From be2eda4650ea8ee01166c217452f72317ec9dce4 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <nikolai.hartmann@gmx.de>
Date: Tue, 20 Nov 2018 09:45:27 +0100
Subject: [PATCH] add another hidden layer on top of regression targets
---
toolkit.py | 18 +++++++++++++-----
1 file changed, 13 insertions(+), 5 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index fb51f65..d673121 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -791,14 +791,22 @@ class ClassificationProject(object):
if (dropout_fraction is not None) and (dropout_fraction > 0):
hidden_layer = Dropout(rate=dropout_fraction)(hidden_layer)
- # one output node for binary classification
- output_layer = Dense(1, activation=self.activation_function_output)(hidden_layer)
- outputs = [output_layer]
-
# optional regression targets
+ extra_targets = []
for target_field in self.target_fields:
extra_target = Dense(1, activation="linear", name="target_{}".format(target_field))(hidden_layer)
- outputs.append(extra_target)
+ extra_targets.append(extra_target)
+
+ if not self.target_fields:
+ # one output node for binary classification
+ output_layer = Dense(1, activation=self.activation_function_output)(hidden_layer)
+ outputs = [output_layer]
+ else:
+ # add another hidden layer on top of the regression targets and previous hidden layers
+ merge = concatenate([hidden_layer]+extra_targets)
+ hidden_layer2 = Dense(64, activation=self.activation_function)(merge)
+ output_class = Dense(1, activation=self.activation_function_output)(hidden_layer2)
+ outputs = [output_class]+extra_targets
self._model = Model(inputs=[input_layer], outputs=outputs)
self._compile_or_load_model()
--
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