221,7 @@ be unstable in when `input` doesn't have distinct singular values. See-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -186,28 +194,23 @@ be unstable in when `input` doesn't have distinct singular values. See
#> torch_tensor -#> -0.583641 +#> 1.53809 #> [ CPUFloatType{} ]torch_logdet(A)#> torch_tensor -#> nan +#> 0.430541 #> [ CPUFloatType{} ]A#> torch_tensor -#> -0.9045 2.1737 -0.1822 -#> 0.2464 -0.3835 -0.8773 -#> 0.5088 -0.3878 -0.5142 +#> -0.4016 1.8304 -1.8544 +#> 0.2144 -0.0205 -1.1661 +#> -0.7520 0.1449 -0.0779 #> [ CPUFloatType{3,3} ]A$det()#> torch_tensor -#> -0.583641 +#> 1.53809 #> [ CPUFloatType{} ]#> torch_tensor -#> nan +#> 0.430541 #> [ CPUFloatType{} ]
- @@ -213,7 +217,7 @@ treated as@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -195,14 +203,10 @@ treated as
True. torch_logical_and(a, b, out=torch_empty(4, dtype=torch_bool())) }
True. diff --git a/docs/reference/torch_logical_not.html b/docs/reference/torch_logical_not.html index d68ab2f0309ed53a9789d88a38aec2836f273d16..8e30b01a80c8475f0696c153a7711bbf95a6f8ac 100644 --- a/docs/reference/torch_logical_not.html +++ b/docs/reference/torch_logical_not.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -207,7 +211,7 @@ dtype. If the input tensor is not a bool tensor, zeros are treated as@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -189,14 +197,10 @@ dtype. If the input tensor is not a bool tensor, zeros are treated as
Fals #> 0 #> [ CPUBoolType{3} ]
Fals diff --git a/docs/reference/torch_logical_or.html b/docs/reference/torch_logical_or.html index df477856de70cc705489781b3221afc629b171fc..0ceba3c7449dd17c9c9ee260fbac46eead3b11cf 100644 --- a/docs/reference/torch_logical_or.html +++ b/docs/reference/torch_logical_or.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -215,7 +219,7 @@ treated as@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -197,14 +205,10 @@ treated as
True. torch_logical_or(a, b, out=torch_empty(4, dtype=torch_bool())) }
True. diff --git a/docs/reference/torch_logical_xor.html b/docs/reference/torch_logical_xor.html index 7fcf24fd2c0afd250b7401c9027f3ab8d3890c77..733aa892915369001e8132df32be08dff35a44e8 100644 --- a/docs/reference/torch_logical_xor.html +++ b/docs/reference/torch_logical_xor.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -221,7 +225,7 @@ treated as@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -203,14 +211,10 @@ treated as
True. #> 0 #> [ CPUBoolType{4} ]
True. diff --git a/docs/reference/torch_logspace.html b/docs/reference/torch_logspace.html index 5644b5b46c87f922ff13ef686bb1126e6e2bc1e8..8bf751739fcd6e74bb47ea6f4666d08115625294 100644 --- a/docs/reference/torch_logspace.html +++ b/docs/reference/torch_logspace.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -224,14 +232,10 @@ logarithmically spaced with base
basebetween #> 4 #> [ CPUFloatType{1} ]
-@@ -242,7 +246,7 @@ logarithmically spaced with baseContents
--
-
- Arguments -
- logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+basebetween diff --git a/docs/reference/torch_logsumexp.html b/docs/reference/torch_logsumexp.html index 1c48bd987d1718bcb7eb977689b5821ff8cdd285..7971a06f197fa54bf72496f48eef71418016ef0c 100644 --- a/docs/reference/torch_logsumexp.html +++ b/docs/reference/torch_logsumexp.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_lstsq.html b/docs/reference/torch_lstsq.html index cd1765aac6de5e03d27cf96ebe17c8a8f60087e5..028530523cc4f8a13a8c390578560d783430312e 100644 --- a/docs/reference/torch_lstsq.html +++ b/docs/reference/torch_lstsq.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -218,7 +222,7 @@ output tensor having 1 (or-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -195,19 +203,15 @@ output tensor having 1 (or
len(dim)) fewer dimension(s).#> torch_tensor -#> 1.9985 -#> 1.5078 -#> 1.8004 +#> 1.4916 +#> 0.5338 +#> 0.4478 #> [ CPUFloatType{3} ]
len(dim)) fewer dimension(s).- @@ -243,7 +246,7 @@ remaining \(m - n\) rows of that column. diff --git a/docs/reference/torch_lt.html b/docs/reference/torch_lt.html index 1563a6efe6899d376d53ee4ccee1c2f7bcf34c64..6c7b16c11f61d9098d5b20cf0429d6c06fb88913 100644 --- a/docs/reference/torch_lt.html +++ b/docs/reference/torch_lt.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -173,7 +181,7 @@
Note
-The case when \eqn{m < n} is not supported on the GPU. +The case when \eqn{m < n} is not supported on the GPU.lstsq(input, A, out=None) -> Tensor
@@ -224,15 +232,10 @@ remaining \(m - n\) rows of that column. #> 8.9332 5.2418 #> [ CPUFloatType{5,2} ]
- @@ -204,7 +208,7 @@ broadcastable with the first argument. diff --git a/docs/reference/torch_lu.html b/docs/reference/torch_lu.html index 342a0a0d8e51e0adea74aa5d129b4dce990638c4..2b78c84c2da389fd685d934e1866b9d399b8c15a 100644 --- a/docs/reference/torch_lu.html +++ b/docs/reference/torch_lu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ is set to True." /> - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -186,14 +194,10 @@ broadcastable with the first argument. #> 1 0 #> [ CPUBoolType{2,2} ]
- @@ -223,7 +228,7 @@ elements in the tuple are Tensor, IntTensor. Default: NULL diff --git a/docs/reference/torch_lu_solve.html b/docs/reference/torch_lu_solve.html index 3cf38588b43adf58cde5dcc6734873e0689a4e4c..460f8c4895ce2c2d5a2fdbaea453e1e7eaa3ed0a 100644 --- a/docs/reference/torch_lu_solve.html +++ b/docs/reference/torch_lu_solve.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -90,12 +93,17 @@ is set to True." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -127,7 +135,7 @@ is set to True." />
- - + @@ -188,31 +196,28 @@ elements in the tuple are Tensor, IntTensor. Default: NULL torch_lu(A)
#> [[1]] #> torch_tensor #> (1,.,.) = -#> 0.6938 0.9729 1.2666 -#> 0.3948 -0.5709 -1.8963 -#> 0.9797 0.5815 -0.5899 +#> 2.3578 1.4992 1.0227 +#> -0.4723 0.7524 0.4669 +#> -0.3497 0.2362 0.7378 #> #> (2,.,.) = -#> -1.8531 -0.2316 -0.6086 -#> 0.1135 1.3647 -0.1498 -#> 0.2346 0.4005 0.0214 +#> -1.1227 -0.0584 -0.0944 +#> -0.3623 2.3518 -0.1488 +#> 0.4147 0.1782 0.2812 #> [ CPUFloatType{2,3,3} ] #> #> [[2]] #> torch_tensor -#> 2 2 3 +#> 2 3 3 #> 3 3 3 #> [ CPUIntType{2,3} ] #>-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -188,17 +196,13 @@ LU factorization of A from
torch_lu. out = torch_lu(A) x = torch_lu_solve(b, out[[1]], out[[2]]) torch_norm(torch_bmm(A, x) - b)
#> torch_tensor -#> 1.47514e-07 +#> 2.17252e-07 #> [ CPUFloatType{} ]-@@ -209,7 +213,7 @@ LU factorization of A fromContents
- - ++torch_lu. diff --git a/docs/reference/torch_masked_select.html b/docs/reference/torch_masked_select.html index e5b6b7f5c01592d3e283452bf51d86bc87457378..f445077fe12f5482991da1444eecd7acc6604c74 100644 --- a/docs/reference/torch_masked_select.html +++ b/docs/reference/torch_masked_select.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -225,7 +226,7 @@ to match, but they must be broadcastable . diff --git a/docs/reference/torch_matmul.html b/docs/reference/torch_matmul.html index 161bee5d36e9776dbb4ee24e7a9989df089ada5c..2a73a4f50e77c383903f3977fa5756d67ad0f632 100644 --- a/docs/reference/torch_matmul.html +++ b/docs/reference/torch_matmul.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -188,33 +196,26 @@ to match, but they must be broadcastable .
+#> 0.9272 +#> 0.7298 +#> 1.5119 +#> 1.2583 +#> 0.5245 +#> [ CPUFloatType{5} ]#> torch_tensor -#> 1.2835 -0.7208 0.0672 1.0638 -#> 1.3561 -1.8924 0.8533 0.7159 -#> 2.9560 0.6744 -0.1289 0.4879 +#> -1.0189 0.9272 0.2813 0.7298 +#> -0.9519 -1.4434 -0.8169 1.5119 +#> -0.7218 1.2583 0.5245 -0.5202 #> [ CPUFloatType{3,4} ]mask = x$ge(0.5) mask#> torch_tensor -#> 1 0 0 1 -#> 1 0 1 1 -#> 1 1 0 0 +#> 0 1 0 1 +#> 0 0 0 1 +#> 0 1 1 0 #> [ CPUBoolType{3,4} ]torch_masked_select(x, mask)#> torch_tensor -#> 1.2835 -#> 1.0638 -#> 1.3561 -#> 0.8533 -#> 0.7159 -#> 2.9560 -#> 0.6744 -#> [ CPUFloatType{7} ]
- @@ -357,7 +360,7 @@ tensor,@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -208,145 +216,140 @@ tensor,
outwill be an \((j \times k \times n \times p)\) tensor.tensor1 = torch_randn(c(3)) tensor2 = torch_randn(c(3)) torch_matmul(tensor1, tensor2)
#> torch_tensor -#> -1.98052 +#> -0.0241494 #> [ CPUFloatType{} ]# matrix x vector tensor1 = torch_randn(c(3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2)#> torch_tensor -#> 0.0915 -#> -0.4803 -#> 0.1216 +#> -1.2363 +#> 3.0268 +#> 0.1035 #> [ CPUFloatType{3} ]# batched matrix x broadcasted vector tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2)#> torch_tensor -#> -1.8133 1.1063 1.3090 -#> 4.3649 -0.9861 -0.9484 -#> -0.7668 0.2129 -0.2296 -#> -1.1764 -4.4576 -1.0427 -#> -2.1544 -1.7819 -2.3941 -#> -4.9113 3.1843 0.5698 -#> -1.3242 -0.0767 0.7157 -#> 0.2347 -1.2068 3.8221 -#> 1.3283 -2.5615 -1.3238 -#> 0.3641 0.4202 5.2516 +#> -2.7407 2.5049 0.0359 +#> -0.2428 1.9005 -0.0311 +#> 1.5064 -1.1403 4.7911 +#> -1.1711 1.5313 4.8539 +#> 1.0677 -1.4385 3.5871 +#> -3.6176 2.1055 -0.4337 +#> 2.7659 3.3263 -1.1249 +#> 3.1125 -1.0300 0.8533 +#> -7.2295 -3.2742 1.9161 +#> 1.7768 -0.6715 2.4132 #> [ CPUFloatType{10,3} ]# batched matrix x batched matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(10, 4, 5)) torch_matmul(tensor1, tensor2)#> torch_tensor #> (1,.,.) = -#> -1.2165 -2.7346 0.1847 3.1132 -0.9576 -#> -0.1750 -0.1394 0.7393 1.1322 -0.3289 -#> 1.6392 0.0277 -0.2499 -0.5371 2.3237 +#> 2.1537 -0.9727 0.0868 3.0558 -0.9135 +#> -2.6331 -8.2079 4.6051 -6.7919 5.9808 +#> 0.4733 -2.2162 1.0598 2.4622 -0.4438 #> #> (2,.,.) = -#> -0.1097 0.7331 -0.0397 -1.3870 2.8443 -#> -0.2409 0.3011 -0.9890 -0.2164 1.0473 -#> -0.6430 0.2691 -0.7538 -0.3657 1.0435 +#> -3.2293 -1.7143 -2.6607 -0.0832 -1.6717 +#> -2.5618 1.4231 -0.5654 0.1853 -0.0330 +#> -1.1593 -0.6543 0.1057 0.7253 0.2542 #> #> (3,.,.) = -#> 0.7113 -1.2512 -0.6192 1.3470 0.0697 -#> 0.5567 -0.8821 0.4482 1.1729 0.3508 -#> -1.9476 -2.9420 5.9115 -1.9642 -3.3421 +#> 1.8733 -2.5163 3.7143 1.6841 0.1385 +#> 1.2127 -2.9773 2.3041 2.5720 1.3138 +#> -0.0171 -0.9660 3.6009 0.7866 -1.5337 #> #> (4,.,.) = -#> -2.7279 -2.3519 -3.4962 -1.8454 0.2952 -#> -0.0917 3.2803 2.2842 2.6555 0.6128 -#> 1.6096 -0.3718 -1.3668 -1.4122 -0.3896 +#> 0.3547 -3.9708 -2.5380 -1.0752 -5.3527 +#> 1.7091 -2.9129 0.9074 1.0743 1.0275 +#> 1.1593 -1.8107 0.8382 0.4825 1.2793 #> #> (5,.,.) = -#> -2.7603 -1.3881 1.6565 -2.6275 2.5845 -#> 2.5211 2.6449 -0.6835 -1.0770 -0.8359 -#> 4.4098 1.5104 -0.0084 -0.5178 -3.6947 +#> -0.5769 -4.2236 -1.0838 0.4376 2.2441 +#> 0.7824 1.9849 -1.4272 0.1560 -0.0576 +#> 0.7344 -2.5135 -0.5494 -0.3676 0.4109 #> #> (6,.,.) = -#> -2.8421 -0.6536 2.3330 1.6759 1.8693 -#> -0.1875 0.4286 1.1292 -0.8807 -0.1597 -#> 1.4919 1.0388 1.8531 -4.0056 -3.1985 +#> -1.7212 2.1676 -0.9803 0.0793 2.8358 +#> 0.9456 0.0361 0.5637 -1.1537 -0.1105 +#> -0.5617 1.4245 -0.3344 0.0940 1.5494 #> #> (7,.,.) = -#> 1.5208 -0.8174 0.7788 -3.3769 0.4279 -#> -0.4062 1.2204 0.3962 6.0043 -0.2476 -#> -1.1297 -0.8162 -1.1938 -0.6805 -0.2886 +#> 2.5519 -0.1848 2.4864 1.5132 1.0586 +#> 0.6640 -0.3814 -0.6928 1.5510 1.9655 +#> -0.8183 -0.3852 -2.2143 1.1407 2.0630 #> #> (8,.,.) = -#> -0.3097 -0.5236 0.0541 -1.3140 -1.8386 -#> -2.3916 1.1753 -0.7198 -0.0499 1.6123 -#> -1.4409 2.0329 -1.0229 3.2992 -0.9494 +#> -0.0168 1.3795 -1.1796 3.2791 0.9693 +#> -0.2317 0.4384 -1.5643 2.2430 -0.6618 +#> -0.0996 0.6698 0.4613 0.2960 1.1599 #> #> (9,.,.) = -#> -3.9666 2.1826 1.0016 -2.0595 -0.6597 -#> 0.9553 0.8955 0.3203 -1.0528 -1.7101 -#> -1.2053 0.0330 -0.1557 -1.2127 -0.5608 +#> 7.9252 0.2456 -7.1531 -2.9645 3.2929 +#> 2.1319 0.3302 -2.2897 -0.6544 1.2224 +#> 4.0157 -1.7977 -1.3135 2.6495 0.4928 #> #> (10,.,.) = -#> 0.3902 0.6571 2.2867 -1.8772 3.0385 -#> -0.6810 -0.2411 -1.0153 -0.0459 -1.0307 -#> -1.6677 -0.2461 1.8351 0.2226 1.6543 +#> 0.1402 0.4785 -3.5549 -1.0089 0.2646 +#> 1.8108 0.0249 -0.4183 -0.9522 1.3001 +#> -0.6563 -0.2119 -0.7839 -0.4225 0.3819 #> [ CPUFloatType{10,3,5} ]# batched matrix x broadcasted matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4, 5)) torch_matmul(tensor1, tensor2)#> torch_tensor #> (1,.,.) = -#> 1.1474 1.9511 -1.0121 -0.0405 1.3895 -#> 0.5580 -4.3577 1.3727 -0.9916 -2.2133 -#> -0.4171 1.0962 0.2108 -0.6524 1.5692 +#> 3.3780 -1.0720 -1.3765 -5.1701 -0.5722 +#> -1.2270 -0.7055 -0.0240 -1.6081 0.2402 +#> 3.7207 -0.8630 -1.5693 -3.1595 -0.2475 #> #> (2,.,.) = -#> -0.9469 -0.1175 0.8171 -0.7192 0.7886 -#> -2.7153 -6.3253 4.1977 -1.7583 -2.5220 -#> -0.1269 -2.1454 1.3397 -2.5725 1.2167 +#> -3.5109 -2.6442 -0.8076 1.2871 1.0722 +#> 0.7154 -0.0828 -0.1345 2.5352 -0.6859 +#> 2.2720 2.0566 -0.0048 1.3469 1.0371 #> #> (3,.,.) = -#> -5.3878 -1.7436 4.3569 -1.9035 1.3024 -#> 0.9807 2.0425 -1.2561 0.7209 0.6420 -#> 7.9363 4.4045 -6.8691 2.4657 -0.1838 +#> 1.7905 -3.0443 -0.9543 -4.1480 -3.4618 +#> -5.6131 2.7879 2.6081 1.3717 2.4207 +#> -0.2866 0.0182 0.0553 -1.1232 0.2584 #> #> (4,.,.) = -#> -1.3222 -1.2315 0.8491 0.4263 -1.2562 -#> 0.2211 2.1194 -0.5375 -0.7384 2.3603 -#> -0.3109 0.9888 -0.0489 -0.3535 1.1502 +#> 2.8548 -0.5644 -0.4335 -1.3244 -2.2084 +#> 0.7430 0.1274 0.0114 3.4985 -0.8686 +#> 1.3035 1.0213 -0.9364 4.6209 2.4461 #> #> (5,.,.) = -#> -0.5486 0.9569 -0.2541 1.1549 -0.4924 -#> -0.7094 -2.1146 1.2335 -0.1820 -1.3032 -#> -3.3788 -3.7787 3.1325 0.5616 -3.0915 +#> -4.1717 1.7929 2.3653 -0.7239 0.3967 +#> 0.2925 2.7194 1.1932 -0.4505 0.8761 +#> 2.4023 -0.9088 -0.0130 -5.5450 -3.0284 #> #> (6,.,.) = -#> 3.0435 3.8228 -2.7917 -0.9955 3.6304 -#> 2.8293 3.3970 -2.6648 -0.3077 2.5864 -#> -0.7713 -1.4287 1.0548 -0.5711 -0.3656 +#> 6.0704 -1.7497 -2.1878 -3.5899 -2.0988 +#> 0.4229 0.6318 0.6280 -3.0756 -0.7544 +#> 0.6998 -0.0218 -1.0562 -0.8221 2.2329 #> #> (7,.,.) = -#> 1.1784 -0.1683 -0.5254 -0.6180 0.4325 -#> 0.2213 -0.9106 -0.0427 0.5210 -1.2674 -#> 4.3432 2.8587 -3.4393 -0.5140 2.2615 +#> -5.3377 -0.5594 1.1731 3.1231 0.5482 +#> -5.5583 -0.7444 1.3053 0.8009 0.3432 +#> 0.4578 -4.8776 -2.4537 -2.8592 -1.2562 #> #> (8,.,.) = -#> -0.4073 -0.9500 0.6260 -0.1885 -0.4608 -#> 0.0163 -0.7709 -0.1913 1.4753 -2.1824 -#> -2.6043 1.7585 0.9636 0.3255 1.2075 +#> 4.1146 -2.6102 -2.8294 1.8040 -0.5032 +#> -4.8004 -0.3467 0.8817 3.2605 1.1562 +#> 1.9305 -1.0591 -1.5835 -2.6062 1.0635 #> #> (9,.,.) = -#> 1.4516 2.5749 -1.7179 0.4731 1.2603 -#> 2.4206 0.9187 -1.9504 0.6515 -0.2627 -#> -2.9325 -2.0715 2.4717 -0.4191 -0.7998 +#> -0.9492 -0.1283 0.9508 -1.7143 -1.6992 +#> 6.1829 3.3914 -0.0867 2.4016 -0.3277 +#> -2.9519 -2.7414 -0.7203 3.1738 -0.0198 #> #> (10,.,.) = -#> -2.0689 0.6484 1.0804 0.6741 -0.0458 -#> -2.6179 -3.6950 2.7358 0.5041 -3.0391 -#> -4.0565 -1.1599 2.9709 0.3315 -0.8232 +#> -3.2094 2.7451 2.9043 0.2943 -0.5282 +#> 1.9241 0.7552 -0.0242 4.1561 -0.9324 +#> 2.2007 -0.6206 -0.1254 -4.8430 -2.1609 #> [ CPUFloatType{10,3,5} ]outwill be an \((j \times k \times n \times p)\) tensor. diff --git a/docs/reference/torch_matrix_power.html b/docs/reference/torch_matrix_power.html index 41e2c9977dc36c5b6e251dad98242b4a1220ba88..f0ca9bfa0d04dbe0289e1a9408edd24906a6150a 100644 --- a/docs/reference/torch_matrix_power.html +++ b/docs/reference/torch_matrix_power.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -217,7 +223,7 @@ is returned. diff --git a/docs/reference/torch_matrix_rank.html b/docs/reference/torch_matrix_rank.html index 02940b5c2abf03fa509616a12c211e0fa4ef349f..3dd213a897c7c020d645b40e1d92060dc1cfbe3f 100644 --- a/docs/reference/torch_matrix_rank.html +++ b/docs/reference/torch_matrix_rank.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -183,30 +191,28 @@ is returned. a = torch_randn(c(2, 2, 2)) a
#> torch_tensor #> (1,.,.) = -#> 1.4273 -1.4460 -#> 0.2066 -0.4784 +#> -0.2071 0.3780 +#> -0.5045 0.0826 #> #> (2,.,.) = -#> 0.6855 0.1272 -#> 1.1732 -0.2822 +#> -0.3898 -0.2683 +#> 0.5863 0.2666 #> [ CPUFloatType{2,2,2} ]torch_matrix_power(a, 3)#> torch_tensor #> (1,.,.) = -#> 2.1979 -1.8575 -#> 0.2654 -0.2500 +#> 0.01 * +#> 5.4362 -5.9758 +#> 7.9752 0.8559 #> #> (2,.,.) = -#> 0.4845 0.0643 -#> 0.5928 -0.0044 +#> 0.01 * +#> 2.1487 1.0261 +#> -2.2421 -0.3611 #> [ CPUFloatType{2,2,2} ]-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -192,14 +200,10 @@ is the epsilon value for the datatype of
input. #> 10 #> [ CPULongType{} ]
-@@ -210,7 +214,7 @@ is the epsilon value for the datatype ofContents
- - ++input. diff --git a/docs/reference/torch_max.html b/docs/reference/torch_max.html index 500c39922bc322484c532e47e7f2cdd717d71f20..18f4036cf3d633b7504265b33829bc3f8f09b726 100644 --- a/docs/reference/torch_max.html +++ b/docs/reference/torch_max.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_mean.html b/docs/reference/torch_mean.html index cd5c6887d2ff760abfa12fd46ba50d8a818a5465..ae59ee031817c5474660d64185bfca531e7b4f08 100644 --- a/docs/reference/torch_mean.html +++ b/docs/reference/torch_mean.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -226,65 +234,57 @@ $$
#> torch_tensor -#> 0.3459 0.6809 -0.6423 +#> 0.3126 -1.9398 0.6811 #> [ CPUFloatType{1,3} ]torch_max(a)#> torch_tensor -#> 0.680911 +#> 0.681081 #> [ CPUFloatType{} ]#> torch_tensor -#> 0.6331 -1.2533 2.1704 0.4931 -#> 0.4625 -0.5182 -0.3714 -0.8252 -#> -0.2593 -0.6299 2.0184 -1.2016 -#> -1.2750 -1.2259 -1.6439 0.8795 +#> 0.8510 0.7024 0.0489 0.5159 +#> 0.1469 2.6616 -1.7811 1.8440 +#> -0.1878 -0.5359 0.1757 -0.3853 +#> -0.6329 -0.6897 -0.3555 -1.3305 #> [ CPUFloatType{4,4} ]torch_max(a, dim = 1)#> [[1]] #> torch_tensor -#> 2.1704 -#> 0.4625 -#> 2.0184 -#> 0.8795 +#> 0.8510 +#> 2.6616 +#> 0.1757 +#> -0.3555 #> [ CPUFloatType{4} ] #> #> [[2]] #> torch_tensor -#> 2 #> 0 +#> 1 +#> 2 #> 2 -#> 3 #> [ CPULongType{4} ] #>#> torch_tensor -#> 1.8196 -#> 0.4605 -#> -1.0669 -#> 0.7493 +#> 0.4658 +#> -0.9029 +#> 0.6370 +#> 0.1878 #> [ CPUFloatType{4} ]#> torch_tensor -#> 0.4266 -#> -0.5421 -#> -0.5235 -#> -2.4285 +#> 0.2649 +#> 0.3266 +#> 0.5535 +#> 0.3014 #> [ CPUFloatType{4} ]torch_max(a, other = b)#> torch_tensor -#> 1.8196 -#> 0.4605 -#> -0.5235 -#> 0.7493 +#> 0.4658 +#> 0.3266 +#> 0.6370 +#> 0.3014 #> [ CPUFloatType{4} ]
-@@ -295,7 +295,7 @@ $$Contents
--
-
- Arguments -
- Note -
- max(input) -> Tensor -
- max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) -
- Warning -
- max(input, other, out=None) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_median.html b/docs/reference/torch_median.html index 38a168f5a956870b75c74c5ee482fb01537402f0..99f079994e50f8180ef92fc493cbc3c02dd2f688 100644 --- a/docs/reference/torch_median.html +++ b/docs/reference/torch_median.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -197,38 +205,33 @@ output tensor having 1 (or
len(dim)) fewer dimension(s).#> torch_tensor -#> -0.1050 -0.6577 0.3595 +#> -0.2829 0.7446 -0.3286 #> [ CPUFloatType{1,3} ]torch_mean(a)#> torch_tensor -#> -0.134412 +#> 0.0443644 #> [ CPUFloatType{} ]#> torch_tensor -#> 0.5930 -0.1867 0.9454 1.2361 -#> -1.3861 0.3607 -0.6367 0.0875 -#> 1.0533 0.0997 1.2075 0.9616 -#> 0.7345 1.7434 -0.4099 3.0034 +#> -0.9323 1.6725 0.2138 -1.9346 +#> 0.2564 -0.0475 -0.2788 -1.0667 +#> 0.3069 1.2129 0.2888 -0.1409 +#> 1.3432 -0.5312 -0.4730 -0.8594 #> [ CPUFloatType{4,4} ]torch_mean(a, 1)#> torch_tensor -#> 0.6470 -#> -0.3936 -#> 0.8305 -#> 1.2678 +#> -0.2452 +#> -0.2842 +#> 0.4169 +#> -0.1301 #> [ CPUFloatType{4} ]torch_mean(a, 1, TRUE)#> torch_tensor -#> 0.6470 -#> -0.3936 -#> 0.8305 -#> 1.2678 +#> -0.2452 +#> -0.2842 +#> 0.4169 +#> -0.1301 #> [ CPUFloatType{4,1} ]
-@@ -239,7 +242,7 @@ output tensor having 1 (orContents
--
-
- Arguments -
- mean(input) -> Tensor -
- mean(input, dim, keepdim=False, out=None) -> Tensor -
- Examples -
+len(dim)) fewer dimension(s).diff --git a/docs/reference/torch_meshgrid.html b/docs/reference/torch_meshgrid.html index 82df281a13e72509372fcb407d1753e0272ff561..15d2ac72a686e34946fc90f90d280205b10f3169 100644 --- a/docs/reference/torch_meshgrid.html +++ b/docs/reference/torch_meshgrid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -198,23 +206,23 @@ the outputs tensor having 1 fewer dimension than
input.#> torch_tensor -#> -0.4378 -0.5767 1.4844 +#> 1.5362 -1.4032 0.6089 #> [ CPUFloatType{1,3} ]torch_median(a)#> torch_tensor -#> -0.437788 +#> 0.608883 #> [ CPUFloatType{} ]#> torch_tensor -#> -1.3530 -1.1157 -0.3118 0.9587 0.0023 -#> -0.3140 1.2068 1.6569 0.1390 0.7882 -#> -0.0182 -0.7019 1.0649 1.3824 -0.1467 -#> 1.0978 0.5075 0.8237 -0.2026 0.1677 +#> 0.8665 -0.2775 -0.5280 -0.9086 -0.5305 +#> -0.0766 0.7030 0.2714 -1.9572 0.1124 +#> -0.2088 -0.0836 -0.0411 -2.1958 -1.5354 +#> -1.2656 -0.2068 0.0691 -1.3210 0.0746 #> [ CPUFloatType{4,5} ]torch_median(a, 1)#> [[1]] #> torch_tensor -#> -0.3118 -#> 0.7882 -#> -0.0182 -#> 0.5075 +#> -0.5280 +#> 0.1124 +#> -0.2088 +#> -0.2068 #> [ CPUFloatType{4} ] #> #> [[2]] @@ -226,15 +234,10 @@ the outputs tensor having 1 fewer dimension thaninput. #> [ CPULongType{4} ] #>
-@@ -245,7 +248,7 @@ the outputs tensor having 1 fewer dimension thanContents
--
-
- Arguments -
- median(input) -> Tensor -
- median(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) -
- Examples -
+input.- @@ -213,7 +217,7 @@ expanding the \(i\)@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -195,14 +203,10 @@ expanding the \(i\)
thinput over dimensions defined by other input #> [ CPUFloatType{3,3} ] #>
thinput over dimensions defined by other input diff --git a/docs/reference/torch_min.html b/docs/reference/torch_min.html index f2090621318b018839f3b43845f2fbdb54bce067..aa308da1c4559f67d54a30de66129f7b7ace084f 100644 --- a/docs/reference/torch_min.html +++ b/docs/reference/torch_min.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_mm.html b/docs/reference/torch_mm.html index f015d19182e2c3ba318f45bf1f94d600bdd455bd..d62e75c0532c94d53b141015e202ada29c1f1001 100644 --- a/docs/reference/torch_mm.html +++ b/docs/reference/torch_mm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -227,65 +235,57 @@ $$
#> torch_tensor -#> 0.4582 1.3809 0.5156 +#> 1.4925 0.4818 0.5961 #> [ CPUFloatType{1,3} ]torch_min(a)#> torch_tensor -#> 0.458193 +#> 0.481831 #> [ CPUFloatType{} ]#> torch_tensor -#> 0.5208 -0.1816 -0.4899 0.7631 -#> -0.7353 1.1245 2.4261 -1.4030 -#> 0.6275 -0.2326 -1.7820 0.3831 -#> 2.4267 -0.2818 -0.6354 0.0011 +#> -0.9382 1.2352 0.0466 0.0032 +#> -1.3634 -0.0877 -0.0361 0.5226 +#> -0.6694 1.0441 -1.0105 -1.7537 +#> -1.7962 0.6367 0.1105 0.0989 #> [ CPUFloatType{4,4} ]torch_min(a, dim = 1)#> [[1]] #> torch_tensor -#> -0.4899 -#> -1.4030 -#> -1.7820 -#> -0.6354 +#> -0.9382 +#> -1.3634 +#> -1.7537 +#> -1.7962 #> [ CPUFloatType{4} ] #> #> [[2]] #> torch_tensor -#> 2 +#> 0 +#> 0 #> 3 -#> 2 -#> 2 +#> 0 #> [ CPULongType{4} ] #>#> torch_tensor -#> -1.9326 -#> 0.3032 -#> 0.4792 -#> -1.3625 +#> 0.3925 +#> 0.1551 +#> -0.2151 +#> 0.5649 #> [ CPUFloatType{4} ]#> torch_tensor -#> 0.1833 -#> 0.6937 -#> -0.0165 -#> 0.2920 +#> 0.0309 +#> 0.5256 +#> -0.6878 +#> 0.3369 #> [ CPUFloatType{4} ]torch_min(a, other = b)#> torch_tensor -#> -1.9326 -#> 0.3032 -#> -0.0165 -#> -1.3625 +#> 0.0309 +#> 0.1551 +#> -0.6878 +#> 0.3369 #> [ CPUFloatType{4} ]
