fix: use tf.nn.softmax() only with cross_entropy loss

Songbo729066989 · May 6, 2016 · 20d1b59 · 20d1b59
1 parent 1bcc1ef
commit 20d1b59
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Showing 3 changed files with 3 additions and 2 deletions.
diff --git a/model.py b/model.py
@@ -102,6 +102,7 @@ def _create_cost_function_node(self, loss_func, model_output, ref_input, regterm
 
         with tf.name_scope("cost"):
             if loss_func == 'cross_entropy':
+                model_output = tf.nn.softmax(model_output)
                 cost = - tf.reduce_mean(ref_input * tf.log(tf.clip_by_value(model_output, 1e-10, float('inf'))) +
                                         (1 - ref_input) * tf.log(tf.clip_by_value(1 - model_output, 1e-10, float('inf'))))
                 _ = tf.scalar_summary("cross_entropy", cost)

diff --git a/models/autoencoder_models/stacked_denoising_autoencoder.py b/models/autoencoder_models/stacked_denoising_autoencoder.py
@@ -355,7 +355,7 @@ def _create_softmax_layer(self, last_layer, n_classes):
         self.softmax_b = tf.Variable(tf.constant(0.1, shape=[n_classes]), name='sm-biases')
 
         with tf.name_scope("softmax_layer"):
-            self.softmax_out = tf.nn.softmax(tf.matmul(last_layer, self.softmax_W) + self.softmax_b)
+            self.softmax_out = tf.matmul(last_layer, self.softmax_W) + self.softmax_b
             self.layer_nodes.append(self.softmax_out)
 
     def _create_test_node(self):

diff --git a/models/rbm_models/dbn.py b/models/rbm_models/dbn.py
@@ -276,7 +276,7 @@ def _create_softmax_layer(self, next_train, n_classes):
         self.softmax_b = tf.Variable(tf.constant(0.1, shape=[n_classes]), name='softmax-biases')
 
         with tf.name_scope("softmax_layer"):
-            self.y = tf.nn.softmax(tf.matmul(next_train, self.softmax_W) + self.softmax_b)
+            self.y = tf.matmul(next_train, self.softmax_W) + self.softmax_b
 
     def _create_test_node(self):