torch.nn.functional
Attributes
Members list
Grouped members
Loss functions
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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