-@@ -296,7 +296,7 @@ $$Contents
--
-
- Arguments -
- Note -
- min(input) -> Tensor -
- min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) -
- Warning -
- min(input, other, out=None) -> Tensor -
- Examples -
+- @@ -211,7 +214,7 @@ For broadcasting matrix products, see@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -188,19 +196,14 @@ For broadcasting matrix products, see
torch_ma mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_mm(mat1, mat2)
#> torch_tensor -#> -0.8354 1.3728 0.0689 -#> 0.1298 0.2881 -2.4872 +#> 1.5549 -0.0951 1.5225 +#> -1.9478 -0.2738 -1.5651 #> [ CPUFloatType{2,3} ]torch_ma diff --git a/docs/reference/torch_mode.html b/docs/reference/torch_mode.html index e6c81844454d7c389d95e10bc41f38c8bfe96db9..c05588a657261cc45e7b8f9b62b75de717ed19a1 100644 --- a/docs/reference/torch_mode.html +++ b/docs/reference/torch_mode.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_mul.html b/docs/reference/torch_mul.html index 6ec6e15731c3b9e3aac6e17fa212489fd79f6ebf..9a7a0d4f21fd7fe63dd5f9552f80e6ad7f367054 100644 --- a/docs/reference/torch_mul.html +++ b/docs/reference/torch_mul.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -196,31 +204,26 @@ in the output tensors having 1 fewer dimension than
input.#> torch_tensor -#> 44 #> 9 -#> 42 -#> 48 -#> 2 +#> 12 +#> 6 +#> 28 +#> 36 #> [ CPUFloatType{5} ]torch_mode(a, 0)#> [[1]] #> torch_tensor -#> 2 +#> 6 #> [ CPUFloatType{} ] #> #> [[2]] #> torch_tensor -#> 4 +#> 2 #> [ CPULongType{} ] #>
-@@ -231,7 +234,7 @@ in the output tensors having 1 fewer dimension thanContents
--
-
- Arguments -
- Note -
- mode(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) -
- Examples -
+input.diff --git a/docs/reference/torch_multinomial.html b/docs/reference/torch_multinomial.html index 76f2b141cede0fdd0dcba45fe2fbe51315da0d04..dab6ce2e7f6af6adc59f280503862f2c28f86534 100644 --- a/docs/reference/torch_multinomial.html +++ b/docs/reference/torch_multinomial.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -251,7 +255,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -208,39 +216,35 @@ $$
#> torch_tensor -#> -0.6326 -#> 1.3105 -#> 1.4708 +#> -1.8773 +#> -0.1587 +#> -0.5829 #> [ CPUFloatType{3} ]torch_mul(a, 100)#> torch_tensor -#> -63.2551 -#> 131.0495 -#> 147.0758 +#> -187.7341 +#> -15.8737 +#> -58.2864 #> [ CPUFloatType{3} ]#> torch_tensor -#> -0.8102 -#> 0.2200 -#> -0.4980 -#> -1.1600 +#> 0.1865 +#> 1.6510 +#> 0.1468 +#> 1.4875 #> [ CPUFloatType{4,1} ]#> torch_tensor -#> 0.5411 -0.2962 3.5346 -0.1035 +#> -0.5098 0.6219 -0.7591 -0.4392 #> [ CPUFloatType{1,4} ]torch_mul(a, b)#> torch_tensor -#> -0.4384 0.2400 -2.8638 0.0839 -#> 0.1190 -0.0652 0.7775 -0.0228 -#> -0.2695 0.1475 -1.7602 0.0515 -#> -0.6277 0.3437 -4.1003 0.1201 +#> -0.0951 0.1160 -0.1415 -0.0819 +#> -0.8417 1.0268 -1.2533 -0.7252 +#> -0.0748 0.0913 -0.1114 -0.0645 +#> -0.7583 0.9251 -1.1292 -0.6534 #> [ CPUFloatType{4,4} ]
diff --git a/docs/reference/torch_mv.html b/docs/reference/torch_mv.html index e04371067229995c232d6a4a04b2aee328be03db..bc0f83ccbcadb7cc151804dda6d3fbca822cd8a7 100644 --- a/docs/reference/torch_mv.html +++ b/docs/reference/torch_mv.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -212,8 +220,8 @@ of tensor
input.weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights torch_multinomial(weights, 2)#> torch_tensor -#> 1 #> 2 +#> 1 #> [ CPULongType{2} ]torch_multinomial(weights, 4, replacement=TRUE)#> torch_tensor #> 1 #> 1 @@ -221,15 +229,10 @@ of tensorinput. #> 1 #> [ CPULongType{4} ]
-@@ -240,7 +243,7 @@ of tensorContents
--
-
- Arguments -
- Note -
- multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor -
- Examples -
+input.- @@ -211,7 +214,7 @@ size \(m\),@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -188,19 +196,14 @@ size \(m\),
outwill be 1-D of size \(n\). mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_mv(mat, vec)
#> torch_tensor -#> -0.2478 -#> 0.7627 +#> -2.2015 +#> -3.3366 #> [ CPUFloatType{2} ]outwill be 1-D of size \(n\). diff --git a/docs/reference/torch_mvlgamma.html b/docs/reference/torch_mvlgamma.html index ece10b8822671dc2310451d22c8ff27e107ed7da..af925760200528a107729a501383e52ca683ecac 100644 --- a/docs/reference/torch_mvlgamma.html +++ b/docs/reference/torch_mvlgamma.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_narrow.html b/docs/reference/torch_narrow.html index 849eaf92f9bc1df64d042d9b06ce5640d417d15d..eedcc998443baa4b6312d6e719acf888c1844ff9 100644 --- a/docs/reference/torch_narrow.html +++ b/docs/reference/torch_narrow.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -208,7 +212,7 @@ where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -183,21 +191,17 @@ where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the
#> torch_tensor -#> 1.3902 1.7034 1.1663 -#> 1.6929 1.6962 1.7477 +#> 1.5628 1.0006 1.1691 +#> 1.4843 1.0556 1.2789 #> [ CPUFloatType{2,3} ]torch_mvlgamma(a, 2)#> torch_tensor -#> 0.5273 0.3909 0.8011 -#> 0.3918 0.3915 0.3897 +#> 0.4226 1.1432 0.7965 +#> 0.4604 1.0132 0.6409 #> [ CPUFloatType{2,3} ]
- @@ -213,7 +217,7 @@ returned tensor and@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -195,14 +203,10 @@ returned tensor and
inputtensor share the same underlying storage. #> 8 9 #> [ CPUIntType{3,2} ]
inputtensor share the same underlying storage. diff --git a/docs/reference/torch_ne.html b/docs/reference/torch_ne.html index fc0de2352b16c926d6aa63bbfd8bcf3e703d094f..55801cc837da82077c174ad4bc9d4eb41944f50f 100644 --- a/docs/reference/torch_ne.html +++ b/docs/reference/torch_ne.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -204,7 +208,7 @@ broadcastable with the first argument. diff --git a/docs/reference/torch_neg.html b/docs/reference/torch_neg.html index c40a98461462a63b1a994803acfd716cd43d10a7..411539a381feb01eee1ff5d85f13866f144537ac 100644 --- a/docs/reference/torch_neg.html +++ b/docs/reference/torch_neg.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -186,14 +194,10 @@ broadcastable with the first argument. #> 1 0 #> [ CPUBoolType{2,2} ]
diff --git a/docs/reference/torch_nonzero.html b/docs/reference/torch_nonzero.html index 415a8b40149c42d980f3b515e0e0d9154eea5dc4..226205cb762c7bc1e257cf28fdafb234b9aaa7bf 100644 --- a/docs/reference/torch_nonzero.html +++ b/docs/reference/torch_nonzero.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -211,7 +215,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,27 +188,23 @@ $$
#> torch_tensor -#> -1.0735 -#> 0.4161 -#> 1.0936 -#> -1.6355 -#> 0.1721 +#> 1.3288 +#> -0.2646 +#> 0.9299 +#> -2.3627 +#> 0.4755 #> [ CPUFloatType{5} ]torch_neg(a)#> torch_tensor -#> 1.0735 -#> -0.4161 -#> -1.0936 -#> 1.6355 -#> -0.1721 +#> -1.3288 +#> 0.2646 +#> -0.9299 +#> 2.3627 +#> -0.4755 #> [ CPUFloatType{5} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -169,10 +177,10 @@
Note
-[`torch_nonzero(..., as_tuple=False) <torch.nonzero>`] (default) returns a +
[`torch_nonzero(..., as_tuple=False) <torch.nonzero>`] (default) returns a 2-D tensor where each row is the index for a nonzero value. -[`torch_nonzero(..., as_tuple=True) <torch.nonzero>`] returns a tuple of 1-D +[`torch_nonzero(..., as_tuple=True) <torch.nonzero>`] returns a tuple of 1-D index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]`` gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor contains nonzero indices for a certain dimension. @@ -212,15 +220,10 @@ value, it is treated as a one-dimensional tensor with one element. #> 4 #> [ CPULongType{4,1} ]
-@@ -231,7 +234,7 @@ value, it is treated as a one-dimensional tensor with one element. diff --git a/docs/reference/torch_norm.html b/docs/reference/torch_norm.html index de8134b511b5b63215fe4fdbb0b8679ee8e16987..1bf19ced3ed50bf3d470d340763b231d4a092015 100644 --- a/docs/reference/torch_norm.html +++ b/docs/reference/torch_norm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors -
- Examples -
+- @@ -222,7 +226,7 @@ diff --git a/docs/reference/torch_normal.html b/docs/reference/torch_normal.html index 7f26055834dc786fd5115a5f5887b7fecf28fcbd..1e09a5899c4601fb1bda4ac85f8d095fbbc50869 100644 --- a/docs/reference/torch_normal.html +++ b/docs/reference/torch_normal.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -204,14 +212,10 @@ #> [ CPUFloatType{} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-@@ -254,7 +254,7 @@ among all drawn elements. The resulting tensor has size given byContents
--
-
- Arguments -
- Note -
- normal(mean, std, *, generator=None, out=None) -> Tensor -
- normal(mean=0.0, std, out=None) -> Tensor -
- normal(mean, std=1.0, out=None) -> Tensor -
- normal(mean, std, size, *, out=None) -> Tensor -
- Examples -
+size diff --git a/docs/reference/torch_ones.html b/docs/reference/torch_ones.html index 414b0feb9d2a66eca491aa2d12365ff40d1bd08b..b1ed6bbc969641912b641fdb7ccb1d768d4b8770 100644 --- a/docs/reference/torch_ones.html +++ b/docs/reference/torch_ones.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -203,14 +211,10 @@ by the variable argument
size. #> 1 #> [ CPUFloatType{5} ]
-@@ -221,7 +225,7 @@ by the variable argumentContents
--
-
- Arguments -
- ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+size. diff --git a/docs/reference/torch_ones_like.html b/docs/reference/torch_ones_like.html index fdfeaf37e52c2e2a2f4d3178d4e3512cdfcd2b66..63bb15813473d951afaefb8143d62e208a0622cb 100644 --- a/docs/reference/torch_ones_like.html +++ b/docs/reference/torch_ones_like.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -206,15 +214,10 @@ the old
torch_ones_like(input, out=output)is equivalent to #> 1 1 1 #> [ CPUFloatType{2,3} ]
-@@ -225,7 +228,7 @@ the oldContents
--
-
- Arguments -
- ones_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor -
- Warning -
- Examples -
+torch_ones_like(input, out=output)is equivalent to diff --git a/docs/reference/torch_orgqr.html b/docs/reference/torch_orgqr.html index 958b750e9797d7cd2419df52007a45fe327766c9..7d0661b8a9ad30ba3566d18991cf2a80138d7e82 100644 --- a/docs/reference/torch_orgqr.html +++ b/docs/reference/torch_orgqr.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -199,7 +203,7 @@ See@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,14 +189,10 @@ See
LAPACK documentation for orgqr_ for further details.
LAPACK documentation for orgqr_ for further details. diff --git a/docs/reference/torch_ormqr.html b/docs/reference/torch_ormqr.html index c5abf42038a8a5601bec3426c6593629e9e46f32..37f1b262abc7123ec9d1b0883882a4af4d942ef8 100644 --- a/docs/reference/torch_ormqr.html +++ b/docs/reference/torch_ormqr.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -185,14 +193,10 @@ See
LAPACK documentation for ormqr_ for further details.
