tf.layers.MaxPooling3D

Class MaxPooling3D

Defined in tensorflow/python/layers/pooling.py.

Max pooling layer for 3D inputs (e.g. volumes).

Arguments:

• pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.
• strides: An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.
• padding: A string. The padding method, either 'valid' or 'same'. Case-insensitive.
• data_format: A string. The ordering of the dimensions in the inputs. channels_last (default) and channels_first are supported. channels_last corresponds to inputs with shape (batch, depth, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, depth, height, width).
• name: A string, the name of the layer.

Properties

activity_regularizer

Optional regularizer function for the output of this layer.

dtype

graph

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises:

• AttributeError: if the layer is connected to more than one incoming layers.

Raises:

• RuntimeError: If called in Eager mode.
• AttributeError: If no inbound nodes are found.

input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns:

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises:

• AttributeError: if the layer has no defined input_shape.
• RuntimeError: if called in Eager mode.

losses

name

non_trainable_variables

non_trainable_weights

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns:

Output tensor or list of output tensors.

Raises:

• AttributeError: if the layer is connected to more than one incoming layers.
• RuntimeError: if called in Eager mode.

output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns:

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises:

• AttributeError: if the layer has no defined output shape.
• RuntimeError: if called in Eager mode.

scope_name

trainable_variables

trainable_weights

updates

variables

Returns the list of all layer variables/weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

__init__

__init__(
    pool_size,
    strides,
    padding='valid',
    data_format='channels_last',
    name=None,
    **kwargs
)

__call__

__call__(
    inputs,
    *args,
    **kwargs
)

Wraps call, applying pre- and post-processing steps.

Arguments:

• inputs: input tensor(s).
• *args: additional positional arguments to be passed to self.call.
• **kwargs: additional keyword arguments to be passed to self.call. Note: kwarg scope is reserved for use by the layer.

Returns:

Output tensor(s).

Note: - If the layer's call method takes a scope keyword argument, this argument will be automatically set to the current variable scope. - If the layer's call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.

Raises:

• ValueError: if the layer's call method returns None (an invalid value).

__deepcopy__

__deepcopy__(memo)

add_loss

add_loss(
    losses,
    inputs=None
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Arguments:

• losses: Loss tensor, or list/tuple of tensors.
• inputs: Optional input tensor(s) that the loss(es) depend on. Must match the inputs argument passed to the __call__ method at the time the losses are created. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).

Raises:

• RuntimeError: If called in Eager mode.

add_update

add_update(
    updates,
    inputs=None
)

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored in Eager mode.

Arguments:

• updates: Update op, or list/tuple of update ops.
• inputs: Optional input tensor(s) that the update(s) depend on. Must match the inputs argument passed to the __call__ method at the time the updates are created. If None is passed, the updates are assumed to be unconditional, and will apply across all dataflows of the layer.

add_variable

add_variable(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=True,
    constraint=None
)

Adds a new variable to the layer, or gets an existing one; returns it.

Arguments:

• name: variable name.
• shape: variable shape.
• dtype: The type of the variable. Defaults to self.dtype or float32.
• initializer: initializer instance (callable).
• regularizer: regularizer instance (callable).
• trainable: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
• constraint: constraint instance (callable).

Returns:

The created variable.

Raises:

• RuntimeError: If called in Eager mode with regularizers.

apply

apply(
    inputs,
    *args,
    **kwargs
)

Apply the layer on a input.

This simply wraps self.__call__.

Arguments:

• inputs: Input tensor(s).
• *args: additional positional arguments to be passed to self.call.
• **kwargs: additional keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

build

build(_)

Creates the variables of the layer.

call

call(inputs)

count_params

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

• ValueError: if the layer isn't yet built (in which case its weights aren't yet defined).

get_input_at

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Arguments:

• node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

Raises:

• RuntimeError: If called in Eager mode.

get_input_shape_at

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Arguments:

• node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises:

• RuntimeError: If called in Eager mode.

get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

• inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the losses were created. If you pass inputs=None, unconditional losses are returned, such as weight regularization losses.

Returns:

List of loss tensors of the layer that depend on inputs.

Raises:

• RuntimeError: If called in Eager mode.

get_output_at

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Arguments:

• node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

Raises:

• RuntimeError: If called in Eager mode.

get_output_shape_at

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Arguments:

• node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises:

• RuntimeError: If called in Eager mode.

get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

• inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the updates were created. If you pass inputs=None, unconditional updates are returned.

Returns:

List of update ops of the layer that depend on inputs.

Raises:

• RuntimeError: If called in Eager mode.