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docs/torch/torch.nn/torch.nn.functional

torch.nn.functional

package torch.nn.functional

Attributes

Members list

Grouped members

Loss functions

def binaryCrossEntropyWithLogits[I <: BFloat16 | Float32 | Float64, O <: BFloat16 | Float16 | Float32 | Float64](input: Tensor[I], target: Tensor[O]): Tensor[O]

Function that measures Binary Cross Entropy between target and input logits.

Function that measures Binary Cross Entropy between target and input logits.

TODO support weight, reduction, pos_weight

Attributes

Inherited from:
Loss (hidden)
Source
Loss.scala

Linear functions

def bilinear[D <: DType](input1: Tensor[D], input2: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]]): Tensor[D]

Applies a bilinear transformation to the incoming data: $y = x_1^T A x_2 + b$

Applies a bilinear transformation to the incoming data: $y = x_1^T A x_2 + b$

Shape:

  • input1: $(N, , H_{in1})$ where $H_{in1}=\text{in1_features}$ and $$ means any number of additional dimensions. All but the last dimension of the inputs should be the same.
  • input2: $(N, *, H_{in2})$ where $H_{in2}=\text{in2_features}$
  • weight: $(\text{out_features}, \text{in1_features}, \text{in2_features})$
  • bias: $(\text{out_features})$
  • output: $(N, *, H_{out})$ where $H_{out}=\text{out_features}$ and all but the last dimension are the same shape as the input.

Attributes

Inherited from:
Linear (hidden)
Source
Linear.scala
def linear[D <: DType](input: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]]): Tensor[D]

Applies a linear transformation to the incoming data: $y = xA^T + b$.

Applies a linear transformation to the incoming data: $y = xA^T + b$.

This operation supports 2-D weight with sparse layout

Warning

Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, or may not have autograd support. If you notice missing functionality please open a feature request.

This operator supports TensorFloat32<tf32_on_ampere>

Shape:

  • Input: $(*, in_features)$ where [*] means any number of additional dimensions, including none
  • Weight: $(out_features, in_features)$ or $(in_features)$
  • Bias: $(out_features)$ or $()$
  • Output: $(, out_features)$ or $()$, based on the shape of the weight

Attributes

Inherited from:
Linear (hidden)
Source
Linear.scala

Sparse functions

def oneHot(input: Tensor[Int64], numClasses: Long): Tensor[Int64]

Takes LongTensor with index values of shape (*) and returns a tensor of shape (*, numClasses) 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.

Takes LongTensor with index values of shape (*) and returns a tensor of shape (*, numClasses) 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.

Attributes

Inherited from:
Sparse (hidden)
Source
Sparse.scala

Pooling functions

def avgPool1d[D <: FloatNN | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int), stride: Int | None.type, padding: Int, ceilMode: Boolean, countIncludePad: Boolean): Tensor[D]

Applies a 1D max pooling over an input signal composed of several input planes.

Applies a 1D max pooling over an input signal composed of several input planes.

Value parameters

ceilMode

If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

countIncludePad

when true, will include the zero-padding in the averaging calculation.

input

input tensor of shape $(\text{minibatch} , \text{in_channels} , iW)$

kernelSize

the size of the window.

padding

implicit zero paddings on both sides of the input. Can be a single number or a tuple (padW,).

stride

the stride of the window. Default: kernelSize

Attributes

See also

torch.nn.AdaptiveAvgPool1d for details and output shape.

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def avgPool2d[D <: FloatNN | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int), stride: Int | (Int, Int) | None.type, padding: Int | (Int, Int), ceilMode: Boolean, countIncludePad: Boolean, divisor_override: Int | None.type): Tensor[D]

Applies a 2D max pooling over an input signal composed of several input planes.

Applies a 2D max pooling over an input signal composed of several input planes.

Attributes

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def avgPool3d[D <: Float16 | Float32 | Float64 | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int, Int), stride: Int | (Int, Int, Int) | None.type, padding: Int | (Int, Int, Int), ceilMode: Boolean, countIncludePad: Boolean, divisor_override: Int | None.type): Tensor[D]

Applies a 3D max pooling over an input signal composed of several input planes.

Applies a 3D max pooling over an input signal composed of several input planes.

Attributes

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def maxPool1d[D <: FloatNN | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int), stride: Int | None.type, padding: Int, dilation: Int, ceilMode: Boolean): Tensor[D]

Applies a 1D max pooling over an input signal composed of several input planes.

Applies a 1D max pooling over an input signal composed of several input planes.

Value parameters

ceilMode

If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

dilation

The stride between elements within a sliding window, must be > 0.

input

input tensor of shape $(\text{minibatch} , \text{in_channels} , iW)$, minibatch dim optional.

kernelSize

the size of the window.

padding

Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.

stride

the stride of the window.

