這篇文章主要介紹了關於用TensorFlow實現lasso回歸和嶺回歸演算法的範例,有著一定的參考價值,現在分享給大家,有需要的朋友可以參考一下
也有些正規方法可以限制迴歸演算法輸出結果中係數的影響,其中最常用的兩種正規方法是lasso迴歸和嶺迴歸。
lasso迴歸和嶺迴歸演算法跟常規線性迴歸演算法極為相似,有一點不同的是,在公式中增加正規項來限制斜率(或淨斜率)。這樣做的主要原因是限制特徵對因變數的影響,透過增加一個依賴斜率A的損失函數來實現。
對於lasso迴歸演算法,在損失函數上增加一項:斜率A的某個給定倍數。我們使用TensorFlow的邏輯操作,但沒有這些操作相關的梯度,而是使用階躍函數的連續估計,也稱為連續階躍函數,其會在截止點跳躍擴大。一會兒就可以看到如何使用lasso迴歸演算法。
對於嶺迴歸演算法,增加一個L2範數,即斜率係數的L2正則。
# LASSO and Ridge Regression # lasso回归和岭回归 # # This function shows how to use TensorFlow to solve LASSO or # Ridge regression for # y = Ax + b # # We will use the iris data, specifically: # y = Sepal Length # x = Petal Width # import required libraries import matplotlib.pyplot as plt import sys import numpy as np import tensorflow as tf from sklearn import datasets from tensorflow.python.framework import ops # Specify 'Ridge' or 'LASSO' regression_type = 'LASSO' # clear out old graph ops.reset_default_graph() # Create graph sess = tf.Session() ### # Load iris data ### # iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)] iris = datasets.load_iris() x_vals = np.array([x[3] for x in iris.data]) y_vals = np.array([y[0] for y in iris.data]) ### # Model Parameters ### # Declare batch size batch_size = 50 # Initialize placeholders x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) # make results reproducible seed = 13 np.random.seed(seed) tf.set_random_seed(seed) # Create variables for linear regression A = tf.Variable(tf.random_normal(shape=[1,1])) b = tf.Variable(tf.random_normal(shape=[1,1])) # Declare model operations model_output = tf.add(tf.matmul(x_data, A), b) ### # Loss Functions ### # Select appropriate loss function based on regression type if regression_type == 'LASSO': # Declare Lasso loss function # 增加损失函数,其为改良过的连续阶跃函数,lasso回归的截止点设为0.9。 # 这意味着限制斜率系数不超过0.9 # Lasso Loss = L2_Loss + heavyside_step, # Where heavyside_step ~ 0 if A < constant, otherwise ~ 99 lasso_param = tf.constant(0.9) heavyside_step = tf.truep(1., tf.add(1., tf.exp(tf.multiply(-50., tf.subtract(A, lasso_param))))) regularization_param = tf.multiply(heavyside_step, 99.) loss = tf.add(tf.reduce_mean(tf.square(y_target - model_output)), regularization_param) elif regression_type == 'Ridge': # Declare the Ridge loss function # Ridge loss = L2_loss + L2 norm of slope ridge_param = tf.constant(1.) ridge_loss = tf.reduce_mean(tf.square(A)) loss = tf.expand_dims(tf.add(tf.reduce_mean(tf.square(y_target - model_output)), tf.multiply(ridge_param, ridge_loss)), 0) else: print('Invalid regression_type parameter value',file=sys.stderr) ### # Optimizer ### # Declare optimizer my_opt = tf.train.GradientDescentOptimizer(0.001) train_step = my_opt.minimize(loss) ### # Run regression ### # Initialize variables init = tf.global_variables_initializer() sess.run(init) # Training loop loss_vec = [] for i in range(1500): rand_index = np.random.choice(len(x_vals), size=batch_size) rand_x = np.transpose([x_vals[rand_index]]) rand_y = np.transpose([y_vals[rand_index]]) sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y}) temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y}) loss_vec.append(temp_loss[0]) if (i+1)%300==0: print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b))) print('Loss = ' + str(temp_loss)) print('\n') ### # Extract regression results ### # Get the optimal coefficients [slope] = sess.run(A) [y_intercept] = sess.run(b) # Get best fit line best_fit = [] for i in x_vals: best_fit.append(slope*i+y_intercept) ### # Plot results ### # Plot regression line against data points plt.plot(x_vals, y_vals, 'o', label='Data Points') plt.plot(x_vals, best_fit, 'r-', label='Best fit line', linewidth=3) plt.legend(loc='upper left') plt.title('Sepal Length vs Pedal Width') plt.xlabel('Pedal Width') plt.ylabel('Sepal Length') plt.show() # Plot loss over time plt.plot(loss_vec, 'k-') plt.title(regression_type + ' Loss per Generation') plt.xlabel('Generation') plt.ylabel('Loss') plt.show()
輸出結果:
Step #300 A = [[ 0.77170753]] b = [[ 1.82499862]]
Loss = [[ 10.26473045]]
Step #600 A = [[ 0.75908542]] b = [[ 3.2220633]]
Loss = [[ 3.06292033]]##Step #007485. ] b = [[ 3.9975822]]
Loss = [[ 1.23220456]]
Step #1200 A = [[ 0.73752165]] b = [[ 4.42974091]]##Loss5 = [ ] b = [[ 4.42974091]]##Loss5 = [ 0.57]#7057]#。 #Step #1500 A = [[ 0.72942668]] b = [[ 4.67253113]]
Loss = [[ 0.40874988]]
透過在標準線性迴歸估計的基礎上,增加一個連續的階躍函數,實現lasso迴歸演算法。由於階躍函數的坡度,我們需要注意步長,因為太大的步長會導致最終不收斂。
相關推薦:
用TensorFlow實作戴明迴歸演算法的範例
以上是用TensorFlow實作lasso迴歸和嶺迴歸演算法的範例的詳細內容。更多資訊請關注PHP中文網其他相關文章!