Tensorflow - Implement for generating some 3-dimensional phony data and fitting them with a plane.

Coding according to TensorFlow 官方文档中文版

 import tensorflow as tf
 import numpy as np

 ''' Intro. for this python file.
 Objective:
     Implement for generating some 3-dimensional phony data and fitting them with a plane.
 Operating Environment:
     python = 3.6.4
     tensorflow = 1.5.0
     numpy = 1.15.1
 '''

 # Generate phony data using NumPy. There is totally 100 points.
 ''' numpy.random.rand(d0, d1, ..., dn)
 Explanation:
     Random values in a given shape.
     Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).
 '''
 ''' numpy.dot(a, b, out=None)
 Explanation:
     Dot product of two arrays.
 '''
 x_data = np.float32(np.random.rand(2, 100))
 y_data = np.dot([0.100, 0.200], x_data) + 0.300

 # Generate a linear model.
 ''' tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)
 Explanation:
     shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
     minval: A 0-D Tensor or Python value of type dtype. The lower bound on the range of random values to generate.
             Defaults to 0.
     maxval: A 0-D Tensor or Python value of type dtype. The upper bound on the range of random values to generate.
             Defaults to 1 if dtype is floating point.
     dtype: The type of the output: float16, float32, float64, int32, or int64.
     seed: A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.
     name: A name for the operation (optional).
 '''
 ''' tf.zeros(shape, dtype=tf.float32, name=None)
 Explanation:
     shape: A list of integers, a tuple of integers, or a 1-D Tensor of type int32.
     dtype: The type of an element in the resulting Tensor.
     name: A name for the operation (optional).
 '''
 W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
 b = tf.Variable(tf.zeros([1]))
 y = tf.matmul(W, x_data) + b

 # Minimize variance
 ''' tf.square(x, name=None)
 Explanation:
     Computes square of x element-wise.
 '''
 ''' tf.reduce_mean(input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None)
 Explanation:
     input_tensor: The tensor to reduce. Should have numeric type.
     axis: The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range
           [-rank(input_tensor), rank(input_tensor)].
     keepdims: If true, retains reduced dimensions with length 1.
     name: A name for the operation (optional).
     reduction_indices: The old (deprecated) name for axis.
     keep_dims: Deprecated alias for keepdims.
 '''
 loss = tf.reduce_mean(tf.square(y - y_data))
 optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)
 train = optimizer.minimize(loss)

 # Initialize variables
 init = tf.initialize_all_variables()

 # Launch the graph in a session.
 sess = tf.Session()
 sess.run(init)

 # Fitting
 for step in range(0, 201):
     sess.run(train)
     if step % 20 == 0:
         print(step, sess.run(W), sess.run(b))

 # The best fitting result: W = [[0.10000069 0.20000069]], b = [0.29999927]