pre-release 0.10.0

rl_coach/architectures/tensorflow_components/middlewares/middleware.py

@@ -0,0 +1,68 @@
+#
+# 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 typing import Type, Union, List
+
+import tensorflow as tf
+from rl_coach.base_parameters import MiddlewareScheme, Parameters
+
+from rl_coach.core_types import MiddlewareEmbedding
+
+
+class MiddlewareParameters(Parameters):
+    def __init__(self, parameterized_class: Type['Middleware'],
+                 activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium,
+                 batchnorm: bool=False, dropout: bool=False,
+                 name='middleware'):
+        super().__init__()
+        self.activation_function = activation_function
+        self.scheme = scheme
+        self.batchnorm = batchnorm
+        self.dropout = dropout
+        self.name = name
+        self.parameterized_class_name = parameterized_class.__name__
+
+
+class Middleware(object):
+    """
+    A middleware embedder is the middle part of the network. It takes the embeddings from the input embedders,
+    after they were aggregated in some method (for example, concatenation) and passes it through a neural network
+    which can be customizable but shared between the heads of the network
+    """
+    def __init__(self, activation_function=tf.nn.relu,
+                 scheme: MiddlewareScheme = MiddlewareScheme.Medium,
+                 batchnorm: bool = False, dropout: bool = False, name="middleware_embedder"):
+        self.name = name
+        self.input = None
+        self.output = None
+        self.activation_function = activation_function
+        self.batchnorm = batchnorm
+        self.dropout = dropout
+        self.dropout_rate = 0
+        self.scheme = scheme
+        self.return_type = MiddlewareEmbedding
+
+    def __call__(self, input_layer):
+        with tf.variable_scope(self.get_name()):
+            self.input = input_layer
+            self._build_module()
+
+        return self.input, self.output
+
+    def _build_module(self):
+        pass
+
+    def get_name(self):
+        return self.name