准确率只有70%，cpu版本的TF居然跑了两天才跑完，其他方法将继续尝试。

生成数据目录：

import numpy as np

import os

train_label = {}

for i in range(10):

    search_path = './data/train/{}'.format(i)

    file_list = os.listdir(search_path)

    for file in file_list:

        train_label[os.path.join(search_path, file)] = i

np.save('label.npy', train_label)

test_label = {}

for i in range(10):

    search_path = './data/test/{}'.format(i)

    file_list = os.listdir(search_path)

    for file in file_list:

        test_label[os.path.join(search_path, file)] = i

np.save('test-label.npy', test_label)

训练：

import tensorflow as tf

import numpy as np

import random

import cv2

# 将传入的label转换成one hot的形式。

def getOneHotLabel(label, depth):

    m = np.zeros([len(label), depth])

    for i in range(len(label)):

        m[i][label[i]] = 1

    return m

# 建立神经网络。

def alexnet(image, keepprob=0.5):

    # 定义卷积层1，卷积核大小，偏置量等各项参数参考下面的程序代码，下同。

    with tf.name_scope("conv1") as scope:

        kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(image, kernel, [1, 4, 4, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[64]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv1 = tf.nn.relu(bias, name=scope)

        pass

    # LRN层

    lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001/9, beta=0.75, name="lrn1")

    # 最大池化层

    pool1 = tf.nn.max_pool(lrn1, ksize=[1,3,3,1], strides=[1,2,2,1],padding="VALID", name="pool1")

    # 定义卷积层2

    with tf.name_scope("conv2") as scope:

        kernel = tf.Variable(tf.truncated_normal([5,5,64,192], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[192]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv2 = tf.nn.relu(bias, name=scope)

        pass

    # LRN层

    lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9, beta=0.75, name="lrn2")

    # 最大池化层

    pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID", name="pool2")

    # 定义卷积层3

    with tf.name_scope("conv3") as scope:

        kernel = tf.Variable(tf.truncated_normal([3,3,192,384], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[384]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv3 = tf.nn.relu(bias, name=scope)

        pass

    # 定义卷积层4

    with tf.name_scope("conv4") as scope:

        kernel = tf.Variable(tf.truncated_normal([3,3,384,256], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv4 = tf.nn.relu(bias, name=scope)

        pass

    # 定义卷积层5

    with tf.name_scope("conv5") as scope:

        kernel = tf.Variable(tf.truncated_normal([3,3,256,256], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv5 = tf.nn.relu(bias, name=scope)

        pass

    # 最大池化层

    pool5 = tf.nn.max_pool(conv5, ksize=[1,3,3,1], strides=[1,2,2,1], padding="VALID", name="pool5")

    # 全连接层

    flatten = tf.reshape(pool5, [-1, 6*6*256])

    weight1 = tf.Variable(tf.truncated_normal([6*6*256, 4096], mean=0, stddev=0.01))

    fc1 = tf.nn.sigmoid(tf.matmul(flatten, weight1))

    dropout1 = tf.nn.dropout(fc1, keepprob)

    weight2 = tf.Variable(tf.truncated_normal([4096, 4096], mean=0, stddev=0.01))

    fc2 = tf.nn.sigmoid(tf.matmul(dropout1, weight2))

    dropout2 = tf.nn.dropout(fc2, keepprob)

    weight3 = tf.Variable(tf.truncated_normal([4096, 10], mean=0, stddev=0.01))

    fc3 = tf.nn.sigmoid(tf.matmul(dropout2, weight3))

    return fc3

def alexnet_main():

    # 加载使用的训练集文件名和标签。

    files = np.load("label.npy",  allow_pickle=True , encoding='bytes')[()]

    # 提取文件名。

    keys = [i for i in files]

    print(len(keys))

    myinput = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name='input')

    mylabel = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='label')

    # 建立网络，keepprob为0.6。

    myoutput = alexnet(myinput, 0.6)

    # 定义训练的loss函数。

    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=myoutput, labels=mylabel))

    # 定义优化器，学习率设置为0.09，学习率可以设置为其他的数值。

    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.09).minimize(loss)

    # 定义准确率

    valaccuracy = tf.reduce_mean(

        tf.cast(

            tf.equal(

                tf.argmax(myoutput, 1),

                tf.argmax(mylabel, 1)),

            tf.float32))

    # tensorflow的saver，可以用于保存模型。

    saver = tf.train.Saver()

    init = tf.global_variables_initializer()

    with tf.Session() as sess:

        sess.run(init)

        # 40个epoch

        for loop in range(40):

            # 生成并打乱训练集的顺序。

            indices = np.arange(50000)

            random.shuffle(indices)

            # batch size此处定义为200。

            # 训练集一共50000张图片，前40000张用于训练，后10000张用于验证集。

            for i in range(0, 0+40000, 200):

                photo = []

                label = []

                for j in range(0, 200):

