Tensorflow学习笔记：CNN篇（3）——CIFAR-10数据集的CNN实现

Tensorflow学习笔记：CNN篇（3）——CIFAR-10数据集的CNN实现

前序

—在前面的介绍中，使用卷积神经网络对MNIST数据集做了应用，然而MNIST数据集仅限于对手写数字的识别，而且手写数字相对于自然物体和图片非常简单，也缺少相应的噪声和变换。

—本文将使用CNN对CIFAR-10数据集进行验证，同时会比较不同参数作用下卷积神经网络对准确率产生的影响。

CIFAR-10数据集

CIFAR-10数据集官网

从网站首页可以看到，这里提供10个分类的现实物体的图片，与前面所讲的成熟的人工手写识别相比，现实物体识别挑战巨大，而且图片中含有大量特征、噪声，识别物体比例不一，也加大了识别的难度，使其非常具有挑战性。



官网提供了数据集的下载，这里选用python版本



此外，官网提供了CIFAR-10的数据结构介绍：



可以看到，数据集中的数据分成了两部分：第一部分是特征部分，使用一个[10000,3072的uint8的矩阵进行存储，每一行向量都是3X3大小的3通道图片，构成的格式类似于[3,3,3]；第二部分为标签部分，使用一个10000数据的list进行存储，每个list对应的是0-9中的一个数字，对应于物品分类。另外对于python的数据集，还有一个标签为“label_names”，例如label_names[0] == “airplane”等。

对于具体的数据读取，官网上也提供了相应的代码：

代码实例

1、数据读取

前面说到，label是一个包含0-9的list列表，根据之前我们用到的one-hot方法，采用稀疏性列表法，即10个列表数字中只有对应的那个值为1，其他值都为0，因此需要将list格式转化成对应的one-hot矩阵。

def unpickle(filename):
with open(filename, 'rb') as f:
d = pickle.load(f, encoding='latin1')
return d

def onehot(labels):
'''one-hot 编码'''
n_sample = len(labels)
n_class = max(labels) + 1
onehot_labels = np.zeros((n_sample, n_class))
onehot_labels[np.arange(n_sample), labels] = 1
return onehot_labels

# 训练数据集
data1 = unpickle('cifar10-dataset/data_batch_1')
data2 = unpickle('cifar10-dataset/data_batch_2')
data3 = unpickle('cifar10-dataset/data_batch_3')
data4 = unpickle('cifar10-dataset/data_batch_4')
data5 = unpickle('cifar10-dataset/data_batch_5')
X_train = np.concatenate((data1['data'], data2['data'], data3['data'], data4['data'], data5['data']), axis=0)
y_train = np.concatenate((data1['labels'], data2['labels'], data3['labels'], data4['labels'], data5['labels']), axis=0)
y_train = onehot(y_train)
# 测试数据集
test = unpickle('cifar10-dataset/test_batch')
X_test = test['data'][:5000, :]
y_test = onehot(test['labels'])[:5000, :]

print('Training dataset shape:', X_train.shape)
print('Training labels shape:', y_train.shape)
print('Testing dataset shape:', X_test.shape)
print('Testing labels shape:', y_test.shape)

这里使用unpick函数依次读取5个batch中的数据，生成5个dict格式文件，而其中的数据以[data, labels]格式存放，之后链接对应的5个特征数据和标签数据生成最终的训练集，采用前5000个数据作为测试集进行使用。

2、模型参数

learning_rate = 1e-3
training_iters = 200
batch_size = 50
display_step = 5
n_features = 3072  # 32*32*3
n_classes = 10
n_fc1 = 384
n_fc2 = 192

3、模型构建

# 构建模型
x = tf.placeholder(tf.float32, [None, n_features])
y = tf.placeholder(tf.float32, [None, n_classes])

W_conv = {
'conv1': tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.0001)),
'conv2': tf.Variable(tf.truncated_normal([5, 5, 32, 64],stddev=0.01)),
'fc1': tf.Variable(tf.truncated_normal([8*8*64, n_fc1], stddev=0.1)),
'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)),
'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=0.1))
}
b_conv = {
'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[32])),
'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[64])),
'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])),
'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_
4000
fc2])),
'fc3': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[n_classes]))
}

x_image = tf.reshape(x, [-1, 32, 32, 3])
# 卷积层 1
conv1 = tf.nn.conv2d(x_image, W_conv['conv1'], strides=[1, 1, 1, 1], padding='SAME')
conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])
conv1 = tf.nn.relu(conv1)
# 池化层 1
pool1 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# LRN层，Local Response Normalization
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 卷积层 2
conv2 = tf.nn.conv2d(norm1, W_conv['conv2'], strides=[1, 1, 1, 1], padding='SAME')
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = tf.nn.relu(conv2)
# LRN层，Local Response Normalization
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 池化层 2
pool2 = tf.nn.avg_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
reshape = tf.reshape(pool2, [-1, 8*8*64])

fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv['fc1'])
fc1 = tf.nn.relu(fc1)
# 全连接层 2
fc2 = tf.add(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2'])
fc2 = tf.nn.relu(fc2)
# 全连接层 3, 即分类层
fc3 = tf.nn.softmax(tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3']))

# 定义损失
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc3, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
# 评估模型
correct_pred = tf.equal(tf.argmax(fc3, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
sess.run(init)
c = []
total_batch = int(X_train.shape[0] / batch_size)
#    for i in range(training_iters):
start_time = time.time()
for i in range(200):
for batch in range(total_batch):
batch_x = X_train[batch*batch_size : (batch+1)*batch_size, :]
batch_y = y_train[batch*batch_size : (batch+1)*batch_size, :]
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
print(acc)
c.append(acc)
end_time = time.time()
print('time: ', (end_time - start_time))
start_time = end_time
print("---------------%d onpech is finished-------------------",i)
print("Optimization Finished!")

