TensorFlow MNIST 入门 代码

其实就是按照TensorFlow中文教程的内容一步步跟着敲的代码。

 

不过在运行代码的时候遇到代码中加载不到MNIST数据资源，似乎是被墙了（(⊙﹏⊙)b）

于是自己手动下载了数据包，放到 MNIST_data/ 文件夹下，代码就能正常运转了。

资源链接如下：

http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz

http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz

http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz

http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz

 

完整版代码如下：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

w = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))+0.1

x = tf.placeholder(tf.float32,[None,784])
y_ = tf.placeholder(tf.float32,[None,10])

y = tf.nn.softmax(tf.matmul(x,w)+b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init)

for i in range(800):
    batch_xs,batch_ys=mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={x:batch_xs,y_:batch_ys})
    if i % 50 ==0:
        correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
        print(sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels}))