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TensorFlow 1 version
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View source on GitHub
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This wrapper allows to apply a layer to every temporal slice of an input.
Inherits From: Wrapper, Layer, Module
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 the same 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.shapeTensorShape([None, 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.
Arguments | |
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layer
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a tf.keras.layers.Layer instance.
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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 | |
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ValueError
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If not initialized with a tf.keras.layers.Layer instance.
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