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

佚名 6年前 (2018-08-28) 人工智能 601人围观抢沙发百度已收录

 1 import tensorflow as tf
 2 import numpy as np
 3 
 4 
 5 ''' Intro. for this python file.
 6 Objective:
 7     Implement for generating some 3-dimensional phony data and fitting them with a plane.
 8 Operating Environment:
 9     python = 3.6.4
10     tensorflow = 1.5.0
11     numpy = 1.15.1
12 '''
13 
14 
15 # Generate phony data using NumPy. There is totally 100 points.
16 ''' numpy.random.rand(d0, d1, ..., dn)
17 Explanation:
18     Random values in a given shape.
19     Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).
20 '''
21 ''' numpy.dot(a, b, out=None)
22 Explanation:
23     Dot product of two arrays.
24 '''
25 x_data = np.float32(np.random.rand(2, 100))
26 y_data = np.dot([0.100, 0.200], x_data) + 0.300
27 
28 
29 # Generate a linear model.
30 ''' tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)
31 Explanation:
32     shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
33     minval: A 0-D Tensor or Python value of type dtype. The lower bound on the range of random values to generate. 
34             Defaults to 0.
35     maxval: A 0-D Tensor or Python value of type dtype. The upper bound on the range of random values to generate. 
36             Defaults to 1 if dtype is floating point.
37     dtype: The type of the output: float16, float32, float64, int32, or int64.
38     seed: A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.
39     name: A name for the operation (optional).
40 '''
41 ''' tf.zeros(shape, dtype=tf.float32, name=None)
42 Explanation:
43     shape: A list of integers, a tuple of integers, or a 1-D Tensor of type int32.
44     dtype: The type of an element in the resulting Tensor.
45     name: A name for the operation (optional).
46 '''
47 W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
48 b = tf.Variable(tf.zeros([1]))
49 y = tf.matmul(W, x_data) + b
50 
51 # Minimize variance
52 ''' tf.square(x, name=None)
53 Explanation:
54     Computes square of x element-wise.
55 '''
56 ''' tf.reduce_mean(input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None)
57 Explanation:
58     input_tensor: The tensor to reduce. Should have numeric type.
59     axis: The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range 
60           [-rank(input_tensor), rank(input_tensor)].
61     keepdims: If true, retains reduced dimensions with length 1.
62     name: A name for the operation (optional).
63     reduction_indices: The old (deprecated) name for axis.
64     keep_dims: Deprecated alias for keepdims.
65 '''
66 loss = tf.reduce_mean(tf.square(y - y_data))
67 optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)
68 train = optimizer.minimize(loss)
69 
70 # Initialize variables
71 init = tf.initialize_all_variables()
72 
73 # Launch the graph in a session.
74 sess = tf.Session()
75 sess.run(init)
76 
77 # Fitting
78 for step in range(0, 201):
79     sess.run(train)
80     if step % 20 == 0:
81         print(step, sess.run(W), sess.run(b))
82 
83 # The best fitting result: W = [[0.10000069 0.20000069]], b = [0.29999927]