def __init__(self, **kwargs):
dp = DataPreprocessor()
file_name_formatted = dp.write_to_file()
dc = DataCleaner(file_name_formatted)
dc.clean_data_pipeline().trim_rare_words()
self.data_loader = DataLoader(dc.vocabulary, dc.pairs)
self.dp = dp
self.dc = dc
load_embedding = kwargs.get('pretrained_embedding', False)
embedding_file = kwargs.get('pretrained_embedding_file', None)
load_enc_dec = kwargs.get('pretrained_enc_dec', False)
load_enc_file = kwargs.get('pretrained_enc_file', None)
load_dec_file = kwargs.get('pretrained_dec_file', None)
self.model_name = kwargs.get('model_name', 'cb_model')
attn_model = kwargs.get('attention_type', 'dot')
self.hidden_size = kwargs.get('hidden_size', 500)
self.encoder_nr_layers = kwargs.get('enc_nr_layers', 2)
self.decoder_nr_layers = kwargs.get('dec_nr_layers', 2)
dropout = kwargs.get('dropout', 0.1)
self.batch_size = kwargs.get('batch_size', 64)
self.clip = kwargs.get('clip', 50.0)
self.teacher_forcing_ratio = kwargs.get('teacher_forcing_ratio', 1.0)
self.learning_rate = kwargs.get('lr', 0.0001)
self.decoder_learning_ratio = kwargs.get('decoder_learning_ratio', 5.0)
self.nr_iteration = kwargs.get('nr_iterations', 4000)
self.print_every = kwargs.get('print_every', 1)
self.save_every = 500
self.embedding = nn.Embedding(self.dc.vocabulary.num_words, self.hidden_size)
if load_embedding:
self.embedding.load_state_dict(embedding_file)
# Initialize encoder & decoder models
encoder = EncoderRNN(self.hidden_size, self.embedding, self.encoder_nr_layers, dropout)
decoder = DecoderRNN(
attn_model,
self.embedding,
self.hidden_size,
self.dc.vocabulary.num_words,
self.decoder_nr_layers,
dropout
)
if load_enc_dec:
encoder.load_state_dict(load_enc_file)
decoder.load_state_dict(load_dec_file)
# Use appropriate device
encoder = encoder.to(device)
decoder = decoder.to(device)
self.encoder = encoder
self.decoder = decoder
self.encoder_optimizer = optim.Adam(encoder.parameters(), lr=self.learning_rate)
self.decoder_optimizer = optim.Adam(decoder.parameters(), lr=self.learning_rate * self.decoder_learning_ratio)
return
def main():
"""primary entry
"""
voc, pairs = loadPreparedData()
print('Building encoder and decoder ...')
# Initialize word embeddings
#embedding = nn.Embedding(voc.num_words, params.hidden_size)
embedding = nn.Embedding(voc.num_words, params.embedding_size)
# Initialize encoder & decoder models
encoder = EncoderRNN(embedding, params.hidden_size,
params.encoder_n_layers, params.dropout)
decoder = LuongAttnDecoderRNN(params.attn_model, embedding,
params.hidden_size, voc.num_words,
params.decoder_n_layers, params.dropout)
# Use appropriate device
encoder = encoder.to(params.device)
decoder = decoder.to(params.device)
print('Models built and ready to go!')
# Ensure dropout layers are in train mode
encoder.train()
decoder.train()
# Initialize optimizers
print('Building optimizers ...')
encoder_optimizer = optim.Adam(encoder.parameters(),
lr=params.learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(),
lr=params.learning_rate *
params.decoder_learning_ratio)
# Run training iterations
print("Starting Training!")
trainIters(voc,
pairs,
encoder,
decoder,
encoder_optimizer,
decoder_optimizer,
embedding,
params.encoder_n_layers,
params.decoder_n_layers,
params.save_dir,
params.n_iteration,
params.batch_size,
params.print_every,
params.save_every,
params.clip,
params.corpus_name,
load_filename=None)
def initialize_models(voc):
print('Building encoder and decoder ...')
# initialize word embeddings
embedding = nn.Embedding(voc.num_words, c.HIDDEN_SIZE)
if c.LOAD_FILENAME:
embedding.load_state_dict(embedding_sd)
# initialize encoder & decoder models
encoder = EncoderRNN(c.HIDDEN_SIZE, embedding, c.ENCODER_N_LAYERS,
c.DROPOUT)
decoder = LuongAttnDecoderRNN(c.ATTN_MODEL, embedding, c.HIDDEN_SIZE,
voc.num_words, c.DECODER_N_LAYERS, c.DROPOUT)
if c.LOAD_FILENAME:
encoder.load_state_dict(encoder_sd)
decoder.load_state_dict(decoder_sd)
# Use appropriate device
encoder = encoder.to(c.DEVICE)
decoder = decoder.to(c.DEVICE)
print('Models built and ready to go!')
return embedding, encoder, decoder
encoder_sd = checkpoint['en']
decoder_sd = checkpoint['de']
encoder_optimizer_sd = checkpoint['en_opt']
decoder_optimizer_sd = checkpoint['de_opt']
embedding_sd = checkpoint['embedding']
voc.__dict__ = checkpoint['voc_dict']
# Initialize word embeddings
embedding = nn.Embedding(voc.num_words, hidden_size)
embedding.load_state_dict(embedding_sd)
# Initialize encoder & decoder models
encoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)
decoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size,
voc.num_words, decoder_n_layers, dropout)
encoder.load_state_dict(encoder_sd)
decoder.load_state_dict(decoder_sd)
encoder = encoder.to(device)
decoder = decoder.to(device)
# Set dropout layers to eval mode
encoder.eval()
decoder.eval()
# Initialize search module
searcher = GreedySearchDecoder(encoder, decoder)
# Begin chatting (uncomment and run the following line to begin)
evaluateInput(encoder, decoder, searcher, voc)
