MainPath = 'C:/Users/Francesco/Desktop/chatbot/' path = MainPath + 'chatbot_data/data/' with open(path + 'DataDizE.json', 'r') as f: DataDizE = json.load(f) dataset = [(DataDizE[k]['question'], DataDizE[k]['domain'][0], DataDizE[k]['relation'], DataDizE[k]['answer']) for k in sorted(DataDizE.keys())] dataset = sorted( set([(x + ' | ' + y + ' | ' + w, z) for x, y, w, z in dataset])) dataset = dataset * 10 random.seed(123) random.shuffle(dataset) LSTM_instance_model = Utilities.LSTMModel() LSTM_instance_model.process_dataset(dataset, 1) ModelPath = MainPath + 'chatbot_data/models/' # Initialization NN hidden_size = latent_dim = 128 batch_size = 512 embedding_length = 100 #K.clear_session() epochs = 30 y_data = LSTM_instance_model.process_data_Y(LSTM_instance_model.Y, LSTM_instance_model.Y_max_len, LSTM_instance_model.Y_word_to_ix) # Epochs: ~30 to 0.99
path = MainPath + 'chatbot_data/data/' with open(path + 'DataDizYN.json', 'r') as f: DataDizYN = json.load(f) # Select Num of observations used for training the Model Data = [( DataDizYN[k]['question'], DataDizYN[k]['domain'][0], DataDizYN[k]['relation'], DataDizYN[k]['answer'] ) for k in sorted(DataDizYN.keys())] Data = sorted(set([(x + ' | ' + y + ' | ' + w, z) for x, y, w, z in Data])) # set model Model = Utilities.LSTMModel() Model.process_dataset(Data, 1) # Initialization NN hidden_size = 128 batch_size = 256 AtTime = len(Data) embedding_length = 10**2 # Create NN-Architecture epochs = 5 ModelPath = MainPath + 'chatbot_data/models/' K.clear_session() if 'PredictYN.keras' not in os.listdir(ModelPath): Model.model = Sequential()