TensorFlow生成三色线性训练集

Python生成三色训练集（可直接使用）

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

# 总训练数量
batchSize=100
# 单个训练集数量
eachBatchSize = 10

# Red points
redPointsX = np.random.normal(loc=2.0, scale=1.5, size=batchSize)
print("redX=", redPointsX)
redPointsY = np.random.normal(loc=2.0, scale=1.0, size=batchSize)
print("redY=", redPointsY)

'''
numpy中reshape函数的三种常见相关用法
reshape(1,-1)转化成1行：
reshape(2,-1)转换成两行：
reshape(-1,1)转换成1列： 
reshape(-1,2)转化成两列
'''
redP = np.concatenate(([redPointsX], [redPointsY]), 0)
print("red=", redP)
print("redPxEachBatch=", redP[0, 1:eachBatchSize], "redPyEachBatch=", redP[1, 1:eachBatchSize])

# Blue Points
bluePointsX = np.random.normal(loc=5.0, scale=1.5, size=batchSize)
print("blueX=", bluePointsX)
bluePointsY = np.random.normal(loc=5.0, scale=2.3, size=batchSize)
print("blueY=", bluePointsY)

blueP = np.concatenate(([bluePointsX], [bluePointsY]), 0)
print("blue=", blueP)
print("bluePxEachBatch=", blueP[0, 1:eachBatchSize], "bluePyEachBatch=", blueP[1, 1:eachBatchSize])

# Yellow points
yellowPointsX = np.random.normal(loc=5.0, scale=1.2, size=batchSize)
print("yellowX=", yellowPointsX)
yellowPointsY = np.random.normal(loc=2.0, scale=1.6, size=batchSize)
print("yellowY=", yellowPointsY)

yellowP = np.concatenate(([yellowPointsX], [yellowPointsY]), 0)
print("yellow=", yellowP)
print("yellowPxEachBatch=", yellowP[0, 1:eachBatchSize], "yellowPyEachBatch=", yellowP[1, 1:eachBatchSize])

# one-hot编码
X0 = np.concatenate(([redPointsX], [bluePointsX], [yellowPointsX]), 0)
Y0 = np.concatenate(([redPointsX], [bluePointsY], [yellowPointsY]), 0)
oneHotIndex = np.concatenate((np.zeros((1, len(redPointsX))), np.zeros((1, len(bluePointsX))),
                              np.zeros((1, len(yellowPointsX)))), 1)
print("oneHotIndex=", oneHotIndex)

plt.figure(1)
plt.scatter(redP[0, :], redP[1, :], c='red')
plt.scatter(blueP[0, :], blueP[1, :], c='blue')
plt.scatter(yellowP[0, :], yellowP[1, :], c='yellow')
plt.title('Exercise Set')
plt.show()