tensorflow使用神经网络实现mnist分类_Python

tensorflow使用神经网络实现mnist分类

2021-04-01 00:25Missayaa Python

这篇文章主要为大家详细介绍了tensorflow使用神经网络实现mnist分类，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

本文实例为大家分享了tensorflow 神经网络实现mnist 分类的具体代码，供大家参考，具体内容如下

只有两层的神经网络，直接上代码

									#引入包

									import tensorflow as tf

									import numpy as np

									import matplotlib.pyplot as plt

									#引入input_data文件

									from tensorflow.examples.tutorials.mnist import input_data

									#读取文件

									mnist = input_data.read_data_sets('F:/mnist/data/',one_hot=True)

									#定义第一个隐藏层和第二个隐藏层，输入层输出层

									n_hidden_1 = 256

									n_hidden_2 = 128

									n_input = 784

									n_classes = 10

									#由于不知道输入图片个数，所以用placeholder

									x = tf.placeholder("float",[None,n_input])

									y = tf.placeholder("float",[None,n_classes])

									stddev = 0.1

									#定义权重

									weights = {

									    'w1':tf.Variable(tf.random_normal([n_input,n_hidden_1],stddev = stddev)),

									    '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))    

									    }

									#定义偏置

									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 is Ready")

									#前向传播

									def multilayer_perceptrin(_X,_weights,_biases):

									  layer1 = tf.nn.sigmoid(tf.add(tf.matmul(_X,_weights['w1']),_biases['b1']))

									  layer2 = tf.nn.sigmoid(tf.add(tf.matmul(layer1,_weights['w2']),_biases['b2']))

									  return (tf.matmul(layer2,_weights['out'])+_biases['out'])

									#定义优化函数，精准度等

									pred = multilayer_perceptrin(x,weights,biases)

									cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred,labels=y))

									optm = tf.train.GradientDescentOptimizer(learning_rate = 0.001).minimize(cost)

									corr = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))

									accr = tf.reduce_mean(tf.cast(corr,"float"))

									print("Functions is ready")

									#定义超参数

									training_epochs = 80

									batch_size = 200

									display_step = 4

									#会话开始

									init = tf.global_variables_initializer()

									sess = tf.Session()

									sess.run(init)

									#优化

									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_xs,batch_ys = mnist.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,training_epochs,avg_cost))

									    feeds = {x:batch_xs,y:batch_ys}

									    train_acc = sess.run(accr,feed_dict = feeds)

									    print("Train accuracy:%.3f"%(train_acc))

									    feeds = {x:mnist.test.images,y:mnist.test.labels}

									    test_acc = sess.run(accr,feed_dict = feeds)

									    print("Test accuracy:%.3f"%(test_acc))

									print("Optimization Finished")

程序部分运行结果如下：

									Train accuracy:0.605

									Test accuracy:0.633

									Epoch:071/080 cost:1.810029302

									Train accuracy:0.600

									Test accuracy:0.645

									Epoch:075/080 cost:1.761531130

									Train accuracy:0.690

									Test accuracy:0.649

									Epoch:079/080 cost:1.711757494

									Train accuracy:0.640

									Test accuracy:0.660

									Optimization Finished