Tensorflow学习—— AdamOptimizer

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#载入数据集
mnist = input_data.read_data_sets("F:\\TensorflowProject\\MNIST_data",one_hot=True)

#每个批次的大小，训练时一次100张放入神经网络中训练
batch_size = 100

#计算一共有多少个批次
n_batch = mnist.train.num_examples//batch_size

#定义两个placeholder
x = tf.placeholder(tf.float32,[None,784])
#0-9十个数字
y = tf.placeholder(tf.float32,[None,10])

#创建一个神经网络
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x,W)+b)

#二次代价函数
#loss = tf.reduce_mean(tf.square(y-prediction))
#交叉熵代价函数
#使用交叉熵定义代价函数，可以加快模型收敛速度
#loss = tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
#使用梯度下降法
#train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
train_step = tf.train.AdamOptimizer(0.01).minimize(loss) #1e-2

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

#结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

#
with tf.Session() as sess:
　　sess.run(init)
　　for epoch in range(21):
　　　　for batch in range(n_batch):
　　　　　　batch_xs,batch_ys = mnist.train.next_batch(batch_size)
　　　　　　sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})

　　　　#测试准确率
　　　　acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
　　　　print("Iter: "+str(epoch)+" ,Testing Accuracy "+str(acc))

 

###########运行结果

Extracting F:\TensorflowProject\MNIST_data\train-images-idx3-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\train-labels-idx1-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\t10k-images-idx3-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\t10k-labels-idx1-ubyte.gz
Iter: 0  ,Testing Accuracy  0.9221
Iter: 1  ,Testing Accuracy  0.9133
Iter: 2  ,Testing Accuracy  0.9271
Iter: 3  ,Testing Accuracy  0.9262
Iter: 4  ,Testing Accuracy  0.9299
Iter: 5  ,Testing Accuracy  0.9293
Iter: 6  ,Testing Accuracy  0.9301
Iter: 7  ,Testing Accuracy  0.9299
Iter: 8  ,Testing Accuracy  0.9287
Iter: 9  ,Testing Accuracy  0.9319
Iter: 10  ,Testing Accuracy  0.9317
Iter: 11  ,Testing Accuracy  0.9315
Iter: 12  ,Testing Accuracy  0.9307
Iter: 13  ,Testing Accuracy  0.932
Iter: 14  ,Testing Accuracy  0.9314
Iter: 15  ,Testing Accuracy  0.9316
Iter: 16  ,Testing Accuracy  0.9311
Iter: 17  ,Testing Accuracy  0.9333
Iter: 18  ,Testing Accuracy  0.9318
Iter: 19  ,Testing Accuracy  0.9318
Iter: 20  ,Testing Accuracy  0.9289