• TF



    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    mnist = input_data.read_data_sets("data/", one_hot=True)
    
    Extracting data/train-images-idx3-ubyte.gz
    Extracting data/train-labels-idx1-ubyte.gz
    Extracting data/t10k-images-idx3-ubyte.gz
    Extracting data/t10k-labels-idx1-ubyte.gz
    

    单层神经网络


    参数设置

    numClasses = 10 
    inputSize = 784 
    numHiddenUnits = 50 # 隐层单元个数,将 784 个像素点,映射成 50个特征
    trainingIterations = 10000 
    batchSize = 100 
    
    X = tf.placeholder(tf.float32, shape = [None, inputSize])
    y = tf.placeholder(tf.float32, shape = [None, numClasses])
    

    参数初始化

    W1 = tf.Variable(tf.truncated_normal([inputSize, numHiddenUnits], stddev=0.1))
    B1 = tf.Variable(tf.constant(0.1), [numHiddenUnits])
    W2 = tf.Variable(tf.truncated_normal([numHiddenUnits, numClasses], stddev=0.1))
    B2 = tf.Variable(tf.constant(0.1), [numClasses])
    

    网络结构

    hiddenLayerOutput = tf.matmul(X, W1) + B1
    hiddenLayerOutput = tf.nn.relu(hiddenLayerOutput)
    finalOutput = tf.matmul(hiddenLayerOutput, W2) + B2
    finalOutput = tf.nn.relu(finalOutput)
    

    网络迭代

    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = finalOutput))
    opt = tf.train.GradientDescentOptimizer(learning_rate = .1).minimize(loss)
    
    correct_prediction = tf.equal(tf.argmax(finalOutput,1), tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    
    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)
    
    for i in range(trainingIterations):
        batch = mnist.train.next_batch(batchSize)
        batchInput = batch[0]
        batchLabels = batch[1]
        _, trainingLoss = sess.run([opt, loss], feed_dict={X: batchInput, y: batchLabels})
        if i%1000 == 0:
            trainAccuracy = accuracy.eval(session=sess, feed_dict={X: batchInput, y: batchLabels})
            print ("step %d, training accuracy %g"%(i, trainAccuracy))
    
    step 0, training accuracy 0.13
    step 1000, training accuracy 0.79
    step 2000, training accuracy 0.83
    step 3000, training accuracy 0.88
    step 4000, training accuracy 0.91
    step 5000, training accuracy 0.87
    step 6000, training accuracy 0.89
    step 7000, training accuracy 0.84
    step 8000, training accuracy 0.89
    step 9000, training accuracy 1
    

    两层神经网络

    
    numHiddenUnitsLayer2 = 100
    trainingIterations = 10000
    
    X = tf.placeholder(tf.float32, shape = [None, inputSize])
    y = tf.placeholder(tf.float32, shape = [None, numClasses])
    
    W1 = tf.Variable(tf.random_normal([inputSize, numHiddenUnits], stddev=0.1))
    B1 = tf.Variable(tf.constant(0.1), [numHiddenUnits])
    W2 = tf.Variable(tf.random_normal([numHiddenUnits, numHiddenUnitsLayer2], stddev=0.1))
    B2 = tf.Variable(tf.constant(0.1), [numHiddenUnitsLayer2])
    W3 = tf.Variable(tf.random_normal([numHiddenUnitsLayer2, numClasses], stddev=0.1))
    B3 = tf.Variable(tf.constant(0.1), [numClasses])
    
    hiddenLayerOutput = tf.matmul(X, W1) + B1
    hiddenLayerOutput = tf.nn.relu(hiddenLayerOutput)
    hiddenLayer2Output = tf.matmul(hiddenLayerOutput, W2) + B2
    hiddenLayer2Output = tf.nn.relu(hiddenLayer2Output)
    finalOutput = tf.matmul(hiddenLayer2Output, W3) + B3
    
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = finalOutput))
    opt = tf.train.GradientDescentOptimizer(learning_rate = .1).minimize(loss)
    
    correct_prediction = tf.equal(tf.argmax(finalOutput,1), tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    
    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)
    
    for i in range(trainingIterations):
        batch = mnist.train.next_batch(batchSize)
        batchInput = batch[0]
        batchLabels = batch[1]
        _, trainingLoss = sess.run([opt, loss], feed_dict={X: batchInput, y: batchLabels})
        if i%1000 == 0:
            train_accuracy = accuracy.eval(session=sess, feed_dict={X: batchInput, y: batchLabels})
            print ("step %d, training accuracy %g"%(i, train_accuracy))
    
    testInputs = mnist.test.images
    testLabels = mnist.test.labels
    acc = accuracy.eval(session=sess, feed_dict = {X: testInputs, y: testLabels})
    print("testing accuracy: {}".format(acc))
    
    step 0, training accuracy 0.1
    step 1000, training accuracy 0.97
    step 2000, training accuracy 0.98
    step 3000, training accuracy 1
    step 4000, training accuracy 0.99
    step 5000, training accuracy 1
    step 6000, training accuracy 0.99
    step 7000, training accuracy 1
    step 8000, training accuracy 0.99
    step 9000, training accuracy 1
    testing accuracy: 0.9700999855995178
    
    
    

  • 相关阅读:
    检测单链表是否含有环
    巧用正则和document.location.search获取URL参数的值
    网站前端性能优化
    Ubuntu 10.10 wubi安装错误处理
    SQL中随机数函数rand()简介
    C#实现智能提示(提示补全)功能
    重装系统使之svn再次复活
    MongoDB安装
    mongodb查询
    解决iframe中跨域session丢失的问题
  • 原文地址:https://www.cnblogs.com/fldev/p/14403318.html
Copyright © 2020-2023  润新知