CIFAR10是一个对图片进行10种分类的项目
官网提供了数据集的下载,此外官网还有对于数据集的介绍。数据集中数据被分为了两部分。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32*32大小的3通道图片,构成的格式类似于[32,32,3];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,对应物品的分类。此外在python数据集中还有一个标签为‘label_names’,例如label_names[0] ==’airplane’等。
对于数据的读取,官网也提供了相应的代码
def unpickle(file):
import pickle
with open(file, 'rb') as fo;
dict = pickle.load(fo, encoding='bytes')
return dict
代码示例
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是1,其他的值都是0,因此需要将list格式化成对应的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,之后连接对应的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_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
poo11 = 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, E_conv['conv2'], ttrides=[1, 1, 1, 1], padding='SAME')
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = tf.nn.relu(conv2)
# LRN层
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])
# 全连接层1
fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv1['fc1'])
fc1 = tf.nn.relu(fc1)
# 全连接层2
fc2 = tf.add(tf.matmul(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))
4、运行部分
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
c = []
total_batch = int(X_train.shape[0] / batch_size)
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)
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)
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
poo11 = 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, E_conv['conv2'], ttrides=[1, 1, 1, 1], padding='SAME')
conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
conv2 = tf.nn.relu(conv2)
# LRN层
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])
# 全连接层1
fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv1['fc1'])
fc1 = tf.nn.relu(fc1)
# 全连接层2
fc2 = tf.add(tf.matmul(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)
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)