# -*- coding: utf-8 -*-
'''A simple multilayer perceptron
'''
from keras.models import Sequential
from keras.layers import Dense, BatchNormalization, Dropout, Activation, ELU
def create_model(input_shape, hidden_layers=[1024, 512, 256], input_dropout=0.1, hidden_dropout=0.5):
'''Define a simple multilayer perceptron.
Args:
input_shape (tuple): input shape to the model. For this model, should be of shape (dim,)
input_dropout (float): fraction of input features to drop out during training
hidden_layers (tuple): a tuple/list with number of hidden units in each hidden layer
Returns:
keras.models.Sequential : a model to train
'''
model = Sequential()
# dropout the input to prevent overfitting to any one feature
# (a similar concept to randomization in random forests,
# but we choose less severe feature sampling )
model.add(Dropout(input_dropout, input_shape=input_shape))
# set up hidden layers
for n_hidden_units in hidden_layers:
# the layer...activation will come later
model.add(Dense(n_hidden_units))
# dropout to prevent overfitting
model.add(Dropout(hidden_dropout))
# batchnormalization helps training
model.add(BatchNormalization())
# ...the activation!
model.add(ELU())
# the output layer
model.add(Dense(1, activation='sigmoid'))
# we'll optimize with plain old sgd
model.compile(loss='binary_crossentropy',
optimizer='sgd', metrics=['accuracy'])
return model
