Class Maximum
Defined in tensorflow/python/keras/layers/merge.py.
Layer that computes the maximum (element-wise) a list of inputs.
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
__init__
__init__(**kwargs)
Properties
activity_regularizer
Optional regularizer function for the output of this layer.
dtype
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_mask
Retrieves the input mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Input mask tensor (potentially None) or list of input mask tensors.
Raises:
AttributeError: if the layer is connected to more than one incoming layers.
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
Losses which are associated with this Layer.
Note that when executing eagerly, getting this property evaluates regularizers. When using graph execution, variable regularization ops have already been created and are simply returned here.
Returns:
A list of tensors.
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_mask
Retrieves the output mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Output mask tensor (potentially None) or list of output mask tensors.
Raises:
AttributeError: if the layer is connected to more than one incoming layers.
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.
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
__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 toself.call.**kwargs: additional keyword arguments to be passed toself.call.
Returns:
Output tensor(s).
Raises:
ValueError: if the layer'scallmethod returns None (an invalid value).
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 toself.call.**kwargs: additional keyword arguments to be passed toself.call.
Returns:
Output tensor(s).
build
build(
instance,
input_shape
)
compute_mask
compute_mask(
inputs,
mask=None
)
compute_output_shape
compute_output_shape(
instance,
input_shape
)
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).
from_config
from_config(
cls,
config
)
Creates a layer from its config.
This method is the reverse of get_config,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights).
Arguments:
config: A Python dictionary, typically the output of get_config.
Returns:
A layer instance.
get_config
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network (one layer of abstraction above).
Returns:
Python dictionary.
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=0will 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_mask_at
get_input_mask_at(node_index)
Retrieves the input mask 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=0will correspond to the first time the layer was called.
Returns:
A mask tensor (or list of tensors if the layer has multiple inputs).
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=0will 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.
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=0will 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_mask_at
get_output_mask_at(node_index)
Retrieves the output mask 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=0will correspond to the first time the layer was called.
Returns:
A mask tensor (or list of tensors if the layer has multiple outputs).
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=0will 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.
Returns:
List of update ops of the layer that depend on inputs.
Raises:
RuntimeError: If called in Eager mode.
get_weights
get_weights()
Returns the current weights of the layer.
Returns:
Weights values as a list of numpy arrays.
set_weights
set_weights(weights)
Sets the weights of the layer, from Numpy arrays.
Arguments:
weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output ofget_weights).
Raises:
ValueError: If the provided weights list does not match the layer's specifications.