Tensorflow手写数字识别训练（梯度下降法）
# coding: utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#print("hello")
#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))
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#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(100):
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 0.8322 Iter: 1 ,Testing Accuracy 0.872 Iter: 2 ,Testing Accuracy 0.8808 Iter: 3 ,Testing Accuracy 0.888 Iter: 4 ,Testing Accuracy 0.8938 Iter: 5 ,Testing Accuracy 0.8969 Iter: 6 ,Testing Accuracy 0.899 Iter: 7 ,Testing Accuracy 0.9015 Iter: 8 ,Testing Accuracy 0.9038 Iter: 9 ,Testing Accuracy 0.9055 Iter: 10 ,Testing Accuracy 0.9063 Iter: 11 ,Testing Accuracy 0.9077 Iter: 12 ,Testing Accuracy 0.9078 ...... Iter: 38 ,Testing Accuracy 0.9192 Iter: 39 ,Testing Accuracy 0.9195 Iter: 40 ,Testing Accuracy 0.92 Iter: 41 ,Testing Accuracy 0.9199 Iter: 42 ,Testing Accuracy 0.9205 Iter: 43 ,Testing Accuracy 0.9201 Iter: 44 ,Testing Accuracy 0.921 Iter: 45 ,Testing Accuracy 0.9207 Iter: 46 ,Testing Accuracy 0.9214 Iter: 47 ,Testing Accuracy 0.9212 Iter: 48 ,Testing Accuracy 0.9215 Iter: 49 ,Testing Accuracy 0.9213 ..... Iter: 93 ,Testing Accuracy 0.9254 Iter: 94 ,Testing Accuracy 0.9259 Iter: 95 ,Testing Accuracy 0.926 Iter: 96 ,Testing Accuracy 0.9262 Iter: 97 ,Testing Accuracy 0.9263 Iter: 98 ,Testing Accuracy 0.9262 Iter: 99 ,Testing Accuracy 0.926