Attributes

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def maxPool1dWithIndices[D <: FloatNN | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int), stride: Int | None.type, padding: Int, dilation: Int, ceilMode: Boolean): TensorTuple[D]

Applies a 1D max pooling over an input signal composed of several input planes.

Applies a 1D max pooling over an input signal composed of several input planes.

Value parameters

ceilMode

If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

dilation

The stride between elements within a sliding window, must be > 0.

input

input tensor of shape $(\text{minibatch} , \text{in_channels} , iW)$, minibatch dim optional.

kernelSize

the size of the window.

padding

Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.

stride

the stride of the window.

Attributes

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def maxPool2d[D <: FloatNN | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int), stride: Int | (Int, Int) | None.type, padding: Int | (Int, Int), dilation: Int | (Int, Int), ceilMode: Boolean): Tensor[D]

Applies a 2D max pooling over an input signal composed of several input planes.

Applies a 2D max pooling over an input signal composed of several input planes.

Value parameters

ceilMode

If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

dilation

The stride between elements within a sliding window, must be > 0.

input

input tensor $(\text{minibatch} , \text{in_channels} , iH , iW)$, minibatch dim optional.

kernelSize

size of the pooling region. Can be a single number or a tuple (kH, kW)

padding

Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.

stride

stride of the pooling operation. Can be a single number or a tuple (sH, sW)

Attributes

See also

torch.nn.MaxPool2d for details.

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def maxPool2dWithIndices[D <: FloatNN | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int), stride: Int | (Int, Int) | None.type, padding: Int | (Int, Int), dilation: Int | (Int, Int), ceilMode: Boolean): TensorTuple[D]

Applies a 2D max pooling over an input signal composed of several input planes.

Applies a 2D max pooling over an input signal composed of several input planes.

Value parameters

ceilMode

If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

dilation

The stride between elements within a sliding window, must be > 0.

input

input tensor $(\text{minibatch} , \text{in_channels} , iH , iW)$, minibatch dim optional.

kernelSize

size of the pooling region. Can be a single number or a tuple (kH, kW)

padding

Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.

stride

stride of the pooling operation. Can be a single number or a tuple (sH, sW)

Attributes

See also

torch.nn.MaxPool2d for details.

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def maxPool3d[D <: Float32 | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int, Int), stride: Int | (Int, Int, Int) | None.type, padding: Int | (Int, Int, Int), dilation: Int | (Int, Int, Int), ceilMode: Boolean): Tensor[D]

Applies a 3D max pooling over an input signal composed of several input planes.

Applies a 3D max pooling over an input signal composed of several input planes.

Attributes

Inherited from:
Pooling (hidden)
Source
Pooling.scala
def maxPool3dWithIndices[D <: Float16 | Float32 | Float64 | Complex32](input: Tensor[D], kernelSize: Int | (Int, Int, Int), stride: Int | (Int, Int, Int) | None.type, padding: Int | (Int, Int, Int), dilation: Int | (Int, Int, Int), ceilMode: Boolean): TensorTuple[D]

Applies a 3D max pooling over an input signal composed of several input planes.

Applies a 3D max pooling over an input signal composed of several input planes.

Attributes

Inherited from:
Pooling (hidden)
Source
Pooling.scala

Convolution functions

def conv1d[D <: FloatNN | ComplexNN](input: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]], stride: Int, padding: Int, dilation: Int, groups: Int): Tensor[D]

Applies a 1D convolution over an input signal composed of several input planes.

Applies a 1D convolution over an input signal composed of several input planes.

Attributes

Inherited from:
Convolution (hidden)
Source
Convolution.scala
def conv2d[D <: FloatNN | ComplexNN](input: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]], stride: Int | (Int, Int), padding: Int | (Int, Int), dilation: Int | (Int, Int), groups: Int): Tensor[D]

Applies a 2D convolution over an input signal composed of several input planes.

Applies a 2D convolution over an input signal composed of several input planes.

Attributes

Inherited from:
Convolution (hidden)
Source
Convolution.scala
def conv3d[D <: FloatNN | ComplexNN](input: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]], stride: Int, padding: Int, dilation: Int, groups: Int): Tensor[D]

Applies a 3D convolution over an input image composed of several input planes.

Applies a 3D convolution over an input image composed of several input planes.

Attributes

Inherited from:
Convolution (hidden)
Source
Convolution.scala
def convTranspose1d[D <: FloatNN | ComplexNN](input: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]], stride: Int | (Int, Int), padding: Int | (Int, Int), outputPadding: Int | (Int, Int), groups: Int, dilation: Int | (Int, Int)): Tensor[D]

Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called “deconvolution”.

Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called “deconvolution”.

Attributes

Inherited from:
Convolution (hidden)
Source
Convolution.scala
def convTranspose2d[D <: FloatNN | ComplexNN](input: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]], stride: Int | (Int, Int), padding: Int | (Int, Int), outputPadding: Int | (Int, Int), groups: Int, dilation: Int | (Int, Int)): Tensor[D]

Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”.

Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”.

Attributes

Inherited from:
Convolution (hidden)
Source
Convolution.scala
def convTranspose3d[D <: FloatNN | ComplexNN](input: Tensor[D], weight: Tensor[D], bias: Tensor[D] | Option[Tensor[D]], stride: Int | (Int, Int, Int), padding: Int | (Int, Int, Int), outputPadding: Int | (Int, Int, Int), groups: Int, dilation: Int | (Int, Int)): Tensor[D]

Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”.

Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”.

Attributes

Inherited from:
Convolution (hidden)
Source
Convolution.scala

Dropout functions

def dropout[D <: DType](input: Tensor[D], p: Double, training: Boolean): Tensor[D]

During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.

During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.

Attributes

See also

torch.nn.Dropout for details.

Inherited from:
Dropout (hidden)
Source
Dropout.scala

Non-linear activation functions

def logSoftmax[In <: DType, Out <: FloatNN | Derive](input: Tensor[In], dim: Long, dtype: Out): Tensor[DTypeOrDeriveFromTensor[In, Out]]

Applies a softmax followed by a logarithm.

Applies a softmax followed by a logarithm.

While mathematically equivalent to log(softmax(x)), doing these two operations separately is slower and numerically unstable. This function uses an alternative formulation to compute the output and gradient correctly.

See torch.nn.LogSoftmax for more details.

Attributes

Inherited from:
Activations (hidden)
Source
Activations.scala
def relu[D <: DType](input: Tensor[D]): Tensor[D]

Applies the rectified linear unit function element-wise.

Applies the rectified linear unit function element-wise.

See torch.nn.ReLU for more details.

Attributes

Inherited from:
Activations (hidden)
Source
Activations.scala
def sigmoid[D <: DType](input: Tensor[D]): Tensor[D]

Applies the element-wise function $\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}$

Applies the element-wise function $\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}$

See torch.nn.Sigmoid for more details.

Attributes

Inherited from:
Activations (hidden)
Source
Activations.scala
def silu[D <: DType](input: Tensor[D]): Tensor[D]

Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.

Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.

Attributes

Inherited from:
Activations (hidden)
Source
Activations.scala
def softmax[In <: DType, Out <: FloatNN | Derive](input: Tensor[In], dim: Long, dtype: Out): Tensor[DTypeOrDeriveFromTensor[In, Out]]

Applies a softmax function.

Applies a softmax function.

Attributes

Inherited from:
Activations (hidden)
Source
Activations.scala

Value members

Inherited methods

def crossEntropy[I <: BFloat16 | Float32 | Float64, O <: NumericRealNN](input: Tensor[I], target: Tensor[O]): Tensor[I]

This criterion computes the cross entropy loss between input logits and target. See torch.nn.loss.CrossEntropyLoss for details.

This criterion computes the cross entropy loss between input logits and target. See torch.nn.loss.CrossEntropyLoss for details.

Shape:

  • Input: Shape $(C)$, $(N,C)$ or $(N,C,d_1,d_2,...,d_K)$ with $K≥1$ in the case of K-dimensional loss.
  • Target: If containing class indices, shape $()$, $(N)$ or $(N,d_1,d_2,...,d_K)$ with $K≥1$ in the case of K-dimensional loss where each value should be between $[0,C)$. If containing class probabilities, same shape as the input and each value should be between [0,1][0,1].

where:

  • C = number of classes
  • N = batch size​

Value parameters

ignore_index

Specifies a target value that is ignored and does not contribute to the input gradient. When size_average is true, the loss is averaged over non-ignored targets. Note that ignore_index is only applicable when the target contains class indices. Default: -100

input

Predicted unnormalized logits; see Shape section above for supported shapes.

label_smoothing

A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets become a mixture of the original ground truth and a uniform distribution as described in Rethinking the Inception Architecture for Computer Vision. Default: 0.0

reduce

Deprecated (see reduction). By default, the losses are averaged or summed over observations for each mini-batch depending on size_average. When reduce is false, returns a loss per batch element instead and ignores size_average. Default: true

reduction

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 and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

size_average

Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to false, the losses are instead summed for each mini-batch. Ignored when reduce is false. Default: true

target

Ground truth class indices or class probabilities; see Shape section below for supported shapes.

weight

a manual rescaling weight given to each class. If given, has to be a Tensor of size C

Attributes

Returns

torch.Tensor

See also
Example 
 // Example of target with class indices
 val input = torch.randn(3, 5, requires_grad=True)
 val target = torch.randint(5, (3,), dtype=torch.int64)
 val loss = F.cross_entropy(input, target)
 loss.backward()
 // Example of target with class probabilities
 val input = torch.randn(3, 5, requires_grad=True)
 val target = torch.randn(3, 5).softmax(dim=1)
 val loss = F.crossEntropy(input, target)
 loss.backward()
Inherited from:
Loss (hidden)
Source
Loss.scala
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