tf.keras.layers.TimeDistributed

This wrapper allows to apply a layer to every temporal slice of an input.

Inherits From: Wrapper, Layer, Operation

tf.keras.layers.TimeDistributed(
    layer, **kwargs
)

Used in the notebooks

Used in the tutorials

Every input should be at least 3D, and the dimension of index one of the first input will be considered to be the temporal dimension.

Consider a batch of 32 video samples, where each sample is a 128x128 RGB image with channels_last data format, across 10 timesteps. The batch input shape is (32, 10, 128, 128, 3).

You can then use TimeDistributed to apply the same Conv2D layer to each of the 10 timesteps, independently:

inputs = layers.Input(shape=(10, 128, 128, 3), batch_size=32)
conv_2d_layer = layers.Conv2D(64, (3, 3))
outputs = layers.TimeDistributed(conv_2d_layer)(inputs)
outputs.shape
(32, 10, 126, 126, 64)

Because TimeDistributed applies the same instance of Conv2D to each of the timestamps, the same set of weights are used at each timestamp.

layer a keras.layers.Layer instance.

inputs Input tensor of shape (batch, time, ...) or nested tensors, and each of which has shape (batch, time, ...).
training Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the wrapped layer (only if the layer supports this argument).
mask Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked. This argument is passed to the wrapped layer (only if the layer supports this argument).

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

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config, custom_objects=None
)

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).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)