LostTech.TensorFlow : API Documentation

Type TimeDistributed

Namespace tensorflow.keras.layers

Parent Wrapper

Interfaces ITimeDistributed

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

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: The output will then have shape `(32, 10, 8)`.

In subsequent layers, there is no need for the `input_shape`: 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:
Show Example
# 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) 


Public properties

PythonFunctionContainer activity_regularizer get;

object activity_regularizer_dyn get;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

InputSpec input_spec get; set;

object input_spec_dyn get; set;

Layer layer get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object PythonObject get;

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

bool trainable get; set;

object trainable_dyn get; set;

object trainable_variables get;

object trainable_variables_dyn get;

IList<object> trainable_weights get;

object trainable_weights_dyn get;

IList<object> updates get;

object updates_dyn get;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;