Tensor Type
Represents a multi-dimensional data type containing elements of a single data type.
Example
A tensor can be constructed from a list or sequence using FurnaceImage.tensor
let t = FurnaceImage.tensor([[1.; -1.]; [1.; -1.]])
Instance members
| Instance member | Description |
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Full Usage:
this.allclose (tensor, ?relativeTolerance, ?absoluteTolerance)
Parameters:
Tensor
?relativeTolerance : float
?absoluteTolerance : float
Returns: bool
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The resulting tensor does not participate in reverse or forward differentiation. It can be used as input to another operation such as
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Full Usage:
this.argmax ()
Returns: int[]
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The resulting tensor does not participate in reverse or forward differentiation. It can be used as input to another operation such as
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Full Usage:
this.argmin ()
Returns: int[]
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Full Usage:
this.bceLoss (target, ?weight, ?reduction)
Parameters:
Tensor
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The target tensor.
?weight : Tensor
-
A manual rescaling weight given to the loss of each batch element.
?reduction : string
-
Optionally 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'.
Returns: Tensor
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Full Usage:
this.bernoulli (?device, ?dtype, ?backend)
Parameters:
Device
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The desired device of returned tensor. Default: if None, uses Device.Default.
?dtype : Dtype
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The desired element type of returned tensor. Default: if None, uses Dtype.Default.
?backend : Backend
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The desired backend of returned tensor. Default: if None, uses Backend.Default.
Returns: Tensor
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Full Usage:
this.conv2d (filters, ?stride, ?padding, ?dilation, ?strides, ?paddings, ?dilations)
Parameters:
Tensor
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The filters.
?stride : int
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The stride of the convolving kernel.
?padding : int
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The implicit padding on corresponding sides of the input.
?dilation : int
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The spacing between kernel elements.
?strides : int seq
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The strides of the convolving kernel.
?paddings : int seq
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The implicit paddings on corresponding sides of the input.
?dilations : int seq
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The spacings between kernel elements.
Returns: Tensor
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Full Usage:
this.conv3d (filters, ?stride, ?padding, ?dilation, ?strides, ?paddings, ?dilations)
Parameters:
Tensor
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The filters.
?stride : int
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The stride of the convolving kernel.
?padding : int
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The implicit padding on corresponding sides of the input.
?dilation : int
-
The spacing between kernel elements.
?strides : int seq
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The strides of the convolving kernel.
?paddings : int seq
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The implicit paddings on corresponding sides of the input.
?dilations : int seq
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The spacings between kernel elements.
Returns: Tensor
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Full Usage:
this.convTranspose1d (filters, ?stride, ?padding, ?dilation, ?outputPadding)
Parameters:
Tensor
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The filters.
?stride : int
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The stride of the convolving kernel.
?padding : int
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The implicit padding on both sides of the input.
?dilation : int
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The spacing between kernel elements.
?outputPadding : int
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The additional size added to one side of each dimension in the output shape.
Returns: Tensor
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Full Usage:
this.convTranspose2d (filters, ?stride, ?padding, ?dilation, ?outputPadding, ?strides, ?paddings, ?dilations, ?outputPaddings)
Parameters:
Tensor
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The filters.
?stride : int
-
The stride of the convolving kernel.
?padding : int
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The implicit padding on both sides of the input.
?dilation : int
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The spacing between kernel elements.
?outputPadding : int
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The additional size added to one side of each dimension in the output shape.
?strides : int seq
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The strides of the convolving kernel.
?paddings : int seq
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The implicit paddings on corresponding sides of the input.
?dilations : int seq
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The spacings between kernel elements.
?outputPaddings : int seq
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The additional sizes added to one side of each dimension in the output shape.
Returns: Tensor
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Full Usage:
this.convTranspose3d (filters, ?stride, ?padding, ?dilation, ?outputPadding, ?strides, ?paddings, ?dilations, ?outputPaddings)
Parameters:
Tensor
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The filters.
?stride : int
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The stride of the convolving kernel.
?padding : int
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The implicit padding on both sides of the input.
?dilation : int
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The spacing between kernel elements.
?outputPadding : int
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The additional size added to one side of each dimension in the output shape.
?strides : int seq
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The strides of the convolving kernel.
?paddings : int seq
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The implicit paddings on corresponding sides of the input.
?dilations : int seq
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The spacings between kernel elements.
?outputPaddings : int seq
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The additional sizes added to one side of each dimension in the output shape.
