def getmodel(milayer, HIDDEN_DIM, INPUT_DIM, modelname='undefined',hidden_layers=1): model = Sequential() model.modelname = modelname #model.add(Dense(2*HIDDEN_DIM, input_dim=INPUT_DIM, activation='tanh')) for _ in range(hidden_layers): model.add(Dense(HIDDEN_DIM,input_dim=INPUT_DIM, activation='tanh')) if milayer is not None: model.add(milayer) model.milayer = milayer model.add(Dense(d.nb_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) return model