In [1]:

    

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf


    

    




/Users/atsushi.hara/.anyenv/envs/pyenv/versions/anaconda3-5.0.1/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
  return f(*args, **kwds)
/Users/atsushi.hara/.anyenv/envs/pyenv/versions/anaconda3-5.0.1/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

In [2]:

    

sess = tf.Session()


    

In [3]:

    

x_vals = np.linspace(0, 10, 100)
y_vals = x_vals + np.random.normal(0, 1, 100)


    

In [4]:

    

x_vals_column = np.transpose(np.matrix(x_vals))
ones_column = np.transpose(np.matrix(np.repeat(1, 100)))
A = np.column_stack((x_vals_column, ones_column))

b = np.transpose(np.matrix(y_vals))


    

In [5]:

    

A_tensor = tf.constant(A)
b_tensor = tf.constant(b)


    

In [6]:

    

tA_A = tf.matmul(tf.transpose(A_tensor), A_tensor)
tA_A_inv = tf.matrix_inverse(tA_A)
product = tf.matmul(tA_A_inv, tf.transpose(A_tensor))
solution = tf.matmul(product, b_tensor)

solution_eval = sess.run(solution)


    

In [7]:

    

slope = solution_eval[0][0]
y_intercept = solution_eval[1][0]
# 傾きと切片
print('slope: {}'.format(slope))
print('y_intecept: {}'.format(y_intercept))


    

    




slope: 0.9692888103873192
y_intecept: 0.1987270931472158

In [8]:

    

best_fit = []
for i in x_vals:
    best_fit.append(slope*i+y_intercept)
    
plt.plot(x_vals, y_vals, 'o', label='Data')
plt.plot(x_vals, best_fit, 'r-', label='Best fit line', linewidth=3)
plt.legend(loc='upper left')
plt.show()


    

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