使用tensorflow的softmax进行mnist识别

tensorflow真是方便，看来深度学习需要怎么使用框架、如何建模～

 '''
 softmax classifier for mnist  

 created on 2019.9.28
 author: vince
 '''
 import math
 import logging
 import numpy
 import random
 import matplotlib.pyplot as plt
 import tensorflow as tf
 from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
 from sklearn.metrics import accuracy_score

 def main():
     logging.basicConfig(level = logging.INFO,
             format = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
             datefmt = '%a, %d %b %Y %H:%M:%S');

     logging.info("trainning begin.");

     mnist = read_data_sets('../data/MNIST',one_hot=True)    # MNIST_data指的是存放数据的文件夹路径，one_hot=True 为采用one_hot的编码方式编码标签

     x = tf.placeholder(tf.float32, [None, 784]);
     w = tf.Variable(tf.zeros([784, 10]));
     b = tf.Variable(tf.zeros([10]));
     y = tf.matmul(x, w) + b;

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

     cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y, labels = y_));
     train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy);

     sess = tf.InteractiveSession();
     tf.global_variables_initializer().run();
     for _ in range(1000):
         batch_xs, batch_ys = mnist.train.next_batch(100);
         sess.run(train_step, feed_dict = {x : batch_xs, y_ : batch_ys});

     logging.info("trainning end.");
     logging.info("testing begin.");

     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}));

     logging.info("testing end.");

 if __name__ == "__main__":
     main();