我想通过使用Tensorflow构建一个多元线性回归模型.

一个数据示例：2104,3,399900(前两个是功能,最后一个是房价;我们有47个示例)

代码如下：

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

# model parameters as external flags

flags = tf.app.flags

FLAGS = flags.FLAGS

flags.DEFINE_float('learning_rate', 1.0, 'Initial learning rate.')

flags.DEFINE_integer('max_steps', 100, 'Number of steps to run trainer.')

flags.DEFINE_integer('display_step', 100, 'Display logs per step.')

def run_training(train_X, train_Y):

X = tf.placeholder(tf.float32, [m, n])

Y = tf.placeholder(tf.float32, [m, 1])

# weights

W = tf.Variable(tf.zeros([n, 1], dtype=np.float32), name="weight")

b = tf.Variable(tf.zeros([1], dtype=np.float32), name="bias")

# linear model

activation = tf.add(tf.matmul(X, W), b)

cost = tf.reduce_sum(tf.square(activation - Y)) / (2*m)

optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(cost)

with tf.Session() as sess:

init = tf.initialize_all_variables()

sess.run(init)

for step in range(FLAGS.max_steps):

sess.run(optimizer, feed_dict={X: np.asarray(train_X), Y: np.asarray(train_Y)})

if step % FLAGS.display_step == 0:

print "Step:", "%04d" % (step+1), "Cost=", "{:.2f}".format(sess.run(cost, \

feed_dict={X: np.asarray(train_X), Y: np.asarray(train_Y)})), "W=", sess.run(W), "b=", sess.run(b)

print "Optimization Finished!"

training_cost = sess.run(cost, feed_dict={X: np.asarray(train_X), Y: np.asarray(train_Y)})

print "Training Cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n'

print "Predict.... (Predict a house with 1650 square feet and 3 bedrooms.)"

predict_X = np.array([1650, 3], dtype=np.float32).reshape((1, 2))

# Do not forget to normalize your features when you make this prediction

predict_X = predict_X / np.linalg.norm(predict_X)

predict_Y = tf.add(tf.matmul(predict_X, W),b)

print "House price(Y) =", sess.run(predict_Y)

def read_data(filename, read_from_file = True):

global m, n

if read_from_file:

with open(filename) as fd:

data_list = fd.read().splitlines()

m = len(data_list) # number of examples

n = 2 # number of features

train_X = np.zeros([m, n], dtype=np.float32)

train_Y = np.zeros([m, 1], dtype=np.float32)

for i in range(m):

datas = data_list[i].split(",")

for j in range(n):

train_X[i][j] = float(datas[j])

train_Y[i][0] = float(datas[-1])

else:

m = 47

n = 2

train_X = np.array( [[ 2.10400000e+03, 3.00000000e+00],

[ 1.60000000e+03, 3.00000000e+00],

[ 2.40000000e+03, 3.00000000e+00],

[ 1.41600000e+03, 2.00000000e+00],

[ 3.00000000e+03, 4.00000000e+00],

[ 1.98500000e+03, 4.00000000e+00],

[ 1.53400000e+03, 3.00000000e+00],

[ 1.42700000e+03, 3.00000000e+00],

[ 1.38000000e+03, 3.00000000e+00],

[ 1.49400000e+03, 3.00000000e+00],

[ 1.94000000e+03, 4.00000000e+00],

[ 2.00000000e+03, 3.00000000e+00],

[ 1.89000000e+03, 3.00000000e+00],

[ 4.47800000e+03, 5.00000000e+00],

[ 1.26800000e+03, 3.00000000e+00],

[ 2.30000000e+03, 4.00000000e+00],

[ 1.32000000e+03, 2.00000000e+00],

[ 1.23600000e+03, 3.00000000e+00],

[ 2.60900000e+03, 4.00000000e+00],

[ 3.03100000e+03, 4.00000000e+00],

[ 1.76700000e+03, 3.00000000e+00],

[ 1.88800000e+03, 2.00000000e+00],

[ 1.60400000e+03, 3.00000000e+00],

[ 1.96200000e+03, 4.00000000e+00],

[ 3.89000000e+03, 3.00000000e+00],

[ 1.10000000e+03, 3.00000000e+00],

[ 1.45800000e+03, 3.00000000e+00],

[ 2.52600000e+03, 3.00000000e+00],

[ 2.20000000e+03, 3.00000000e+00],

[ 2.63700000e+03, 3.00000000e+00],

[ 1.83900000e+03, 2.00000000e+00],

[ 1.00000000e+03, 1.00000000e+00],

[ 2.04000000e+03, 4.00000000e+00],

[ 3.13700000e+03, 3.00000000e+00],

[ 1.81100000e+03, 4.00000000e+00],

[ 1.43700000e+03, 3.00000000e+00],

[ 1.23900000e+03, 3.00000000e+00],

[ 2.13200000e+03, 4.00000000e+00],

[ 4.21500000e+03, 4.00000000e+00],

[ 2.16200000e+03, 4.00000000e+00],

[ 1.66400000e+03, 2.00000000e+00],

[ 2.23800000e+03, 3.00000000e+00],

[ 2.56700000e+03, 4.00000000e+00],

[ 1.20000000e+03, 3.00000000e+00],

[ 8.52000000e+02, 2.00000000e+00],

[ 1.85200000e+03, 4.00000000e+00],

[ 1.20300000e+03, 3.00000000e+00]]

).astype('float32')

train_Y = np.array([[ 399900.],

[ 329900.],

[ 369000.],

[ 232000.],

[ 539900.],

[ 299900.],

[ 314900.],

[ 198999.],

[ 212000.],

[ 242500.],

[ 239999.],

[ 347000.],

[ 329999.],

[ 699900.],

[ 259900.],

[ 449900.],

[ 299900.],

[ 199900.],

[ 499998.],

[ 599000.],

[ 252900.],

[ 255000.],

[ 242900.],

[ 259900.],

[ 573900.],

[ 249900.],

[ 464500.],

[ 469000.],

[ 475000.],

[ 299900.],

[ 349900.],

[ 169900.],

[ 314900.],

[ 579900.],

[ 285900.],

[ 249900.],

[ 229900.],

[ 345000.],

[ 549000.],

[ 287000.],

[ 368500.],

[ 329900.],

[ 314000.],

[ 299000.],

[ 179900.],

[ 299900.],

[ 239500.]]

).astype('float32')

return train_X, train_Y

def feature_normalize(train_X):

train_X_tmp = train_X.transpose()

for N in range(2):

train_X_tmp[N] = train_X_tmp[N] / np.linalg.norm(train_X_tmp[N])

train_X = train_X_tmp.transpose()

return train_X

import sys

def main(argv):

if not argv:

print "Enter data filename."

sys.exit()

filename = argv[1]

train_X, train_Y = read_data(filename, False)

train_X = feature_normalize(train_X)

run_training(train_X, train_Y)

if __name__ == '__main__':

tf.app.run()

结果我得到：

with learning rate 1.0 and 100 iterations, the model finally predicts a house with 1650 square feet and 3 bedrooms get a price $752,903, with:

Training Cost= 4.94429e+09

W= [[ 505305.375] [ 177712.625]]

b= [ 247275.515625]

我的代码中肯定会有一些错误,因为不同学习率的成本函数图与solution不一样

我应该得到以下结果：

theta_0: 340,413

theta_1: 110,631

theta_2: -6,649

The predicted price of the house should be $293,081.

我使用tensorflow有什么问题吗？