tensorflow分类任务MNIST数据集

分手后的思念是犯贱 2022-02-22 13:38 427阅读 0赞

import tensorflow as tf
    import numpy as np
    from tensorflow.examples.tutorials.mnist import input_data
    import matplotlib.pyplot as plt
    
    mnist = input_data.read_data_sets('MNIST_data/',one_hot=True)
    batch_size = 50
    learning_rate = 0.001
    keep_prob = tf.placeholder(tf.float32)
    n_batch = mnist.train.num_examples // batch_size
    #import data
    
    train_images = mnist.train.images
    train_labels = mnist.train.labels
    test_images = mnist.test.images
    test_labels = mnist.test.labels
    print("Loading data...")
    print("training start....")
    #construct graph
    x_data = tf.placeholder(tf.float32,[None,784])
    y_data = tf.placeholder(tf.float32,[None,10])
    
    
    #initial variable
    w1 =tf.Variable(tf.truncated_normal([784,600],stddev=0.1))
    b1 = tf.Variable(tf.zeros([600])+0.1)
    pre1 = tf.nn.tanh(tf.matmul(x_data,w1)+b1)
    dro_pre1 = tf.nn.dropout(pre1,keep_prob)
    
    w2 =tf.Variable(tf.truncated_normal([600,400],stddev=0.1))
    b2 = tf.Variable(tf.zeros([400])+0.1)
    pre2 = tf.nn.tanh(tf.matmul(pre1,w2)+b2)
    dro_pre2 = tf.nn.dropout(pre2,keep_prob)
    
    w3 =tf.Variable(tf.truncated_normal([400,200],stddev=0.1))
    b3 = tf.Variable(tf.zeros([200])+0.1)
    pre3 = tf.nn.tanh(tf.matmul(pre2,w3)+b3)
    dro_pre3 = tf.nn.dropout(pre3,keep_prob)
    
    w4 =tf.Variable(tf.truncated_normal([200,100],stddev=0.1))
    b4 = tf.Variable(tf.zeros([100])+0.1)
    pre4 = tf.nn.tanh(tf.matmul(pre3,w4)+b4)
    dro_pre4 = tf.nn.dropout(pre4,keep_prob)
    
    w5 =tf.Variable(tf.truncated_normal([100,10],stddev=0.1))
    b5 = tf.Variable(tf.zeros([10])+0.1)
    pre5 = tf.nn.softmax(tf.matmul(pre4,w5)+b5)
    # dro_pre5 = tf.nn.dropout(pre5,keep_prob)
    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_data,logits=pre5))
    
    correct_pre = tf.equal(tf.argmax(y_data,1),tf.argmax(pre5,1))
    
    accuracy = tf.reduce_mean(tf.cast(correct_pre,tf.float32))
    #梯度下降
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
    
    #innitial variable
    init = tf.global_variables_initializer()
    epoch_ = []
    t_acc = []
    with tf.Session() as sess:
        sess.run(init)
        for epoch in range (51):
                epoch_.append(epoch)
            # if epoch % 2 == 0:
                for batch in range(n_batch):
                    batch_x,batch_y = mnist.train.next_batch(batch_size)
                    sess.run(optimizer,feed_dict={x_data:batch_x,y_data:batch_y,keep_prob:0.7})
    
                train_acc = sess.run(accuracy,feed_dict={x_data:train_images,y_data:train_labels,keep_prob:0.7})
                test_acc = sess.run(accuracy,feed_dict={x_data:test_images,y_data:test_labels,keep_prob:0.7})
                t_acc.append(test_acc)
                print("Iter:" + str(epoch) + ", Training Accuracy:" + str(train_acc)+ ", Testing Accuracy:" + str(test_acc))
    
    plt.figure()
    plt.plot(epoch_,t_acc,'ro--',lw = 1)
    plt.show()
    plt.savefig('Test06 image')

因为是个练手程序，所以没有使用CNN或RNN，就是一个加了dropout的小程序，效果还行。