tensorflow逻辑回归框架

- 日理万妓 2023-07-11 08:49 2阅读 0赞

小彩笔的摸爬滚打之路orz

import numpy as np
    import tensorflow as tf
    import matplotlib.pyplot as plt
    import input_data

mnist      = input_data.read_data_sets('data/', one_hot=True)
    trainimg   = mnist.train.images
    trainlabel = mnist.train.labels
    testimg    = mnist.test.images
    testlabel  = mnist.test.labels
    print ("MNIST loaded")

print (trainimg.shape)
    print (trainlabel.shape)
    print (testimg.shape)
    print (testlabel.shape)
    #print (trainimg)
    print (trainlabel[0])

![在这里插入图片描述][20200304215408669.png]

x = tf.placeholder("float", [None, 784]) 
    y = tf.placeholder("float", [None, 10])  # None is for infinite 
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    # LOGISTIC REGRESSION MODEL
    actv = tf.nn.softmax(tf.matmul(x, W) + b) 
    # COST FUNCTION
    cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1)) 
    # OPTIMIZER
    learning_rate = 0.01
    optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# PREDICTION
    pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))   
    # ACCURACY
    accr = tf.reduce_mean(tf.cast(pred, "float"))
    # INITIALIZER
    init = tf.global_variables_initializer()

# argmax函数讲解
    sess = tf.InteractiveSession()
    
    arr = np.array([[31, 23,  4, 24, 27, 34],
                    [18,  3, 25,  0,  6, 35],
                    [28, 14, 33, 22, 20,  8],
                    [13, 30, 21, 19,  7,  9],
                    [16,  1, 26, 32,  2, 29],
                    [17, 12,  5, 11, 10, 15]])
    #tf.rank(arr).eval() 矩阵维度
    #tf.shape(arr).eval()
    #tf.argmax(arr, 0).eval() 求最大值索引，0对应每一列，1对应每一行，返回的都是所在的列号
    # 0 -> 31 (arr[0, 0])
    # 3 -> 30 (arr[3, 1])
    # 2 -> 33 (arr[2, 2])
    tf.argmax(arr, 1).eval()
    # 5 -> 34 (arr[0, 5])
    # 5 -> 35 (arr[1, 5])
    # 2 -> 33 (arr[2, 2])

![在这里插入图片描述][20200305010830968.png]

training_epochs = 50
    batch_size      = 100
    display_step    = 5
    # SESSION
    sess = tf.Session()
    sess.run(init)
    # MINI-BATCH LEARNING
    for epoch in range(training_epochs):
        avg_cost = 0.
        num_batch = int(mnist.train.num_examples/batch_size)
        for i in range(num_batch): 
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(optm, feed_dict={ x: batch_xs, y: batch_ys})
            feeds = { x: batch_xs, y: batch_ys}
            avg_cost += sess.run(cost, feed_dict=feeds)/num_batch
        # DISPLAY
        if epoch % display_step == 0:
            feeds_train = { x: batch_xs, y: batch_ys}
            feeds_test = { x: mnist.test.images, y: mnist.test.labels}
            train_acc = sess.run(accr, feed_dict=feeds_train)
            test_acc = sess.run(accr, feed_dict=feeds_test)
            print ("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f" 
                   % (epoch, training_epochs, avg_cost, train_acc, test_acc))
    print ("DONE")

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70]

[20200304215408669.png]: https://img-blog.csdnimg.cn/20200304215408669.png
[20200305010830968.png]: https://img-blog.csdnimg.cn/20200305010830968.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200305010859987.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg==,size_16,color_FFFFFF,t_70