Extended dropout support

toolkit.py

@@ -110,7 +110,9 @@ class ClassificationProject(object):
 
     :param nodes: list number of nodes in each layer. If only a single number is given, use this number for every layer
 
-    :param dropout: dropout fraction after each hidden layer. Set to None for no Dropout
+    :param dropout: dropout fraction after each hidden layer. You can also pass a list for dropout fractions for each layer. Set to None for no Dropout.
+
+    :param dropout_input: dropout fraction for the input layer. Set to None for no Dropout.
 
     :param batch_size: size of the training batches
 
@@ -196,6 +198,7 @@ class ClassificationProject(object):
                         layers=3,
                         nodes=64,
                         dropout=None,
+                        dropout_input=None,
                         batch_size=128,
                         validation_split=0.33,
                         activation_function='relu',
@@ -243,6 +246,11 @@ class ClassificationProject(object):
             logger.warning("Number of layers not equal to the given nodes "
                            "per layer - adjusted to " + str(self.layers))
         self.dropout = dropout
+        if not isinstance(self.dropout, list):
+            self.dropout = [self.dropout for i in range(self.layers)]
+        if len(self.dropout) != self.layers:
+            raise ValueError("List of dropout fractions has to be of equal size as the number of layers!")
+        self.dropout_input = dropout_input
         self.batch_size = batch_size
         self.validation_split = validation_split
         self.activation_function = activation_function
@@ -588,15 +596,21 @@ class ClassificationProject(object):
 
             self._model = Sequential()
 
-            # first hidden layer
-            self._model.add(Dense(self.nodes[0], input_dim=len(self.fields), activation=self.activation_function))
-            # the other hidden layers
-            for node_count, layer_number in zip(self.nodes[1:], range(self.layers-1)):
+            if self.dropout_input is None:
+                self._model.add(Dense(self.nodes[0], input_dim=len(self.fields), activation=self.activation_function))
+                # in case of no Dropout we already have the first hidden layer
+                start_layer = 1
+            else:
+                self._model.add(Dropout(rate=self.dropout_input, input_shape=(len(self.fields),)))
+                start_layer = 0
+            # the (other) hidden layers
+            for node_count, dropout_fraction in zip(self.nodes[start_layer:], self.dropout[start_layer:]):
                 self._model.add(Dense(node_count, activation=self.activation_function))
-                if self.dropout is not None:
-                    self._model.add(Dropout(rate=self.dropout))
+                if dropout_fraction > 0:
+                    self._model.add(Dropout(rate=dropout_fraction))
             # last layer is one neuron (binary classification)
             self._model.add(Dense(1, activation=self.activation_function_output))
+
             logger.info("Using {}(**{}) as Optimizer".format(self.optimizer, self.optimizer_opts))
             Optimizer = getattr(keras.optimizers, self.optimizer)
             optimizer = Optimizer(**self.optimizer_opts)
@@ -607,9 +621,11 @@ class ClassificationProject(object):
                                 loss=self.loss,
                                 metrics=['accuracy'])
             np.random.set_state(rn_state)
+
             if os.path.exists(os.path.join(self.project_dir, "weights.h5")):
                 if self.is_training:
-                    continue_training = self.query_yn("Found previously trained weights - continue training (choosing N will restart)? (Y/N) ")
+                    continue_training = self.query_yn("Found previously trained weights - "
+                                                      "continue training (choosing N will restart)? (Y/N) ")
                 else:
                     continue_training = True
                 if continue_training: