Deep Learning with Tensorfow:cha

Softmax 1 layer分类

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from random import randint
import numpy as np
import matplotlib.pyplot as plt

logs_patch = 'log_simple_stats_softmax'
batch_size = 100 #批次大小
learning_rate = 0.5 #学习率
training_epochs = 10 #训练次数

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

#Y_ = W*X+b
X = tf.placeholder(tf.float32,[None,28,28,1],name="X")
Y_ = tf.placeholder(tf.float32,[None,10],name="Y")
W = tf.Variable(tf.zeros([784,10]),name="W")

#将X展开为一维
XX = tf.reshape(X,[-1,784])
b = tf.Variable(tf.zeros([10]),name="b")

#XX*W+b
evidence = tf.matmul(XX,W)+b
#softmax分类
Y = tf.nn.softmax(evidence,name="output")
#交叉熵作为损失函数
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y))*1000.0
#优化器，通过梯度下降减小损失
train_step = tf.train.AdamOptimizer(0.005).minimize(cross_entropy)
#计算精度，判断是否准确
correct_prediction = tf.equal(tf.argmax(Y,1),tf.argmax(Y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
#统计
tf.summary.scalar("cost",cross_entropy)
tf.summary.scalar("accuracy",accuracy)
summary_op = tf.summary.merge_all()

with tf.Session() as sess:
    #初始化
    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(logs_patch,graph=tf.get_default_graph())
    for epoch in range(training_epochs):
        #批次训练
        batch_count = int(mnist.train.num_examples/batch_size)
        for i in range(batch_count):
            batch_x,batch_y = mnist.train.next_batch(batch_size)
            #传入值
            sess.run(train_step,feed_dict={XX:batch_x,Y_:batch_y})
        print("Epoch: ",epoch)
    #评估u精度
    print("Accuracy: ",accuracy.eval(feed_dict={X:np.reshape(mnist.test.images,[-1,28,28,1]),Y_:mnist.test.labels}))
    print("done")
    #预测
    num = randint(0,mnist.test.images.shape[0])#随机图像
    test_img = np.reshape(mnist.test.images[num],[28,28,1])
    test_label = mnist.test.labels[num]
    #预测label
    classification = sess.run(tf.argmax(Y,1),feed_dict={X:[test_img]})
    plt.imshow(np.reshape(test_img,[28,28]))
    plt.show()
    print("predict_label: ",classification[0])
    print("true_label: ",np.argmax(test_label))

Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
Epoch:  0
Epoch:  1
Epoch:  2
Epoch:  3
Epoch:  4
Epoch:  5
Epoch:  6
Epoch:  7
Epoch:  8
Epoch:  9
Accuracy:  0.9218
done
predict_label:  5
true_label:  5