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59 lines
2.8 KiB
Python
59 lines
2.8 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 List, Type, Union
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from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameters
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class MiddlewareParameters(NetworkComponentParameters):
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def __init__(self, parameterized_class_name: str,
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activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium,
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batchnorm: bool=False, dropout_rate: float=0.0, name='middleware', dense_layer=None, is_training=False):
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super().__init__(dense_layer=dense_layer)
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self.activation_function = activation_function
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self.scheme = scheme
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self.batchnorm = batchnorm
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self.dropout_rate = dropout_rate
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self.name = name
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self.is_training = is_training
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self.parameterized_class_name = parameterized_class_name
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@property
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def path(self):
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return 'rl_coach.architectures.tensorflow_components.middlewares:' + self.parameterized_class_name
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class FCMiddlewareParameters(MiddlewareParameters):
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def __init__(self, activation_function='relu',
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scheme: Union[List, 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=None, is_training=False, num_streams=1):
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super().__init__(parameterized_class_name="FCMiddleware", activation_function=activation_function,
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scheme=scheme, batchnorm=batchnorm, dropout_rate=dropout_rate, name=name, dense_layer=dense_layer,
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is_training=is_training)
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self.num_streams = num_streams
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class LSTMMiddlewareParameters(MiddlewareParameters):
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def __init__(self, activation_function='relu', number_of_lstm_cells=256,
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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_lstm_embedder", dense_layer=None, is_training=False):
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super().__init__(parameterized_class_name="LSTMMiddleware", activation_function=activation_function,
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scheme=scheme, batchnorm=batchnorm, dropout_rate=dropout_rate, name=name, dense_layer=dense_layer,
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is_training=is_training)
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self.number_of_lstm_cells = number_of_lstm_cells |