-@@ -203,7 +207,7 @@ SeeContents
- - ++LAPACK documentation for ormqr_ for further details. diff --git a/docs/reference/torch_pdist.html b/docs/reference/torch_pdist.html index b637895cc89e08dcc197560848795d477f91fe2d..2a22269a03c78db35fce1d0edfae7728793e829b 100644 --- a/docs/reference/torch_pdist.html +++ b/docs/reference/torch_pdist.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -205,7 +209,7 @@ When \(p = \infty\), the closest scipy function is diff --git a/docs/reference/torch_pinverse.html b/docs/reference/torch_pinverse.html index 695b5356b16fcd9754eaf76cc30748e390d79508..d0a0e05efcd9a922fa78ffc0398bf2400861d410 100644 --- a/docs/reference/torch_pinverse.html +++ b/docs/reference/torch_pinverse.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -187,14 +195,10 @@ When \(p = \infty\), the closest scipy function is
diff --git a/docs/reference/torch_pixel_shuffle.html b/docs/reference/torch_pixel_shuffle.html index ced66eec1101e9ccd5de0da8bde9a65521bdebca..c23bfac057d4580c74ef657b5a31785cfb9c0cd7 100644 --- a/docs/reference/torch_pixel_shuffle.html +++ b/docs/reference/torch_pixel_shuffle.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -234,7 +237,7 @@ Please look at-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -191,39 +199,34 @@ Please look at
Moore-Penrose inverse_ for more details#> torch_tensor -#> -0.5093 -1.3586 0.4624 0.2282 -0.7146 -#> -1.1385 0.0685 0.3987 0.1347 1.3165 -#> -0.9570 1.9990 -1.2909 -0.4181 -0.9217 +#> -0.2264 -0.0329 0.6990 -1.6327 -0.1358 +#> 0.1323 0.0109 1.5492 1.8007 -0.4490 +#> -1.5412 1.0334 -0.0066 0.1515 1.3396 #> [ CPUFloatType{3,5} ]torch_pinverse(input)#> torch_tensor -#> -0.4397 -0.4374 -0.2888 -#> -0.3414 0.0243 0.1631 -#> 0.0543 0.1028 -0.1461 -#> 0.0543 0.0397 -0.0359 -#> -0.3845 0.3448 -0.2103 +#> -0.1018 -0.0362 -0.2989 +#> 0.0102 0.0231 0.1990 +#> 0.4503 0.4091 0.0503 +#> -0.3993 0.1863 0.0386 +#> -0.0776 -0.0785 0.2450 #> [ CPUFloatType{5,3} ]#> torch_tensor #> (1,.,.) = -#> 1.0000e+00 1.3784e-07 1.0803e-07 -#> -3.7253e-08 1.0000e+00 2.5891e-07 -#> -5.9605e-08 -3.3528e-08 1.0000e+00 +#> 1.0000 0.0000 -0.0000 +#> 0.0000 1.0000 0.0000 +#> 0.0000 0.0000 1.0000 #> #> (2,.,.) = -#> 1.0000e+00 5.2154e-08 -1.7881e-07 -#> -5.9605e-08 1.0000e+00 8.9407e-08 -#> -5.9605e-08 -3.7253e-08 1.0000e+00 +#> 1.0000e+00 5.0664e-07 -3.4273e-07 +#> 1.6391e-07 1.0000e+00 -1.0431e-07 +#> 6.3330e-08 -4.4703e-08 1.0000e+00 #> [ CPUFloatType{2,3,3} ]
Moore-Penrose inverse_ for more details- @@ -196,7 +200,7 @@ tensor of shape \((*, C, H \times r, W \times r)\). diff --git a/docs/reference/torch_poisson.html b/docs/reference/torch_poisson.html index 16e468b9d77493571b3bb63819170b917a4d9a4d..4036f9bb7fa8cfe74aa0b3caff2be2f78c6bac75 100644 --- a/docs/reference/torch_poisson.html +++ b/docs/reference/torch_poisson.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -178,14 +186,10 @@ tensor of shape \((*, C, H \times r, W \times r)\).
diff --git a/docs/reference/torch_polygamma.html b/docs/reference/torch_polygamma.html index 629210d3faa46fa98a0baa208e952bc7f8a7a778..ac539e156d248e9b4e4cb5f01cdd92458916042a 100644 --- a/docs/reference/torch_polygamma.html +++ b/docs/reference/torch_polygamma.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -206,7 +210,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -182,20 +190,16 @@ $$
#> torch_tensor -#> 0 3 1 2 -#> 2 1 5 3 -#> 4 2 4 1 -#> 1 0 3 0 +#> 0 2 0 8 +#> 0 2 3 1 +#> 5 1 7 3 +#> 2 3 4 0 #> [ CPUFloatType{4,4} ]
- @@ -212,7 +215,7 @@ $$ diff --git a/docs/reference/torch_pow.html b/docs/reference/torch_pow.html index aa9101328303ff92c1e665fdf26e7a64c6161cc8..c37a14376f374ae889632d4f9e1b5ce3079761e1 100644 --- a/docs/reference/torch_pow.html +++ b/docs/reference/torch_pow.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -193,15 +201,10 @@ $$ torch_polygamma(1, a) }
diff --git a/docs/reference/torch_prod.html b/docs/reference/torch_prod.html index 77f10a48d17d3da2bf4aa6bd6916da0b6b9d0a6c..b6feb2cea042fae8e65c77780b1ff597b3bbf299 100644 --- a/docs/reference/torch_prod.html +++ b/docs/reference/torch_prod.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -209,15 +217,15 @@ $$
#> torch_tensor -#> -1.0757 -#> -0.3088 -#> -0.2272 -#> 1.4382 +#> -1.0589 +#> -1.0449 +#> 0.0256 +#> 0.4742 #> [ CPUFloatType{4} ]torch_pow(a, 2)#> torch_tensor -#> 1.1572 -#> 0.0954 -#> 0.0516 -#> 2.0684 +#> 1.1212 +#> 1.0918 +#> 0.0007 +#> 0.2248 #> [ CPUFloatType{4} ]#> torch_tensor @@ -246,15 +254,10 @@ $$ #> 16 #> [ CPUFloatType{4} ]
-@@ -265,7 +268,7 @@ $$Contents
--
-
- Arguments -
- pow(input, exponent, out=None) -> Tensor -
- pow(self, exponent, out=None) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_promote_types.html b/docs/reference/torch_promote_types.html index a906f0ab3ecedbce8d694945bfbaf0b35c1add60..2d5b5d587b0011a56c136d59e5a62ab2e30aaad5 100644 --- a/docs/reference/torch_promote_types.html +++ b/docs/reference/torch_promote_types.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -196,33 +204,28 @@ the output tensor having 1 fewer dimension than
input.#> torch_tensor -#> -0.1975 1.2815 -1.3789 +#> -0.9504 0.4454 -2.0986 #> [ CPUFloatType{1,3} ]torch_prod(a)#> torch_tensor -#> 0.34899 +#> 0.888286 #> [ CPUFloatType{} ]#> torch_tensor -#> -1.8966 0.6320 -#> 0.0146 1.8568 -#> 0.7756 0.8296 -#> -0.6350 -0.0319 +#> -0.4386 1.0782 +#> 0.8182 0.0358 +#> -0.5379 -1.0059 +#> -0.6535 -2.1604 #> [ CPUFloatType{4,2} ]torch_prod(a, 1)#> torch_tensor -#> -1.1986 -#> 0.0270 -#> 0.6434 -#> 0.0202 +#> -0.4729 +#> 0.0293 +#> 0.5411 +#> 1.4117 #> [ CPUFloatType{4} ]
-@@ -233,7 +236,7 @@ the output tensor having 1 fewer dimension thanContents
--
-
- Arguments -
- prod(input, dtype=None) -> Tensor -
- prod(input, dim, keepdim=False, dtype=None) -> Tensor -
- Examples -
+input.diff --git a/docs/reference/torch_qr.html b/docs/reference/torch_qr.html index cb5eaf57396d60adc8571e999fe15cf92229bf49..b7b9eae60a8f3139097836af16c703e12bb007ad 100644 --- a/docs/reference/torch_qr.html +++ b/docs/reference/torch_qr.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -198,7 +202,7 @@ promotion logic.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,14 +188,10 @@ promotion logic.
torch_promote_types(torch_int32(), torch_float32())#> torch_Floattorch_promote_types(torch_uint8(), torch_long())#> torch_Long
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -205,15 +213,10 @@ Otherwise, if
someisFalse, this function returns the #> 0 -0 1 #> [ CPUFloatType{3,3} ]
-@@ -224,7 +227,7 @@ Otherwise, ifContents
- - ++someisFalse, this function returns the diff --git a/docs/reference/torch_quantize_per_channel.html b/docs/reference/torch_quantize_per_channel.html index 06b6a0b612e4c1ebe11884763f906ac3fc0c65ad..dc3e9be40f14cddf3f3af2fe47909e07a2de2d77 100644 --- a/docs/reference/torch_quantize_per_channel.html +++ b/docs/reference/torch_quantize_per_channel.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -195,14 +203,10 @@ #> 100 200 #> [ CPUByteType{2,2} ]
-@@ -213,7 +217,7 @@ diff --git a/docs/reference/torch_quantize_per_tensor.html b/docs/reference/torch_quantize_per_tensor.html index 66197839a8b38020d6b695ddabc86b771cb7e122..99a6111a25fbc810803d40cbd924234ebfca46ae 100644 --- a/docs/reference/torch_quantize_per_tensor.html +++ b/docs/reference/torch_quantize_per_tensor.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -194,14 +202,10 @@ #> 30 #> [ CPUByteType{4} ]
-@@ -212,7 +216,7 @@ diff --git a/docs/reference/torch_rand.html b/docs/reference/torch_rand.html index c596cb86437e54d2c3a22c7577d26174da40cb4c..3ca338b7b4d55c177d185ecaf4b3f171145cb095 100644 --- a/docs/reference/torch_rand.html +++ b/docs/reference/torch_rand.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
- - ++diff --git a/docs/reference/torch_rand_like.html b/docs/reference/torch_rand_like.html index 29f3a942d87ce986bcd6ad72e607cbe420a10e24..0ddeb964196798253f196e278b66827e18555011 100644 --- a/docs/reference/torch_rand_like.html +++ b/docs/reference/torch_rand_like.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -194,23 +202,19 @@ on the interval \([0, 1)\)
Examples
torch_rand(4)#> torch_tensor -#> 0.0700 -#> 0.0146 -#> 0.1299 -#> 0.9493 +#> 0.6047 +#> 0.7560 +#> 0.3577 +#> 0.8623 #> [ CPUFloatType{4} ]#> torch_tensor -#> 0.6918 0.3520 0.6800 -#> 0.0478 0.6168 0.2352 +#> 0.9579 0.0519 0.4924 +#> 0.1820 0.8366 0.8147 #> [ CPUFloatType{2,3} ]
-@@ -221,7 +225,7 @@ on the interval \([0, 1)\)Contents
--
-
- Arguments -
- rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -197,14 +205,10 @@ random numbers from a uniform distribution on the interval \([0, 1)\).
-@@ -215,7 +219,7 @@ random numbers from a uniform distribution on the interval \([0, 1)\). diff --git a/docs/reference/torch_randint.html b/docs/reference/torch_randint.html index 525af69a56f2a8c07d4b08320a6f17be7903cc3c..ad0137d1158897eebaec54fb86dacd3aad3363aa 100644 --- a/docs/reference/torch_randint.html +++ b/docs/reference/torch_randint.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_randint_like.html b/docs/reference/torch_randint_like.html index f0b2059f6382f93271c179ac27591567c6ad2a96..b2187023d3b305cb58fca2ad5e49389b36fe9436 100644 --- a/docs/reference/torch_randint_like.html +++ b/docs/reference/torch_randint_like.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -210,25 +218,21 @@ a tensor with dtype
torch_int64.Examples
#> torch_tensor -#> 4 -#> 4 -#> 4 +#> 3 +#> 3 +#> 3 #> [ CPUFloatType{3} ]#> torch_tensor -#> 6 4 -#> 1 4 +#> 5 4 +#> 9 1 #> [ CPUFloatType{2,2} ]#> torch_tensor -#> 7 7 -#> 4 7 +#> 3 4 +#> 3 6 #> [ CPUFloatType{2,2} ]
-@@ -239,7 +243,7 @@ a tensor with dtypeContents
- - ++torch_int64.-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -208,14 +216,10 @@ a tensor with dtype
torch_int64.
-@@ -226,7 +230,7 @@ a tensor with dtypeContents
--
-
- Arguments -
- randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, -
- Examples -
+torch_int64. diff --git a/docs/reference/torch_randn.html b/docs/reference/torch_randn.html index 2facc26f4f893b3b92e8ae1ba56366e56c3420b3..193833791531d80bb2cf255a8ba90fec3b8b8e60 100644 --- a/docs/reference/torch_randn.html +++ b/docs/reference/torch_randn.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_randperm.html b/docs/reference/torch_randperm.html index 59f43d7a01234795f9b149428c9870c09ea12fc5..7e974cb85c1bc6781eecc379cdde82dacebbfec9 100644 --- a/docs/reference/torch_randperm.html +++ b/docs/reference/torch_randperm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -198,23 +206,19 @@ The shape of the tensor is defined by the variable argument
size.Examples#> torch_tensor -#> 0.1769 -#> 1.5478 -#> -0.8871 -#> -0.8477 +#> 0.7377 +#> -1.0120 +#> 0.3791 +#> 0.1634 #> [ CPUFloatType{4} ]#> torch_tensor -#> 1.2484 1.0939 -0.1743 -#> 0.2503 -0.0494 2.2941 +#> -1.2550 -0.5020 -0.2737 +#> 1.2364 -2.0740 -0.5460 #> [ CPUFloatType{2,3} ]
-@@ -225,7 +229,7 @@ The shape of the tensor is defined by the variable argumentContents
--
-
- Arguments -
- randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+size. diff --git a/docs/reference/torch_randn_like.html b/docs/reference/torch_randn_like.html index 5ee8af06a0bf5f9dfe88768235d95223004afdd9..6c2b74f70b9dc94d8804bcda5e470cdf5021b4a9 100644 --- a/docs/reference/torch_randn_like.html +++ b/docs/reference/torch_randn_like.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -215,7 +219,7 @@ random numbers from a normal distribution with mean 0 and variance 1.@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -197,14 +205,10 @@ random numbers from a normal distribution with mean 0 and variance 1.
