Module: tf.keras.backend | TensorFlow v2.0.0 (original) (raw)
Keras backend API.
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_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_keras_tensor(...): Returns whether x is a Keras tensor.
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.
name_scope(...): A context manager for use when defining a Python op.
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_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_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.