allow different number of nodes per layer

toolkit.py

@@ -106,7 +106,7 @@ class ClassificationProject(object):
 
     :param layers: number of layers in the neural network
 
-    :param nodes: number of nodes in each layer
+    :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
 
@@ -230,6 +230,12 @@ class ClassificationProject(object):
         self.identifiers = identifiers
         self.layers = layers
         self.nodes = nodes
+        if not isinstance(self.nodes, list):
+            self.nodes = [self.nodes for i in range(self.layers)]
+        if len(self.nodes) != self.layers:
+            self.layers = len(self.nodes)
+            logger.warning("Number of layers not equal to the given nodes "
+                           "per layer - adjusted to " + str(self.layers))
         self.dropout = dropout
         self.batch_size = batch_size
         self.validation_split = validation_split
@@ -551,10 +557,10 @@ class ClassificationProject(object):
             self._model = Sequential()
 
             # first hidden layer
-            self._model.add(Dense(self.nodes, input_dim=len(self.branches), activation=self.activation_function))
+            self._model.add(Dense(self.nodes[0], input_dim=len(self.branches), activation=self.activation_function))
             # the other hidden layers
-            for layer_number in range(self.layers-1):
-                self._model.add(Dense(self.nodes, activation=self.activation_function))
+            for node_count, layer_number in zip(self.nodes[1:], range(self.layers-1)):
+                self._model.add(Dense(node_count, activation=self.activation_function))
                 if self.dropout is not None:
                     self._model.add(Dropout(rate=self.dropout))
             # last layer is one neuron (binary classification)