                    # print(keys[indices[i + j]])

                    photo.append(cv2.resize(cv2.imread(keys[indices[i + j]]), (224, 224))/225)

                    label.append(files[keys[indices[i + j]]])

                m = getOneHotLabel(label, depth=10)

                a, b = sess.run([optimizer, loss], feed_dict={myinput: photo, mylabel: m})

                print("\r%lf"%b, end='')

            acc = 0

            # 每次取验证集的200张图片进行验证，返回这200张图片的正确率。

            for i in range(40000, 40000+10000, 200):

                photo = []

                label = []

                for j in range(i, i + 200):

                    photo.append(cv2.resize(cv2.imread(keys[indices[j]]), (224, 224))/225)

                    label.append(files[keys[indices[j]]])

                m = getOneHotLabel(label, depth=10)

                acc += sess.run(valaccuracy, feed_dict={myinput: photo, mylabel: m})

            # 输出，一共有50次验证集数据相加，所以需要除以50。

            print("Epoch ", loop, ': validation rate: ', acc/50)

        # 保存模型。

        saver.save(sess, "model/model.ckpt")

if __name__ == '__main__':

    alexnet_main()

测试：

import tensorflow as tf

import numpy as np

import random

import cv2

def getOneHotLabel(label, depth):

    m = np.zeros([len(label), depth])

    for i in range(len(label)):

        m[i][label[i]] = 1

    return m

# 建立神经网络

def alexnet(image, keepprob=0.5):

    # 定义卷积层1，卷积核大小，偏置量等各项参数参考下面的程序代码，下同

    with tf.name_scope("conv1") as scope:

        kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(image, kernel, [1, 4, 4, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[64]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv1 = tf.nn.relu(bias, name=scope)

        pass

    # LRN层

    lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001/9, beta=0.75, name="lrn1")

    # 最大池化层

    pool1 = tf.nn.max_pool(lrn1, ksize=[1,3,3,1], strides=[1,2,2,1],padding="VALID", name="pool1")

    # 定义卷积层2

    with tf.name_scope("conv2") as scope:

        kernel = tf.Variable(tf.truncated_normal([5,5,64,192], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[192]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv2 = tf.nn.relu(bias, name=scope)

        pass

    # LRN层

    lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9, beta=0.75, name="lrn2")

    # 最大池化层

    pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID", name="pool2")

    # 定义卷积层3

    with tf.name_scope("conv3") as scope:

        kernel = tf.Variable(tf.truncated_normal([3,3,192,384], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[384]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv3 = tf.nn.relu(bias, name=scope)

        pass

    # 定义卷积层4

    with tf.name_scope("conv4") as scope:

        kernel = tf.Variable(tf.truncated_normal([3,3,384,256], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv4 = tf.nn.relu(bias, name=scope)

        pass

    # 定义卷积层5

    with tf.name_scope("conv5") as scope:

        kernel = tf.Variable(tf.truncated_normal([3,3,256,256], dtype=tf.float32, stddev=1e-1, name="weights"))

        conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding="SAME")

        biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")

        bias = tf.nn.bias_add(conv, biases)

        conv5 = tf.nn.relu(bias, name=scope)

        pass

    # 最大池化层

    pool5 = tf.nn.max_pool(conv5, ksize=[1,3,3,1], strides=[1,2,2,1], padding="VALID", name="pool5")

    # 全连接层

    flatten = tf.reshape(pool5, [-1, 6*6*256])

    weight1 = tf.Variable(tf.truncated_normal([6*6*256, 4096], mean=0, stddev=0.01))

    fc1 = tf.nn.sigmoid(tf.matmul(flatten, weight1))

    dropout1 = tf.nn.dropout(fc1, keepprob)

    weight2 = tf.Variable(tf.truncated_normal([4096, 4096], mean=0, stddev=0.01))

    fc2 = tf.nn.sigmoid(tf.matmul(dropout1, weight2))

    dropout2 = tf.nn.dropout(fc2, keepprob)

    weight3 = tf.Variable(tf.truncated_normal([4096, 10], mean=0, stddev=0.01))

    fc3 = tf.nn.sigmoid(tf.matmul(dropout2, weight3))

    return fc3

def alexnet_main():

    # 加载测试集的文件名和标签。

    files = np.load("test-label.npy", encoding='bytes')[()]

    keys = [i for i in files]

    print(len(keys))

    myinput = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name='input')

    mylabel = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='label')

    myoutput = alexnet(myinput, 0.6)

    prediction = tf.argmax(myoutput, 1)

    truth = tf.argmax(mylabel, 1)

    valaccuracy = tf.reduce_mean(

        tf.cast(

            tf.equal(

                prediction,

                truth),

            tf.float32))

    saver = tf.train.Saver()

    with tf.Session() as sess:

        # 加载训练好的模型，路径根据自己的实际情况调整

        saver.restore(sess, r"model/model.ckpt")

        cnt = 0

        for i in range(10000):

            photo = []

            label = []

            photo.append(cv2.resize(cv2.imread(keys[i]), (224, 224))/225)

            label.append(files[keys[i]])

            m = getOneHotLabel(label, depth=10)

            a, b= sess.run([prediction, truth], feed_dict={myinput: photo, mylabel: m})

            print(a, ' ', b)

            if a[0] == b[0]:

                cnt += 1

        print("Epoch ", 1, ': prediction rate: ', cnt / 10000)

if __name__ == '__main__':

    alexnet_main()

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