Returns: Tensor
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Full Usage:
this.corrcoef ()
Returns: Tensor
The correlation coefficient matrix \(R\) is computed from the covariance
matrix
Returns a square tensor representing the correlation coefficient matrix.
Given a tensor with \(N\) variables \(X=[x_1,x_2,\ldots,x_N]\) the
\(R_{i,j}\) entry on the correlation matrix is the correlation between
\(x_i\) and \(x_j\).
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The correlation between variables \(x\) and \(y\) is \[cor(x,y)= \frac{\sum^{N}_{i = 1}(x_{i} - \mu_x)(y_{i} - \mu_y)}{\sigma_x \sigma_y (N ~-~1)}\] where \(\mu_x\) and \(\mu_y\) are the sample means and \(\sigma_x\) and \(\sigma_x\) are the sample standard deviations.
Example
val x: obj
val y: obj
val xy: obj
Evaluates to
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Full Usage:
this.cov (?correction, ?fweights, ?aweights)
Parameters:
int64
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Difference between the sample size and the sample degrees of freedom. Defaults to 1 (Bessel's correction).
?fweights : Tensor
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Frequency weights represent the number of times each observation was observed.
Should be given as a tensor of integers. Defaults to no weights.
?aweights : Tensor
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Relative importance weights, larger weights for observations that
should have a larger effect on the estimate.
Should be given as a tensor of floating point numbers. Defaults to no weights.
Returns: Tensor
Returns a square tensor representing the covariance matrix.
Given a tensor with \(N\) variables \(X=[x_1,x_2,\ldots,x_N]\) the
\(C_{i,j}\) entry on the covariance matrix is the covariance between
\(x_i\) and \(x_j\).
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If no weights are given, the covariance between variables \(x\) and \(y\) is
\[cov(x,y)= \frac{\sum^{N}_{i = 1}(x_{i} - \mu_x)(y_{i} - \mu_y)}{N~-~\text{correction}}\]
where \(\mu_x\) and \(\mu_y\) are the sample means.
If there are fweights or aweights then the covariance is
\[cov(x,y)=\frac{\sum^{N}_{i = 1}w_i(x_{i} - \mu_x^*)(y_{i} - \mu_y^*)}{\text{normalization factor}}\]
where \(w\) is either fweights or aweights if one weight type is provided.
If both weight types are provided \(w=\text{fweights}\times\text{aweights}\).
\(\mu_x^* = \frac{\sum^{N}_{i = 1}w_ix_{i} }{\sum^{N}_{i = 1}w_i}\)
is the weighted mean of variables.
The normalization factor is \(\sum^{N}_{i=1} w_i\) if only fweights are provided or if aweights are provided and
Example
val x: obj
val y: obj
val xy: obj
Evaluates to
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Full Usage:
this.crossEntropyLoss (target, ?weight, ?reduction)
Parameters:
Tensor
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The target tensor.
?weight : Tensor
-
A optional manual rescaling weight given to the loss of each batch element.
?reduction : string
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Optionally 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'.
Returns: Tensor
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Full Usage:
this.depth
Returns: int
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Full Usage:
this.derivative
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Full Usage:
this.diagonal (?offset, ?dim1, ?dim2)
Parameters:
int
?dim1 : int
?dim2 : int
Returns: Tensor
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Full Usage:
this.dilate dilations
Parameters:
int seq
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The dilations to use.
Returns: Tensor
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Full Usage:
this.dim
Returns: int
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Full Usage:
this.dropout ?p
Parameters:
double
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The probability of an element to be zeroed. Default: 0.5.
Returns: Tensor
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Full Usage:
this.dropout2d ?p
Parameters:
double
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The probability of an element to be zeroed. Default: 0.5.
Returns: Tensor
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Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor \text{input}[i, j]input[i,j] ). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution
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Full Usage:
this.dropout3d ?p
Parameters:
double
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The probability of an element to be zeroed. Default: 0.5.
Returns: Tensor
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Randomly zero out entire channels (a channel is a 3D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 3D tensor \text{input}[i, j]input[i,j] ). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.
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Full Usage:
this.elementSize
Returns: int
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Passing -1 as the size for a dimension means not changing the size of that dimension. The tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1. Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the stride to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.
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Full Usage:
this.fanout
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Full Usage:
this.flatten (?startDim, ?endDim)
Parameters:
int
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The first dim to flatten.
?endDim : int
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The last dim to flatten.
