From 87b25c46926863f5973f502217c1a4cedcdf79b8 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Tue, 7 Aug 2018 11:37:04 +0200
Subject: [PATCH] allow different number of nodes per layer
---
toolkit.py | 14 ++++++++++----
1 file changed, 10 insertions(+), 4 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index a75459e..dfd8f33 100755
--- a/toolkit.py
+++ b/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)
--
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