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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
from ray.rllib.models.model import Model
from ray.rllib.models.misc import normc_initializer, get_activation_fn
from ray.rllib.utils.annotations import override
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:
label = "fc{}".format(i)
last_layer = slim.fully_connected(
last_layer,
size,
weights_initializer=normc_initializer(1.0),
activation_fn=activation,
scope=label)
i += 1
label = "fc_out"
output = slim.fully_connected(
last_layer,
num_outputs,
weights_initializer=normc_initializer(0.01),
activation_fn=None,
scope=label)
return output, last_layer