Returns: Tensor
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Full Usage:
this.hasinf ()
Returns: bool
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Full Usage:
this.hasinfnan ()
Returns: bool
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Full Usage:
this.hasnan ()
Returns: bool
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Full Usage:
this.isForwardDiff
Returns: bool
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Full Usage:
this.isNoDiff
Returns: bool
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Full Usage:
this.isReverseDiff
Returns: bool
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Full Usage:
this.leakyRelu ?negativeSlope
Parameters:
float
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Controls the angle of the negative slope. Default: 0.01.
Returns: Tensor
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\[\text{leakyRelu}(x) = \max(0, x) + \text{negativeSlope} * \min(0, x)\]
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Full Usage:
this.logsoftmax dim
Parameters:
int
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A dimension along which softmax will be computed.
Returns: Tensor
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Full Usage:
this.logsumexp (dim, ?keepDim)
Parameters:
int
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The dimension to reduce.
?keepDim : bool
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Whether the output tensor has dim retained or not.
Returns: Tensor
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The behavior depends on the dimensionality of the tensors as follows: If both tensors are 1-dimensional, the dot product (scalar) is returned. If both arguments are 2-dimensional, the matrix-matrix product is returned. If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed. If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned. If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable). For example, if input is a (j \times 1 \times n \times m)(j×1×n×m) tensor and other is a (k \times m \times p)(k×m×p) tensor, out will be an (j \times k \times n \times p)(j×k×n×p) tensor.
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Full Usage:
this.maxpool1d (kernelSize, ?stride, ?padding)
Parameters:
int
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The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
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The implicit zero padding to be added on both sides.
Returns: Tensor
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Full Usage:
this.maxpool2d (?kernelSize, ?stride, ?padding, ?kernelSizes, ?strides, ?paddings)
Parameters:
int
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The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
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The implicit zero padding to be added on both sides.
?kernelSizes : int seq
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The sizes of the window to take a max over.
?strides : int seq
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The strides of the window. Default value is kernelSize.
?paddings : int seq
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The implicit zero paddings to be added on corresponding sides.
Returns: Tensor
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Full Usage:
this.maxpool2di (?kernelSize, ?stride, ?padding, ?kernelSizes, ?strides, ?paddings)
Parameters:
int
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The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
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The implicit zero padding to be added on both sides.
?kernelSizes : int seq
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The sizes of the window to take a max over.
?strides : int seq
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The strides of the window. Default value is kernelSize.
?paddings : int seq
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The implicit zero paddings to be added on corresponding sides.
Returns: Tensor * Tensor
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Full Usage:
this.maxpool3d (?kernelSize, ?stride, ?padding, ?kernelSizes, ?strides, ?paddings)
Parameters:
int
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The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
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The implicit zero padding to be added on both sides.
?kernelSizes : int seq
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The sizes of the window to take a max over.
?strides : int seq
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The strides of the window. Default value is kernelSizes.
?paddings : int seq
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The implicit zero paddings to be added on corresponding sides.
Returns: Tensor
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Full Usage:
this.maxpool3di (?kernelSize, ?stride, ?padding, ?kernelSizes, ?strides, ?paddings)
Parameters:
int
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The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
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The implicit zero padding to be added on both sides.
?kernelSizes : int seq
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The sizes of the window to take a max over.
?strides : int seq
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The strides of the window. Default value is kernelSize.
?paddings : int seq
-
The implicit zero paddings to be added on corresponding sides.
Returns: Tensor * Tensor
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Full Usage:
this.maxunpool1d (indices, kernelSize, ?stride, ?padding, ?outputSize)
Parameters:
Tensor
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The indices selected by maxpool1di.
kernelSize : int
-
The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
-
The implicit zero padding to be added on both sides.
?outputSize : int seq
-
The targeted output size.
Returns: Tensor
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Full Usage:
this.maxunpool2d (indices, ?kernelSize, ?stride, ?padding, ?kernelSizes, ?strides, ?paddings, ?outputSize)
Parameters:
Tensor
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The indices selected by maxpool2di.
?kernelSize : int
-
The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
-
The implicit zero padding to be added on both sides.
?kernelSizes : int seq
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The sizes of the window to take a max over.
?strides : int seq
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The strides of the window. Default value is kernelSizes.
?paddings : int seq
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The implicit zero paddings to be added on corresponding sides.
?outputSize : int seq
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The targeted output size.
Returns: Tensor
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Full Usage:
this.maxunpool3d (indices, ?kernelSize, ?stride, ?padding, ?kernelSizes, ?strides, ?paddings, ?outputSize)
Parameters:
Tensor
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The indices selected by maxpool3di.
?kernelSize : int
-
The size of the window to take a max over.
?stride : int
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The stride of the window. Default value is kernelSize.
