from keras.layers import Dense, Merge
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.regularizers import l2
from objectives import cca_loss
def create_model(layer_sizes1, layer_sizes2, input_size1, input_size2,
learning_rate, reg_par, outdim_size, use_all_singular_values):
"""
builds the whole model
the structure of each sub-network is defined in build_mlp_net,
and it can easily get substituted with a more efficient and powerful network like CNN
"""
view1_model = build_mlp_net(layer_sizes1, input_size1, reg_par)
view2_model = build_mlp_net(layer_sizes2, input_size2, reg_par)
model = Sequential()
model.add(Merge([view1_model, view2_model], mode='concat'))
model_optimizer = RMSprop(lr=learning_rate)
model.compile(loss=cca_loss(outdim_size, use_all_singular_values), optimizer=model_optimizer)
return model
def build_mlp_net(layer_sizes, input_size, reg_par):
model = Sequential()
for l_id, ls in enumerate(layer_sizes):
if l_id == 0:
input_dim = input_size
else:
input_dim = []
if l_id == len(layer_sizes)-1:
activation = 'linear'
else:
activation = 'sigmoid'
model.add(Dense(ls, input_dim=input_dim,
activation=activation,
kernel_regularizer=l2(reg_par)))
return model
