Python tiny_cifar10_resnet_v2_generatorの例

コード例 #1

ファイル: resnet_test.py プロジェクト: BhaskarNallani/gradient-checkpointing

def create_loss():
  """Creates loss tensor for resnet model."""
  images = tf.random_uniform((BATCH_SIZE, HEIGHT, WIDTH, DEPTH))
  labels = tf.random_uniform((BATCH_SIZE, NUM_CLASSES))
  # channels_last for CPU
  if USE_TINY:
    network = resnet_model.tiny_cifar10_resnet_v2_generator(RESNET_SIZE, NUM_CLASSES, data_format='channels_last')
  else:
    network = resnet_model.cifar10_resnet_v2_generator(RESNET_SIZE, NUM_CLASSES, data_format='channels_last')
  inputs = tf.reshape(images, [BATCH_SIZE, HEIGHT, WIDTH, DEPTH])
  logits = network(inputs,True)
  cross_entropy = tf.losses.softmax_cross_entropy(logits=logits,
                                                  onehot_labels=labels)
  l2_penalty = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
  loss = cross_entropy + _WEIGHT_DECAY * l2_penalty
  return loss

コード例 #2

def create_loss():
  """Creates loss tensor for resnet model."""
  images = tf.random_uniform((BATCH_SIZE, HEIGHT, WIDTH, DEPTH))
  labels = tf.random_uniform((BATCH_SIZE, NUM_CLASSES))
  # channels_last for CPU
  if USE_TINY:
    network = resnet_model.tiny_cifar10_resnet_v2_generator(RESNET_SIZE, NUM_CLASSES, data_format='channels_last')
  else:
    network = resnet_model.cifar10_resnet_v2_generator(RESNET_SIZE, NUM_CLASSES, data_format='channels_last')
  inputs = tf.reshape(images, [BATCH_SIZE, HEIGHT, WIDTH, DEPTH])
  logits = network(inputs,True)
  cross_entropy = tf.losses.softmax_cross_entropy(logits=logits,
                                                  onehot_labels=labels)
  l2_penalty = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
  loss = cross_entropy + _WEIGHT_DECAY * l2_penalty
  return loss

コード例 #3

def _create_cifar_resnet_loss():
  """Creates loss tensor for resnet model."""
  HEIGHT = 32
  WIDTH = 32
  DEPTH = 3
  NUM_CLASSES = 10
  BATCH_SIZE=1
  _WEIGHT_DECAY = 2e-4
  _INITIAL_LEARNING_RATE = 0.1 * BATCH_SIZE / 128
  _MOMENTUM = 0.9
  RESNET_SIZE=8
  
  images = tf.random_uniform((BATCH_SIZE, HEIGHT, WIDTH, DEPTH))
  labels = tf.random_uniform((BATCH_SIZE, NUM_CLASSES))
  # channels_last for CPU
  network = resnet_model.tiny_cifar10_resnet_v2_generator(RESNET_SIZE, NUM_CLASSES, data_format='channels_last')
  inputs = tf.reshape(images, [BATCH_SIZE, HEIGHT, WIDTH, DEPTH])
  logits = network(inputs,True)
  cross_entropy = tf.losses.softmax_cross_entropy(logits=logits,
                                                  onehot_labels=labels)
  l2_penalty = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
  loss = cross_entropy + _WEIGHT_DECAY * l2_penalty
  return loss