?padding : int
-
The implicit zero padding to be added on both sides.
?kernelSizes : int seq
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The sizes of the window to take a max over.
?strides : int seq
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The strides of the window. Default value is kernelSizes.
?paddings : int seq
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The implicit zero paddings to be added on corresponding sides.
?outputSize : int seq
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The targeted output size.
Returns: Tensor
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Full Usage:
this.mean (dim, ?keepDim)
Parameters:
int
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The dimension to reduce.
?keepDim : bool
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Whether the output tensor has dim retained or not.
Returns: Tensor
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If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed, resulting in the output tensor having 1 fewer dimension.
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Full Usage:
this.memorySize
Returns: int64
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Full Usage:
this.mseLoss (target, ?reduction)
Parameters:
Tensor
-
The target tensor.
?reduction : string
-
Optionally 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'.
Returns: Tensor
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Full Usage:
this.multinomial (numSamples, ?normalize, ?device, ?dtype, ?backend)
Parameters:
int
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The number of samples to draw.
?normalize : bool
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Indicates where the probabilities should first be normalized by their sum.
?device : Device
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The desired device of returned tensor. Default: if None, uses Device.Default.
?dtype : Dtype
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The desired element type of returned tensor. Default: if None, uses Dtype.Default.
?backend : Backend
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The desired backend of returned tensor. Default: if None, uses Backend.Default.
Returns: Tensor
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Full Usage:
this.nelement
Returns: int
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Full Usage:
this.nestingTag
Returns: uint32
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Full Usage:
this.nllLoss (target, ?weight, ?reduction)
Parameters:
Tensor
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The target tensor.
?weight : Tensor
-
A optional manual rescaling weight given to the loss of each batch element.
?reduction : string
-
Optionally 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'.
Returns: Tensor
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Full Usage:
this.pad paddings
Parameters:
int seq
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The implicit paddings on corresponding sides of the input.
Returns: Tensor
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Full Usage:
this.permute permutation
Parameters:
int seq
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The desired ordering of dimensions.
Returns: Tensor
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Full Usage:
this.repeat (dim, times)
Parameters:
int
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The dimension along which to repeat values.
times : int
-
The number of repetitions for each element.
Returns: Tensor
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Full Usage:
this.reverse (?value, ?zeroDerivatives)
Parameters:
Tensor
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The derivative value to propagate backwards. Should have the same shape with this tensor.
?zeroDerivatives : bool
-
Indicates whether any existing derivatives in the computation graph (for example from a previous reverse propagation that was executed) should be zeroed or not before starting this propagation. Default: true
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Full Usage:
this.reverseDiff (?derivative, ?nestingTag)
Parameters:
Tensor
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The derivative (adjoint) to assign to the new reverse-mode tensor. Defaults to an empty placeholder tensor.
?nestingTag : uint32
-
The level nestingTag for nested differentiation. Defaults to the current global nesting level
Returns: Tensor
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Any tensors produced using this tensor will also support reverse-mode propagation. After the completion
of the corresponding
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Full Usage:
this.reverseReset zeroDerivatives
Parameters:
bool
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Full Usage:
this.save fileName
Parameters:
string
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Full Usage:
this.softmax dim
Parameters:
int
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A dimension along which softmax will be computed.
Returns: Tensor
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Softmax is defined as: \text{softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}.
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Full Usage:
this.split (sizes, ?dim)
Parameters:
int seq
-
List of sizes for each chunk
?dim : int
-
The dimension along which to split the tensor, defaults to 0.
Returns: Tensor[]
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Full Usage:
this.squeeze ?dim
Parameters:
int
-
If given, the input will be squeezed only in this dimension.
Returns: Tensor
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If the tensor has a batch dimension of size 1, then squeeze(input) will also remove the batch dimension, which can lead to unexpected errors.
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Full Usage:
this.std ?unbiased
Parameters:
bool
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Whether to use the unbiased estimation or not.
Returns: Tensor
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If unbiased is False, then the standard deviation will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.
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Full Usage:
this.std (dim, ?keepDim, ?unbiased)
Parameters:
int
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The dimension to reduce.
?keepDim : bool
-
Whether the output tensor has dim retained or not.
?unbiased : bool
-
Whether to use the unbiased estimation or not.
Returns: Tensor
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If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed, resulting in the output tensor having 1 fewer dimension(s). If unbiased is False, then the standard deviation will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.
