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