def run(args):
reverse, fil, n_epoch, print_every, learning_rate, n_layers, hidden_size, batch_size, beam_size, input = args.reverse, args.filter, args.epoch, args.print, args.learning_rate, args.layer, args.hidden, args.batch_size, args.beam, args.input
if args.train and not args.load:
print("==" * 20, "train", "==" * 20)
trainIters(args, args.train, reverse, n_epoch, learning_rate,
batch_size, n_layers, hidden_size, print_every)
elif args.load:
print("==" * 20, "load", "==" * 20)
n_layers, hidden_size = parseFilename(args.load)
trainIters(args,
args.train,
reverse,
n_epoch,
learning_rate,
batch_size,
n_layers,
hidden_size,
print_every,
loadFilename=args.load)
# load parameters from filename
elif args.test:
print("==" * 20, "test", "==" * 20)
n_layers, hidden_size = parseFilename(args.test, True)
runTest(args, n_layers, hidden_size, reverse, args.test, beam_size,
batch_size, input, args.corpus)
def run(args):
learning_rate, lr_decay_epoch, lr_decay_ratio, n_layers, hidden_size, embed_size, \
attr_size, attr_num, batch_size, beam_size, overall, max_length, min_length, save_dir = \
args.learning_rate, args.lr_decay_epoch, args.lr_decay_ratio, args.layer, args.hidden_size, args.embed_size, \
args.attr_size, args.attr_num, args.batch_size, args.beam_size, args.overall, args.max_length, args.min_length, args.save_dir
if args.train and not args.load:
trainIters(args.train, learning_rate, lr_decay_epoch, lr_decay_ratio,
batch_size, n_layers, hidden_size, embed_size, attr_size,
attr_num, overall, save_dir)
elif args.load:
n_layers, hidden_size = parseFilename(args.load)
trainIters(args.train,
learning_rate,
lr_decay_epoch,
lr_decay_ratio,
batch_size,
n_layers,
hidden_size,
embed_size,
attr_size,
attr_num,
overall,
save_dir,
loadFilename=args.load)
elif args.test:
n_layers, hidden_size = parseFilename(args.review_model)
runTest(args.test, n_layers, hidden_size, embed_size, attr_size,
attr_num, overall, args.review_model, args.sketch_model,
args.topic_model, beam_size, max_length, min_length, save_dir)
def run(args):
reverse, fil, n_iteration, print_every, save_every, learning_rate, \
n_layers, hidden_size, batch_size, beam_size, inp, dropout = \
args.reverse, args.filter, args.iteration, args.print, args.save, args.learning_rate, \
args.layer, args.hidden, args.batch_size, args.beam, args.input, args.dropout
if args.train and not args.load:
trainIters(args.train, reverse, n_iteration, learning_rate, batch_size,
n_layers, hidden_size, print_every, save_every, dropout)
elif args.load:
n_layers, hidden_size, reverse = parseFilename(args.load)
trainIters(args.train,
reverse,
n_iteration,
learning_rate,
batch_size,
n_layers,
hidden_size,
print_every,
save_every,
dropout,
loadFilename=args.load)
elif args.test:
n_layers, hidden_size, reverse = parseFilename(args.test, True)
runTest(n_layers, hidden_size, reverse, args.test, beam_size, inp,
args.corpus)
def run(args):
tab_printer(args)
learning_rate, lr_decay_epoch, lr_decay_ratio, weight_decay, embed_size, hidden_size, \
node_size, capsule_size, gcn_layers, gcn_filters, rnn_layers, capsule_num, batch_size, epochs = \
args.learning_rate, args.lr_decay_epoch, args.lr_decay_ratio, args.weight_decay, args.embed_size, \
args.hidden_size, args.node_size, args.capsule_size, args.gcn_layers, args.gcn_filters, \
args.rnn_layers, args.capsule_num, args.batch_size, args.epochs
if args.train:
trainIters(args.train, learning_rate, lr_decay_epoch, lr_decay_ratio,
weight_decay, batch_size, rnn_layers, hidden_size,
embed_size, node_size, epochs, args.save_dir)
elif args.load:
trainIters(args.load, learning_rate, lr_decay_epoch, lr_decay_ratio,
weight_decay, batch_size, rnn_layers, hidden_size,
embed_size, node_size, epochs, args.save_dir,
args.load_file)
elif args.test:
runTest(args.test, rnn_layers, hidden_size, embed_size, node_size,
capsule_size, gcn_layers, gcn_filters, capsule_num,
args.aspect_model, args.review_model, args.beam_size,
args.max_length, args.min_length, args.save_dir)
else:
print("mode error!")
