teras.layers.GumbelSoftmax

teras.layers.GumbelSoftmax#

class teras.layers.GumbelSoftmax(temperature=0.2, hard=False, seed=1337, **kwargs)[source]#

Implementation of the Gumbel Softmax activation proposed by Eric Jang et al. in the paper, “Categorical Reparameterization with Gumbel-Softmax”

Reference(s):

https://arxiv.org/abs/1611.01144

Parameters:

  • temperature (float) – float, Controls the sharpness or smoothness of the resulting probability distribution. A higher temperature value leads to a smoother and more uniform probability distribution. Conversely, a lower temperature value makes the distribution concentrated around the category with the highest probability.

  • hard (bool) – bool, Whether to return soft probabilities or hard one hot

  • False. (vectors. Defaults to)

  • seed (int) – int, Seed for random sampling.

__init__(temperature=0.2, hard=False, seed=1337, **kwargs)[source]#

Methods

__init__([temperature, hard, seed])

add_loss(loss)

Can be called inside of the call() method to add a scalar loss.

add_metric()

add_variable(shape, initializer[, dtype, ...])

Add a weight variable to the layer.

add_weight([shape, initializer, dtype, ...])

Add a weight variable to the layer.

build(input_shape)

build_from_config(config)

Builds the layer's states with the supplied config dict.

call(logits)

compute_mask(inputs, previous_mask)

compute_output_shape(*args, **kwargs)

compute_output_spec(*args, **kwargs)

count_params()

Count the total number of scalars composing the weights.

from_config(config)

Creates an operation from its config.

get_build_config()

Returns a dictionary with the layer's input shape.

get_config()

Returns the config of the object.

get_weights()

Return the values of layer.weights as a list of NumPy arrays.

load_own_variables(store)

Loads the state of the layer.

quantize(mode)

quantized_call(*args, **kwargs)

save_own_variables(store)

Saves the state of the layer.

set_weights(weights)

Sets the values of layer.weights from a list of NumPy arrays.

stateless_call(trainable_variables, ...[, ...])

Call the layer without any side effects.

symbolic_call(*args, **kwargs)

Attributes

compute_dtype

The dtype of the computations performed by the layer.

dtype

Alias of layer.variable_dtype.

dtype_policy

input

Retrieves the input tensor(s) of a symbolic operation.

input_dtype

The dtype layer inputs should be converted to.

input_spec

losses

List of scalar losses from add_loss, regularizers and sublayers.

metrics

List of all metrics.

metrics_variables

List of all metric variables.

non_trainable_variables

List of all non-trainable layer state.

non_trainable_weights

List of all non-trainable weight variables of the layer.

output

Retrieves the output tensor(s) of a layer.

path

The path of the layer.

quantization_mode

The quantization mode of this layer, None if not quantized.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

Settable boolean, whether this layer should be trainable or not.

trainable_variables

List of all trainable layer state.

trainable_weights

List of all trainable weight variables of the layer.

variable_dtype

The dtype of the state (weights) of the layer.

variables

List of all layer state, including random seeds.

weights

List of all weight variables of the layer.
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