diff --git a/docs/reference/torch_range.html b/docs/reference/torch_range.html index 5cce7d3e80dc5caa1c871e84258b6b1dd90493aa..16e52e6fde01f1b3c31c3976f6368d31ed538706 100644 --- a/docs/reference/torch_range.html +++ b/docs/reference/torch_range.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -216,7 +220,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -193,19 +201,15 @@
torch_randperm(4)#> torch_tensor #> 2 -#> 3 -#> 1 #> 0 +#> 1 +#> 3 #> [ CPULongType{4} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -222,15 +230,10 @@ $$ #> 3.5000 #> [ CPUFloatType{6} ]
-@@ -241,7 +244,7 @@ $$ diff --git a/docs/reference/torch_real.html b/docs/reference/torch_real.html index 63268bd4a65e3901326022b882454b17fef54b08..ee7ea180e1e92a06ef2e184d3b09edf3ea3fd294 100644 --- a/docs/reference/torch_real.html +++ b/docs/reference/torch_real.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Warning -
- Examples -
+- @@ -207,7 +210,7 @@ $$ diff --git a/docs/reference/torch_reciprocal.html b/docs/reference/torch_reciprocal.html index d4a9cc9a6adfce73d5e0515f7e972a8c1355d2fe..c1711c2bdbd3b0a306fe7801c2f4d17279e9c2ed 100644 --- a/docs/reference/torch_reciprocal.html +++ b/docs/reference/torch_reciprocal.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
diff --git a/docs/reference/torch_relu_.html b/docs/reference/torch_relu_.html index 9543cb0b0372cb7fb085c368b4c81bd95cd11dd3..70d05bc27359b775a88a2ae063a4ffe3d28b67ca 100644 --- a/docs/reference/torch_relu_.html +++ b/docs/reference/torch_relu_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -209,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,25 +188,21 @@ $$
#> torch_tensor -#> -0.9518 -#> 0.0427 -#> 1.3018 -#> 0.1766 +#> -0.0628 +#> 0.1928 +#> -0.6503 +#> 0.1817 #> [ CPUFloatType{4} ]torch_reciprocal(a)#> torch_tensor -#> -1.0506 -#> 23.3955 -#> 0.7682 -#> 5.6640 +#> -15.9280 +#> 5.1856 +#> -1.5377 +#> 5.5042 #> [ CPUFloatType{4} ]
- @@ -183,7 +188,7 @@ diff --git a/docs/reference/torch_remainder.html b/docs/reference/torch_remainder.html index 12832997744226b4f7c4838b08cd80af16459a0a..f80e464b1cb062ad9d7b4af33d34eb3937108372 100644 --- a/docs/reference/torch_remainder.html +++ b/docs/reference/torch_remainder.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,13 +174,10 @@
- @@ -216,7 +220,7 @@ numbers. The remainder has the same sign as the divisor. diff --git a/docs/reference/torch_renorm.html b/docs/reference/torch_renorm.html index b6f8861ac6eacc063da33e5dfc0e68f93f02dea8..10c29b21fc0242f7538bb7c84ed7e8ab524a18b4 100644 --- a/docs/reference/torch_renorm.html +++ b/docs/reference/torch_renorm.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -198,14 +206,10 @@ numbers. The remainder has the same sign as the divisor. #> 0.5000 #> [ CPUFloatType{5} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -210,15 +218,10 @@ than the value
maxnorm#> 1.6667 1.6667 1.6667 #> [ CPUFloatType{3,3} ]
-@@ -229,7 +232,7 @@ than the valueContents
- - ++maxnormdiff --git a/docs/reference/torch_repeat_interleave.html b/docs/reference/torch_repeat_interleave.html index bf1058645833950efb008cabccb1215dac2e34c2..60b0965a819f421d254cb74261a0064ac5d10508 100644 --- a/docs/reference/torch_repeat_interleave.html +++ b/docs/reference/torch_repeat_interleave.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-@@ -221,7 +223,7 @@ diff --git a/docs/reference/torch_reshape.html b/docs/reference/torch_reshape.html index 188d8e53bae78cd51aff68c753c1ec94965e9488..c8250feb03d41043242f0e781c1b52bebdd6e5c6 100644 --- a/docs/reference/torch_reshape.html +++ b/docs/reference/torch_reshape.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- repeat_interleave(input, repeats, dim=None) -> Tensor -
- Warning -
- repeat_interleave(repeats) -> Tensor -
- Examples -
+- @@ -212,7 +216,7 @@ dimensions and the number of elements in@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -194,14 +202,10 @@ dimensions and the number of elements in
input. #> 3 #> [ CPUFloatType{4} ]
input. diff --git a/docs/reference/torch_result_type.html b/docs/reference/torch_result_type.html index e187833a6e5b5371ffa26b6234ff030f15223b6c..b3766956e72163d5a7bd32117201e956cbc5f179 100644 --- a/docs/reference/torch_result_type.html +++ b/docs/reference/torch_result_type.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -198,7 +202,7 @@ for more information on the type promotion logic. diff --git a/docs/reference/torch_rfft.html b/docs/reference/torch_rfft.html index 4962edb426fbef63bcd4e136360f3eda75f15f3e..f5c0f7ac4d258825e9c2f5e9330e991072bd77b1 100644 --- a/docs/reference/torch_rfft.html +++ b/docs/reference/torch_rfft.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
#> torch_Float# torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -222,76 +230,70 @@ of
input, but instead the last dimension will be halfed as of size x = torch_randn(c(5, 5)) torch_rfft(x, 2)
#> torch_tensor #> (1,.,.) = -#> -0.7981 0.0000 -#> 0.6722 -2.6845 -#> 5.2374 -1.3287 +#> 3.9069 0.0000 +#> 6.1332 -7.1960 +#> 1.1799 3.1917 #> #> (2,.,.) = -#> 1.7845 -1.1174 -#> 8.4891 -3.9620 -#> 3.0784 -4.7354 +#> 2.7608 -2.5656 +#> 2.9372 2.4412 +#> -5.0360 5.2319 #> #> (3,.,.) = -#> 2.7803 2.9670 -#> 2.6271 5.7163 -#> -0.6144 -7.1863 +#> 2.1129 -2.5218 +#> 0.5721 0.4728 +#> 2.7971 4.8230 #> #> (4,.,.) = -#> 2.7803 -2.9670 -#> -0.8117 -3.2777 -#> 2.0968 -1.9049 +#> 2.1129 2.5218 +#> 0.2330 3.8823 +#> 7.7532 0.6730 #> #> (5,.,.) = -#> 1.7845 1.1174 -#> -4.0628 -2.2012 -#> 1.9542 -3.9232 +#> 2.7608 2.5656 +#> 0.6659 -0.7688 +#> -1.2298 7.1500 #> [ CPUFloatType{5,3,2} ]torch_rfft(x, 2, onesided=FALSE)#> torch_tensor #> (1,.,.) = -#> -0.7981 0.0000 -#> 0.6722 -2.6845 -#> 5.2374 -1.3287 -#> 5.2374 1.3287 -#> 0.6722 2.6845 +#> 3.9069 0.0000 +#> 6.1332 -7.1960 +#> 1.1799 3.1917 +#> 1.1799 -3.1917 +#> 6.1332 7.1960 #> #> (2,.,.) = -#> 1.7845 -1.1174 -#> 8.4891 -3.9620 -#> 3.0784 -4.7354 -#> 1.9542 3.9232 -#> -4.0628 2.2012 +#> 2.7608 -2.5656 +#> 2.9372 2.4412 +#> -5.0360 5.2319 +#> -1.2298 -7.1500 +#> 0.6659 0.7688 #> #> (3,.,.) = -#> 2.7803 2.9670 -#> 2.6271 5.7163 -#> -0.6144 -7.1863 -#> 2.0968 1.9049 -#> -0.8117 3.2777 +#> 2.1129 -2.5218 +#> 0.5721 0.4728 +#> 2.7971 4.8230 +#> 7.7532 -0.6730 +#> 0.2330 -3.8823 #> #> (4,.,.) = -#> 2.7803 -2.9670 -#> -0.8117 -3.2777 -#> 2.0968 -1.9049 -#> -0.6144 7.1863 -#> 2.6271 -5.7163 +#> 2.1129 2.5218 +#> 0.2330 3.8823 +#> 7.7532 0.6730 +#> 2.7971 -4.8230 +#> 0.5721 -0.4728 #> #> (5,.,.) = -#> 1.7845 1.1174 -#> -4.0628 -2.2012 -#> 1.9542 -3.9232 -#> 3.0784 4.7354 -#> 8.4891 3.9620 +#> 2.7608 2.5656 +#> 0.6659 -0.7688 +#> -1.2298 7.1500 +#> -5.0360 -5.2319 +#> 2.9372 -2.4412 #> [ CPUFloatType{5,5,2} ]-@@ -302,7 +304,7 @@ ofContents
--
-
- Arguments -
- Note -
- rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor -
- Warning -
- Examples -
+input, but instead the last dimension will be halfed as of size diff --git a/docs/reference/torch_roll.html b/docs/reference/torch_roll.html index f0db1620a7a8c71132ad00c6e2ff3ce7881f096e..38c6f585c314cc0fe166529107ec9f056ecab210 100644 --- a/docs/reference/torch_roll.html +++ b/docs/reference/torch_roll.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -223,7 +227,7 @@ to the original shape. diff --git a/docs/reference/torch_rot90.html b/docs/reference/torch_rot90.html index 06a388031acf04200533094c0799f331afa7c208..9fdeaa36aff6cf52fc1859b5b1fa42c3bdabefca 100644 --- a/docs/reference/torch_rot90.html +++ b/docs/reference/torch_rot90.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -205,14 +213,10 @@ to the original shape. #> 4 3 #> [ CPUFloatType{4,2} ]
- @@ -224,7 +228,7 @@ Rotation direction is from the first towards the second axis if k > 0, and fr diff --git a/docs/reference/torch_round.html b/docs/reference/torch_round.html index 75eafa701a7dd7f4178b8b6cd313cbb8b9be2fb0..0bf4be7b77f2d43170d77146e90c0b1d1fb13172 100644 --- a/docs/reference/torch_round.html +++ b/docs/reference/torch_round.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -206,14 +214,10 @@ Rotation direction is from the first towards the second axis if k > 0, and fr #> 4 6 #> [ CPUFloatType{2,2,2} ]
diff --git a/docs/reference/torch_rrelu_.html b/docs/reference/torch_rrelu_.html index 105cd8fe3f550d6b056f0ee9b7f09c34f078f5d9..52c31cc549163d6d43c7167f76553966a42fcaab 100644 --- a/docs/reference/torch_rrelu_.html +++ b/docs/reference/torch_rrelu_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -207,7 +211,7 @@ to the closest integer.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -178,25 +186,21 @@ to the closest integer.
#> torch_tensor -#> -0.2791 -#> -0.0998 -#> 1.0750 -#> 0.9208 +#> 0.7415 +#> -1.4748 +#> -0.8683 +#> -1.3706 #> [ CPUFloatType{4} ]torch_round(a)#> torch_tensor -#> -0 -#> -0 -#> 1 #> 1 +#> -1 +#> -1 +#> -1 #> [ CPUFloatType{4} ]
- @@ -183,7 +188,7 @@ diff --git a/docs/reference/torch_rsqrt.html b/docs/reference/torch_rsqrt.html index 9a3c26d203ae2220d158fd88ad7fb6117f3305a9..67bd62c13b47c3d2b1276b9a4f167f37a4789271 100644 --- a/docs/reference/torch_rsqrt.html +++ b/docs/reference/torch_rsqrt.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,13 +174,10 @@
diff --git a/docs/reference/torch_save.html b/docs/reference/torch_save.html index 21c71ef048d086cef8d622d6f5541fb85f0efa32..472ba553012f7ea876ca03c9aca9148608cc518a 100644 --- a/docs/reference/torch_save.html +++ b/docs/reference/torch_save.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ term storage." /> - +@@ -88,12 +91,17 @@- @@ -210,7 +214,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,25 +189,21 @@ $$
#> torch_tensor -#> -0.0303 -#> -0.3958 -#> 0.0159 -#> 0.1759 +#> -0.9927 +#> 0.7252 +#> -0.0975 +#> 1.1863 #> [ CPUFloatType{4} ]torch_rsqrt(a)#> torch_tensor #> nan +#> 1.1742 #> nan -#> 7.9420 -#> 2.3843 +#> 0.9181 #> [ CPUFloatType{4} ]
- @@ -192,7 +197,7 @@ term storage. diff --git a/docs/reference/torch_selu_.html b/docs/reference/torch_selu_.html index 3e3cfed8ba7abbc85fa4292aff21e7e6b4c9de62..28087bd683062a1de7a6bacaf2ed281b8c60a7ae 100644 --- a/docs/reference/torch_selu_.html +++ b/docs/reference/torch_selu_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ term storage." />-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ term storage." />
-
-
+
@@ -175,13 +183,10 @@ term storage.
torch_load()
- @@ -183,7 +188,7 @@ diff --git a/docs/reference/torch_sigmoid.html b/docs/reference/torch_sigmoid.html index c065d3bca3b66feb932b892a689297e57924891a..a58e2e93273c3e3634dac243a001a0ab964671a0 100644 --- a/docs/reference/torch_sigmoid.html +++ b/docs/reference/torch_sigmoid.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,13 +174,10 @@
diff --git a/docs/reference/torch_sign.html b/docs/reference/torch_sign.html index 6297b32a63fd38e6d775b49a8636fe9f17a8e96a..caa84b27082cabc05fa51284774e4885f90b1110 100644 --- a/docs/reference/torch_sign.html +++ b/docs/reference/torch_sign.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -209,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,25 +188,21 @@ $$
#> torch_tensor -#> 0.0173 -#> 0.5940 -#> -0.4700 -#> -0.0982 +#> 1.0266 +#> 0.1165 +#> 0.6234 +#> -0.2500 #> [ CPUFloatType{4} ]torch_sigmoid(a)#> torch_tensor -#> 0.5043 -#> 0.6443 -#> 0.3846 -#> 0.4755 +#> 0.7363 +#> 0.5291 +#> 0.6510 +#> 0.4378 #> [ CPUFloatType{4} ]
- @@ -209,7 +213,7 @@ $$ diff --git a/docs/reference/torch_sin.html b/docs/reference/torch_sin.html index 83ed398042a630d433e3c8bd11920225666729c3..21e7dddfdaa54e49f7b7486484703ce46205823e 100644 --- a/docs/reference/torch_sin.html +++ b/docs/reference/torch_sin.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -191,14 +199,10 @@ $$ #> 1 #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_sinh.html b/docs/reference/torch_sinh.html index 2a54d808adea606b6924f1839ec0066a61c21c7a..4292daafdd14f64ed1afce9ed034fc30431b967b 100644 --- a/docs/reference/torch_sinh.html +++ b/docs/reference/torch_sinh.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -209,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,25 +188,21 @@ $$
#> torch_tensor -#> 0.2759 -#> -0.9342 -#> 0.2415 -#> 1.8438 +#> -1.3616 +#> 0.1876 +#> 0.3002 +#> -1.0332 #> [ CPUFloatType{4} ]torch_sin(a)#> torch_tensor -#> 0.2724 -#> -0.8041 -#> 0.2392 -#> 0.9630 +#> -0.9782 +#> 0.1865 +#> 0.2957 +#> -0.8590 #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_slogdet.html b/docs/reference/torch_slogdet.html index 5e156b050bc7bd4ce0621f003e652e5d9d00279d..a46d381a32770718abfe6e7d041ebe26636f8fda 100644 --- a/docs/reference/torch_slogdet.html +++ b/docs/reference/torch_slogdet.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -210,7 +214,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,25 +189,21 @@ $$
#> torch_tensor -#> -0.1060 -#> -0.7216 -#> -1.9582 -#> 0.6978 +#> -0.3551 +#> 0.5707 +#> -1.5595 +#> 0.7611 #> [ CPUFloatType{4} ]torch_sinh(a)#> torch_tensor -#> -0.1062 -#> -0.7859 -#> -3.4728 -#> 0.7558 +#> -0.3626 +#> 0.6022 +#> -2.2732 +#> 0.8368 #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_solve.html b/docs/reference/torch_solve.html index b025272908ba86ef03ad148f11d02f8e110f9b96..7833737f97ca7f0a3ef6984cddfcf15096a75993 100644 --- a/docs/reference/torch_solve.html +++ b/docs/reference/torch_solve.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -222,7 +225,7 @@ See `~torch.svd` for details.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -185,33 +193,28 @@ See `~torch.svd` for details.
#> torch_tensor -#> 0.8257 -3.4845 0.4019 -#> -1.0083 -0.8181 0.2510 -#> 1.2254 0.3792 0.1787 +#> 0.5550 -0.6741 0.8316 +#> -0.8512 0.5469 -0.4875 +#> -2.4404 -1.0506 -1.0179 #> [ CPUFloatType{3,3} ]torch_det(A)#> torch_tensor -#> -1.65003 +#> 1.04236 #> [ CPUFloatType{} ]torch_logdet(A)#> torch_tensor -#> nan +#> 0.0414878 #> [ CPUFloatType{} ]torch_slogdet(A)#> [[1]] #> torch_tensor -#> -1 +#> 1 #> [ CPUFloatType{} ] #> #> [[2]] #> torch_tensor -#> 0.500794 +#> 0.0414878 #> [ CPUFloatType{} ] #>
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -214,18 +222,13 @@ batched outputs
solution, LU. X = out[[1]] LU = out[[2]] torch_dist(B, A$matmul(X))
#> torch_tensor -#> 5.62799e-06 +#> 2.19235e-06 #> [ CPUFloatType{} ]-@@ -236,7 +239,7 @@ batched outputsContents
- - ++solution, LU. diff --git a/docs/reference/torch_sort.html b/docs/reference/torch_sort.html index d09a044114b19c824004b3d671a4fb45c3dbf14b..cfe5b7b9b5f7e22ddc155005d0a9733d7d8e8ca4 100644 --- a/docs/reference/torch_sort.html +++ b/docs/reference/torch_sort.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -194,41 +202,37 @@ sorted values and
indicesare the indices of the elements in the or out = torch_sort(x) out
#> [[1]] #> torch_tensor -#> -1.2858 0.0331 1.0314 1.7694 -#> -1.7488 -1.1888 0.9208 0.9682 -#> -0.7885 -0.6494 0.5639 0.9872 +#> -1.8744 -0.4719 0.2687 1.5399 +#> -1.3519 -0.6021 -0.4366 1.5268 +#> -1.2490 0.1535 0.2824 1.6506 #> [ CPUFloatType{3,4} ] #> #> [[2]] #> torch_tensor -#> 0 1 2 3 -#> 1 2 0 3 -#> 0 1 3 2 +#> 3 2 0 1 +#> 3 1 2 0 +#> 0 3 1 2 #> [ CPULongType{3,4} ] #>out = torch_sort(x, 0) out#> [[1]] #> torch_tensor -#> -1.2858 -1.7488 -1.1888 0.5639 -#> -0.7885 -0.6494 0.9872 0.9682 -#> 0.9208 0.0331 1.0314 1.7694 +#> -1.2490 -0.6021 -0.4719 -1.8744 +#> 0.2687 0.2824 -0.4366 -1.3519 +#> 1.5268 1.5399 1.6506 0.1535 #> [ CPUFloatType{3,4} ] #> #> [[2]] #> torch_tensor -#> 0 1 1 2 -#> 2 2 2 1 -#> 1 0 0 0 +#> 2 1 0 0 +#> 0 2 1 1 +#> 1 0 2 2 #> [ CPULongType{3,4} ] #>-@@ -239,7 +243,7 @@ sorted values andContents
- - ++indicesare the indices of the elements in the or diff --git a/docs/reference/torch_sparse_coo_tensor.html b/docs/reference/torch_sparse_coo_tensor.html index cde74ec7aaa0b93f515b9793857d1624bf59420e..55580405372c97f5a62ba4d30a9b9ef53e72225f 100644 --- a/docs/reference/torch_sparse_coo_tensor.html +++ b/docs/reference/torch_sparse_coo_tensor.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-@@ -253,7 +257,7 @@ coordinates in the indices, and the value at that index is the sum of all duplic diff --git a/docs/reference/torch_split.html b/docs/reference/torch_split.html index 3ba35b5ddd03fbec9ae00f6a3e7ada7352e3e1e5..95d223b52784e6601493b7449b8c48ef94b46370 100644 --- a/docs/reference/torch_split.html +++ b/docs/reference/torch_split.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor -
- Examples -
+- @@ -209,7 +213,7 @@ to `split_size_or_sections`. diff --git a/docs/reference/torch_sqrt.html b/docs/reference/torch_sqrt.html index 7f0376e34129f1f46eabfe415989a53d198d78f2..c318b2f3135e40e0e3058fafc1c747c67269b998 100644 --- a/docs/reference/torch_sqrt.html +++ b/docs/reference/torch_sqrt.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -191,14 +199,10 @@ to `split_size_or_sections`.
