A tensor_spec.BoundedTensorSpec detailing the shape and
dtypes of samples pulled from the output distribution.
activation_fn
Activation function to use in dense layer.
init_means_output_factor
Output factor for initializing action means
weights.
std_bias_initializer_value
Initial value for the bias of the
stddev_projection_layer or the direct bias_layer depending on the
state_dependent_std flag.
mean_transform
Transform to apply to the calculated means. Uses
tanh_squash_to_spec by default.
std_transform
Transform to apply to the stddevs.
state_dependent_std
If true, stddevs will be produced by MLP from state.
else, stddevs will be an independent variable.
scale_distribution
Whether or not to use a bijector chain to scale
distributions to match the sample spec. Note the TransformedDistribution
does not support certain operations required by some agents or policies
such as KL divergence calculations or Mode.
seed
seed used for Keras kernal initializers.
seed_stream_class
The seed stream class. This is almost always
tfp.util.SeedStream, except for in unit testing, when one may want to
seed all the layers deterministically.
name
A string representing name of the network.
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[]]