def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN, )))
model.add(
Dense(input_dim=nn_input_dim_NN, output_dim=110, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=110, output_dim=350, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=350, output_dim=150, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=150, output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(
Dense(input_dim=20,
output_dim=2,
init='he_normal',
activation='softmax'))
sgd = SGD(lr=0.02, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='binary_crossentropy',
class_mode='binary')
return KerasClassifier(nn=model, **self.params)
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN, )))
model.add(
Dense(input_dim=nn_input_dim_NN, output_dim=62, init='he_normal'))
model.add(LeakyReLU(alpha=.001))
model.add(Dropout(0.3))
model.add(Dense(input_dim=62, output_dim=158, init='he_normal'))
model.add(LeakyReLU(alpha=.001))
model.add(Dropout(0.25))
model.add(Dense(input_dim=158, output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.2))
model.add(
Dense(input_dim=20,
output_dim=2,
init='he_normal',
activation='softmax'))
#model.add(Activation('softmax'))
sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='binary_crossentropy',
class_mode='binary')
return KerasClassifier(nn=model, **self.params)
def build_model(self):
model = Sequential()
model.add(Dropout(0.2, input_shape=(nn_input_dim_NN, )))
model.add(
Dense(input_dim=nn_input_dim_NN, output_dim=140, init='uniform'))
model.add(LeakyReLU(alpha=.00001))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=140, output_dim=250, init='uniform'))
model.add(LeakyReLU(alpha=.00001))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(
Dense(input_dim=250,
output_dim=90,
init='uniform',
activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(
Dense(input_dim=90,
output_dim=2,
init='uniform',
activation='softmax'))
#model.add(Activation('softmax'))
sgd = SGD(lr=0.013, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='binary_crossentropy',
class_mode='binary')
return KerasClassifier(nn=model, **self.params)
def build_model(self):
model = Sequential()
model.add(Dense(64, input_shape=nn_input_dim_NN, init='he_normal'))
model.add(LeakyReLU(alpha=.00001))
model.add(Dropout(0.5))
model.add(Dense(2, init='he_normal'))
model.add(Activation('softmax'))
sgd = SGD(lr=0.1, decay=1e-5, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=["accuracy"])
return KerasClassifier(nn=model,**self.params)