Keras实现简单分类神经网络

#keras搭建神经网络import sklearnfrom keras.models import Sequentialfrom keras.layers import Dense,Activationfrom keras.optimizers import SGDimport numpy as npfrom sklearn.datasets import load_irisiris=load_iris()x=iris.datay=iris.targetprint(y)#进行结果的标签化处理one-hot处理from sklearn.preprocessing import LabelBinarizerprint(LabelBinarizer().fit_transform(y))#进行数据的可视化处理from sklearn.model_selection import train_test_splitx_train,x_test,y_train,y_test=train_test_split(x,y)y_train1=LabelBinarizer().fit_transform(y_train)y_test1=LabelBinarizer().fit_transform(y_test) #分类结果标签处理print(x.shape,x_train.shape)print(y_train)model=Sequential(    [        Dense(5,input_dim=4),  #输入层为4个输入结果，隐含层为5个节点        Activation("relu"),    #激活函数为relu函数        Dense(3),               #输出层为3个节点        Activation("sigmoid"), #激活函数为sigmoid函数    ])sgd=SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True) #lr因子，步长，实质因子model.compile(optimizer=sgd,loss="categorical_crossentropy") #损失函数为crossmodel.fit(x_train,y_train1,nb_epoch=300,batch_size=80) #训练200轮，每次取40个数字print(model.predict_classes(x_test))y_pre=model.predict_classes(x_test)print(sklearn.metrics.accuracy_score(y_test,y_pre))