diff --git a/docs/reference/torch_square.html b/docs/reference/torch_square.html index 66b5c439157673deda13e83b91669528653e8ef9..8df7330e3f2033038506e8f4318a26b6661e0d17 100644 --- a/docs/reference/torch_square.html +++ b/docs/reference/torch_square.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -209,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,25 +188,21 @@ $$
#> torch_tensor -#> -1.0049 -#> -2.0852 -#> 1.8124 -#> 0.1530 +#> 1.1742 +#> -0.5793 +#> 1.2887 +#> 0.1927 #> [ CPUFloatType{4} ]torch_sqrt(a)#> torch_tensor +#> 1.0836 #> nan -#> nan -#> 1.3463 -#> 0.3912 +#> 1.1352 +#> 0.4390 #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_squeeze.html b/docs/reference/torch_squeeze.html index d6be21bd5502a8c4391ced450977f0fb87ad3bbf..724d79e4b5d217b668c4e5075fe02bcf1ea83ca4 100644 --- a/docs/reference/torch_squeeze.html +++ b/docs/reference/torch_squeeze.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -206,7 +210,7 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -177,25 +185,21 @@
#> torch_tensor -#> -0.5120 -#> 2.4132 -#> 2.8247 -#> -0.0097 +#> -0.3196 +#> -1.5477 +#> -0.9304 +#> 0.3750 #> [ CPUFloatType{4} ]torch_square(a)#> torch_tensor -#> 2.6215e-01 -#> 5.8234e+00 -#> 7.9788e+00 -#> 9.4250e-05 +#> 0.1021 +#> 2.3954 +#> 0.8656 +#> 0.1406 #> [ CPUFloatType{4} ]
- @@ -259,7 +262,7 @@ will squeeze the tensor to the shape \((A \times B)\). diff --git a/docs/reference/torch_stack.html b/docs/reference/torch_stack.html index 06c9f8bea46a4456872d291e6aa067be83fb0762..25e73efe74c47a6193a5d612f06d83ba6b1812e2 100644 --- a/docs/reference/torch_stack.html +++ b/docs/reference/torch_stack.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -240,15 +248,10 @@ will squeeze the tensor to the shape \((A \times B)\). #> 0 0 #> [ CPUFloatType{2,2,1,2} ]
- @@ -201,7 +205,7 @@ diff --git a/docs/reference/torch_std.html b/docs/reference/torch_std.html index d621fe84472dc68b34dbbeb37b92943c01bb9f58..c5a03e2cb9e0f63ebb8eefc71e1af6c3ecc45c49 100644 --- a/docs/reference/torch_std.html +++ b/docs/reference/torch_std.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -183,14 +191,10 @@
diff --git a/docs/reference/torch_std_mean.html b/docs/reference/torch_std_mean.html index 5c121b584a112f5a5f58cbb9d39541d19a0d9bc7..01850f85fc53598c96cebc51366b4e3f757c99a7 100644 --- a/docs/reference/torch_std_mean.html +++ b/docs/reference/torch_std_mean.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -205,33 +213,28 @@ via the biased estimator. Otherwise, Bessel's correction will be used.
#> torch_tensor -#> 0.4955 -0.0373 -0.4843 +#> 1.2212 -0.0202 0.4927 #> [ CPUFloatType{1,3} ]torch_std(a)#> torch_tensor -#> 0.490552 +#> 0.623829 #> [ CPUFloatType{} ]#> torch_tensor -#> 0.9203 1.1066 -0.8159 -0.5546 -#> 1.5307 0.1133 -0.5696 1.2234 -#> 0.8110 -0.7280 0.7640 -0.7218 -#> 1.9211 0.0538 -0.9575 -1.3945 +#> -0.2743 0.5739 0.6146 0.9361 +#> 0.6085 0.9326 -0.1847 -3.0802 +#> 0.2248 -0.1062 -0.9201 0.3781 +#> 0.4208 -0.6510 -0.7921 0.0070 #> [ CPUFloatType{4,4} ]torch_std(a, dim=1)#> torch_tensor -#> 0.9894 -#> 0.9759 -#> 0.8734 -#> 1.4741 +#> 0.5172 +#> 1.8275 +#> 0.5792 +#> 0.5688 #> [ CPUFloatType{4} ]
-@@ -242,7 +245,7 @@ via the biased estimator. Otherwise, Bessel's correction will be used.Contents
--
-
- Arguments -
- std(input, unbiased=True) -> Tensor -
- std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_stft.html b/docs/reference/torch_stft.html index d6a2fa4ff9aba2b19e26de0a6350aae107d7b68f..72e8fe6b5f3c65d601e3cb25f547bffe4264ab8c 100644 --- a/docs/reference/torch_stft.html +++ b/docs/reference/torch_stft.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -255,7 +258,7 @@ via the biased estimator. Otherwise, Bessel's correction will be used.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -201,50 +209,45 @@ via the biased estimator. Otherwise, Bessel's correction will be used.
#> torch_tensor -#> -0.4156 0.8868 0.1543 +#> 1.3473 0.2410 0.0069 #> [ CPUFloatType{1,3} ]torch_std_mean(a)#> [[1]] #> torch_tensor -#> 0.6529 +#> 0.715928 #> [ CPUFloatType{} ] #> #> [[2]] #> torch_tensor -#> 0.208505 +#> 0.531757 #> [ CPUFloatType{} ] #>#> torch_tensor -#> -0.0759 -2.1466 1.9407 0.8170 -#> -0.7982 -0.4738 -0.5751 -2.5351 -#> 0.0380 -0.7000 0.4211 0.2462 -#> 0.5574 -0.5074 0.7596 0.6575 +#> 1.3337 -0.0816 -0.2708 0.2049 +#> 1.9402 -0.4611 0.4759 0.7745 +#> -1.7412 0.2830 1.0061 -0.4176 +#> -0.3773 0.6271 -0.2783 -0.7665 #> [ CPUFloatType{4,4} ]torch_std_mean(a, 1)#> [[1]] #> torch_tensor -#> 1.7298 -#> 0.9692 -#> 0.4931 -#> 0.5886 +#> 0.7186 +#> 0.9901 +#> 1.1704 +#> 0.5895 #> [ CPUFloatType{4} ] #> #> [[2]] #> torch_tensor -#> 0.1338 -#> -1.0955 -#> 0.0013 -#> 0.3668 +#> 0.2966 +#> 0.6823 +#> -0.2174 +#> -0.1988 #> [ CPUFloatType{4} ] #>
- @@ -278,7 +282,7 @@ imaginary part. diff --git a/docs/reference/torch_sum.html b/docs/reference/torch_sum.html index c898884c19982dd46ffc992c07cd5e2c0d4f4af6..982d2d1d522cefb1238b314908d58e75d3fbcbba 100644 --- a/docs/reference/torch_sum.html +++ b/docs/reference/torch_sum.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -222,7 +230,7 @@ the frequency that \(0 \leq \omega < \text{n\_fft}\). When * `window` can be a 1-D tensor of size `win_length`, e.g., from `torch_hann_window`. If `window` is ``None`` (default), it is treated as if having \eqn{1} everywhere in the window. If - \eqn{\text{win\_length} < \text{n\_fft}}, `window` will be padded on + \eqn{\text{win\_length} < \text{n\_fft}}, `window` will be padded on both sides to length `n_fft` before being applied. * If `center` is ``True`` (default), `input` will be padded on @@ -260,14 +268,10 @@ imaginary part.
diff --git a/docs/reference/torch_svd.html b/docs/reference/torch_svd.html index df627625265ec3b4c00f861e7de9ae0ed9e3685d..5222feae8de54de1bf06942789a56bf13207ad03 100644 --- a/docs/reference/torch_svd.html +++ b/docs/reference/torch_svd.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -197,22 +205,22 @@ output tensor having 1 (or
len(dim)) fewer dimension(s).#> torch_tensor -#> 1.4114 0.1092 0.4277 +#> -0.9979 -0.7203 -0.2968 #> [ CPUFloatType{1,3} ]torch_sum(a)#> torch_tensor -#> 1.94829 +#> -2.01504 #> [ CPUFloatType{} ]#> torch_tensor -#> -0.5463 -0.9058 0.3607 1.1920 -#> 0.2398 -2.3006 0.2159 -1.0321 -#> -0.3772 -2.0077 -0.0511 0.3528 -#> -0.3568 0.8900 0.3464 0.7374 +#> 0.1493 -0.8701 -0.8350 0.5208 +#> -1.4548 0.5161 -0.3966 1.0659 +#> -2.6548 -0.7976 -1.3652 2.0197 +#> 0.1067 0.4712 -0.1923 -1.5907 #> [ CPUFloatType{4,4} ]torch_sum(a, 1)#> torch_tensor -#> 0.1007 -#> -2.8770 -#> -2.0833 -#> 1.6170 +#> -1.0350 +#> -0.2694 +#> -2.7979 +#> -1.2052 #> [ CPUFloatType{4} ]#> torch_tensor #> 435 @@ -221,15 +229,10 @@ output tensor having 1 (orlen(dim)) fewer dimension(s). #> 3135 #> [ CPUFloatType{4} ]
-@@ -240,7 +243,7 @@ output tensor having 1 (orContents
--
-
- Arguments -
- sum(input, dtype=None) -> Tensor -
- sum(input, dim, keepdim=False, dtype=None) -> Tensor -
- Examples -
+len(dim)) fewer dimension(s).diff --git a/docs/reference/torch_symeig.html b/docs/reference/torch_symeig.html index 45dc0a708ae1c3c2c902a7d57f0d164d0998b3e2..4aa1757f631ff05000fdd7a3521655801b90e8b0 100644 --- a/docs/reference/torch_symeig.html +++ b/docs/reference/torch_symeig.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -212,35 +220,30 @@ of shape \((m \times m)\) and \((n \times n)\) respectively.
somew#> torch_tensor -#> -1.3629 -0.9076 0.7189 -#> 0.2428 -1.0190 -1.3498 -#> -1.4778 0.6645 -2.4540 -#> 1.2681 -0.4800 0.6281 -#> -0.2511 -0.0026 0.4706 +#> 0.2401 -0.9543 1.5985 +#> 2.1083 -0.4239 1.0416 +#> 0.4397 -1.3918 0.5759 +#> -0.7973 -0.3136 0.4630 +#> 1.5233 0.2115 -1.0122 #> [ CPUFloatType{5,3} ]out = torch_svd(a) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t()))#> torch_tensor -#> 1.01549e-06 +#> 8.13582e-07 #> [ CPUFloatType{} ]a_big = torch_randn(c(7, 5, 3)) out = torch_svd(a_big) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1)))#> torch_tensor -#> 2.81587e-06 +#> 2.84836e-06 #> [ CPUFloatType{} ]
-@@ -251,7 +254,7 @@ of shape \((m \times m)\) and \((n \times n)\) respectively.Contents
--
-
- Arguments -
- Note -
- svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) -
- Examples -
+somew-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -206,15 +214,10 @@ only the upper triangular portion is used by default.
