This layer wrapper allows to apply a layer to every temporal slice of an input
time_distributed(object, layer, ...)
What to compose the new Layer
instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()
).
The return value depends on object
. If object
is:
missing or NULL
, the Layer
instance is returned.
a Sequential
model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object)
is returned.
a tf.keras.layers.Layer
instance.
standard layer arguments.
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:
input <- layer_input(c(10, 128, 128, 3))
conv_layer <- layer_conv_2d(filters = 64, kernel_size = c(3, 3))
output <- input %>% time_distributed(conv_layer)
output$shape # TensorShape([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.
Other layer wrappers:
bidirectional()