实验14-1使用cnn完成MNIST手写体识别(tf)+实验14-2使用cnn完成MNIST手写体识别(keras)

版本python3.7 tensorflow版本为tensorflow-gpu版本2.6

实验14-1使用cnn完成MNIST手写体识别(tf)运行结果：

 代码：

import tensorflow as tf
# Tensorflow提供了一个类来处理MNIST数据
from tensorflow.examples.tutorials.mnist import input_data
import time

# 载入数据集
mnist = input_data.read_data_sets('MNIST', one_hot=True)
# 设置批次的大小
batch_size = 100
# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

# 定义初始化权值函数
def weight_variable(shape):
    initial = tf.compat.v1.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


# 定义初始化偏置函数
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


# 卷积层
def conv2d(input, filter):
    return tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')


# 池化层
def max_pool_2x2(value):
    return tf.nn.max_pool(value, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# 输入层
# 定义两个placeholder
tf.compat.v1.disable_eager_execution()
x = tf.compat.v1.placeholder(tf.float32, [None, 784])  # 28*28
y = tf.compat.v1.placeholder(tf.float32, [None, 10])
# 改变x的格式转为4维的向量[batch,in_hight,in_width,in_channels]
x_image = tf.reshape(x, [-1, 28, 28, 1])

# 卷积、激励、池化操作
# 初始化第一个卷积层的权值和偏置
W_conv1 = weight_variable([5, 5, 1, 32])  # 5*5的采样窗口，32个卷积核从1个平面抽取特征
b_conv1 = bias_variable([32])  # 每一个卷积核一个偏置值
# 把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)  # 进行max_pooling 池化层

# 初始化第二个卷积层的权值和偏置
W_conv2 = weight_variable([5, 5, 32, 64])  # 5*5的采样窗口，64个卷积核从32个平面抽取特征
b_conv2 = bias_variable([64])
# 把第一个池化层结果和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)  # 池化层

# 28*28的图片第一次卷积后还是28*28，第一次池化后变为14*14
# 第二次卷积后为14*14，第二次池化后变为了7*7
# 经过上面操作后得到64张7*7的平面


# 全连接层
# 初始化第一个全连接层的权值
W_fc1 = weight_variable([7 * 7 * 64, 1024])  # 经过池化层后有7*7*64个神经元，全连接层有1024个神经元
b_fc1 = bias_variable([1024])  # 1024个节点
# 把池化层2的输出扁平化为1维
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
# 求第一个全连接层的输出
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# keep_prob用来表示神经元的输出概率
keep_prob = tf.compat.v1.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 初始化第二个全连接层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

# 输出层
# 计算输出
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 交叉熵代价函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
# 使用AdamOptimizer进行优化
train_step = tf.compat.v1.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 结果存放在一个布尔列表中(argmax函数返回一维张量中最大的值所在的位置)
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
# 求准确率(tf.cast将布尔值转换为float型)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 创建会话
with tf.compat.v1.Session() as sess:
    start_time = time.clock()
    sess.run(tf.compat.v1.global_variables_initializer())  # 初始化变量
    for epoch in range(21):  # 迭代21次（训练21次）
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.7})  # 进行迭代训练
        # 测试数据计算出准确率
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})
        print('Iter' + str(epoch) + ',Testing Accuracy=' + str(acc))
    end_time = time.clock()
    print('Running time:%s Second' % (end_time - start_time))  # 输出运行时间

实验14-2使用cnn完成MNIST手写体识别(keras)：
这个要远程下载数据集，因为有点慢，我就下载数据集到本地，要它读取本地的数据集：
运行结果：

 代码;

from keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

train_X, train_y = mnist.load_data(r"F:\大学\大三\选修\机器学习\机械学习\实验\实验十四\mnist.npz")[0]
train_X = train_X.reshape(-1, 28, 28, 1)
train_X = train_X.astype('float32')
train_X /= 255
train_y = to_categorical(train_y, 10)

from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dropout, Dense
from keras.losses import categorical_crossentropy
from tensorflow.keras.optimizers import Adadelta

model = Sequential()
model.add(Conv2D(32, (5,5), activation='relu', input_shape=[28, 28, 1]))
model.add(Conv2D(64, (5,5), activation='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

model.compile(loss=categorical_crossentropy,
             optimizer=Adadelta(),
             metrics=['accuracy'])

batch_size = 100
epochs = 8
model.fit(train_X, train_y,
         batch_size=batch_size,
         epochs=epochs)

test_X, test_y = mnist.load_data(r"F:\大学\大三\选修\机器学习\机械学习\实验\实验十四\mnist.npz")[1]
test_X = test_X.reshape(-1, 28, 28, 1)
test_X = test_X.astype('float32')
test_X /= 255
test_y = to_categorical(test_y, 10)
loss, accuracy = model.evaluate(test_X, test_y, verbose=1)
print('loss:%.4f accuracy:%.4f' %(loss, accuracy))