TensorFlow多层感知机

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import input_data
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
mnist=input_data.read_data_sets("D:大二Java大三寒假作业大三寒假作业深度学习应用篇",one_hot=True)
learning_rate=0.001
training_epochs=15
batch_size=100
display_step=1
n_hidden_1=256
n_hidden_2=256
n_input=784
n_classes=10
X=tf.placeholder("float",[None,n_input])
Y=tf.placeholder("float",[None,n_classes])
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]))}
def multiplayer_perceptron(x):
    layer_1=tf.add(tf.matmul(x,weights['h1']),biases['b1'])
    layer_2=tf.add(tf.matmul(layer_1,weights['h2']),biases['b2'])
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer
logits=multiplayer_perceptron(X)
loss_op=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits,labels=Y))
train_op=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_op)
init=tf.global_variables_initializer()
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([train_op, loss_op], 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!")
                pred = tf.nn.softmax(logits)
                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}))