datahack2017_4.py

from keras.layers import Dense, Dropout, Input, BatchNormalization
from keras.models import Model
def get_nn_model(dropout=0.6):
    inp = Input(shape = (90,))
    x = Dense(100, activation='relu')(inp)
    x = BatchNormalization()(x)
    x = Dropout(dropout)(x)
    
    x = Dense(50, activation='relu')(x)
    x = BatchNormalization()(x)
    x = Dropout(dropout)(x)
    
    x = Dense(50, activation='relu')(x)
    x = BatchNormalization()(x)
    x = Dropout(dropout)(x)
    
    x = Dense(1, activation='sigmoid')(x)
    model = Model(inputs=[inp], outputs=[x])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model