tf.keras.layers.TimeDistributed

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

Inherits From: Wrapper

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.TimeDistributed

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

The input should be at least 3D, and the dimension of index one 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 a Conv2D layer to each of the 10 timesteps, independently:

inputs = tf.keras.Input(shape=(10, 128, 128, 3))
conv_2d_layer = tf.keras.layers.Conv2D(64, (3, 3))
outputs = tf.keras.layers.TimeDistributed(conv_2d_layer)(inputs)
outputs.shape
TensorShape([None, 10, 126, 126, 64])

Arguments
layer	a tf.keras.layers.Layer instance.

Call arguments:

• inputs: Input tensor.
• 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).

Raises
ValueError	If not initialized with a tf.keras.layers.Layer instance.