keraflow.layers.wrappers.TimeDistributed Class Reference

Wrapper for apply a layer to every temporal slice of an input. More...

Inheritance diagram for keraflow.layers.wrappers.TimeDistributed:

Public Member Functions
def	__init__
Public Member Functions inherited from keraflow.layers.base.Layer
def	__init__
def	set_trainable_params
	Register the layer's trainable parameters. More...
def	input_dimension
	Return the expected dimension of input tensor. More...
def	check_input_shape
	Check if the input shape(s) are valid. More...
def	init_param
	Initializes trainable parameter(s) of the layer. More...
def	output
	Calculates the output tensor of the layer given the input tensor(s). More...
def	output_shape
	Calculates the shape of the output tensor given the shape of the input tensor(s). More...
def	support_mask
	Whether the layer supports to carry on to the output tensor the mask embedded in input tensor. More...
def	pack_init_param
	Check keraflow.utils.generic_utils.serialize.
def	get_tensor_shape
	Get the shape of a tensor. More...
def	get_weights
	Gets trainable parameter name, value (numpy arrays) pairs. More...
def	embed
	Embeds the target layer such that its trainable parameters (along with regularizers and constraints on the parameters) are treated as the host layer's parameters and are updated during traing process. More...
def	__call__
	Feed an input kensor or multiple input kensors to the layer and outputs another Kensor. More...

Additional Inherited Members
Static Public Member Functions inherited from keraflow.layers.base.Layer
def	unpack_init_param
	Check keraflow.utils.generic_utils.unserialize. More...

Detailed Description

Wrapper for apply a layer to every temporal slice of an input.

The input should be at least 3D. The first dimension is the batch dimension, and the second dimension is considered to be the temporal dimension.

Example of applying a dens layer to the output of an Embedding layer:

vocabulary_size = 5000
emb_dim = 50
model = Sequential()
model.add(Input(None, batch_size=32))  # variable length of inputs
model.add(Embedding(vocabulary_size, emb_dim))  # output will be of shape (batch_size, sequence_length, emb_dim)
model.add(TimeDistributed(Dense(32)))  # output will be of shape (batch_size, sequence_length, 32)

Constructor & Destructor Documentation

def keraflow.layers.wrappers.TimeDistributed.__init__	(		self,
			layer,
			kwargs
	)

Parameters

layer	The layer to be wrapped.
kwargs	see Layer.__init__.