Module autograd::ops [] [src]

Modules

gradient_descent_ops

Functions

_hessian_vector_product

(Experimental) Computes hessian vector product

abs

Returns the largest integer less than or equal to a number, element-wise.

acos

Elementwise arccos

acosh

Elementwise hyperbolic arccos

add

Addition.

add_inplace

Inplace addition

add_n

Adds all input tensors.

argmax

Takes argmax along specified axis.

asin

Elementwise arcsin

asinh

Elementwise hyperbolic arcsin

atan

Elementwise arctan

atanh

Elementwise hyperbolic arctan

batch_matmul

Batched matrix multiplication.

batch_norm

Applies batch normalization.

bernoulli

Outputs values sampled from the bernoulli distribution.

bernoulli_rng

Outputs values sampled from the bernoulli distribution.

ceil

Returns the smallest integer greater than or equal to a number, element-wise.

clip

Limits all elements so as to be within [min, max]

concat

Concatenates input tensors along specified axis.

constant

Creates a constant tensor.

convert_to_tensor

Converts ndarray::Array to ag::Tensor.

cos

Elementwise cosine

cosh

Elementwise hyperbolic cosine

div

Division.

elu

Elementwise exponential linear unit.

equal

Compares two tensors and returns a binary tensor.

exp

Elementwise exponential

expand_dims

Expands specified dims.

flatten

Flattens input tensor into 1-ranked (vector)

floor

Returns the largest integer less than or equal to a number, element-wise.

gather

Gathers subviews from the input tensor.

gather_common

Gathers subviews from the input tensor.

grad

Returns gradient tensors wrt input tensors.

grad_with_default

Returns gradient tensors wrt input tensors.

greater

Returns a binary tensor.

greater_equal

Returns a binary tensor.

identity

Identity function without copy.

jacobians

Computes jacobians for variables.

leaky_relu

Elementwise leaky relu.

lesser

Returns a binary tensor.

lesser_equal

Returns a binary tensor.

log

Elementwise log

log_normal

Outputs values sampled from the log-normal distribution.

log_normal_rng

Outputs values sampled from the log-normal distribution.

log_softmax

Log softmax function.

matmul

Matrix multiplication.

matmul_t

Matrix multiplication with inputs's transposition.

maximum

Returns the max of x and y (i.e. x > y ? x : y) element-wise.

minimum

Returns the min of x and y (i.e. x > y ? y : x) element-wise.

mul

Multiplication.

neg

Performs the - operation.

normalize

Normalizes input tensor with its mean and variance along specified axis.

not_equal

Compares two tensors and returns a binary tensor.

ones

Returns ones with given shape

placeholder

Creates a placeholder tensor.

pow

Elementwise pow

random_exp

Outputs values sampled from the exponential distribution.

random_exp_rng

Outputs values sampled from the exponential distribution.

random_gamma

Outputs values sampled from the gamma distribution.

random_gamma_rng

Outputs values sampled from the gamma distribution.

random_normal

Outputs values sampled from the normal distribution.

random_normal_rng

Outputs values sampled from the normal distribution.

random_uniform

Outputs values sampled from the uniform distribution.

random_uniform_rng

Outputs values sampled from the uniform distribution.

range

Returns a range

rank

Returns the (symbolic) rank of input tensor

reciprocal

Returns the 1/x, element-wise.

reduce_logsumexp

Computes log(sum(exp(x))) along specified axis. axis can be negative.

reduce_max

Takes max along specified axes.

reduce_mean

Takes mean along specified axes.

reduce_min

Takes min along specified axes.

reduce_prod

Takes product along specified axes.

reduce_sum

Takes sum along specified axes.

relu

Elementwise rectified linear unit.

reshape

Reshapes input tensor.

scalar

Generates a zero-ranked tensor from a scalar value.

select_ith_of

Gets i th output value of x

setdiff1d

Takes diff between two tensors

shape

Returns the (symbolic) shape of input tensor

sigmoid

Elementwise logistic sigmoid function.

sigmoid_cross_entropy

Computes binary_cross_entropy(sigmoid(y), t).

sign

Returns -1 if x < 0, 0 if x==0, 1 if x > 0, element-wise.

sin

Elementwise sine

sinh

Elementwise hyperbolic sine

size

Returns the (symbolic) size of input tensor

slice

Slices input tensor with indices.

softmax

Computes softmax along specified axis

softmax_cross_entropy

Computes categorical_cross_entropy(softmax(y), t).

softplus

Elementwise softplus.

sparse_softmax_cross_entropy

A variant of softmax_cross_entropy.

split

Splits input tensors into parts.

sqrt

Elementwise sqrt

square

Returns square of the input.

squeeze

Squeezes specified dims.

standard_normal

Outputs values sampled from the standard normal distribution.

standard_normal_rng

Outputs values sampled from the standard normal distribution.

standard_uniform

Outputs values sampled from the standard uniform distribution.

standard_uniform_rng

Outputs values sampled from the standard uniform distribution.

stop_gradient

Stops gradient propagation.

sub

Subtraction.

sub_inplace

Inplace subtraction

tan

Elementwise tangent

tanh

Elementwise hyperbolic tangent

tensordot

Computes tensor dot product (tensor contraction) along specified axes.

tile

Tiles input tensor along specified axis.

transpose

Permutes dimensions.

variable

Creates a shared variable tensor from rust-ndarray's array object.

zeros

Returns zeros with given shape