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If keepdim is
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Full Usage:
this.summary ()
Returns: string
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Full Usage:
this.toArray1D ()
Returns: 'T[]
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Full Usage:
this.toArray2D ()
Returns: 'T[,]
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Full Usage:
this.toArray3D ()
Returns: 'T[,,]
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Full Usage:
this.toArray4D ()
Returns: 'T[,,,]
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Full Usage:
this.toBool ()
Returns: bool
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Full Usage:
this.toByte ()
Returns: byte
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Full Usage:
this.toDouble ()
Returns: float
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Full Usage:
this.toImage (?pixelMin, ?pixelMax, ?normalize, ?gridCols)
Parameters:
double
?pixelMax : double
?normalize : bool
?gridCols : int
Returns: Tensor
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Full Usage:
this.toImageString (?pixelMin, ?pixelMax, ?normalize, ?gridCols, ?asciiPalette)
Parameters:
double
?pixelMax : double
?normalize : bool
?gridCols : int
?asciiPalette : string
Returns: string
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Full Usage:
this.toInt16 ()
Returns: int16
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Full Usage:
this.toInt32 ()
Returns: int
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Full Usage:
this.toInt64 ()
Returns: int64
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Full Usage:
this.toSByte ()
Returns: sbyte
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Full Usage:
this.toSingle ()
Returns: float32
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Full Usage:
this.transpose (dim0, dim1)
Parameters:
int
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The first dimension to be transposed.
dim1 : int
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The second dimension to be transposed.
Returns: Tensor
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Full Usage:
this.undilate dilations
Parameters:
int seq
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The dilations to use.
Returns: Tensor
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Full Usage:
this.unflatten (dim, unflattenedShape)
Parameters:
int
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The dimension to unflatten.
unflattenedShape : int seq
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New shape of the unflattened dimenension.
Returns: Tensor
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Full Usage:
this.unsqueeze dim
Parameters:
int
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The index at which to insert the singleton dimension.
Returns: Tensor
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Full Usage:
this.unstack ?dim
Parameters:
int
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The dimension to remove, defaults to 0.
Returns: Tensor[]
Returns an array of all slices along a given dimension.
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Full Usage:
this.var (dim, ?keepDim, ?unbiased)
Parameters:
int
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The dimension to reduce.
?keepDim : bool
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Whether the output tensor has dim retained or not.
?unbiased : bool
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Whether to use the unbiased estimation or not.
Returns: Tensor
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If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed, resulting in the output tensor having 1 fewer dimension(s). If unbiased is False, then the variance will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.
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Full Usage:
this.var ?unbiased
Parameters:
bool
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Whether to use the unbiased estimation or not.
Returns: Tensor
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If unbiased is False, then the variance will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.
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The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions \(d, d+1, \dots, d+kd,d+1,…,d+k\) that satisfy the following contiguity-like condition that \(\forall i = d, \dots, d+k-1∀i=d,…,d+k−1 ,\) \[\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]\]
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Full Usage:
this.view shape
Parameters:
int seq
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The desired shape of returned tensor.
Returns: Tensor
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The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions \(d, d+1, \dots, d+kd,d+1,…,d+k\) that satisfy the following contiguity-like condition that \(\forall i = d, \dots, d+k-1∀i=d,…,d+k−1 ,\) \[\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]\]
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The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size. The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions \(d, d+1, \dots, d+kd,d+1,…,d+k\) that satisfy the following contiguity-like condition that \(\forall i = d, \dots, d+k-1∀i=d,…,d+k−1 ,\) \[\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]\]
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Static members
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Full Usage:
Tensor.create (value, ?device, ?dtype, ?backend)
Parameters:
obj
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The .NET object used to form the initial values for the tensor.
?device : Device
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The desired device of returned tensor. Default: if None, uses Device.Default.
?dtype : Dtype
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The desired element type of returned tensor. Default: if None, uses Dtype.Default.
?backend : Backend
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The desired backend of returned tensor. Default: if None, uses Backend.Default.
Returns: Tensor
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The fastest creation technique is a one dimensional array matching the desired dtype. Then use 'view' to reshape.
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Full Usage:
Tensor.load (fileName, ?device, ?dtype, ?backend)
Parameters:
string
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The file from which to load the tensor.
?device : Device
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The device of the resulting tensor. Defaults to the current default device.
?dtype : Dtype
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The element type of the resulting tensor. Defaults to the element type of the saved tensor.
?backend : Backend
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The device of the resulting tensor. Defaults to the current default backend.
Returns: Tensor
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The backend at the time of saving the tensor must be available when the tensor is reloaded. The tensor is first loaded into that backend and then moved. As a result, intermediate tensors may be created in the process of reloading.
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