-@@ -225,7 +228,7 @@ only the upper triangular portion is used by default. diff --git a/docs/reference/torch_t.html b/docs/reference/torch_t.html index 040b217f1a62301a9e77662cf0c785eb1dd88837..adddff36e0f3146568f6b3b0110601f211dc6a9a 100644 --- a/docs/reference/torch_t.html +++ b/docs/reference/torch_t.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) -
- Examples -
+diff --git a/docs/reference/torch_take.html b/docs/reference/torch_take.html index 1b63471627421998272ada8056c3d5ef9622187e..edf9936cd947e9daf59f85e6cb5492c03010e8d5 100644 --- a/docs/reference/torch_take.html +++ b/docs/reference/torch_take.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -219,7 +223,7 @@ is equivalent to-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -176,39 +184,35 @@ is equivalent to
transpose(input, 0, 1).#> torch_tensor -#> -0.5236 1.6387 0.5699 -#> 0.3841 2.4055 1.7505 +#> 0.3500 0.2613 0.3582 +#> -0.0810 -0.5862 1.2173 #> [ CPUFloatType{2,3} ]torch_t(x)#> torch_tensor -#> -0.5236 0.3841 -#> 1.6387 2.4055 -#> 0.5699 1.7505 +#> 0.3500 -0.0810 +#> 0.2613 -0.5862 +#> 0.3582 1.2173 #> [ CPUFloatType{3,2} ]#> torch_tensor -#> -0.6833 -#> -0.3923 -#> -0.5017 +#> -0.9663 +#> 1.0086 +#> 0.2139 #> [ CPUFloatType{3} ]torch_t(x)#> torch_tensor -#> -0.6833 -#> -0.3923 -#> -0.5017 +#> -0.9663 +#> 1.0086 +#> 0.2139 #> [ CPUFloatType{3} ]#> torch_tensor -#> 0.8233 -0.8778 0.9721 -#> 0.4296 0.3451 1.2391 +#> 0.7325 0.9076 -1.2570 +#> -0.9799 -1.5550 0.1285 #> [ CPUFloatType{2,3} ]torch_t(x)#> torch_tensor -#> 0.8233 0.4296 -#> -0.8778 0.3451 -#> 0.9721 1.2391 +#> 0.7325 -0.9799 +#> 0.9076 -1.5550 +#> -1.2570 0.1285 #> [ CPUFloatType{3,2} ]
transpose(input, 0, 1).- @@ -202,7 +206,7 @@ takes the same shape as the indices. diff --git a/docs/reference/torch_tan.html b/docs/reference/torch_tan.html index 6ad5998f1062d49ec687a45952de1fab0b9218ce..3048a7057d2e3cde996d5a2b1b54c952db5d55b8 100644 --- a/docs/reference/torch_tan.html +++ b/docs/reference/torch_tan.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,14 +192,10 @@ takes the same shape as the indices. #> 8 #> [ CPUFloatType{3} ]
diff --git a/docs/reference/torch_tanh.html b/docs/reference/torch_tanh.html index 36108f2f39e164fa5244a4f8a7d2c06d7be6361f..1d268348eb651c45c3b970a4e08032ff73b735df 100644 --- a/docs/reference/torch_tanh.html +++ b/docs/reference/torch_tanh.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -211,7 +213,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -180,27 +188,21 @@ $$
#> torch_tensor -#> 0.01 * -#> 7.8220 -#> -27.3532 -#> -10.8996 -#> -124.8186 +#> 0.4196 +#> -0.2736 +#> -0.6374 +#> 0.5404 #> [ CPUFloatType{4} ]torch_tan(a)#> torch_tensor -#> 0.01 * -#> 7.8380 -#> -28.0564 -#> -10.9430 -#> -299.1420 +#> 0.4460 +#> -0.2807 +#> -0.7405 +#> 0.6000 #> [ CPUFloatType{4} ]
diff --git a/docs/reference/torch_tensordot.html b/docs/reference/torch_tensordot.html index eadb7a2d9c83729e220a996f984e0bd225157620..62c8dd42b482a50cb25a74492ec3d3619a128129 100644 --- a/docs/reference/torch_tensordot.html +++ b/docs/reference/torch_tensordot.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -210,7 +214,7 @@ $$-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -181,25 +189,21 @@ $$
#> torch_tensor -#> -0.1985 -#> 0.2548 -#> -0.3502 -#> 0.9367 +#> 2.3257 +#> 0.1872 +#> -1.9069 +#> -1.3260 #> [ CPUFloatType{4} ]torch_tanh(a)#> torch_tensor -#> -0.1959 -#> 0.2494 -#> -0.3365 -#> 0.7337 +#> 0.9811 +#> 0.1850 +#> -0.9568 +#> -0.8683 #> [ CPUFloatType{4} ]
- @@ -202,7 +206,7 @@ diff --git a/docs/reference/torch_threshold_.html b/docs/reference/torch_threshold_.html index bbfd8595c939d2f9094f9fcaa199390ef435a539..fbf548544bc8ab43edabc8088b4acb92e38b66b0 100644 --- a/docs/reference/torch_threshold_.html +++ b/docs/reference/torch_threshold_.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,14 +192,10 @@
- @@ -183,7 +188,7 @@ diff --git a/docs/reference/torch_topk.html b/docs/reference/torch_topk.html index 8b6ac1e7f71945e078b01b8ffdc9b34da2c6f621..9511b81edb75272913ac117ca0ceb2619a6b10be 100644 --- a/docs/reference/torch_topk.html +++ b/docs/reference/torch_topk.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -166,13 +174,10 @@
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -220,14 +228,10 @@ of the elements in the original
inputtensor. #> [ CPULongType{3} ] #>
-@@ -238,7 +242,7 @@ of the elements in the originalContents
--
-
- Arguments -
- topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) -
- Examples -
+inputtensor. diff --git a/docs/reference/torch_trace.html b/docs/reference/torch_trace.html index 14a9252a114e3498f42fc1457305ab7b59086b44..d601790d28de1b5a9bd97cd461a521fb57577341 100644 --- a/docs/reference/torch_trace.html +++ b/docs/reference/torch_trace.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +- @@ -189,7 +194,7 @@ diff --git a/docs/reference/torch_transpose.html b/docs/reference/torch_transpose.html index 967ccdac2c78e3763b9db625b47d3a3a2c751c27..03003387fa1f3a42d96180d1259739a91d6e4665 100644 --- a/docs/reference/torch_transpose.html +++ b/docs/reference/torch_transpose.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -172,13 +180,10 @@ #> 15 #> [ CPUFloatType{} ]
diff --git a/docs/reference/torch_trapz.html b/docs/reference/torch_trapz.html index 678606c6d6da47b45289531282b2edc0615f8109..e8eeebbf26bae778c4e388e4ae566af21a73364f 100644 --- a/docs/reference/torch_trapz.html +++ b/docs/reference/torch_trapz.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -211,7 +215,7 @@ of the other.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -185,22 +193,18 @@ of the other.
#> torch_tensor -#> -0.3190 -0.5501 -1.6680 -#> 0.9620 -2.3017 -1.8312 +#> 0.8275 0.2687 0.4284 +#> 0.2057 -0.2063 0.0312 #> [ CPUFloatType{2,3} ]torch_transpose(x, 0, 1)#> torch_tensor -#> -0.3190 0.9620 -#> -0.5501 -2.3017 -#> -1.6680 -1.8312 +#> 0.8275 0.2057 +#> 0.2687 -0.2063 +#> 0.4284 0.0312 #> [ CPUFloatType{3,2} ]
diff --git a/docs/reference/torch_triangular_solve.html b/docs/reference/torch_triangular_solve.html index 02bed08337a136eebf919bf41eb7059b6bfc912f..b80ae4c6215618679834b7ef3a6164fd1dd0e190 100644 --- a/docs/reference/torch_triangular_solve.html +++ b/docs/reference/torch_triangular_solve.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -191,24 +199,19 @@
#> torch_tensor -#> -1.1714 -0.1222 -1.1778 -#> 1.2347 1.0994 -0.8010 +#> 1.1411 -0.9035 -0.6229 +#> 0.9061 -1.3901 -0.3899 #> [ CPUFloatType{2,3} ]#> torch_tensor -#> -1.9435 -#> 1.3163 +#> -0.5256 +#> -1.1320 #> [ CPUFloatType{2} ]
-@@ -219,7 +222,7 @@Contents
- - ++-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -196,14 +204,10 @@ batched outputs
X
-@@ -214,7 +218,7 @@ batched outputsContents
--
-
- Arguments -
- triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) -
- Examples -
+Xdiff --git a/docs/reference/torch_tril.html b/docs/reference/torch_tril.html index e75a5d8fccd44b63676291e60bc5ba559f30c4dd..647e39932f27571b5f733de9cb3bd849e0ee6feb 100644 --- a/docs/reference/torch_tril.html +++ b/docs/reference/torch_tril.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_tril_indices.html b/docs/reference/torch_tril_indices.html index afbce69af263c31c51603de6a741ebef38fe3ee4..ec95c2051fb6cafcba8561fe87abb72cf9ea4802 100644 --- a/docs/reference/torch_tril_indices.html +++ b/docs/reference/torch_tril_indices.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -234,7 +238,7 @@ the main diagonal. The main diagonal are the set of indices-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -191,39 +199,35 @@ the main diagonal. The main diagonal are the set of indices
#> torch_tensor -#> -0.1394 1.3042 -2.0061 -#> -1.0624 0.1293 2.9790 -#> 0.5792 -0.9699 1.0450 +#> -1.0609 -0.2644 -0.1790 +#> 1.2216 -0.5241 -0.4886 +#> 1.0138 -1.2293 0.1210 #> [ CPUFloatType{3,3} ]torch_tril(a)#> torch_tensor -#> -0.1394 0.0000 0.0000 -#> -1.0624 0.1293 0.0000 -#> 0.5792 -0.9699 1.0450 +#> -1.0609 0.0000 0.0000 +#> 1.2216 -0.5241 0.0000 +#> 1.0138 -1.2293 0.1210 #> [ CPUFloatType{3,3} ]#> torch_tensor -#> -0.0557 -0.6500 0.0711 -0.6071 1.3340 -0.7109 -#> 0.5518 0.7060 -0.8850 3.5853 -1.4234 -0.6294 -#> 0.0441 -2.0146 -1.3148 0.5229 0.1489 -0.5386 -#> -0.3241 -0.6004 1.4160 0.2215 -1.1580 -0.4011 +#> -0.8001 -0.7327 -0.6987 2.7228 1.4684 -0.1581 +#> 0.2280 -0.9131 0.0791 -1.9072 -0.0986 0.4138 +#> 0.5966 0.4041 -1.6242 0.9137 0.2863 0.2439 +#> 0.6229 1.0073 -0.4861 0.4902 -1.1291 1.5767 #> [ CPUFloatType{4,6} ]torch_tril(b, diagonal=1)#> torch_tensor -#> -0.0557 -0.6500 0.0000 0.0000 0.0000 0.0000 -#> 0.5518 0.7060 -0.8850 0.0000 0.0000 0.0000 -#> 0.0441 -2.0146 -1.3148 0.5229 0.0000 0.0000 -#> -0.3241 -0.6004 1.4160 0.2215 -1.1580 0.0000 +#> -0.8001 -0.7327 0.0000 0.0000 0.0000 0.0000 +#> 0.2280 -0.9131 0.0791 0.0000 0.0000 0.0000 +#> 0.5966 0.4041 -1.6242 0.9137 0.0000 0.0000 +#> 0.6229 1.0073 -0.4861 0.4902 -1.1291 0.0000 #> [ CPUFloatType{4,6} ]torch_tril(b, diagonal=-1)#> torch_tensor #> 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 -#> 0.5518 0.0000 0.0000 0.0000 0.0000 0.0000 -#> 0.0441 -2.0146 0.0000 0.0000 0.0000 0.0000 -#> -0.3241 -0.6004 1.4160 0.0000 0.0000 0.0000 +#> 0.2280 0.0000 0.0000 0.0000 0.0000 0.0000 +#> 0.5966 0.4041 0.0000 0.0000 0.0000 0.0000 +#> 0.6229 1.0073 -0.4861 0.0000 0.0000 0.0000 #> [ CPUFloatType{4,6} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -218,15 +226,10 @@ where \(d_{1}, d_{2}\) are the dimensions of the matrix. a }
-@@ -237,7 +240,7 @@ where \(d_{1}, d_{2}\) are the dimensions of the matrix. diff --git a/docs/reference/torch_triu.html b/docs/reference/torch_triu.html index 6b74dc18c38391b2c57ba31740936c4130c7220b..b79cec7d7a57585a786c78cd4ad94f55686f8151 100644 --- a/docs/reference/torch_triu.html +++ b/docs/reference/torch_triu.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_triu_indices.html b/docs/reference/torch_triu_indices.html index 53b0689e0b51e08d41ce46ca58a0dd946760719f..2829f99b347963cdd79f9fff7310e8343e763200 100644 --- a/docs/reference/torch_triu_indices.html +++ b/docs/reference/torch_triu_indices.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -242,7 +246,7 @@ the main diagonal. The main diagonal are the set of indices-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -191,47 +199,43 @@ the main diagonal. The main diagonal are the set of indices
#> torch_tensor -#> -0.7324 -2.7293 1.5403 -#> -0.4862 0.0272 -0.2479 -#> 1.7248 0.3247 1.2056 +#> -0.3862 -0.2357 0.5704 +#> 0.5558 -0.4488 -0.3904 +#> 1.4073 -1.7850 -0.0894 #> [ CPUFloatType{3,3} ]torch_triu(a)#> torch_tensor -#> -0.7324 -2.7293 1.5403 -#> 0.0000 0.0272 -0.2479 -#> 0.0000 0.0000 1.2056 +#> -0.3862 -0.2357 0.5704 +#> 0.0000 -0.4488 -0.3904 +#> 0.0000 0.0000 -0.0894 #> [ CPUFloatType{3,3} ]torch_triu(a, diagonal=1)#> torch_tensor -#> 0.0000 -2.7293 1.5403 -#> 0.0000 0.0000 -0.2479 +#> 0.0000 -0.2357 0.5704 +#> 0.0000 0.0000 -0.3904 #> 0.0000 0.0000 0.0000 #> [ CPUFloatType{3,3} ]torch_triu(a, diagonal=-1)#> torch_tensor -#> -0.7324 -2.7293 1.5403 -#> -0.4862 0.0272 -0.2479 -#> 0.0000 0.3247 1.2056 +#> -0.3862 -0.2357 0.5704 +#> 0.5558 -0.4488 -0.3904 +#> 0.0000 -1.7850 -0.0894 #> [ CPUFloatType{3,3} ]#> torch_tensor -#> 0.5919 0.1448 1.5926 0.8951 -0.6060 0.9366 -#> 1.0576 -0.7288 0.0125 0.2618 0.7012 -1.5835 -#> -0.1562 0.1177 -0.0704 0.2083 0.5690 -0.5008 -#> 0.8982 1.1181 0.1416 -0.1001 0.3505 -0.6827 +#> 1.0168 -0.4837 2.5254 1.0138 0.0936 -0.7116 +#> 1.2263 -1.0299 0.6360 1.8586 1.1980 -0.2932 +#> -1.2835 -0.1012 1.4770 -2.0518 -1.5150 -0.5947 +#> 1.3577 -2.1740 1.3421 0.3728 0.4461 -0.9936 #> [ CPUFloatType{4,6} ]torch_triu(b, diagonal=1)#> torch_tensor -#> 0.0000 0.1448 1.5926 0.8951 -0.6060 0.9366 -#> 0.0000 0.0000 0.0125 0.2618 0.7012 -1.5835 -#> 0.0000 0.0000 0.0000 0.2083 0.5690 -0.5008 -#> 0.0000 0.0000 0.0000 0.0000 0.3505 -0.6827 +#> 0.0000 -0.4837 2.5254 1.0138 0.0936 -0.7116 +#> 0.0000 0.0000 0.6360 1.8586 1.1980 -0.2932 +#> 0.0000 0.0000 0.0000 -2.0518 -1.5150 -0.5947 +#> 0.0000 0.0000 0.0000 0.0000 0.4461 -0.9936 #> [ CPUFloatType{4,6} ]torch_triu(b, diagonal=-1)#> torch_tensor -#> 0.5919 0.1448 1.5926 0.8951 -0.6060 0.9366 -#> 1.0576 -0.7288 0.0125 0.2618 0.7012 -1.5835 -#> 0.0000 0.1177 -0.0704 0.2083 0.5690 -0.5008 -#> 0.0000 0.0000 0.1416 -0.1001 0.3505 -0.6827 +#> 1.0168 -0.4837 2.5254 1.0138 0.0936 -0.7116 +#> 1.2263 -1.0299 0.6360 1.8586 1.1980 -0.2932 +#> 0.0000 -0.1012 1.4770 -2.0518 -1.5150 -0.5947 +#> 0.0000 0.0000 1.3421 0.3728 0.4461 -0.9936 #> [ CPUFloatType{4,6} ]
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -218,15 +226,10 @@ where \(d_{1}, d_{2}\) are the dimensions of the matrix. a }
-@@ -237,7 +240,7 @@ where \(d_{1}, d_{2}\) are the dimensions of the matrix. diff --git a/docs/reference/torch_true_divide.html b/docs/reference/torch_true_divide.html index 963d5bca296eeb9ff4c149128955b15ca2a2bb36..6b45441edae9ecee7932a0ba02bebe0b174c5236 100644 --- a/docs/reference/torch_true_divide.html +++ b/docs/reference/torch_true_divide.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor -
- Examples -
+- @@ -209,7 +213,7 @@ $$ diff --git a/docs/reference/torch_trunc.html b/docs/reference/torch_trunc.html index 99cc6cbfd1c6de539c2bbc4baeb5a7a55dea5c98..e0f517db5a8cb1c05432ad694287093c4e0f8ab0 100644 --- a/docs/reference/torch_trunc.html +++ b/docs/reference/torch_trunc.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -191,14 +199,10 @@ $$ #> 1.5000 #> [ CPUFloatType{2} ]
diff --git a/docs/reference/torch_unbind.html b/docs/reference/torch_unbind.html index 36262ac7a71972aa7b90af6bebbb273d14e804fe..fe26b17f4159a143f65150dee8ee09a08cecb695 100644 --- a/docs/reference/torch_unbind.html +++ b/docs/reference/torch_unbind.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -207,7 +211,7 @@ the elements of-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -178,25 +186,21 @@ the elements of
input.#> torch_tensor -#> -1.3598 -#> -0.2199 -#> 0.9021 -#> -0.2568 +#> -0.1046 +#> 0.3736 +#> -0.4061 +#> 0.4883 #> [ CPUFloatType{4} ]torch_trunc(a)#> torch_tensor -#> -1 #> -0 #> 0 #> -0 +#> 0 #> [ CPUFloatType{4} ]
input.- @@ -216,7 +220,7 @@ diff --git a/docs/reference/torch_unique_consecutive.html b/docs/reference/torch_unique_consecutive.html index 7bb931fccc1a8cfe393cbdbf5af66dd254fca9f9..48d88cd1bf3004e5aae29853765a9f76b72dba5d 100644 --- a/docs/reference/torch_unique_consecutive.html +++ b/docs/reference/torch_unique_consecutive.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -198,14 +206,10 @@ #> [ CPUIntType{3} ] #>
- @@ -269,7 +273,7 @@ diff --git a/docs/reference/torch_unsqueeze.html b/docs/reference/torch_unsqueeze.html index 43e53c6ee29da2044947c341042e8460a5807e70..b94dc3c43ddd954c5eb4d1323bef67355a7c764e 100644 --- a/docs/reference/torch_unsqueeze.html +++ b/docs/reference/torch_unsqueeze.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -251,14 +259,10 @@ #> [ CPULongType{5} ] #>
- @@ -208,7 +212,7 @@ applied at@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -190,14 +198,10 @@ applied at
dim=dim + input.dim() + 1. #> 4 #> [ CPUFloatType{4,1} ]
dim=dim + input.dim() + 1. diff --git a/docs/reference/torch_var.html b/docs/reference/torch_var.html index 7e1776a1209a3da19fb5e801f7013fe9a53f6568..3e5c25d1b28b3cf65176a06019e5a9882acd98d4 100644 --- a/docs/reference/torch_var.html +++ b/docs/reference/torch_var.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +diff --git a/docs/reference/torch_var_mean.html b/docs/reference/torch_var_mean.html index 3ec853734b4452120194337da27c0083297b0e99..4e766f4a3ede303d125b7563208431712a3da535 100644 --- a/docs/reference/torch_var_mean.html +++ b/docs/reference/torch_var_mean.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -204,34 +212,28 @@ biased estimator. Otherwise, Bessel's correction will be used.