# Test
test_acc = sess.run(accuracy, feed_dict={x: X_test, y: y_test})
print("Testing Accuracy:", test_acc)
plt.plot(c)
plt.xlabel('Iter')
plt.ylabel('Cost')
plt.title('lr=%f, ti=%d, bs=%d, acc=%f' % (learning_rate, training_iters, batch_size, test_acc))
plt.tight_layout()
plt.savefig('cnn-tf-cifar10-%s.png' % test_acc, dpi=200)

运行结果

根据计算机的运行速率不同，在笔者的计算机，大概10s运行一个周期（GPU：GTX1060 6G），训练200个周期，准确率为0.62，测试集准确率0.498。

完整代码

# coding: utf-8

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import _pickle as pickle
import time

def unpickle(filename):
with open(filename, 'rb') as f:
d = pickle.load(f, encoding='latin1')
return d

def onehot(labels):
'''one-hot 编码'''
n_sample = len(labels)
n_class = max(labels) + 1
onehot_labels = np.zeros((n_sample, n_class))
onehot_labels[np.arange(n_sample), labels] = 1
return onehot_labels

# 训练数据集
data1 = unpickle('cifar10-dataset/data_batch_1')
data2 = unpickle('cifar10-dataset/data_batch_2')
data3 = unpickle('cifar10-dataset/data_batch_3')
data4 = unpickle('cifar10-dataset/data_batch_4')
data5 = unpickle('cifar10-dataset/data_batch_5')
X_train = np.concatenate((data1['data'], data2['data'], data3['data'], data4['data'], data5['data']), axis=0)
y_train = np.concatenate((data1['labels'], data2['labels'], data3['labels'], data4['labels'], data5['labels']), axis=0)
y_train = onehot(y_train)
# 测试数据集
test = unpickle('cifar10-dataset/test_batch')
X_test = test['data'][:5000, :]
y_test = onehot(test['labels'])[:5000, :]

print('Training dataset shape:', X_train.shape)
print('Training labels shape:', y_train.shape)
print('Testing dataset shape:', X_test.shape)
print('Testing labels shape:', y_test.shape)

# 模型参数
learning_rate = 1e-3
training_iters = 200
batch_size = 50
display_step = 5
n_features = 3072  # 32*32*3
n_classes = 10
n_fc1 = 384
n_fc2 = 192

# 构建模型
x = tf.placeholder(tf.float32, [None, n_features])
y = tf.placeholder(tf.float32, [None, n_classes])

W_conv = {
'conv1': tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.0001)),
'conv2': tf.Variable(tf.truncated_normal([5, 5, 32, 64],stddev=0.01)),
'fc1': tf.Variable(tf.truncated_normal([8*8*64, n_fc1], stddev=0.1)),
'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)),
'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=0.1))
}
b_conv = {
'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[32])),
'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[64])),
'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])),
'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc2])),
'fc3': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[n_classes]))
}

x_image = tf.reshape(x, [-1, 32, 32, 3])
# 卷积层 1
conv1 = tf.nn.conv2d(x_image, W_conv['conv1'], strides=[1, 1, 1, 1], padding='SAME')
conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])
conv1 = tf.nn.relu(conv1)
# 池化层 1
pool1 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# LRN层，Local Response Normalization
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 卷积层 2
conv2 = tf.nn.conv2d(norm1, W_conv['conv2'], strides=[1, 1, 1, 1], padding='SAME')
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = tf.nn.relu(conv2)
# LRN层，Local Response Normalization
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 池化层 2
pool2 = tf.nn.avg_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
reshape = tf.reshape(pool2, [-1, 8*8*64])

fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv['fc1'])
fc1 = tf.nn.relu(fc1)
# 全连接层 2
fc2 = tf.add(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2'])
fc2 = tf.nn.relu(fc2)
# 全连接层 3, 即分类层
fc3 = tf.nn.softmax(tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3']))

# 定义损失
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc3, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
# 评估模型
correct_pred = tf.equal(tf.argmax(fc3, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
sess.run(init)
c = []
total_batch = int(X_train.shape[0] / batch_size)
#    for i in range(training_iters):
start_time = time.time()
for i in range(200):
for batch in range(total_bat
a871
ch):
batch_x = X_train[batch*batch_size : (batch+1)*batch_size, :]
batch_y = y_train[batch*batch_size : (batch+1)*batch_size, :]
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
print(acc)
c.append(acc)
end_time = time.time()
print('time: ', (end_time - start_time))
start_time = end_time
print("---------------%d onpech is finished-------------------",i)
print("Optimization Finished!")

# Test
test_acc = sess.run(accuracy, feed_dict={x: X_test, y: y_test})
print("Testing Accuracy:", test_acc)
plt.plot(c)
plt.xlabel('Iter')
plt.ylabel('Cost')
plt.title('lr=%f, ti=%d, bs=%d, acc=%f' % (learning_rate, training_iters, batch_size, test_acc))
plt.tight_layout()
plt.savefig('cnn-tf-cifar10-%s.png' % test_acc, dpi=200)