Linear_regression

线性回归的Python实现

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltrng = np.random#parameterslearning_rate = 0.01training_epochs = 1000display_step = 50#training Datatrain_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]#tf Graph InputX = tf.placeholder("float")Y = tf.placeholder("float")#Set model weightsW = tf.Variable(rng.randn(),name = "weight")b = tf.Variable(rng.randn(),name = "bias")#Construct a linear modelpred = tf.add(tf.multiply(X,W),b)#Mean squared errorcost = tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)#Gradient descentoptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)#Initializing the variablesinit = tf.global_variables_initializer()#Launch the graphwith tf.Session() as sess:    sess.run(init)    #fit all training data    for epochs in range(training_epochs):        for (x,y) in zip(train_X,train_Y):            sess.run(optimizer,feed_dict={X:x, Y:y})        if (epochs+1) % display_step == 0:            c = sess.run(cost,feed_dict={X: train_X, Y: train_Y})            print('Epoch:','%04d' % (epochs+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    "Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n'    # Graphic display    plt.plot(train_X, train_Y, 'ro', label='Original data')    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')    plt.legend()    plt.show()