tf_agents.networks.Network

A class used to represent networks used by TF-Agents policies and agents.

tf_agents.networks.Network(
    input_tensor_spec=None, state_spec=(), name=None
)

Used in the notebooks

Used in the tutorials

The main differences between a TF-Agents Network and a Keras Layer include: networks keep track of their underlying layers, explicitly represent RNN-like state in inputs and outputs, and simplify variable creation and clone operations.

When calling a network net, typically one passes data through it via:

outputs, next_state = net(observation, network_state=...)
outputs, next_state = net(observation, step_type=..., network_state=...)
outputs, next_state = net(observation)  # net.call must fill an empty state
outputs, next_state = net(observation, step_type=...)
outputs, next_state = net(
    observation, step_type=..., network_state=..., learning=...)

etc.

To force construction of a network's variables:

net.create_variables()
net.create_variables(input_tensor_spec=...)  # To provide an input spec
net.create_variables(training=True)  # Provide extra kwargs
net.create_variables(input_tensor_spec, training=True)

To create a copy of the network:

cloned_net = net.copy()
cloned_net.variables  # Raises ValueError: cloned net does not share weights.
cloned_net.create_variables(...)
cloned_net.variables  # Now new variables have been created.

input_tensor_spec A nest of tf.TypeSpec representing the input observations. Optional. If not provided, create_variables() will fail unless a spec is provided.
state_spec A nest of tensor_spec.TensorSpec representing the state needed by the network. Default is (), which means no state.
name (Optional.) A string representing the name of the network.

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

View source

copy(
    **kwargs
)

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

View source

create_variables(
    input_tensor_spec=None, **kwargs
)

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 a DistributionSpec.

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

get_initial_state

View source

get_initial_state(
    batch_size=None
)

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

View source

get_layer(
    name=None, index=None
)

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).

Args
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

View source

summary(
    line_length=None, positions=None, print_fn=None
)

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.