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74 lines
3.0 KiB
Python
74 lines
3.0 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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import tensorflow as tf
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class InputEmbedder:
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def __init__(self, input_size, activation_function=tf.nn.relu, name="embedder"):
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self.name = name
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self.input_size = input_size
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self.activation_function = activation_function
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self.input = None
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self.output = None
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def __call__(self, prev_input_placeholder=None):
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with tf.variable_scope(self.get_name()):
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if prev_input_placeholder is None:
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self.input = tf.placeholder("float", shape=(None,) + self.input_size, name=self.get_name())
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else:
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self.input = prev_input_placeholder
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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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class ImageEmbedder(InputEmbedder):
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def __init__(self, input_size, input_rescaler=255.0, activation_function=tf.nn.relu, name="embedder"):
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InputEmbedder.__init__(self, input_size, activation_function, name)
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self.input_rescaler = input_rescaler
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def _build_module(self):
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# image observation
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rescaled_observation_stack = self.input / self.input_rescaler
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self.observation_conv1 = tf.layers.conv2d(rescaled_observation_stack,
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filters=32, kernel_size=(8, 8), strides=(4, 4),
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activation=self.activation_function, data_format='channels_last')
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self.observation_conv2 = tf.layers.conv2d(self.observation_conv1,
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filters=64, kernel_size=(4, 4), strides=(2, 2),
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activation=self.activation_function, data_format='channels_last')
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self.observation_conv3 = tf.layers.conv2d(self.observation_conv2,
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filters=64, kernel_size=(3, 3), strides=(1, 1),
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activation=self.activation_function, data_format='channels_last')
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self.output = tf.contrib.layers.flatten(self.observation_conv3)
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class VectorEmbedder(InputEmbedder):
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def __init__(self, input_size, activation_function=tf.nn.relu, name="embedder"):
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InputEmbedder.__init__(self, input_size, activation_function, name)
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def _build_module(self):
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# vector observation
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input_layer = tf.contrib.layers.flatten(self.input)
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self.output = tf.layers.dense(input_layer, 256, activation=self.activation_function)
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