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  • tensorflow线性回归

    一、代码显示

    import tensorflow.compat.v1 as tf
    tf.disable_v2_behavior()
    import numpy as np
    import os
    import matplotlib.pyplot as plt
    os.environ["CUDA_VISIBLE_DEVICES"]="0"
    learning_rate=0.01
    training_epochs=1000
    display_step=50
    train_X=np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
    train_Y=np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
    n_samples=train_X.shape[0]
    X=tf.placeholder("float")
    Y=tf.placeholder("float")
    W=tf.Variable(np.random.randn(),name="weight")
    b=tf.Variable(np.random.randn(),name='bias')
    pred=tf.add(tf.multiply(X,W),b)
    cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)
    optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
    init=tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        for epoch in range(training_epochs):
            for (x, y) in zip(train_X, train_Y):
                sess.run(optimizer, feed_dict={X: x, Y: y})
                if (epoch+1) % display_step == 0:
                    c = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
                    print("Epoch:" , '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c), "W=", sess.run(W), "b=", sess.run(
                        b))
                    print("Optimization Finished!")
                    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
                    print("Train cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b))
                    plt.plot(train_X, train_Y, 'ro', label='Original data')
                    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label="Fitting line")
                    plt.legend()
                    plt.show()
    View Code

    二、截图显示

     

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  • 原文地址:https://www.cnblogs.com/hhjing/p/14290175.html
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