zoukankan      html  css  js  c++  java
  • 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:
    武道之路-炼体期五重天中期
    武道之路-炼体期五重天
    武道之路-炼体期四重天巅峰
    修改Oracle监听端口
    完美解决IE(IE6/IE7/IE8)不兼容HTML5标签的方法
    Create Linked Server SQL Server 2008
    Oracle:ODP.NET Managed 小试牛刀
    jquery ajax跨域请求webservice webconfig配置
    oracle 11g ORA-12541: TNS: 无监听程序 (DBD ERROR: OCIServerAttach)
    oracle 11g 一直提示 严重: 监听程序未启动或数据库服务未注册到该监听程序
  • 原文地址:https://www.cnblogs.com/fldev/p/14403318.html
Copyright © 2011-2022 走看看