代码生成TensorFlow多层感知器识别手写数字

好的，以下是实现多层感知器识别手写数字的 TensorFlow 代码：

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)

# 定义超参数
learning_rate = 0.01
num_steps = 1000
batch_size = 128
display_step = 100

# 定义网络参数
n_hidden_1 = 256 # 第一层隐藏层神经元个数
n_hidden_2 = 256 # 第二层隐藏层神经元个数
num_input = 784 # MNIST数据集每张图片的像素个数（28*28）
num_classes = 10 # MNIST数据集的类别个数（0-9）

# 定义输入输出占位符
X = tf.placeholder("float", [None, num_input])
Y = tf.placeholder("float", [None, num_classes])

# 定义权重和偏置变量
weights = {
    'h1': tf.Variable(tf.random_normal([num_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, num_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([num_classes]))
}

# 定义多层感知器网络模型
def neural_net(x):
    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
logits = neural_net(X)

# 定义损失函数和优化器
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(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))

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

# 开始训练模型
with tf.Session() as sess:
    sess.run(init)

    for step in range(1, num_steps+1):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # 计算损失函数和准确率，并输出
            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))

    print("Optimization Finished!")

    # 计算测试集准确率
    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={X: mnist.test.images,
                                      Y: mnist.test.labels}))

这段代码使用了 TensorFlow 中的 `tf.layers` API 来构建多层感知器网络模型，使用交叉熵损失函数和 Adam 优化器进行训练，最后计算模型在测试集上的准确率。