def model_loading():
_, model_dir, hparams, checkpoint, _, _ = general_utils.initialize_session(
"chat")
#Load the vocabulary
print()
print("Loading vocabulary...")
if hparams.model_hparams.share_embedding:
shared_vocab_filepath = path.join(model_dir,
Vocabulary.SHARED_VOCAB_FILENAME)
input_vocabulary = Vocabulary.load(shared_vocab_filepath)
output_vocabulary = input_vocabulary
else:
input_vocab_filepath = path.join(model_dir,
Vocabulary.INPUT_VOCAB_FILENAME)
input_vocabulary = Vocabulary.load(input_vocab_filepath)
output_vocab_filepath = path.join(model_dir,
Vocabulary.OUTPUT_VOCAB_FILENAME)
output_vocabulary = Vocabulary.load(output_vocab_filepath)
# Setting up the chat
chatlog_filepath = path.join(
model_dir, "chat_logs", "chatlog_{0}.txt".format(
datetime.datetime.now().strftime("%Y%m%d_%H%M%S")))
chat_settings = ChatSettings(hparams.model_hparams,
hparams.inference_hparams)
############# Loading Model #############
reload_model = False
print()
print("Initializing model..."
if not reload_model else "Re-initializing model...")
print()
model = ChatbotModel(mode="infer",
model_hparams=chat_settings.model_hparams,
input_vocabulary=input_vocabulary,
output_vocabulary=output_vocabulary,
model_dir=model_dir)
#Load the weights
print()
print("Loading model weights...")
print()
model.load(checkpoint)
#Show the commands
if not reload_model:
#Uncomment the following line if you want to print commands.
#chat_command_handler.print_commands()
print('Model Reload!')
return model, chatlog_filepath, chat_settings
"""
Script for chatting with a trained chatbot model
"""
import datetime
from os import path
import general_utils
import chat_command_handler
from chat_settings import ChatSettings
from chatbot_model import ChatbotModel
from vocabulary import Vocabulary
#Read the hyperparameters and configure paths
_, model_dir, hparams, checkpoint = general_utils.initialize_session("chat")
#Load the vocabulary
print()
print("Loading vocabulary...")
if hparams.model_hparams.share_embedding:
shared_vocab_filepath = path.join(model_dir,
Vocabulary.SHARED_VOCAB_FILENAME)
input_vocabulary = Vocabulary.load(shared_vocab_filepath)
output_vocabulary = input_vocabulary
else:
input_vocab_filepath = path.join(model_dir,
Vocabulary.INPUT_VOCAB_FILENAME)
input_vocabulary = Vocabulary.load(input_vocab_filepath)
output_vocab_filepath = path.join(model_dir,
Vocabulary.OUTPUT_VOCAB_FILENAME)
output_vocabulary = Vocabulary.load(output_vocab_filepath)
"""
import time
import math
from os import path
from shutil import copytree # Recursively copy an entire directory tree rooted at src.
import general_utils
import train_console_helper
from dataset_readers import dataset_reader_factory
from vocabulary_importers import vocabulary_importer_factory
from vocabulary import Vocabulary
from chatbot_model import ChatbotModel
from training_stats import TrainingStats
#Read the hyperparameters and paths
dataset_dir, model_dir, hparams, resume_checkpoint, encoder_embeddings_dir, decoder_embeddings_dir = general_utils.initialize_session(
"train")
training_stats_filepath = path.join(model_dir, "training_stats.json")
#Read the chatbot dataset and generate / import the vocabulary
dataset_reader = dataset_reader_factory.get_dataset_reader(dataset_dir)
print()
print("Reading dataset '{0}'...".format(dataset_reader.dataset_name))
dataset, dataset_read_stats = dataset_reader.read_dataset(
dataset_dir=dataset_dir,
model_dir=model_dir,
training_hparams=hparams.training_hparams,
share_vocab=hparams.model_hparams.share_embedding,
encoder_embeddings_dir=encoder_embeddings_dir,
decoder_embeddings_dir=decoder_embeddings_dir)
if encoder_embeddings_dir is not None:
"""
Script for training the chatbot model
"""
import time
import math
from os import path
import general_utils
import dataset_reader_factory
from chatbot_model import ChatbotModel
from training_stats import TrainingStats
#Read the hyperparameters and paths
dataset_dir, model_dir, hparams, resume_checkpoint = general_utils.initialize_session(
"train")
training_stats_filepath = path.join(model_dir, "training_stats.json")
#Read the chatbot dataset
dataset_reader = dataset_reader_factory.get_dataset_reader(dataset_dir)
print()
print("Reading dataset '{0}'...".format(dataset_reader.dataset_name))
dataset = dataset_reader.read_dataset(
dataset_dir=dataset_dir,
model_dir=model_dir,
training_hparams=hparams.training_hparams,
share_vocab=hparams.model_hparams.share_embedding)
#Split the chatbot dataset into training & validation datasets
print(
"Splitting {0} samples into training & validation sets ({1}% used for validation)..."