def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not DATA_DIR:
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(DATA_DIR, 'cifar-10-batches-bin')
images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,
batch_size=BATCH_SIZE)
return images, labels
def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not DATA_DIR:
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(DATA_DIR, 'cifar-10-batches-bin')
images, labels = cifar10_input.distorted_inputs(
data_dir=data_dir, batch_size=BATCH_SIZE)
return images, labels
max_steps = 3000
batch_size = 128
data_dir = 'cifar10_data/cifar-10-batches-bin'
def variable_with_weight_loss(shape, stddev, wl):
var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))
if wl is not None:
weight_loss = tf.multiply(tf.nn.l2_loss(var), wl, name='weight_loss')
tf.add_to_collection('losses', weight_loss)
return var
# cifar10.maybe_download_and_extract()
images_train, labels_train = cifar10_input.distorted_inputs(
data_dir=data_dir, batch_size=batch_size)
images_test, labels_test = cifar10_input.inputs(
eval_data=True, data_dir=data_dir, batch_size=batch_size)
image_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])
label_holder = tf.placeholder(tf.int32, [batch_size])
weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, wl=0.0)
kernel1 = tf.nn.conv2d(image_holder, weight1, [1, 1, 1, 1], padding='SAME')
bias1 = tf.Variable(tf.constant(0.0, shape=[64]))
conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1))
pool1 = tf.nn.max_pool(
conv1, ksize=[
1, 3, 3, 1], strides=[
1, 2, 2, 1], padding='SAME')
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)