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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from ray.rllib.models.model import Model
from ray.rllib.models.tf.misc import normc_initializer, get_activation_fn
from ray.rllib.utils.annotations import override
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
# Deprecated: see as an alternative models/tf/fcnet_v2.py
class FullyConnectedNetwork(Model):
"""Generic fully connected network."""
@override(Model)
def _build_layers(self, inputs, num_outputs, options):
"""Process the flattened inputs.
Note that dict inputs will be flattened into a vector. To define a
model that processes the components separately, use _build_layers_v2().
"""
hiddens = options.get("fcnet_hiddens")
activation = get_activation_fn(options.get("fcnet_activation"))
with tf.name_scope("fc_net"):
i = 1
last_layer = inputs
for size in hiddens:
# skip final linear layer
if options.get("no_final_linear") and i == len(hiddens):
output = tf.layers.dense(
last_layer,
num_outputs,
kernel_initializer=normc_initializer(1.0),
activation=activation,
name="fc_out")
return output, output
label = "fc{}".format(i)
last_layer = tf.layers.dense(
last_layer,
size,
kernel_initializer=normc_initializer(1.0),
activation=activation,
name=label)
i += 1
output = tf.layers.dense(
last_layer,
num_outputs,
kernel_initializer=normc_initializer(0.01),
activation=None,
name="fc_out")
return output, last_layer
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