Removed Bias Terms from L2 Loss

SuyashLakhotia · Mar 13, 2018 · f91fb23 · f91fb23
1 parent 83fcc47
commit f91fb23
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diff --git a/cnn_fchollet.py b/cnn_fchollet.py
@@ -78,7 +78,6 @@ def __init__(self, sequence_length, num_classes, vocab_size, embedding_size, emb
                 b = tf.Variable(tf.constant(0.1, shape=[num_units]), name="b")
 
                 l2_loss += tf.nn.l2_loss(W)
-                l2_loss += tf.nn.l2_loss(b)
 
                 self.x = tf.nn.xw_plus_b(self.x, W, b)
                 self.x = tf.nn.relu(self.x)
@@ -92,7 +91,6 @@ def __init__(self, sequence_length, num_classes, vocab_size, embedding_size, emb
             b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
 
             l2_loss += tf.nn.l2_loss(W)
-            l2_loss += tf.nn.l2_loss(b)
 
             self.scores = tf.nn.xw_plus_b(self.x, W, b, name="scores")
             self.predictions = tf.argmax(self.scores, 1, name="predictions")

diff --git a/cnn_ykim.py b/cnn_ykim.py
@@ -72,7 +72,6 @@ def __init__(self, sequence_length, num_classes, vocab_size, embedding_size, emb
                 b = tf.Variable(tf.constant(0.1, shape=[num_units]), name="b")
 
                 l2_loss += tf.nn.l2_loss(W)
-                l2_loss += tf.nn.l2_loss(b)
 
                 self.x = tf.nn.xw_plus_b(self.x, W, b)
                 self.x = tf.nn.relu(self.x)
@@ -86,7 +85,6 @@ def __init__(self, sequence_length, num_classes, vocab_size, embedding_size, emb
             b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
 
             l2_loss += tf.nn.l2_loss(W)
-            l2_loss += tf.nn.l2_loss(b)
 
             self.scores = tf.nn.xw_plus_b(self.x, W, b, name="scores")
             self.predictions = tf.argmax(self.scores, 1, name="predictions")

diff --git a/graph_cnn.py b/graph_cnn.py
@@ -89,7 +89,6 @@ def __init__(self, filter_name, L, K, F, P, FC, batch_size, num_vertices, num_cl
                 b = tf.Variable(tf.constant(0.1, shape=[num_units]), name="b")
 
                 l2_loss += tf.nn.l2_loss(W)
-                l2_loss += tf.nn.l2_loss(b)
 
                 x = tf.nn.xw_plus_b(x, W, b)
                 x = tf.layers.batch_normalization(x, training=self.train_flag)
@@ -104,7 +103,6 @@ def __init__(self, filter_name, L, K, F, P, FC, batch_size, num_vertices, num_cl
             b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
 
             l2_loss += tf.nn.l2_loss(W)
-            l2_loss += tf.nn.l2_loss(b)
 
             self.scores = tf.nn.xw_plus_b(x, W, b, name="scores")
             self.predictions = tf.argmax(self.scores, 1, name="predictions")

diff --git a/mlp.py b/mlp.py
@@ -26,7 +26,6 @@ def __init__(self, vocab_size, num_classes, layers, l2_reg_lambda):
                 b = tf.Variable(tf.constant(0.1, shape=[num_units]), name="b")
 
                 l2_loss += tf.nn.l2_loss(W)
-                l2_loss += tf.nn.l2_loss(b)
 
                 x = tf.nn.xw_plus_b(x, W, b)
                 x = tf.nn.relu(x)
@@ -40,7 +39,6 @@ def __init__(self, vocab_size, num_classes, layers, l2_reg_lambda):
             b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
 
             l2_loss += tf.nn.l2_loss(W)
-            l2_loss += tf.nn.l2_loss(b)
 
             self.scores = tf.nn.xw_plus_b(x, W, b, name="scores")
             self.predictions = tf.argmax(self.scores, 1, name="predictions")