tf_agents.networks.lstm_encoding_network.LSTMEncodingNetwork

Recurrent network.

Inherits From: Network

input_tensor_spec A nest of tensor_spec.TensorSpec representing the observations.
preprocessing_layers (Optional.) A nest of tf.keras.layers.Layer representing preprocessing for the different observations. All of these layers must not be already built.
preprocessing_combiner (Optional.) A keras layer that takes a flat list of tensors and combines them. Good options include tf.keras.layers.Add and tf.keras.layers.Concatenate(axis=-1). This layer must not be already built.
conv_layer_params Optional list of convolution layers parameters, where each item is a length-three tuple indicating (filters, kernel_size, stride).
input_fc_layer_params Optional list of fully connected parameters, where each item is the number of units in the layer. These feed into the recurrent layer.
lstm_size An iterable of ints specifying the LSTM cell sizes to use.
output_fc_layer_params Optional list of fully connected parameters, where each item is the number of units in the layer. These are applied on top of the recurrent layer.
activation_fn Activation function, e.g. tf.keras.activations.relu,.
rnn_construction_fn (Optional.) Alternate RNN construction function, e.g. tf.keras.layers.LSTM, tf.keras.layers.CuDNNLSTM. It is invalid to provide both rnn_construction_fn and lstm_size.
rnn_construction_kwargs (Optional.) Dictionary or arguments to pass to rnn_construction_fn.

The RNN will be constructed via:

rnn_layer = rnn_construction_fn(**rnn_construction_kwargs)

dtype The dtype to use by the convolution, LSTM, and fully connected layers.
name A string representing name of the network.

ValueError If any of preprocessing_layers is already built.
ValueError If preprocessing_combiner is already built.
ValueError If neither lstm_size nor rnn_construction_fn are provided.
ValueError If both lstm_size and rnn_construction_fn are provided.

input_tensor_spec Returns the spec of the input to the network of type InputSpec.
layers Get the list of all (nested) sub-layers used in this Network.
state_spec

Methods

copy

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Create a shallow copy of this network.

Args
**kwargs Args to override when recreating this network. Commonly overridden args include 'name'.

Returns
A shallow copy of this network.

create_variables

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Force creation of the network's variables.

Return output specs.

Args
input_tensor_spec (Optional). Override or provide an input tensor spec when creating variables.
**kwargs Other arguments to network.call(), e.g. training=True.

Returns
Output specs - a nested spec calculated from the outputs (excluding any batch dimensions). If any of the output elements is a tfp Distribution, the associated spec entry returned is None.

Raises
ValueError If no input_tensor_spec is provided, and the network did not provide one during construction.

get_initial_state

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Returns an initial state usable by the network.

Args
batch_size Tensor or constant: size of the batch dimension. Can be None in which case not dimensions gets added.

Returns
A nested object of type self.state_spec containing properly initialized Tensors.

get_layer

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Retrieves a layer based on either its name (unique) or index.

If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

Arguments
name String, name of layer.
index Integer, index of layer.

Returns
A layer instance.

Raises
ValueError In case of invalid layer name or index.

summary

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Prints a string summary of the network.

Args
line_length Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
positions Relative or absolute positions of log elements in each line. If not provided, defaults to [.33, .55, .67, 1.].
print_fn Print function to use. Defaults to print. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.

Raises
ValueError if summary() is called before the model is built.