TensorFlow 1 version | View source on GitHub |
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)))
Arguments | |
---|---|
layer
|
a 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 Layer instance.
|