coach v0.8.0

architectures/tensorflow_components/embedders.py

@@ -0,0 +1,73 @@
+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import tensorflow as tf
+
+
+class InputEmbedder:
+    def __init__(self, input_size, activation_function=tf.nn.relu, name="embedder"):
+        self.name = name
+        self.input_size = input_size
+        self.activation_function = activation_function
+        self.input = None
+        self.output = None
+
+    def __call__(self, prev_input_placeholder=None):
+        with tf.variable_scope(self.get_name()):
+            if prev_input_placeholder is None:
+                self.input = tf.placeholder("float", shape=(None,) + self.input_size, name=self.get_name())
+            else:
+                self.input = prev_input_placeholder
+            self._build_module()
+
+        return self.input, self.output
+
+    def _build_module(self):
+        pass
+
+    def get_name(self):
+        return self.name
+
+
+class ImageEmbedder(InputEmbedder):
+    def __init__(self, input_size, input_rescaler=255.0, activation_function=tf.nn.relu, name="embedder"):
+        InputEmbedder.__init__(self, input_size, activation_function, name)
+        self.input_rescaler = input_rescaler
+
+    def _build_module(self):
+        # image observation
+        rescaled_observation_stack = self.input / self.input_rescaler
+        self.observation_conv1 = tf.layers.conv2d(rescaled_observation_stack,
+                                             filters=32, kernel_size=(8, 8), strides=(4, 4),
+                                             activation=self.activation_function, data_format='channels_last')
+        self.observation_conv2 = tf.layers.conv2d(self.observation_conv1,
+                                             filters=64, kernel_size=(4, 4), strides=(2, 2),
+                                             activation=self.activation_function, data_format='channels_last')
+        self.observation_conv3 = tf.layers.conv2d(self.observation_conv2,
+                                             filters=64, kernel_size=(3, 3), strides=(1, 1),
+                                             activation=self.activation_function, data_format='channels_last')
+
+        self.output = tf.contrib.layers.flatten(self.observation_conv3)
+
+
+class VectorEmbedder(InputEmbedder):
+    def __init__(self, input_size, activation_function=tf.nn.relu, name="embedder"):
+        InputEmbedder.__init__(self, input_size, activation_function, name)
+
+    def _build_module(self):
+        # vector observation
+        input_layer = tf.contrib.layers.flatten(self.input)
+        self.output = tf.layers.dense(input_layer, 256, activation=self.activation_function)