A nest of tensor_spec.TensorSpec representing the
A nest of tensor_spec.BoundedTensorSpec representing
Optional list of fully_connected parameters, where each
item is the number of units in the layer.
Optional list of dropout layer parameters, each item
is the fraction of input units to drop or a dictionary of parameters
according to the keras.Dropout documentation. The additional parameter
permanent, if set to True, allows to apply dropout at inference for
approximated Bayesian inference. The dropout layers are interleaved with
the fully connected layers; there is a dropout layer after each fully
connected layer, except if the entry in the list is None. This list must
have the same length of fc_layer_params, or be None.
Optional list of convolution layers parameters, where
each item is a length-three tuple indicating (filters, kernel_size,
Activation function, e.g. tf.nn.relu, slim.leaky_relu, ...
kernel initializer for all layers except for the value
regression layer. If None, a VarianceScaling initializer will be used.
kernel initializer for the value regression
layer. If None, a RandomUniform initializer will be used.
A string representing name of the network.
If input_tensor_spec or action_spec contains more than one
item, or if the action data type is not float.
Returns the spec of the input to the network of type InputSpec.
Get the list of all (nested) sub-layers used in this Network.
(Optional). Override or provide an input tensor spec
when creating variables.
Other arguments to network.call(), e.g. training=True.
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.
If no input_tensor_spec is provided, and the network did
not provide one during construction.