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92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Union, List
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import tensorflow as tf
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from rl_coach.architectures.tensorflow_components.layers import Dense
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from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware
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from rl_coach.base_parameters import MiddlewareScheme
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from rl_coach.core_types import Middleware_FC_Embedding
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from rl_coach.utils import force_list
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class FCMiddleware(Middleware):
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def __init__(self, activation_function=tf.nn.relu,
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scheme: MiddlewareScheme = MiddlewareScheme.Medium,
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batchnorm: bool = False, dropout_rate: float = 0.0,
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name="middleware_fc_embedder", dense_layer=Dense, is_training=False, num_streams: int = 1):
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super().__init__(activation_function=activation_function, batchnorm=batchnorm,
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dropout_rate=dropout_rate, scheme=scheme, name=name, dense_layer=dense_layer,
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is_training=is_training)
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self.return_type = Middleware_FC_Embedding
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assert(isinstance(num_streams, int) and num_streams >= 1)
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self.num_streams = num_streams
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def _build_module(self):
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self.output = []
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for stream_idx in range(self.num_streams):
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layers = [self.input]
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for idx, layer_params in enumerate(self.layers_params):
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layers.extend(force_list(
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layer_params(layers[-1], name='{}_{}'.format(layer_params.__class__.__name__,
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idx + stream_idx * len(self.layers_params)),
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is_training=self.is_training)
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))
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self.output.append((layers[-1]))
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@property
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def schemes(self):
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return {
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MiddlewareScheme.Empty:
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[],
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# ppo
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MiddlewareScheme.Shallow:
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[
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self.dense_layer(64)
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],
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# dqn
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MiddlewareScheme.Medium:
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[
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self.dense_layer(512)
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],
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MiddlewareScheme.Deep: \
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[
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self.dense_layer(128),
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self.dense_layer(128),
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self.dense_layer(128)
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]
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}
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def __str__(self):
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stream = [str(l) for l in self.layers_params]
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if self.layers_params:
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if self.num_streams > 1:
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stream = [''] + ['\t' + l for l in stream]
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result = stream * self.num_streams
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result[0::len(stream)] = ['Stream {}'.format(i) for i in range(self.num_streams)]
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else:
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result = stream
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return '\n'.join(result)
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else:
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return 'No layers'
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