A nest of tensor_spec.TensorSpec representing the
input.
output_tensor_spec
A nest of tensor_spec.BoundedTensorSpec representing
the output.
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. For more details see the documentation
of networks.EncodingNetwork.
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. For more details see the documentation of
networks.EncodingNetwork.
conv_layer_params
Optional list of convolution layers parameters, where
each item is a length-three tuple indicating (filters, kernel_size,
stride).
fc_layer_params
Optional list of fully_connected parameters, where each
item is the number of units in the layer.
dropout_layer_params
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.
activation_fn
Activation function, e.g. tf.nn.relu, slim.leaky_relu, ...
kernel_initializer
Initializer to use for the kernels of the conv and
dense layers. If none is provided a default glorot_uniform.
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.
seed
seed used for Keras kernal initializers for NormalProjectionNetwork.
batch_squash
If True the outer_ranks of the observation are squashed into
the batch dimension. This allow encoding networks to be used with
observations with shape [BxTx...].
dtype
The dtype to use by the convolution and fully connected layers.
discrete_projection_net
Callable that generates a discrete projection
network to be called with some hidden state and the outer_rank of the
state.
continuous_projection_net
Callable that generates a continuous projection
network to be called with some hidden state and the outer_rank of the
state.
name
A string representing name of the network.
Raises
ValueError
If input_tensor_spec contains more than one observation.
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."],[],[]]