代码实现

1、基于Keras设计的简单二分类问题开发的神经网络模型案例

# 训练一个最终分类的模型

from keras.models import Sequential

from keras.layers import Dense

from sklearn.datasets.samples_generator import make_blobs

from sklearn.preprocessing import MinMaxScaler

# 生成一个二分类问题的数据集

X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)

scalar = MinMaxScaler()

scalar.fit(X)

X = scalar.transform(X)

# 定义并拟合模型

model = Sequential()

model.add(Dense(4, input_dim=2, activation='relu'))

model.add(Dense(4, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam')

model.fit(X, y, epochs=200, verbose=0)