本节仿真实现《深度学习之TensorFlow》（机械工业出版社）第七章 P120
代码7-1 线性逻辑回归
1 import tensorflow as tf 2 import numpy as np 3 import matplotlib.pyplot as plt 4 5 6 def generate(sample_size, mean, cov, regression): 7 # mean=[[x1, y1], 8 # [x2, y2], 9 # [x3, y3]] 10 # mean=[[2, 2], 11 # [5, 5], 12 # [5, 2]] 13 14 # cov=[ [x1, y1], 15 # [x2, y2], 16 # [x3, y3]] 17 # cov=[ [1.5, 1], 18 # [1.5, 2.3], 19 # [1.2, 1.6]] 20 21 # regression=Ture: 转化为one-hot label 22 23 import numpy as np 24 import tensorflow as tf 25 import matplotlib.pyplot as plt 26 27 # 总训练数量 28 batchSize = np.array(sample_size, dtype=np.int16) 29 # 单个训练集数量 30 eachBatchSize = 10 31 32 # mean:位置均值 33 meaLoc = np.array(mean, dtype=np.float16) 34 35 # cov:位置标准差 36 covLoc = np.array(cov, np.float16) 37 38 # Red points 39 redPointsX = np.random.normal(loc=meaLoc[0, 0], scale=covLoc[0, 0], size=batchSize[0]) 40 print("redX=", redPointsX) 41 redPointsY = np.random.normal(loc=meaLoc[0, 1], scale=covLoc[0, 1], size=batchSize[0]) 42 print("redY=", redPointsY) 43 44 ''' 45 numpy中reshape函数的三种常见相关用法 46 reshape(1,-1)转化成1行： 47 reshape(2,-1)转换成两行： 48 reshape(-1,1)转换成1列： 49 reshape(-1,2)转化成两列 50 ''' 51 redP = np.concatenate(([redPointsX], [redPointsY]), 0) 52 print("red=", redP) 53 print("redPxEachBatch=", redP[0, 1:eachBatchSize], 54 "redPyEachBatch=", redP[1, 1:eachBatchSize]) 55 56 # Blue Points 57 bluePointsX = np.random.normal(loc=meaLoc[1, 0], scale=covLoc[1, 0], size=batchSize[1]) 58 print("blueX=", bluePointsX) 59 bluePointsY = np.random.normal(loc=meaLoc[1, 1], scale=covLoc[1, 1], size=batchSize[1]) 60 print("blueY=", bluePointsY) 61 62 blueP = np.concatenate(([bluePointsX], [bluePointsY]), 0) 63 print("blue=", blueP) 64 print("bluePxEachBatch=", blueP[0, 1:eachBatchSize], 65 "bluePyEachBatch=", blueP[1, 1:eachBatchSize]) 66 67 # Yellow points 68 yellowPointsX = np.random.normal(loc=meaLoc[2, 0], scale=covLoc[2, 0], size=batchSize[2]) 69 print("yellowX=", yellowPointsX) 70 yellowPointsY = np.random.normal(loc=meaLoc[2, 1], scale=covLoc[2, 1], size=batchSize[2]) 71 print("yellowY=", yellowPointsY) 72 73 yellowP = np.concatenate(([yellowPointsX], [yellowPointsY]), 0) 74 print("yellow=", yellowP) 75 print("yellowPxEachBatch=", yellowP[0, 1:eachBatchSize], 76 "yellowPyEachBatch=", yellowP[1, 1:eachBatchSize]) 77 78 # one-hot编码 79 XLabel = np.concatenate((redPointsX, bluePointsX, yellowPointsX), 0) 80 YLabel = np.concatenate((redPointsX, bluePointsY, yellowPointsY), 0) 81 X0 = np.concatenate(([XLabel], [YLabel]), 0) 82 X0 = X0.T 83 84 print("X0=", X0) 85 LabelIndex = np.concatenate((np.zeros((len(redPointsX))), np.ones((len(bluePointsX))), 86 2 * np.ones((len(yellowPointsX)))), 0) 87 Y0 = LabelIndex.copy() 88 print("LabelIndex=", LabelIndex) 89 print("Y0=", Y0) 90 91 plt.figure(1) 92 plt.scatter(redP[0, :], redP[1, :], c='red') 93 plt.scatter(blueP[0, :], blueP[1, :], c='blue') 94 plt.scatter(yellowP[0, :], yellowP[1, :], c='yellow') 95 plt.title('Exercise Set') 96 plt.show() 97 98 print("LabelIndex.shape=", LabelIndex.shape) 99 b = LabelIndex.shape[0] 100 print("b=", b) 101 102 103 if regression == True: 104 oneHotLabel = np.zeros((b, 3)) 105 print("oneHotLabel=", oneHotLabel) 106 for i, ele in enumerate(LabelIndex): 107 print("i=", i, "ele=", ele) 108 if ele == 0: 109 oneHotLabel[i, 2] = 1 110 elif ele == 1: 111 oneHotLabel[i, 1] = 1 112 else: 113 oneHotLabel[i, 0] = 1 114 print("oneHotLabel=", oneHotLabel) 115 return X0, Y0, oneHotLabel 116 else: 117 return X0, Y0, LabelIndex 118 119 120 mean = [[2, 2], [5, 5], [5, 2]] 121 cov = [[1.5, 1.0], 122 [1.5, 2.3], 123 [1.2, 1.6]] 124 regression = True 125 126 X0, Y0, OneHotLabel = generate([100, 100, 100], mean, cov, regression) 127 print("X0=", X0, " ", "Y0=", Y0, " ", "OneHotLabel=", OneHotLabel)
X0= [[ 1.29076547e+00 1.29076547e+00]
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[ 2.88065974e+00 2.88065974e+00]
[ 2.60421278e+00 2.60421278e+00]
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[ 3.51963078e+00 3.51963078e+00]
[ -3.24017993e-01 -3.24017993e-01]
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[ -4.90235098e-01 -4.90235098e-01]
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LabelIndex= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]
Y0= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]