基于mnist数据集构建MLP,并绘制训练次数和准确率的图像

以下是基于MNIST数据集构建MLP的Python代码，并绘制训练次数和准确率的图像：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
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)

# 定义超参数
learning_rate = 0.1
num_epochs = 100
batch_size = 100
display_step = 1

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

# 定义权重和偏置变量
W1 = tf.Variable(tf.random_normal([784, 256]))
b1 = tf.Variable(tf.random_normal([256]))
W2 = tf.Variable(tf.random_normal([256, 10]))
b2 = tf.Variable(tf.random_normal([10]))

# 定义MLP模型
hidden_layer = tf.nn.relu(tf.add(tf.matmul(x, W1), b1))
output_layer = tf.add(tf.matmul(hidden_layer, W2), b2)

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

# 定义准确率计算方式
correct_pred = tf.equal(tf.argmax(output_layer, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

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

# 开始训练MLP模型
with tf.Session() as sess:
    sess.run(init)
    num_batches = int(mnist.train.num_examples / batch_size)
    training_costs = []
    training_accs = []
    for epoch in range(num_epochs):
        for i in range(num_batches):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            _, c, acc = sess.run([optimizer, cost, accuracy], feed_dict={x: batch_x, y: batch_y})
        if epoch % display_step == 0:
            training_costs.append(c)
            training_accs.append(acc)
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c), "accuracy=", "{:.9f}".format(acc))
    print("Optimization Finished!")
    
    # 测试MLP模型
    test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
    print("Test Accuracy:", test_acc)
    
    # 绘制训练次数和准确率的图像
    plt.plot(range(num_epochs), training_accs)
    plt.xlabel('Training Epochs')
    plt.ylabel('Training Accuracy')
    plt.show()

运行代码后，将输出每个epoch的损失和准确率，并在训练结束时输出测试准确率。此外，还将绘制训练次数和准确率的图像，以便更好地了解模型的训练情况。