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()
