keras的分类模型

基于keras的神经网络分类模型(二分类、多分类)

from matplotlib import pyplot
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

'''
keras实现神经网络分类模型
'''
# 读取数据
path = 'df2.csv'
train_df = pd.read_csv(path)
# 删掉不用字符串字段
dataset = train_df.drop('jh',axis=1)
# df转array
values = dataset.values

y = values[:, -1]
X = values[:, 0:-1]
# 必须标准化,否则难以收敛
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)

# 随机拆分训练集与测试集
from sklearn.model_selection import train_test_split
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.25, stratify=y)
# 多分类转换label
#nb_classes = 3
#train_Y = np_utils.to_categorical(train_y, nb_classes)
#test_Y = np_utils.to_categorical(test_y, nb_classes)
# 全连接神经网络
model = Sequential()
input = X.shape[1]
# 隐藏层128
model.add(Dense(128, input_shape=(input,)))
model.add(Activation('relu'))
# Dropout层用于防止过拟合
model.add(Dropout(0.2))
# 隐藏层128
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.2))
# 没有激活函数用于输出层,二分类问题,用sigmoid激活函数进行变换,多分类用softmax。
model.add(Dense(1))
model.add(Activation('sigmoid'))
# 使用高效的 ADAM 优化算法以,二分类损失函数binary_crossentropy,多分类的损失函数categorical_crossentropy
model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])
# early stoppping
from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_acc', patience=50, verbose=2)
# 训练
history = model.fit(train_X, train_y, epochs=400, batch_size=20, validation_data=(test_X, test_y), verbose=2, shuffle=False, callbacks=[early_stopping])# loss曲线
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()
# 预测
y_pre = model.predict_classes(test_X)
# 
print(classification_report(test_y, y_pre,labels=[0,1]))
print(confusion_matrix(test_y, y_pre))

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