tensorflow入门03——搭建两层的简单神经网络

#搭建两层的神经网络
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import matplotlib.pyplot as plt
import input_data

#加载数据集
minst=input_data.read_data_sets(‘data/data/‘, one_hot=True)

#设置参数
n_hidden_1=256 #第一层输出
n_hidden_2=128 #第二层输出
n_input=784 #输入像素点
n_classes=10 #分类结果

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

#神经网络参数
stddev=0.1
weights={
    ‘w1‘:tf.Variable(tf.random_normal([n_input,n_hidden_1],stddev=stddev)),  #高斯初始化
    ‘w2‘:tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2],stddev=stddev)),
    ‘out‘:tf.Variable(tf.random_normal([n_hidden_2,n_classes],stddev=stddev))
}
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]))

}
print(‘NetWork Ready‘)

def multilayer_perceptron(_X,_weights,_biases):
    layer_1=tf.nn.sigmoid(tf.add(tf.matmul(_X,_weights[‘w1‘]),biases[‘b1‘]))   #sigmoid激活函数
    layer_2=tf.nn.sigmoid(tf.add(tf.matmul(layer_1,_weights[‘w2‘]),_biases[‘b2‘]))
    return (tf.matmul(layer_2,_weights[‘out‘])+_biases[‘out‘])

#预测
pred=multilayer_perceptron(x,weights,biases)

#损失和优化器
learning_rate=0.01
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(y,pred))
optm=tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)
corr=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accr=tf.reduce_mean(tf.cast(corr,"float"))

#初始化
init=tf.global_variables_initializer()
print(‘function ready‘)

#训练参数
training_epochs=20
batch_size=100
display_step=4
#开始训练
sess=tf.Session()
sess.run(init)
feeds={}
for epoch in range(training_epochs):
    avg_cost=0.
    total_batch=int(minst.train.num_examples/batch_size)
    for i in range(total_batch):
        batch_xs,batch_ys=minst.train.next_batch(batch_size)
        feeds={x:batch_xs,y:batch_ys}
        sess.run(optm,feed_dict=feeds)
        avg_cost+=sess.run(cost,feed_dict=feeds)
    avg_cost=avg_cost/total_batch
    if(epoch+1)%display_step==0:
        print(‘Epoch:%03d/%03d cost:%.9f‘%(epoch+1,training_epochs,avg_cost))
        train_acc=sess.run(accr,feed_dict=feeds)
        print(‘TRAIN ACCURACY:%.3f‘%(train_acc))
        feeds={x:minst.test.images,y:minst.test.labels}
        test_acc=sess.run(accr,feed_dict=feeds)
        print(‘TEST ACCURACY:%.3f‘%(test_acc))
print(‘训练完成‘)