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69 lines
2.5 KiB
Python
69 lines
2.5 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 Type, Union, List
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import tensorflow as tf
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from rl_coach.base_parameters import MiddlewareScheme, Parameters
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from rl_coach.core_types import MiddlewareEmbedding
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class MiddlewareParameters(Parameters):
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def __init__(self, parameterized_class: Type['Middleware'],
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activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium,
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batchnorm: bool=False, dropout: bool=False,
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name='middleware'):
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super().__init__()
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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 = dropout
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self.name = name
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self.parameterized_class_name = parameterized_class.__name__
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class Middleware(object):
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"""
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A middleware embedder is the middle part of the network. It takes the embeddings from the input embedders,
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after they were aggregated in some method (for example, concatenation) and passes it through a neural network
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which can be customizable but shared between the heads of the network
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"""
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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: bool = False, name="middleware_embedder"):
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self.name = name
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self.input = None
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self.output = None
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self.activation_function = activation_function
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self.batchnorm = batchnorm
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self.dropout = dropout
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self.dropout_rate = 0
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self.scheme = scheme
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self.return_type = MiddlewareEmbedding
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def __call__(self, input_layer):
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with tf.variable_scope(self.get_name()):
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self.input = input_layer
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self._build_module()
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return self.input, self.output
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def _build_module(self):
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pass
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def get_name(self):
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return self.name
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