tensorflow真是方便,看来深度学习需要怎么使用框架、如何建模~
1 ''' 2 softmax classifier for mnist 3 4 created on 2019.9.28 5 author: vince 6 ''' 7 import math 8 import logging 9 import numpy 10 import random 11 import matplotlib.pyplot as plt 12 import tensorflow as tf 13 from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets 14 from sklearn.metrics import accuracy_score 15 16 def main(): 17 logging.basicConfig(level = logging.INFO, 18 format = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', 19 datefmt = '%a, %d %b %Y %H:%M:%S'); 20 21 logging.info("trainning begin."); 22 23 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 24 25 x = tf.placeholder(tf.float32, [None, 784]); 26 w = tf.Variable(tf.zeros([784, 10])); 27 b = tf.Variable(tf.zeros([10])); 28 y = tf.matmul(x, w) + b; 29 30 y_ = tf.placeholder(tf.float32, [None, 10]); 31 32 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y, labels = y_)); 33 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy); 34 35 sess = tf.InteractiveSession(); 36 tf.global_variables_initializer().run(); 37 for _ in range(1000): 38 batch_xs, batch_ys = mnist.train.next_batch(100); 39 sess.run(train_step, feed_dict = {x : batch_xs, y_ : batch_ys}); 40 41 logging.info("trainning end."); 42 logging.info("testing begin."); 43 44 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)); 45 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)); 46 print(sess.run(accuracy, feed_dict = {x : mnist.test.images, y_:mnist.test.labels})); 47 48 logging.info("testing end."); 49 50 if __name__ == "__main__": 51 main();