def get_inputs(train):
"""Construct distorted input for CIFAR using the Reader ops.
"""
if train:
images, labels = cifar10_input.distorted_inputs(data_dir=FLAGS.data_dir, batch_size=FLAGS.batch_size)
else:
images, labels = cifar10_input.inputs(eval_data=train, data_dir=FLAGS.data_dir, batch_size=FLAGS.batch_size)
print(images)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return images, labels
def get_inputs(train):
"""Construct distorted input for CIFAR using the Reader ops.
"""
if train:
images, labels = cifar10_input.distorted_inputs(
data_dir=FLAGS.data_dir, batch_size=FLAGS.batch_size)
else:
images, labels = cifar10_input.inputs(eval_data=train,
data_dir=FLAGS.data_dir,
batch_size=FLAGS.batch_size)
print(images)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return images, labels
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')
