51 lines
2.2 KiB
Python
51 lines
2.2 KiB
Python
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#===============================================
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# 训练简单的神经网络,并显示运行时间
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# 数据集：mnnist
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#===============================================
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import datetime
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starttime = datetime.datetime.now()
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import tensorflow as tf
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import numpy as np
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# Import data
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from tensorflow.examples.tutorials.mnist import input_data
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flags = tf.app.flags
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FLAGS = flags.FLAGS
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flags.DEFINE_string('data_dir', '/learn/tensorflow/python/data/', 'Directory for storing data') # 把数据放在/data文件夹中
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mnist_data = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) # 读取数据集
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# 建立抽象模型
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x = tf.placeholder(tf.float32, [None, 784]) # 占位符
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y = tf.placeholder(tf.float32, [None, 10])
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W = tf.Variable(tf.zeros([784, 10]))
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b = tf.Variable(tf.zeros([10]))
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a = tf.nn.softmax(tf.matmul(x, W) + b)
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# 定义损失函数和训练方法
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cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(a), reduction_indices=[1])) # 损失函数为交叉熵，学习速率要设为0.3量级
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#cross_entropy = -tf.reduce_sum(y * tf.log(a)) # 损失函数为交叉熵
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optimizer = tf.train.GradientDescentOptimizer(0.3) # 梯度下降法，学习速率要设为0.001量级
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train_next = optimizer.minimize(cross_entropy) # 训练目标：最小化损失函数
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# Train
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sess = tf.InteractiveSession() # 建立交互式会话
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# tf.global_variables_initializer().run()
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sess.run(tf.global_variables_initializer())
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for i in range(1000):
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batch_xs, batch_ys = mnist_data.train.next_batch(100) # 随机抓取100个数据
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# train_next.run({x: batch_xs, y: batch_ys})
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sess.run(train_next, feed_dict={x: batch_xs, y: batch_ys})
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#测试
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correct_prediction = tf.equal(tf.argmax(a, 1), tf.argmax(y, 1))
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# tf.cast先将数据转换成float，防止求平均不准确:比如 tf.float32就是正确，写成tf.float16导致不准确，超出范围。
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
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print(sess.run(accuracy,feed_dict={x:mnist_data.test.images,y:mnist_data.test.labels}))
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endtime=datetime.datetime.now()
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print('total time endtime-starttime:', endtime-starttime)
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