tensorflow学习之（九）classification 分类问题之分类手写数字0-9

时间：2023-03-09 06:50:36
tensorflow学习之（九）classification 分类问题之分类手写数字0-9 
#classification 分类问题

#例子 分类手写数字0-9

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#数据包,如果没有自动下载 number 0 to 9 data

mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

# 定义一个神经层

def add_layer(inputs, in_size, out_size, activation_function=None):

    #add one more layer and return the output of the layer

    Weights = tf.Variable(tf.random_normal([in_size, out_size]))

    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)

    Wx_plus_b = tf.matmul(inputs, Weights) + biases

    if activation_function is None:

        outputs = Wx_plus_b

    else:

        outputs = activation_function(Wx_plus_b)

    return outputs

#用测试集来评估神经网络的准确度

def computer_accuracy(v_xs,v_ys):

    global prediction

    y_pre = sess.run(prediction,feed_dict={xs:v_xs})

    #tf.argmax()返回最大数值的下标

    #tf.equal(A, B)是对比这两个矩阵或者向量的相等的元素，如果是相等的那就返回True，反正返回False，返回的值的矩阵维度和A是一样的

    '''

    tf.argmax(input, axis=None, name=None, dimension=None)

    此函数是对矩阵按行或列计算最大值

    参数

    input：输入Tensor

    axis：0表示按列，1表示按行

    name：名称

    dimension：和axis功能一样，默认axis取值优先。新加的字段

    返回：Tensor  一般是行或列的最大值下标向量

    '''

    '''

    A = [[1,3,4,5,6]]

    B = [[1,3,4,3,2]]

    with tf.Session() as sess:

    print(sess.run(tf.equal(A, B)))

    输出：[[ True  True  True False False]]

    '''

    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))#tf.argmax(y_pre,1)表示预测出的值，tf.argmax(v_ys,1)表示实际值

    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#将correct_prediction的数据格式转换为tf.float32

    result = sess.run(accuracy,feed_dict={xs: v_xs, ys: v_ys})

    return result

#define placeholder for inputs to network

xs = tf.placeholder(tf.float32, [None, 784])  # none表示无论给多少个例子都行，784=28*28

ys = tf.placeholder(tf.float32, [None, 10])  #表示10个需要识别的数字

# add output layer

prediction = add_layer(xs, 784, 10 , activation_function=tf.nn.softmax)

#the error between prediction and real data

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1])) #loss function

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=xs,labels=ys))

sess = tf.Session()

#important step

sess.run(tf.initialize_all_variables())

for i in range(1000):

    batch_xs,batch_ys = mnist.train.next_batch(100) #由于计算能力有限，每次只提取数据集的一部分

    sess.run(train_step,feed_dict={xs: batch_xs, ys: batch_ys})

    if i % 50 == 0:

        #打印计算准确度

        print(computer_accuracy(mnist.test.images,mnist.test.labels))

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