以 mnist 手写数字识别为例，讲解tensorflow的分类
# 去掉 warning
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
# 去掉 warning
old_v = tf.logging.get_verbosity()
tf.logging.set_verbosity(tf.logging.ERROR)
# 引入 input_data 文件,这个文件用于去mnist页面获取mnist数据
from tensorflow.examples.tutorials.mnist import input_data
# 读取 mnist 数据集
mnist = input_data.read_data_sets('E:\mnist', one_hot = True)
# 定义全连接层
def add_layer(inputs, in_size, out_size, activation_function = None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
if activation_function == None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
# 计算精确度
def computer_accuracy(v_xs, v_ys):
global prediction
y_pre = sess.run(prediction, feed_dict = {xs: v_xs})
correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
result = sess.run(accuracy, feed_dict = {xs: v_xs, ys: v_ys})
return result
# 搭建网络
xs = tf.placeholder(tf.float32, [None, 784]) # 28*28
ys = tf.placeholder(tf.float32, [None, 10])
# 调用 add_layer 搭建一个最简单的训练神经网络，只有输入层和输出层
prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)
# 损失函数使用交叉熵损失函数 cross_entropy
cross_entropy = tf.reduce_mean(- tf.reduce_sum(ys * tf.log(prediction), reduction_indices = [1]))
# 使用梯度下降法进行训练
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# 全局变量初始化
init = tf.global_variables_initializer()
# 开始训练
with tf.Session() as sess:
sess.run(init)
for i in range(1001):
batch_xs, batch_ys = mnist.train.next_batch(100) # 小批量梯度下降
sess.run(train_step, feed_dict = {xs: batch_xs, ys: batch_ys})
if i % 50 == 0:
print('%4d: %6.4f' %(i, computer_accuracy(mnist.test.images, mnist.test.labels)))
