利用TensorFlow实现多元线性回归
2017-05-12 11:19 猎手家园 阅读(3329) 评论(0) 编辑 收藏 举报利用TensorFlow实现多元线性回归,代码如下:
# -*- coding:utf-8 -*- import tensorflow as tf import numpy as np from sklearn import linear_model from sklearn import preprocessing # Read x and y x_data = np.loadtxt("ex3x.dat").astype(np.float32) y_data = np.loadtxt("ex3y.dat").astype(np.float32) # We evaluate the x and y by sklearn to get a sense of the coefficients. reg = linear_model.LinearRegression() reg.fit(x_data, y_data) print ("Coefficients of sklearn: K=%s, b=%f" % (reg.coef_, reg.intercept_)) # Now we use tensorflow to get similar results. # Before we put the x_data into tensorflow, we need to standardize it # in order to achieve better performance in gradient descent; # If not standardized, the convergency speed could not be tolearated. # Reason: If a feature has a variance that is orders of magnitude larger than others, # it might dominate the objective function # and make the estimator unable to learn from other features correctly as expected. scaler = preprocessing.StandardScaler().fit(x_data) print (scaler.mean_, scaler.scale_) x_data_standard = scaler.transform(x_data) W = tf.Variable(tf.zeros([2, 1])) b = tf.Variable(tf.zeros([1, 1])) y = tf.matmul(x_data_standard, W) + b loss = tf.reduce_mean(tf.square(y - y_data.reshape(-1, 1)))/2 optimizer = tf.train.GradientDescentOptimizer(0.3) train = optimizer.minimize(loss) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) for step in range(100): sess.run(train) if step % 10 == 0: print (step, sess.run(W).flatten(), sess.run(b).flatten()) print ("Coefficients of tensorflow (input should be standardized): K=%s, b=%s" % (sess.run(W).flatten(), sess.run(b).flatten())) print ("Coefficients of tensorflow (raw input): K=%s, b=%s" % (sess.run(W).flatten() / scaler.scale_, sess.run(b).flatten() - np.dot(scaler.mean_ / scaler.scale_, sess.run(W))))
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