手写数字识别代码

好的，手写数字识别是一个经典的机器学习问题，可以使用各种算法来解决，比如传统的机器学习算法和深度学习算法。下面给您提供一个基于深度学习算法的手写数字识别代码，基于Python和TensorFlow框架实现。

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

# 加载数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# 定义超参数
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1

# 定义网络参数
n_input = 784  # 输入层大小（28*28）
n_hidden_1 = 256  # 隐藏层1大小
n_hidden_2 = 256  # 隐藏层2大小
n_classes = 10  # 输出层大小（0-9）

# 定义输入输出占位符
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

# 定义权重和偏置变量
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# 定义网络结构
def multilayer_perceptron(x, weights, biases):
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer

# 构建模型
pred = multilayer_perceptron(x, weights, biases)

# 定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 初始化变量
init = tf.global_variables_initializer()

# 训练模型
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
            avg_cost += c / total_batch
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
    print("Optimization Finished!")
    # 测试模型
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

这个代码使用的是多层感知器（Multilayer Perceptron, MLP）模型，可以实现手写数字的识别任务。您可以根据自己的需求进行修改和调整。