说明
- 在之前的mnist数据集中，由于数据特征太少，十分简单，仅用简单的cnn就能实现99.2%的准确率，这里尝试测试更加复杂的cifar10数据集
准备
- 需要cifar10的数据集，可以在代码里实现下载，并指定文件夹
需要下载预处理cifar数据集的一些类，用以下代码即可得到
git clone https://github.com/tensorflow/models.git cd models/tutorials/image/cifar10
为使用其数据集预处理的类，需要进入该文件夹下，并新建python文件，代码具体如下
代码
import cifar10, cifar10_input
import tensorflow as tf
import numpy as np
import time
max_steps = 3000 # 最大迭代轮数
batch_size = 128 # 批大小
#下载好的数据集所在的文件夹
data_dir = '/home/jinhanjun/caffe/data/cifar-10-batches-bin' # 数据所在路径
# 初始化weight函数，通过wl参数控制L2正则化大小
def variable_with_weight_loss(shape, stddev, wl):
var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))
if wl is not None:
# L2正则化可用tf.contrib.layers.l2_regularizer(lambda)(w)实现，自带正则化参数
weight_loss = tf.multiply(tf.nn.l2_loss(var), wl, name='weight_loss')
tf.add_to_collection('losses', weight_loss)
return var
# 如果没有下载，则需要将下面一句话取消注释并运行
#cifar10.maybe_download_and_extract()
# 此处的cifar10_input.distorted_inputs()和cifar10_input.inputs()函数
# 都是TensorFlow的操作operation，需要在会话中run来实际运行
# distorted_inputs()函数对数据进行了数据增强
images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir,
batch_size=batch_size)
# 裁剪图片正中间的24*24大小的区块并进行数据标准化操作
images_test, labels_test = cifar10_input.inputs(eval_data=True,
data_dir=data_dir,
batch_size=batch_size)
# 定义placeholder
# 注意此处输入尺寸的第一个值应该是batch_size而不是None
image_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])
label_holder = tf.placeholder(tf.int32, [batch_size])
# 卷积层1，不对权重进行正则化
weight1 = variable_with_weight_loss([5, 5, 3, 64], stddev=5e-2, wl=0.0) # 0.05
kernel1 = tf.nn.conv2d(image_holder, weight1,
strides=[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)
# 卷积层2
weight2 = variable_with_weight_loss([5, 5, 64, 64], stddev=5e-2, wl=0.0)
kernel2 = tf.nn.conv2d(norm1, weight2, strides=[1, 1, 1, 1], padding='SAME')
bias2 = tf.Variable(tf.constant(0.1, shape=[64]))
conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2))
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1], padding='SAME')
# 全连接层3
reshape = tf.reshape(pool2, [batch_size, -1]) # 将每个样本reshape为一维向量
dim = reshape.get_shape()[1].value # 取每个样本的长度
weight3 = variable_with_weight_loss([dim, 384], stddev=0.04, wl=0.004)
bias3 = tf.Variable(tf.constant(0.1, shape=[384]))
local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bias3)
# 全连接层4
weight4 = variable_with_weight_loss([384, 192], stddev=0.04, wl=0.004)
bias4 = tf.Variable(tf.constant(0.1, shape=[192]))
local4 = tf.nn.relu(tf.matmul(local3, weight4) + bias4)
# 全连接层5
weight5 = variable_with_weight_loss([192, 10], stddev=1 / 192.0, wl=0.0)
bias5 = tf.Variable(tf.constant(0.0, shape=[10]))
logits = tf.matmul(local4, weight5) + bias5
# 定义损失函数loss
def loss(logits, labels):
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=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)
return tf.add_n(tf.get_collection('losses'), name='total_loss')
loss = loss(logits, label_holder) # 定义loss
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss) # 定义优化器
top_k_op = tf.nn.in_top_k(logits, label_holder, 1)
# 定义会话并开始迭代训练
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# 启动图片数据增强的线程队列
tf.train.start_queue_runners()
# 迭代训练
for step in range(max_steps):
start_time = time.time()
image_batch, label_batch = sess.run([images_train, labels_train]) # 获取训练数据
_, loss_value = sess.run([train_op, loss],
feed_dict={image_holder: image_batch,
label_holder: label_batch})
duration = time.time() - start_time # 计算每次迭代需要的时间
if step % 10 == 0:
examples_per_sec = batch_size / duration # 每秒处理的样本数
sec_per_batch = float(duration) # 每批需要的时间
format_str = ('step %d, loss=%.2f (%.1f examples/sec; %.3f sec/batch)')
print(format_str % (step, loss_value, examples_per_sec, sec_per_batch))
# 在测试集上测评准确率
num_examples = 10000
import math
num_iter = int(math.ceil(num_examples / batch_size))
true_count = 0
total_sample_count = num_iter * batch_size
step = 0
while step < num_iter:
image_batch, label_batch = sess.run([images_test, labels_test])
predictions = sess.run([top_k_op],
feed_dict={image_holder: image_batch,
label_holder: label_batch})
true_count += np.sum(predictions)
step += 1
precision = true_count / total_sample_count
print('precision @ 1 =%.3f' % precision)
'''
precision @ 1 =0.720
'''