利用卷积神经网络处理cifar图像分类

这是一个图像分类的比赛CIFAR( CIFAR-10 - Object Recognition in Images )

首先我们需要下载数据文件，地址：

http://www.cs.toronto.edu/~kriz/cifar.html

CIFAR-10数据集包含10个类别的60000个32x32彩色图像，每个类别6000个图像。有50000张训练图像和10000张测试图像。

数据集分为五个训练批次和一个测试批次，每个批次具有10000张图像。测试批次包含每个类别中恰好1000张随机选择的图像。训练批次按随机顺序包含其余图像，但是某些训练批次可能包含比另一类更多的图像。在它们之间，培训批次精确地包含每个班级的5000张图像。

这些类是完全互斥的。汽车和卡车之间没有重叠。“汽车”包括轿车，SUV和类似的东西。“卡车”仅包括大型卡车。都不包括皮卡车。

详细代码：

1.导包

import numpy as np

# 序列化和反序列化
import pickle

from sklearn.preprocessing import OneHotEncoder

import warnings
warnings.filterwarnings(‘ignore‘)

import tensorflow as tf

2.数据加载

def unpickle(file):
     3     with open(file, ‘rb‘) as fo:
        dict = pickle.load(fo, encoding=‘ISO-8859-1‘)
    return dict

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

labels = []
X_train = []
for i in range(1,6):
    data = unpickle(‘./cifar-10-batches-py/data_batch_%d‘%(i))
    labels.append(data[‘labels‘])
    X_train.append(data[‘data‘])
    
# 将list类型转换为ndarray
y_train = np.array(labels).reshape(-1)
X_train = np.array(X_train)

# reshape
X_train = X_train.reshape(-1,3072)

# 目标值概率
one_hot = OneHotEncoder()
y_train =one_hot.fit_transform(y_train.reshape(-1,1)).toarray()
display(X_train.shape,y_train.shape)

3.构建神经网络

X = tf.placeholder(dtype=tf.float32,shape = [None,3072])
y = tf.placeholder(dtype=tf.float32,shape = [None,10])
kp = tf.placeholder(dtype=tf.float32)

def gen_v(shape):
    return tf.Variable(tf.truncated_normal(shape = shape))

def conv(input_,filter_,b):
    conv = tf.nn.relu(tf.nn.conv2d(input_,filter_,strides=[1,1,1,1],padding=‘SAME‘) + b)
    return tf.nn.max_pool(conv,[1,3,3,1],[1,2,2,1],‘SAME‘)

def net_work(input_,kp):
    
#     形状改变，4维
    input_ = tf.reshape(input_,shape = [-1,32,32,3])
#     第一层
    filter1 = gen_v(shape = [3,3,3,64])
    b1 = gen_v(shape = [64])
    conv1 = conv(input_,filter1,b1)
#     归一化
    conv1 = tf.layers.batch_normalization(conv1,training=True)
    
#     第二层
    filter2 = gen_v([3,3,64,128])
    b2 = gen_v(shape = [128])
    conv2 = conv(conv1,filter2,b2)
    conv2 = tf.layers.batch_normalization(conv2,training=True)
    
#     第三层
    filter3 = gen_v([3,3,128,256])
    b3 = gen_v([256])
    conv3 = conv(conv2,filter3,b3)
    conv3 = tf.layers.batch_normalization(conv3,training=True)
    
#     第一层全连接层
    dense = tf.reshape(conv3,shape = [-1,4*4*256])
    fc1_w = gen_v(shape = [4*4*256,1024])
    fc1_b = gen_v([1024])
    fc1 = tf.matmul(dense,fc1_w) + fc1_b
    fc1 = tf.layers.batch_normalization(fc1,training=True)
    fc1 = tf.nn.relu(fc1)
#     fc1.shape = [-1,1024]
    
    
#     dropout
    dp = tf.nn.dropout(fc1,keep_prob=kp)
    
#     第二层全连接层
    fc2_w = gen_v(shape = [1024,1024])
    fc2_b = gen_v(shape = [1024])
    fc2 = tf.nn.relu(tf.layers.batch_normalization(tf.matmul(dp,fc2_w) + fc2_b,training=True))
    
#     输出层
    out_w = gen_v(shape = [1024,10])
    out_b = gen_v(shape = [10])
    out = tf.matmul(fc2,out_w) + out_b
    return out

4.损失函数准确率

out = net_work(X,kp)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=out))

# 准确率
y_ = tf.nn.softmax(out)

# equal 相当于 == 
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y,axis = -1),tf.argmax(y_,axis = 1)),tf.float16))
accuracy

5.最优化

1 opt = tf.train.AdamOptimizer().minimize(loss)
2 opt

6.开启训练

epoches = 50000
saver = tf.train.Saver()

index = 0
def next_batch(X,y):
    global index
    batch_X = X[index*128:(index+1)*128]
    batch_y = y[index*128:(index+1)*128]
    index+=1
    if index == 390:
        index = 0
    return batch_X,batch_y

test = unpickle(‘./cifar-10-batches-py/test_batch‘)
y_test = test[‘labels‘]
y_test = np.array(y_test)
X_test = test[‘data‘]
y_test = one_hot.transform(y_test.reshape(-1,1)).toarray()
y_test[:10]

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(epoches):
        batch_X,batch_y = next_batch(X_train,y_train)
        opt_,loss_ = sess.run([opt,loss],feed_dict = {X:batch_X,y:batch_y,kp:0.5})
        print(‘----------------------------‘,loss_)
        if i % 100 == 0:
            score_test = sess.run(accuracy,feed_dict = {X:X_test,y:y_test,kp:1.0})
            score_train = sess.run(accuracy,feed_dict = {X:batch_X,y:batch_y,kp:1.0})
            print(‘iter count:%d。mini_batch loss:%0.4f。训练数据上的准确率：%0.4f。测试数据上准确率：%0.4f‘%
                  (i+1,loss_,score_train,score_test))

这个准确率只达到了百分之80

如果想提高准确率，还需要进一步优化，调参