Module: tf.keras.backend

Keras backend API.

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Compat aliases for migration

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tf.compat.v1.keras.backend

Classes

class name_scope: A context manager for use when defining a Python op.

Functions

abs(...): Element-wise absolute value.

all(...): Bitwise reduction (logical AND).

any(...): Bitwise reduction (logical OR).

arange(...): Creates a 1D tensor containing a sequence of integers.

argmax(...): Returns the index of the maximum value along an axis.

argmin(...): Returns the index of the minimum value along an axis.

backend(...): Publicly accessible method for determining the current backend.

batch_dot(...): Batchwise dot product.

batch_flatten(...): Turn a nD tensor into a 2D tensor with same 0th dimension.

batch_get_value(...): Returns the value of more than one tensor variable.

batch_normalization(...): Applies batch normalization on x given mean, var, beta and gamma.

batch_set_value(...): Sets the values of many tensor variables at once.

bias_add(...): Adds a bias vector to a tensor.

binary_crossentropy(...): Binary crossentropy between an output tensor and a target tensor.

cast(...): Casts a tensor to a different dtype and returns it.

cast_to_floatx(...): Cast a Numpy array to the default Keras float type.

categorical_crossentropy(...): Categorical crossentropy between an output tensor and a target tensor.

clear_session(...): Destroys the current TF graph and creates a new one.

clip(...): Element-wise value clipping.

concatenate(...): Concatenates a list of tensors alongside the specified axis.

constant(...): Creates a constant tensor.

conv1d(...): 1D convolution.

conv2d(...): 2D convolution.

conv2d_transpose(...): 2D deconvolution (i.e.

conv3d(...): 3D convolution.

cos(...): Computes cos of x element-wise.

count_params(...): Returns the static number of elements in a variable or tensor.

ctc_batch_cost(...): Runs CTC loss algorithm on each batch element.

ctc_decode(...): Decodes the output of a softmax.

ctc_label_dense_to_sparse(...): Converts CTC labels from dense to sparse.

cumprod(...): Cumulative product of the values in a tensor, alongside the specified axis.

cumsum(...): Cumulative sum of the values in a tensor, alongside the specified axis.

dot(...): Multiplies 2 tensors (and/or variables) and returns a tensor.

dropout(...): Sets entries in x to zero at random, while scaling the entire tensor.

dtype(...): Returns the dtype of a Keras tensor or variable, as a string.

elu(...): Exponential linear unit.

epsilon(...): Returns the value of the fuzz factor used in numeric expressions.

equal(...): Element-wise equality between two tensors.

eval(...): Evaluates the value of a variable.

exp(...): Element-wise exponential.

expand_dims(...): Adds a 1-sized dimension at index "axis".

eye(...): Instantiate an identity matrix and returns it.

flatten(...): Flatten a tensor.

floatx(...): Returns the default float type, as a string.

foldl(...): Reduce elems using fn to combine them from left to right.

foldr(...): Reduce elems using fn to combine them from right to left.

function(...): Instantiates a Keras function.

gather(...): Retrieves the elements of indices indices in the tensor reference.

get_session(...): Returns the TF session to be used by the backend.

get_uid(...): Associates a string prefix with an integer counter in a TensorFlow graph.

get_value(...): Returns the value of a variable.

gradients(...): Returns the gradients of loss w.r.t. variables.

greater(...): Element-wise truth value of (x > y).

greater_equal(...): Element-wise truth value of (x >= y).

hard_sigmoid(...): Segment-wise linear approximation of sigmoid.

image_data_format(...): Returns the default image data format convention.

in_test_phase(...): Selects x in test phase, and alt otherwise.

in_top_k(...): Returns whether the targets are in the top k predictions.

in_train_phase(...): Selects x in train phase, and alt otherwise.

int_shape(...): Returns the shape of tensor or variable as a tuple of int or None entries.

is_sparse(...): Returns whether a tensor is a sparse tensor.

l2_normalize(...): Normalizes a tensor wrt the L2 norm alongside the specified axis.

learning_phase(...): Returns the learning phase flag.

learning_phase_scope(...): Provides a scope within which the learning phase is equal to value.

less(...): Element-wise truth value of (x < y).

less_equal(...): Element-wise truth value of (x <= y).

local_conv1d(...): Apply 1D conv with un-shared weights.

local_conv2d(...): Apply 2D conv with un-shared weights.

log(...): Element-wise log.

