如何在Tensorflow上使用带有我自己的数据的线性回归模型

Question

I have data set like this

[2016-10-24,23.00,76.00,1015.40,0.00,0.00,100.00,26.00,100.00,100.00,0.00,6.88,186.01,12.26,220.24,27.60,262.50,14.04,2.1] , [15.47]
[2016-10-24,22.00,73.00,1014.70,0.00,0.00,10.20,34.00,0.00,2.00,0.00,6.49,176.82,11.97,201.16,24.27,249.15,7.92,0.669999 ] , [16.14]
....
....

Size of this is [n][19], [n][1]. I want to use Tensorflow Linear Regression to get prediction on Python. I mean I want to use this 19 variable to predict 1 variable. I have large data set. I think it will be enough for training.

However, I am a beginner in Machine Learning and Tensorflow. Can you give me any documentation or clue for this? Thanks in advance.

Answer 1

This is a simple Linear Regression model:

def model(X, w):
    return tf.mul(X, w) # Just X*w so this model line is pretty simple

w = tf.Variable(0.0, name="weights") 
y_model = model(X, w)

cost = tf.square(Y - y_model) # use square error for cost function
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost) 

Then you need to run the train_op under TensorFlow session.

For your dataset, you just need to change the w and x. See more examples at https://github.com/nlintz/TensorFlow-Tutorials/blob/master/01_linear_regression.py .