tensorflow每个变量封装了一个程序，需要通过sess.run 进行调用
接下来我们使用一下使用mnist数据，这是一个手写图像的数据，训练集是55000*28*28, 测试集10000* 28*28
第一步：导入数据
import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data
# 导入数据 mnist = input_data.read_data_sets('data/', one_hot=True) print (" tpye of 'mnist' is %s" % (type(mnist))) print (" number of trian data is %d" % (mnist.train.num_examples)) print (" number of test data is %d" % (mnist.test.num_examples)) training = mnist.train.images traininglable = mnist.train.labels testing = mnist.test.images testinglabel = mnist.test.labels
第二步：初识化变量
#初始化x和y x = tf.placeholder('float', [None, 784]) y = tf.placeholder('float', [None, 10]) # 初始化W和b W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) sess = tf.Session()
第三步： 构造初始化函数
# 构造多分类方程 actv = tf.nn.softmax(tf.matmul(x, W) + b) # 构造代价函数y*log(y1), y1表示的是预测值 cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1)) #训练模型 learning_rate = 0.01 #优化模型，使得cost最小化 optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # 预测结果的最大值索引与真实值的索引进行比对, tf.argmax( , 1) #找出一行中的最大值的索引 pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1)) # 计算正确率, tf.cast 把布尔值转换为数字形式 accr = tf.reduce_mean(tf.cast(pred, 'float'))
第四步：迭代优化参数
init = tf.global_variables_initializer() # 训练次数 train_epoches = 50 # 每次抽取样本数 batch_size = 100 # 每5次循环打印一次结果 display_step = 5 sess = tf.Session() sess.run(init) for train_epoch in range(train_epoches): avg_cost = 0 # 每次选取100个数据，循环的次数 num_batch = int(mnist.train.num_examples/batch_size) for i in range(num_batch): # 抽取数据 bacth_x, bacth_y = mnist.train.next_batch(batch_size) # 进行cost优化 sess.run(optm, feed_dict={x:bacth_x, y:bacth_y}) # 加上cost的值 feeds = {x:bacth_x, y:bacth_y} avg_cost += sess.run(cost, feed_dict=feeds)/num_batch # 每5次打印一次结果 if train_epoch % display_step == 0: feeds_train = {x:bacth_x, y:bacth_y} feed_test = {x:mnist.test.images, y:mnist.test.labels} # 计算训练集的准确率, feed_dict的参数 train_acc = sess.run(accr, feed_dict=feeds_train) # 计算测试集的准确率 test_acc = sess.run(accr, feed_dict=feed_test) print("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f" % (train_epoch, train_epoches, avg_cost, train_acc, test_acc))