CIFAR10是一個對圖片進行10種分類的專案

官網提供了資料集的下載，此外官網還有對於資料集的介紹。資料集中資料被分為了兩部分。第一部分是特徵部分，使用一個[10000,3072]的uint8的矩陣進行儲存，每一行向量都是32*32大小的3通道圖片，構成的格式類似於[32,32,3]；第二部分為標籤部分，使用一個10000資料的list進行儲存，每個list對應的是0-9中的一個數字，對應物品的分類。此外在python資料集中還有一個標籤為‘label_names’，例如label_names[0] ==’airplane’等。
對於資料的讀取，官網也提供了相應的程式碼

def unpickle
 
(file):
    import pickle
    with open(file, 'rb') as fo;
         dict = pickle.load(fo, encoding='bytes')
    return dict

程式碼示例

1、資料讀取

前面說到，label是一個包含0-9的list列表，根據之前我們用到的one-hot方法，採用稀疏性列表法，即10個列表數字中只有對應的那個值是1，其他的值都是0，因此需要將list格式化成對應的one-hot矩陣。

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

def onehot(labels):
    # one-hot編碼
    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

# 訓練資料集
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)
# 測試資料集
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)

這裡使用unpick函式依次讀取5個batch中的資料，生成5個dict格式檔案，而其中的資料以[data, labels]格式存放，之後連線對應的5個特徵資料和標籤資料生成最終的訓練集，採用前5000個數據作為測試集進行使用。

2、模型引數

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、模型構建

# 構建模型
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])
# 卷積層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)
# 池化層1
poo11 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# LRN層，Local Response Normalization
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 卷積層 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層
norm2  = tf.nn.lrn(conv2, 4, bias-1.0, alpha=0.001/9.0, beta=0.75)
# 池化層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])
# 全連線層1
fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv1['fc1'])
fc1 = tf.nn.relu(fc1)
# 全連線層2
fc2 = tf.add(tf.matmul(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2']))
fc2 = tf.nn.relu(fc2)
# 全連線層3，即分類層
fc3 =  tf.nn.softmax(tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3']))

# 定義損失
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)
# 評估模型
correct_pred = tf.equal(tf.argmax(fc3, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

4、執行部分

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)

完整程式碼

# 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編碼
    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

# 訓練資料集
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)
# 測試資料集
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

# 構建模型
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])
# 卷積層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)
# 池化層1
poo11 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# LRN層，Local Response Normalization
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
# 卷積層 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層
norm2  = tf.nn.lrn(conv2, 4, bias-1.0, alpha=0.001/9.0, beta=0.75)
# 池化層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])
# 全連線層1
fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv1['fc1'])
fc1 = tf.nn.relu(fc1)
# 全連線層2
fc2 = tf.add(tf.matmul(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2']))
fc2 = tf.nn.relu(fc2)
# 全連線層3，即分類層
fc3 =  tf.nn.softmax(tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3']))

# 定義損失
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)
# 評估模型
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)