#> torch_tensor -#> 0.01 * -#> -1.5284 -188.8132 -52.1983 +#> 0.3437 0.9060 0.1753 #> [ CPUFloatType{1,3} ]torch_var(a)#> torch_tensor -#> 0.938445 +#> 0.146428 #> [ CPUFloatType{} ]#> torch_tensor -#> -1.2329 0.2645 -0.2550 -0.9550 -#> 1.0364 -0.5202 -2.0826 -1.0278 -#> -0.6257 1.6052 0.1857 -0.9129 -#> -0.0041 0.3224 0.5881 2.4555 +#> 1.7628 0.5658 0.9079 0.4672 +#> -0.4534 -1.1208 0.2843 1.3552 +#> 0.2292 -1.5881 0.3622 0.8889 +#> -1.5956 1.8006 -0.5036 1.6652 #> [ CPUFloatType{4,4} ]torch_var(a, 1)#> torch_tensor -#> 0.4602 -#> 1.6853 -#> 1.2734 -#> 1.2179 +#> 0.3469 +#> 1.1261 +#> 1.1644 +#> 2.7826 #> [ CPUFloatType{4} ]
-@@ -242,7 +244,7 @@ biased estimator. Otherwise, Bessel's correction will be used.Contents
--
-
- Arguments -
- var(input, unbiased=True) -> Tensor -
- var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor -
- Examples -
+diff --git a/docs/reference/torch_where.html b/docs/reference/torch_where.html index d0cd545aa427c343221c8fcd10cbb390a88c6b39..cbc271e18630d507e4c0cd5a615fc8e614101010 100644 --- a/docs/reference/torch_where.html +++ b/docs/reference/torch_where.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -254,7 +257,7 @@ biased estimator. Otherwise, Bessel's correction will be used.-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -200,50 +208,45 @@ biased estimator. Otherwise, Bessel's correction will be used.
#> torch_tensor -#> 1.1877 1.0402 -0.4914 +#> 1.3140 0.4748 -0.4064 #> [ CPUFloatType{1,3} ]torch_var_mean(a)#> [[1]] #> torch_tensor -#> 0.864487 +#> 0.740118 #> [ CPUFloatType{} ] #> #> [[2]] #> torch_tensor -#> 0.578839 +#> 0.460776 #> [ CPUFloatType{} ] #>#> torch_tensor -#> -0.4372 1.4853 -0.5787 0.4902 -#> 1.3714 -1.3596 -0.2643 -0.2872 -#> 0.3618 -0.6693 0.9470 0.2537 -#> -0.4124 0.2220 -0.3256 0.9005 +#> -0.1762 0.7293 0.2519 1.7404 +#> -0.7146 -1.2102 1.4351 0.6839 +#> -0.9013 1.0180 -0.6534 1.0334 +#> -0.5318 0.5831 -0.4978 0.2885 #> [ CPUFloatType{4,4} ]torch_var_mean(a, 1)#> [[1]] #> torch_tensor -#> 0.9141 -#> 1.2696 -#> 0.4468 -#> 0.3665 +#> 0.6786 +#> 1.4977 +#> 1.0939 +#> 0.3159 #> [ CPUFloatType{4} ] #> #> [[2]] #> torch_tensor -#> 0.2399 -#> -0.1349 -#> 0.2233 -#> 0.0961 +#> 0.6363 +#> 0.0486 +#> 0.1242 +#> -0.0395 #> [ CPUFloatType{4} ] #>
-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -209,16 +217,10 @@ $$ torch_where(x > 0, x, y) }
-@@ -229,7 +231,7 @@ $$ diff --git a/docs/reference/torch_zeros.html b/docs/reference/torch_zeros.html index 1172849dcde54fc82d90630fa88154abc1341a1b..f2f8be81027e363d0749138177c0ecf0561b1f95 100644 --- a/docs/reference/torch_zeros.html +++ b/docs/reference/torch_zeros.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +Contents
--
-
- Arguments -
- Note -
- where(condition, x, y) -> Tensor -
- where(condition) -> tuple of LongTensor -
- Examples -
+-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -203,14 +211,10 @@ by the variable argument
size. #> 0 #> [ CPUFloatType{5} ]
-@@ -221,7 +225,7 @@ by the variable argumentContents
--
-
- Arguments -
- zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor -
- Examples -
+size. diff --git a/docs/reference/torch_zeros_like.html b/docs/reference/torch_zeros_like.html index 90952dc2a4291685072f64de14111cd777a4ae42..9fbb8a4f2e29402b0569edc5f8697fe3518ee85a 100644 --- a/docs/reference/torch_zeros_like.html +++ b/docs/reference/torch_zeros_like.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
-
-
+
@@ -206,15 +214,10 @@ the old
torch_zeros_like(input, out=output)is equivalent to #> 0 0 0 #> [ CPUFloatType{2,3} ]
-@@ -225,7 +228,7 @@ the oldContents
--
-
- Arguments -
- zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor -
- Warning -
- Examples -
+torch_zeros_like(input, out=output)is equivalent to diff --git a/docs/reference/with_enable_grad.html b/docs/reference/with_enable_grad.html index 8666e0ad2a579237a7b65be2ece28207f63f0cc6..8148847980cd430fb58370ea6546c3faca585c17 100644 --- a/docs/reference/with_enable_grad.html +++ b/docs/reference/with_enable_grad.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ Enables gradient calculation, if it has been disabled via with_no_grad or set_gr - +- @@ -202,7 +206,7 @@ other threads. diff --git a/docs/reference/with_no_grad.html b/docs/reference/with_no_grad.html index e5602c1d20e9778a962d15d902c09d3fde760948..004f3732ebc6090eb91fb66565556c7037b3b9a9 100644 --- a/docs/reference/with_no_grad.html +++ b/docs/reference/with_no_grad.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -89,12 +92,17 @@ Enables gradient calculation, if it has been disabled via with_no_grad or set_gr-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -126,7 +134,7 @@ Enables gradient calculation, if it has been disabled via with_no_grad or set_gr
- - + @@ -184,14 +192,10 @@ other threads. #> [ CPUFloatType{1} ]
- @@ -201,7 +206,7 @@ diff --git a/vignettes/extending-autograd.Rmd b/vignettes/extending-autograd.Rmd index e6255b8b4150e3a3f9e999e41912a765cef16209..94bd181670f763fe102b2a1680be7d3b7d81fca5 100644 --- a/vignettes/extending-autograd.Rmd +++ b/vignettes/extending-autograd.Rmd @@ -2,7 +2,7 @@ title: "Extending Autograd" output: rmarkdown::html_vignette vignette: > - %\VignetteIndexEntry{extending-autograd} + %\VignetteIndexEntry{Extending Autograd} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- diff --git a/vignettes/indexing.Rmd b/vignettes/indexing.Rmd index 3f368264bfd60429a5becc1e594429a313e49c6a..722eb76cde72aac29f6c5d3a58e189a1b2d08de9 100644 --- a/vignettes/indexing.Rmd +++ b/vignettes/indexing.Rmd @@ -2,7 +2,7 @@ title: "Indexing tensors" output: rmarkdown::html_vignette vignette: > - %\VignetteIndexEntry{Subsetting tensors} + %\VignetteIndexEntry{Indexing tensors} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} ---@@ -88,12 +91,17 @@-
+
- Tensors
- Creating tensors
- Indexing +
- Datasets +
- + Loading Data +
- Autograd
- Using autograd @@ -125,7 +133,7 @@
- - + @@ -184,13 +192,10 @@ #> [ Tensor (undefined) ]
Active bindings
--
+
Method
new()(Dev related) Initializes the context. Not user related.
Usage
-AutogradContext$new( - ptr, - env, - argument_names = NULL, - argument_needs_grad = NULL +
AutogradContext$new( + ptr, + env, + argument_names = NULL, + argument_needs_grad = NULL )
Arguments
--
+
Arguments can also be any kind of R object.
Usage
-+AutogradContext$save_for_backward(...)
AutogradContext$save_for_backward(...)
Arguments
-diff --git a/docs/reference/autograd_set_grad_mode.html b/docs/reference/autograd_set_grad_mode.html index 4984f605f58bbd09b432cc773b81377c167467b4..7ebb3d014750a82ea0addf488994b5595b2f83b6 100644 --- a/docs/reference/autograd_set_grad_mode.html +++ b/docs/reference/autograd_set_grad_mode.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +-
+
diff --git a/docs/reference/autograd_grad.html b/docs/reference/autograd_grad.html index e9435d885e200557e2ddb7c4cb0cb89cf12ae7d4..1dcfb32c596a0ae520e74274bdb3a0967c3fbc7a 100644 --- a/docs/reference/autograd_grad.html +++ b/docs/reference/autograd_grad.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ the outputs. If an output doesn’t require_grad, then the gradient can be None) - +backward(), but it’s always going to be a zero tensor with the same shape as the shape of a corresponding output.This is used e.g. for indices returned from a max Function.
Usage
-+AutogradContext$mark_non_differentiable(...)
AutogradContext$mark_non_differentiable(...)
Arguments
-diff --git a/docs/reference/autograd_function.html b/docs/reference/autograd_function.html index 5406e2c88a953b2fc2240345ff25861672072b16..346af4970fedb05f138ac247fb3d4a8e268ee3aa 100644 --- a/docs/reference/autograd_function.html +++ b/docs/reference/autograd_function.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -42,7 +46,6 @@ retained in the form of a DAG of functions, with edges denoting data dependencies (input <- output). Then, when backward is called, the graph is processed in the topological ordering, by calling backward() methods of each Function object, and passing returned gradients on to next Function's." /> - @@ -60,7 +63,7 @@ Function object, and passing returned gradients on to next Function's." /> - +-
+
@@ -245,20 +253,20 @@ and all arguments should be inputs.Every tensor that’s been modified in-place in a call to
forward()should be given to this function, to ensure correctness of our checks. It doesn’t matter whether the function is called before or after modification.Usage
-+AutogradContext$mark_dirty(...)
AutogradContext$mark_dirty(...)
Arguments
-diff --git a/docs/reference/autograd_backward.html b/docs/reference/autograd_backward.html index 80e2d02670faab0a6b936645c1e01a2c94c43b02..a9e7139836631d817c63e2efae3530469dae27b7 100644 --- a/docs/reference/autograd_backward.html +++ b/docs/reference/autograd_backward.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -44,7 +48,6 @@ matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated function w.r.t. corresponding tensors (None is an acceptable value for all tensors that don’t need gradient tensors)." /> - @@ -62,7 +65,7 @@ tensors)." /> - +-
+
Method
clone()The objects of this class are cloneable with this method.
Usage
-+AutogradContext$clone(deep = FALSE)
AutogradContext$clone(deep = FALSE)
Arguments
-- @@ -284,7 +288,7 @@ matter whether the function is called before or after modification.-
+
@@ -266,14 +274,10 @@ matter whether the function is called before or after modification.Usage
Usage
- @@ -233,7 +237,7 @@ them before calling it.@@ -95,12 +98,17 @@ tensors)." />-
+
- @@ -220,7 +225,7 @@ See AutogradContext for more information abou@@ -93,12 +96,17 @@ Function object, and passing returned gradients on to next Function's." />-
+
- @@ -248,7 +252,7 @@ will still be computed, and will be accumulated into their@@ -90,12 +93,17 @@ the outputs. If an output doesn’t require_grad, then the gradient can be None)-
+
.gradatdiff --git a/docs/reference/cuda_current_device.html b/docs/reference/cuda_current_device.html index 14a6aad88245fb3a18a6043a6a8f19d90b755f9a..b881917f16e0f72ee7523a98bb6fe387b3df80c5 100644 --- a/docs/reference/cuda_current_device.html +++ b/docs/reference/cuda_current_device.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@- @@ -181,7 +187,7 @@-
+
diff --git a/docs/reference/cuda_device_count.html b/docs/reference/cuda_device_count.html index 15bca18e45f6b27bb3c75c3ca2e9e48d3b25e1d8..8362f82402466e3f6188a9e73ff697cc6f570d1c 100644 --- a/docs/reference/cuda_device_count.html +++ b/docs/reference/cuda_device_count.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
-@@ -172,7 +179,7 @@Contents
--
-
+diff --git a/docs/reference/cuda_is_available.html b/docs/reference/cuda_is_available.html index c092c87ee39a605acf0ed7204b4cd2c479f984de..957b23903802c1684a4315cc384e83cbc5f21be2 100644 --- a/docs/reference/cuda_is_available.html +++ b/docs/reference/cuda_is_available.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +@@ -88,12 +91,17 @@--
+
-@@ -172,7 +179,7 @@Contents
--
-
+diff --git a/docs/reference/dataloader.html b/docs/reference/dataloader.html index 43009b466d69d45f99b5709328b465a7d0b8cd9b..f4eb9fcea4799c04e3592524c4b83a2b63eeab65 100644 --- a/docs/reference/dataloader.html +++ b/docs/reference/dataloader.html @@ -11,23 +11,27 @@ single- or multi-process iterators over the dataset. — dataloader • torch - + - + - + + + + + - - + + - + - - + + @@ -40,7 +44,6 @@ single- or multi-process iterators over the dataset. — dataloader • torch - @@ -58,7 +61,7 @@ single- or multi-process iterators over the dataset." /> - +@@ -88,12 +91,17 @@--
+
-@@ -172,7 +179,7 @@Contents
--
-
+@@ -91,12 +94,17 @@ single- or multi-process iterators over the dataset." />-
+
-