def run(args):
reverse, fil, n_iteration, print_every, save_every, learning_rate, n_layers, hidden_size, batch_size, beam_size, inp = \
args.reverse, args.filter, args.iteration, args.print, args.save, args.learning_rate, \
args.layer, args.hidden, args.batch_size, args.beam, args.input
if args.train and not args.load:
trainIters(args.train, reverse, n_iteration, learning_rate, batch_size,
n_layers, hidden_size, print_every, save_every)
elif args.load:
n_layers, hidden_size, reverse = parseFilename(args.load)
trainIters(args.train, reverse, n_iteration, learning_rate, batch_size,
n_layers, hidden_size, print_every, save_every, loadFilename=args.load)
elif args.test:
n_layers, hidden_size, reverse = parseFilename(args.test, True)
runTest(n_layers, hidden_size, reverse, args.test, beam_size, inp, args.corpus)
def run(args):
reverse, fil, n_iteration, print_every, save_every, learning_rate, n_layers, hidden_size, batch_size, beam_size, input = \
args.reverse, args.filter, args.iteration, args.print, args.save, args.learning_rate, \
args.layer, args.hidden, args.batch_size, args.beam, args.input
if args.train and not args.load:
trainIters(args.train, args.corpus_index, args.strip, args.pretrained_model, reverse, n_iteration, learning_rate, batch_size,
n_layers, hidden_size, print_every, save_every)
elif args.load:
n_layers, hidden_size, reverse = parseFilename(args.load)
trainIters(args.train, args.corpus_index, args.strip, args.pretrained_model, reverse, n_iteration, learning_rate, batch_size,
n_layers, hidden_size, print_every, save_every, loadFilename=args.load)
elif args.test:
n_layers, hidden_size, reverse = parseFilename(args.test, True)
runTest(n_layers, args.pretrained_model, hidden_size, reverse, args.test, beam_size, input, args.corpus, args.diff_corpus)
elif args.loss:
loss_graph(args.loss, args.corpus, hidden_size)
def run(args):
reverse, fil, n_iteration, print_every, save_every, learning_rate, n_layers, hidden_size, batch_size, beam_size, input = \
args.reverse, args.filter, args.iteration, args.print, args.save, args.learning_rate, \
args.layer, args.hidden, args.batch_size, args.beam, args.input
if args.train and not args.load:
print("Train new model ... ")
trainIters(args.train, reverse, n_iteration, learning_rate, batch_size,
n_layers, hidden_size, print_every, save_every)
elif args.load:
print("Load existing model ... ")
reverse = parseFilename(args.load)
trainIters(args.train, reverse, n_iteration, learning_rate, batch_size,
n_layers, hidden_size, print_every, save_every, loadFilename=args.load)
elif args.test:
print("Testing ... ")
n_layers, hidden_size, reverse = parseFilename(args.test, True)
runTest(n_layers, hidden_size, reverse, args.test, beam_size, input, args.corpus)
def run(args):
learning_rate, loadFilename, datafile, decoder_n_layers,\
encoder_n_layers, hidden_size, dropout, attn_model, \
n_iteration, batch_size, save_every, print_every, \
decoder_learning_ratio, clip, beam_size, inp = args.learning_rate, args.model_file, args.data_file, args.layers, \
args.layers, args.hidden_size, args.dropout, args.attn_model, args.iteration, \
args.batch_size, args.save_every, args.print_every, \
args.decoder_learning_ratio, args.clip, args.beam_size, args.input
if args.test:
if loadFilename:
print("Starting testing model!")
runTest(decoder_n_layers, hidden_size, False, loadFilename, beam_size, inp, datafile)
else:
raise RuntimeError("Please assign modelFile to load")
elif args.train:
print("Starting Training model!")
trainIters(attn_model=attn_model, hidden_size=hidden_size, encoder_n_layers=encoder_n_layers, \
decoder_n_layers=decoder_n_layers, save_dir=save_dir, n_iteration=n_iteration, batch_size=batch_size, \
learning_rate=learning_rate, decoder_learning_ratio=decoder_learning_ratio, print_every=print_every, \
save_every=save_every, clip=clip, dropout=dropout, corpus_name=corpus_name, datafile=datafile, \
modelFile=loadFilename)
else:
raise RuntimeError("Please specify a running mode between train and test")
# test
import torch
from evaluate import runTest
if __name__ == '__main__':
#load path
modelFile = "./model_param/4_4_512/30000_seq2seq_bidir_model_test.tar"
corpus_name = "test_data"
# The test parameters are consistent with the training model
n_layers = 4
input_size = 300
hidden_size = 512
beam_size = 4
runTest(n_layers, input_size, hidden_size, modelFile, beam_size,
corpus_name)
# 测试对话
from evaluate import runTest
if __name__ == __main__():
# 模型路径
modelFile = "model_path"
# 参数与训练模型保持一致
n_layers = 4
hidden_size = 512
beam_size = 4 #beam search大小
# 语料路径
corpus = "corpus_path"
# 另外需要注意测试时的模型结构需和训练时一致，比如使用注意力，双向LSTM
runTest(n_layers, hidden_size, modelFile, beam_size, corpus)
