################### DEFINING HYPERPARAMETERS ################### dimx = 50 dimy = 50 dimft = 44 batch_size = 70 vocab_size = 8000 embedding_dim = 50 LSTM_neurons = 64 depth = 1 nb_epoch = 3 shared = 1 opt_params = [0.001, 'adam'] ques, ans, label_train, train_len, test_len,\ wordVec_model, res_fname, pred_fname, feat_train, feat_test = wk.load_wiki(model_name, glove_fname) data_l, data_r, embedding_matrix = dl.process_data(ques, ans, wordVec_model, dimx=dimx, dimy=dimy, vocab_size=vocab_size, embedding_dim=embedding_dim) X_train_l, X_test_l, X_dev_l, X_train_r, X_test_r, X_dev_r = wk.prepare_train_test( data_l, data_r, train_len, test_len) lrmodel = lrmodel(embedding_matrix, dimx=dimx, dimy=dimy, LSTM_neurons=LSTM_neurons,
model_name = lrmodel.func_name ################### DEFINING HYPERPARAMETERS ################### dimx = 60 dimy = 60 dimft = 44 batch_size = 50 vocab_size = 8000 embedding_dim = 50 nb_filter = 120, filter_length = (50, 4) depth = 1 nb_epoch = 3 ques, ans, label_train, train_len, test_len, wordVec_model, res_fname, pred_fname, feat_train, feat_test = wk.load_wiki( model_name, glove_fname) data_l, data_r, embedding_matrix = dl.process_data(ques, ans, wordVec_model, dimx=dimx, dimy=dimy, vocab_size=vocab_size, embedding_dim=embedding_dim) X_train_l, X_test_l, X_dev_l, X_train_r, X_test_r, X_dev_r = wk.prepare_train_test( data_l, data_r, train_len, test_len) if model_name == 'cnn_ft': lrmodel = lrmodel(embedding_matrix, dimx=dimx, dimy=dimy,