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This wrapper allows to apply a layer to every temporal slice of an input.
Inherits From: Wrapper
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 samples,
where each sample is a sequence of 10 vectors of 16 dimensions.
The batch input shape of the layer is then (32, 10, 16)
,
and the input_shape
, not including the samples dimension, is (10, 16)
.
You can then use TimeDistributed
to apply a Dense
layer
to each of the 10 timesteps, independently:
# as the first layer in a model
model = Sequential()
model.add(TimeDistributed(Dense(8), input_shape=(10, 16)))
# now model.output_shape == (None, 10, 8)
The output will then have shape (32, 10, 8)
.
In subsequent layers, there is no need for the input_shape
:
model.add(TimeDistributed(Dense(32)))
# now model.output_shape == (None, 10, 32)
The output will then have shape (32, 10, 32)
.
TimeDistributed
can be used with arbitrary layers, not just Dense
,
for instance with a Conv2D
layer:
model = Sequential()
model.add(TimeDistributed(Conv2D(64, (3, 3)),
input_shape=(10, 299, 299, 3)))
layer
: a layer instance.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).ValueError
: If not initialized with a Layer
instance.