train_folds = range(i+1) train_data = join_folds(folds,train_folds) #mins, maxs = MaxMin(train_data['labels']) #T_l = MaxMinFit(train_data['labels'], mins, maxs) #t_l = MaxMinFit(test['labels'], mins, maxs) T_l = train_data['labels'] t_l = test['labels'] Dense_sizes = [300] Dense_l2_regularizers = [0.37173327555716984,0.000165584846072854] Dense_acivity_l2_regularizers = [0.9593094177755246,0.0011426757779919388] CNN_filters = 5 CNN_rows = 6 max_input_length = test['features'].shape[1] is_trainable = True opt = 'adadelta' #sgd, rmsprop, adagrad, adadelta, adam model = create_CNN( CNN_filters, CNN_rows, Dense_sizes, Dense_l2_regularizers, Dense_acivity_l2_regularizers, emb, max_input_length, is_trainable,opt) if(weights!=None): model.set_weights(weights) else: weights = model.get_weights() t = model.fit( train_data['features'], T_l, batch_size=64, nb_epoch=4500) scores_on_train = model.evaluate(train_data['features'],T_l) scores_on_test = model.evaluate(test['features'],t_l) print('mse on train : ' + str(scores_on_train)) print('mse on test : ' + str(scores_on_test)) print('') model = None gc.collect() ret.append([scores_on_train, scores_on_test]) pprint(ret)
Dense_acivity_l2_regularizers = [ 0.01, 0.01 ] CNN_filters = [10] CNN_rows = [2] max_input_length = test['features'].shape[1] is_trainable = False opt = 'adadelta' #sgd, rmsprop, adagrad, adadelta, adam model = create_CNN( CNN_filters, CNN_rows, Dense_sizes, Dense_l2_regularizers, Dense_acivity_l2_regularizers, emb, max_input_length, is_trainable, opt ) t = model.fit( train_data['features'], T_l, batch_size=64, nb_epoch=200 , validation_data=( validation_data['features'], t_l )