本文整理汇总了Python中sklearn.svm.SVC.add方法的典型用法代码示例。如果您正苦于以下问题：Python SVC.add方法的具体用法？Python SVC.add怎么用？Python SVC.add使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.svm.SVC的用法示例。
在下文中一共展示了SVC.add方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: StandardScaler
# 需要导入模块: from sklearn.svm import SVC [as 别名]
# 或者: from sklearn.svm.SVC import add [as 别名]
sc = StandardScaler()
X = sc.fit_transform(X)
test = sc.transform(test)
# Part 2 - Now let's make the ANN!
# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
# Initialising the ANN
classifier = Sequential()
# Adding the input layer and the first hidden layer
classifier.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu', input_dim = 8))
# Adding the second hidden layer
classifier.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu'))
# Adding the output layer
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the ANN to the Training set
classifier.fit(X, y, batch_size = 10, nb_epoch = 100)
# Part 3 - Making the predictions and evaluating the model