def main(argv=None): # pylint: disable=unused-argument
args = parse_args(argv)
cifar10.maybe_download_and_extract(args.data_dir)
if tf.gfile.Exists(args.train_dir):
tf.gfile.DeleteRecursively(args.train_dir)
tf.gfile.MakeDirs(args.train_dir)
train(args)
def main(argv=None): # pylint: disable=unused-argument
cifar10.maybe_download_and_extract()
if gfile.Exists(FLAGS.train_dir):
gfile.DeleteRecursively(FLAGS.train_dir)
gfile.MakeDirs(FLAGS.train_dir)
train()
def main(argv=None): # pylint: disable=unused-argument
cifar10.maybe_download_and_extract()
if tf.gfile.Exists(FLAGS.eval_dir):
tf.gfile.DeleteRecursively(FLAGS.eval_dir)
tf.gfile.MakeDirs(FLAGS.eval_dir)
evaluate()
FLAGS = flags.FLAGS
# define variable_with_weight_loss
# 和之前定义的weight有所不同，
# 这里定义附带loss的weight，通过权重惩罚避免部分权重系数过大，导致overfitting
def variable_with_weight_loss(shape, stddev, w1):
var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))
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
# 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])
from cifar import cifar10
from cifar import cifar10_input
max_steps = 3000
batch_size = 128
data_dir = '/tmp/cifar10_data/cifar-10-batches-bin'
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]))