Removed tensorflow specific code in presets (#59)

* Add generic layer specification for using in presets

* Modify presets to use the generic scheme

rl_coach/architectures/layers.py

@@ -0,0 +1,78 @@
+#
+# 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.
+#
+"""
+Module implementing base classes for common network layers used by preset schemes
+"""
+
+
+class Conv2d(object):
+    """
+    Base class for framework specfic Conv2d layer
+    """
+    def __init__(self, num_filters: int, kernel_size: int, strides: int):
+        self.num_filters = num_filters
+        self.kernel_size = kernel_size
+        self.strides = strides
+
+    def __str__(self):
+        return "Convolution (num filters = {}, kernel size = {}, stride = {})"\
+            .format(self.num_filters, self.kernel_size, self.strides)
+
+
+class BatchnormActivationDropout(object):
+    """
+    Base class for framework specific batchnorm->activation->dropout layer group
+    """
+    def __init__(self, batchnorm: bool=False, activation_function: str=None, dropout_rate: float=0):
+        self.batchnorm = batchnorm
+        self.activation_function = activation_function
+        self.dropout_rate = dropout_rate
+
+    def __str__(self):
+        result = []
+        if self.batchnorm:
+            result += ["Batch Normalization"]
+        if self.activation_function:
+            result += ["Activation (type = {})".format(self.activation_function)]
+        if self.dropout_rate > 0:
+            result += ["Dropout (rate = {})".format(self.dropout_rate)]
+        return "\n".join(result)
+
+
+class Dense(object):
+    """
+    Base class for framework specific Dense layer
+    """
+    def __init__(self, units: int):
+        self.units = units
+
+    def __str__(self):
+        return "Dense (num outputs = {})".format(self.units)
+
+
+class NoisyNetDense(object):
+    """
+    Base class for framework specific factorized Noisy Net layer
+
+    https://arxiv.org/abs/1706.10295.
+    """
+
+    def __init__(self, units: int):
+        self.units = units
+        self.sigma0 = 0.5
+
+    def __str__(self):
+        return "Noisy Dense (num outputs = {})".format(self.units)