def _variable_with_weight_decay(name, shape, stddev, wd): """Helper to create an initialized Variable with weight decay. """ dtype = tf.float16 if FLAGS.use_fp16 else tf.float32 var = _variable_on_cpu( name, shape, tf.truncated_normal_initializer(stddev=stddev, dtype=dtype)) if wd is not None: weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) return var
def _variable_with_weight_decay(name, shape, stddev, wd): """Helper to create an initialized Variable with weight decay. """ dtype = tf.float16 if FLAGS.use_fp16 else tf.float32 var = _variable_on_cpu( name, shape, tf.truncated_normal_initializer(stddev=stddev, dtype=dtype)) if wd is not None: weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) return var
def score(logits, labels): """Add L2Loss to all the trainable variables. """ # Calculate the average cross entropy loss across the batch. labels = tf.cast(labels, tf.int64) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( logits, labels, name='cross_entropy_per_example') cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') tf.add_to_collection('losses', cross_entropy_mean) # The total loss is defined as the cross entropy loss plus all of the weight # decay terms (L2 loss). return tf.add_n(tf.get_collection('losses'), name='total_loss')
def score(logits, labels): """Add L2Loss to all the trainable variables. """ # Calculate the average cross entropy loss across the batch. labels = tf.cast(labels, tf.int64) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( logits, labels, name='cross_entropy_per_example') cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') tf.add_to_collection('losses', cross_entropy_mean) # The total loss is defined as the cross entropy loss plus all of the weight # decay terms (L2 loss). return tf.add_n(tf.get_collection('losses'), name='total_loss')