coach v0.8.0

architectures/architecture.py

@@ -0,0 +1,70 @@
+#
+# 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.
+#
+
+from configurations import Preset
+
+
+class Architecture:
+    def __init__(self, tuning_parameters, name=""):
+        """
+        :param tuning_parameters: A Preset class instance with all the running paramaters
+        :type tuning_parameters: Preset
+        :param name: The name of the network
+        :param name: string
+        """
+        self.batch_size = tuning_parameters.batch_size
+        self.input_depth = tuning_parameters.env.observation_stack_size
+        self.input_height = tuning_parameters.env.desired_observation_height
+        self.input_width = tuning_parameters.env.desired_observation_width
+        self.num_actions = tuning_parameters.env.action_space_size
+        self.measurements_size = tuning_parameters.env.measurements_size \
+            if tuning_parameters.env.measurements_size else 0
+        self.learning_rate = tuning_parameters.learning_rate
+        self.optimizer = None
+        self.name = name
+        self.tp = tuning_parameters
+
+    def get_model(self, tuning_parameters):
+        """
+        :param tuning_parameters: A Preset class instance with all the running parameters
+        :type tuning_parameters: Preset
+        :return: A model
+        """
+        pass
+
+    def predict(self, inputs):
+        pass
+
+    def train_on_batch(self, inputs, targets):
+        pass
+
+    def get_weights(self):
+        pass
+
+    def set_weights(self, weights, rate=1.0):
+        pass
+
+    def reset_accumulated_gradients(self):
+        pass
+
+    def accumulate_gradients(self, inputs, targets):
+        pass
+
+    def apply_and_reset_gradients(self, gradients):
+        pass
+
+    def apply_gradients(self, gradients):
+        pass