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Commit 0901496f authored by frreiss's avatar frreiss
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Removed unused regularization ops

parent 6ab25864
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Showing with 3 additions and 3 deletions
tutorials/image/cifar10/cifar10.py
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...@@ -204,7 +204,7 @@ def inference(images): ...@@ -204,7 +204,7 @@ def inference(images):
kernel = _variable_with_weight_decay('weights', kernel = _variable_with_weight_decay('weights',
shape=[5, 5, 3, 64], shape=[5, 5, 3, 64],
stddev=5e-2, stddev=5e-2,
wd=0.0) wd=None)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0)) biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases) pre_activation = tf.nn.bias_add(conv, biases)
...@@ -223,7 +223,7 @@ def inference(images): ...@@ -223,7 +223,7 @@ def inference(images):
kernel = _variable_with_weight_decay('weights', kernel = _variable_with_weight_decay('weights',
shape=[5, 5, 64, 64], shape=[5, 5, 64, 64],
stddev=5e-2, stddev=5e-2,
wd=0.0) wd=None)
conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME') conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1)) biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv, biases) pre_activation = tf.nn.bias_add(conv, biases)
...@@ -262,7 +262,7 @@ def inference(images): ...@@ -262,7 +262,7 @@ def inference(images):
# and performs the softmax internally for efficiency. # and performs the softmax internally for efficiency.
with tf.variable_scope('softmax_linear') as scope: with tf.variable_scope('softmax_linear') as scope:
weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES], weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],
stddev=1/192.0, wd=0.0) stddev=1/192.0, wd=None)
biases = _variable_on_cpu('biases', [NUM_CLASSES], biases = _variable_on_cpu('biases', [NUM_CLASSES],
tf.constant_initializer(0.0)) tf.constant_initializer(0.0))
softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
......
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