from sklearn import datasets#sklearn提供的自带的数据集
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
# 导入数据
dataset = datasets.load_iris()
x = dataset.data
Y = dataset.target
# 设定随机种子
seed = 7
np.random.seed(seed)
# 构建模型函数
def create_model(optimizer='adam', init='glorot_uniform'):
model = Sequential()#构建顺序模型
model.add(Dense(units=4, activation='relu', input_dim=4, kernel_initializer=init))
model.add(Dense(units=6, activation='relu', kernel_initializer=init))
model.add(Dense(units=3, activation='softmax', kernel_initializer=init))
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_model, epochs=150, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(model, x, Y, cv=kfold)
print('Accuracy: %.2f%% (%.2f)' % (results.mean()*100, results.std()))
'''
Sequential 顺序模型
Dense 全连接层
input_dim 输入层（已知X）
units 隐藏层（求未知数W）
kernel_initializer权重初始化 =>glorot_uniform
model.add第一个是输入层，中间的全部是隐藏层，最后一个是输出层
gamblerofdestinyR14
发布了38 篇原创文章 · 获赞 1 · 访问量 733
私信
关注