我正在学习Aurelien Geron的《Hands-On Machine Learning》一书中的Tensorflow示例。然而,我无法复制这个支持笔记本中的简单线性回归示例。为什么Tensorflow的结果与Numpy / SciKit-Learn不匹配?
据我所知,没有优化(我们使用正规方程,因此只涉及矩阵计算),而且答案看起来太不同了,无法归咎于精度误差。
回答:
据我所知,没有优化(我们使用正规方程,因此只涉及矩阵计算),而且答案看起来太不同了,无法归咎于精度误差。
import numpy as np
import tensorflow as tf
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
m, n = housing.data.shape
housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]
X = tf.constant(housing_data_plus_bias, dtype=tf.float32, name="X")
y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name="y")
XT = tf.transpose(X)
theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y)
with tf.Session() as sess:
theta_value = theta.eval()
theta_value
答案:
array([[ -3.74651413e+01],
[ 4.35734153e-01],
[ 9.33829229e-03],
[ -1.06622010e-01],
[ 6.44106984e-01],
[ -4.25131839e-06],
[ -3.77322501e-03],
[ -4.26648885e-01],
[ -4.40514028e-01]], dtype=float32)
###### 与纯NumPy的比较
X = housing_data_plus_bias
y = housing.target.reshape(-1, 1)
theta_numpy = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)
print(theta_numpy)
回答:
[[ -3.69419202e+01]
[ 4.36693293e-01]
[ 9.43577803e-03]
[ -1.07322041e-01]
[ 6.45065694e-01]
[ -3.97638942e-06]
[ -3.78654265e-03]
[ -4.21314378e-01]
[ -4.34513755e-01]]
###### 与Scikit-Learn比较
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing.data, housing.target.reshape(-1, 1))
print(np.r_[lin_reg.intercept_.reshape(-1, 1), lin_reg.coef_.T])
回答:
[[ -3.69419202e+01]
[ 4.36693293e-01]
[ 9.43577803e-03]
[ -1.07322041e-01]
[ 6.45065694e-01]
[ -3.97638942e-06]
[ -3.78654265e-03]
[ -4.21314378e-01]
[ -4.34513755e-01]]
更新:我的问题听起来与这个类似,但是遵循建议并没有解决问题。