实验11-使用keras完成逻辑回归

版本python3.7 tensorflow版本为tensorflow-gpu版本2.6

运行结果：

 

 代码：

import numpy as np

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
import matplotlib.pyplot as plt
from sklearn import datasets

# 样本数据集，两个特征列，两个分类二分类不需要onehot编码，直接将类别转换为0和1，分别代表正样本的概率。
X,y=datasets.make_classification(n_samples=200, n_features=2, n_informative=2, n_redundant=0,n_repeated=0, n_classes=2, n_clusters_per_class=1)

# 构建神经网络模型
model = Sequential()
model.add(Dense(input_dim=2, units=1))
model.add(Activation('sigmoid'))

# 选定loss函数和优化器
model.compile(loss='binary_crossentropy', optimizer='sgd')

# 训练过程
print('Training -----------')
for step in range(501):
    cost = model.train_on_batch(X, y)
    if step % 50 == 0:
        print("After %d trainings, the cost: %f" % (step, cost))

# 测试过程
print('\nTesting ------------')
cost = model.evaluate(X, y, batch_size=40)
print('test cost:', cost)
W, b = model.layers[0].get_weights()
print('Weights=', W, '\nbiases=', b)

# 将训练结果绘出
Y_pred = model.predict(X)
Y_pred = (Y_pred*2).astype('int')  # 将概率转化为类标号，概率在0-0.5时，转为0，概率在0.5-1时转为1
# 绘制散点图 参数：x横轴 y纵轴
plt.subplot(2,1,1).scatter(X[:,0], X[:,1], c=Y_pred[:,0])
plt.subplot(2,1,2).scatter(X[:,0], X[:,1], c=y)
plt.show()