基于tensorflow使用lenet-5实现对mnist手写数字识别分类

好的，以下是基于TensorFlow使用LeNet-5实现对MNIST手写数字识别分类的步骤：

1. 导入必要的库

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

2. 加载MNIST数据集

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

3. 定义网络结构

# 定义输入和输出的placeholder
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])

# 将输入向量转换为28x28的图像
x_image = tf.reshape(x, [-1, 28, 28, 1])

# 定义第一个卷积层
conv1_weights = tf.Variable(tf.truncated_normal([5, 5, 1, 6], stddev=0.1))
conv1_biases = tf.Variable(tf.constant(0.1, shape=[6]))
conv1 = tf.nn.conv2d(x_image, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
conv1 = tf.nn.relu(conv1 + conv1_biases)

# 定义第一个池化层
pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 定义第二个卷积层
conv2_weights = tf.Variable(tf.truncated_normal([5, 5, 6, 16], stddev=0.1))
conv2_biases = tf.Variable(tf.constant(0.1, shape=[16]))
conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='VALID')
conv2 = tf.nn.relu(conv2 + conv2_biases)

# 定义第二个池化层
pool2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 将第二个池化层的输出展开为一维向量
pool2_flat = tf.reshape(pool2, [-1, 5 * 5 * 16])

# 定义第一个全连接层
fc1_weights = tf.Variable(tf.truncated_normal([5 * 5 * 16, 120], stddev=0.1))
fc1_biases = tf.Variable(tf.constant(0.1, shape=[120]))
fc1 = tf.nn.relu(tf.matmul(pool2_flat, fc1_weights) + fc1_biases)

# 定义第二个全连接层
fc2_weights = tf.Variable(tf.truncated_normal([120, 84], stddev=0.1))
fc2_biases = tf.Variable(tf.constant(0.1, shape=[84]))
fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)

# 定义输出层
fc3_weights = tf.Variable(tf.truncated_normal([84, 10], stddev=0.1))
fc3_biases = tf.Variable(tf.constant(0.1, shape=[10]))
y = tf.matmul(fc2, fc3_weights) + fc3_biases

4. 定义损失函数和优化器

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

5. 定义评估模型的准确率

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

6. 启动会话并训练模型

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(20000):
        batch_xs, batch_ys = mnist.train.next_batch(64)
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
        if i % 1000 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
            print('step %d, training accuracy %g' % (i, train_accuracy))
    test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})
    print('test accuracy %g' % test_accuracy)

以上就是基于TensorFlow使用LeNet-5实现对MNIST手写数字识别分类的全部步骤。