多层感知机用于 mnist 手写数字数据集分类python代码

下面是一个简单的多层感知机（MLP）用于MNIST手写数字数据集分类的Python代码：

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

# 加载MNIST数据集
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# 设置MLP的参数
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1

# 设置MLP的网络结构
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10

# 创建输入和输出的占位符
X = tf.placeholder("float", [None, n_input])
Y = tf.placeholder("float", [None, n_classes])

# 创建MLP的权重和偏置变量
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]))
}

# 创建MLP的模型
def multilayer_perceptron(x):
    layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights['h1']), biases['b1']))
    layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer

# 构建MLP的模型
logits = multilayer_perceptron(X)

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

# 定义评估模型的准确率
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 初始化所有的变量
init = tf.global_variables_initializer()

# 开始训练MLP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)

        # 遍历所有的batch
        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size)

            # 运行优化器来更新权重和偏置
            _, c = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})

            # 计算平均损失值
            avg_cost += c / total_batch

        # 每个epoch打印一次损失值和准确率
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost={:.9f}".format(avg_cost))
            print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))

    print("Optimization Finished!")
    print("Final Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))

这段代码使用TensorFlow库来构建一个两层的MLP模型，用于对MNIST手写数字数据集进行分类。它使用交叉熵作为损失函数，Adam优化器来更新权重和偏置，以及准确率来评估模型的性能。