def inputs(eval_data):
"""Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
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 FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
return cifar10_input.inputs(eval_data=eval_data,
data_dir=data_dir,
batch_size=FLAGS.batch_size)
def inputs(eval_data, data_dir, batch_size, use_fp16):
"""Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
data_dir: Directory for the data.
batch_size: Batch size.
use_fp16: Use fp16.
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.inputs(eval_data=eval_data,
data_dir=data_dir,
batch_size=batch_size)
if use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return images, labels
tf.add_to_collection('losses', weight_loss)
return var
# 下载数据集 － 调用cifar10函数下载并解压
cifar10.maybe_download_and_extract()
# 注意路径
cifar_dir = '.\\cifar10_data\\cifar-10-batches-bin'
# 采用 data augmentation进行数据处理
# 生成训练数据，训练数据通过cifar10_input的distort变化
images_train, labels_train = cifar10_input.distorted_inputs(
data_dir=cifar_dir, batch_size=FLAGS.batch_size)
# 测试数据（eval_data 测试数据）
images_test, labels_test = cifar10_input.inputs(eval_data=True,
data_dir=cifar_dir,
batch_size=FLAGS.batch_size)
# 创建输入数据，采用 placeholder
x_input = tf.placeholder(tf.float32, [FLAGS.batch_size, 24, 24, 3])
y_input = tf.placeholder(tf.int32, [FLAGS.batch_size])
# 创建第一个卷积层 input:3(channel) kernel:64 size:5*5
weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, w1=0.0)
bias1 = tf.Variable(tf.constant(0.0, shape=[64]))
conv1 = tf.nn.conv2d(x_input, weight1, [1, 1, 1, 1], padding='SAME')
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, bias1))
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
def variable_with_weight_loss(shape, stddev, w1): # w1是用来控制L2 loss的大小
var = tf.Variable(tf.truncated_normal(shape, stddev=stddev)) #使用截断正态分布初始化
if w1 is not None:
weight_loss = tf.multiply(tf.nn.l2_loss(var), w1, name='weight_loss')
tf.add_to_collection('losses', weight_loss)
return var
cifar10.maybe_download_and_extract() #下载数据集
images_train, labels_train = cifar10_input.distorted_inputs( #产生训练数据
data_dir=data_dir, batch_size=batch_size)
images_test, labels_test = cifar10_input.inputs(
eval_data=True, #生成测试数据
data_dir=data_dir,
batch_size=batch_size)
images_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])
label_holder = tf.placeholder(tf.int32, [batch_size])
####卷基层1####5x5 3通道 64核
weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, w1=0.0)
kernel1 = tf.nn.conv2d(images_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, bias=1.0, alpha=0.001 / 9.0, beta=0.75)