manual_variable_initialization(...): Sets the manual variable initialization flag.

map_fn(...): Map the function fn over the elements elems and return the outputs.

max(...): Maximum value in a tensor.

maximum(...): Element-wise maximum of two tensors.

mean(...): Mean of a tensor, alongside the specified axis.

min(...): Minimum value in a tensor.

minimum(...): Element-wise minimum of two tensors.

moving_average_update(...): Compute the moving average of a variable.

ndim(...): Returns the number of axes in a tensor, as an integer.

normalize_batch_in_training(...): Computes mean and std for batch then apply batch_normalization on batch.

not_equal(...): Element-wise inequality between two tensors.

one_hot(...): Computes the one-hot representation of an integer tensor.

ones(...): Instantiates an all-ones variable and returns it.

ones_like(...): Instantiates an all-ones variable of the same shape as another tensor.

permute_dimensions(...): Permutes axes in a tensor.

placeholder(...): Instantiates a placeholder tensor and returns it.

pool2d(...): 2D Pooling.

pool3d(...): 3D Pooling.

pow(...): Element-wise exponentiation.

print_tensor(...): Prints message and the tensor value when evaluated.

prod(...): Multiplies the values in a tensor, alongside the specified axis.

random_binomial(...): Returns a tensor with random binomial distribution of values.

random_normal(...): Returns a tensor with normal distribution of values.

random_normal_variable(...): Instantiates a variable with values drawn from a normal distribution.

random_uniform(...): Returns a tensor with uniform distribution of values.

random_uniform_variable(...): Instantiates a variable with values drawn from a uniform distribution.

relu(...): Rectified linear unit.

repeat(...): Repeats a 2D tensor.

repeat_elements(...): Repeats the elements of a tensor along an axis, like np.repeat.

reset_uids(...): Resets graph identifiers.

reshape(...): Reshapes a tensor to the specified shape.

resize_images(...): Resizes the images contained in a 4D tensor.

resize_volumes(...): Resizes the volume contained in a 5D tensor.

reverse(...): Reverse a tensor along the specified axes.

rnn(...): Iterates over the time dimension of a tensor.

round(...): Element-wise rounding to the closest integer.

separable_conv2d(...): 2D convolution with separable filters.

set_epsilon(...): Sets the value of the fuzz factor used in numeric expressions.

set_floatx(...): Sets the default float type.

set_image_data_format(...): Sets the value of the image data format convention.

set_learning_phase(...): Sets the learning phase to a fixed value.

set_session(...): Sets the global TensorFlow session.

set_value(...): Sets the value of a variable, from a Numpy array.

shape(...): Returns the symbolic shape of a tensor or variable.

sigmoid(...): Element-wise sigmoid.

sign(...): Element-wise sign.

sin(...): Computes sin of x element-wise.

softmax(...): Softmax of a tensor.

softplus(...): Softplus of a tensor.

softsign(...): Softsign of a tensor.

sparse_categorical_crossentropy(...): Categorical crossentropy with integer targets.

spatial_2d_padding(...): Pads the 2nd and 3rd dimensions of a 4D tensor.

spatial_3d_padding(...): Pads 5D tensor with zeros along the depth, height, width dimensions.

sqrt(...): Element-wise square root.

square(...): Element-wise square.

squeeze(...): Removes a 1-dimension from the tensor at index "axis".

stack(...): Stacks a list of rank R tensors into a rank R+1 tensor.

std(...): Standard deviation of a tensor, alongside the specified axis.

stop_gradient(...): Returns variables but with zero gradient w.r.t. every other variable.

sum(...): Sum of the values in a tensor, alongside the specified axis.

switch(...): Switches between two operations depending on a scalar value.

tanh(...): Element-wise tanh.

temporal_padding(...): Pads the middle dimension of a 3D tensor.

tile(...): Creates a tensor by tiling x by n.

to_dense(...): Converts a sparse tensor into a dense tensor and returns it.

transpose(...): Transposes a tensor and returns it.

truncated_normal(...): Returns a tensor with truncated random normal distribution of values.

update(...)

update_add(...): Update the value of x by adding increment.

update_sub(...): Update the value of x by subtracting decrement.

var(...): Variance of a tensor, alongside the specified axis.

variable(...): Instantiates a variable and returns it.

zeros(...): Instantiates an all-zeros variable and returns it.

zeros_like(...): Instantiates an all-zeros variable of the same shape as another tensor.