def main(_):
vocab = reader.get_vocab(FLAGS.vocab)
test_ids, test_meta = reader.make_test(PDPATH('/test_data/'+FLAGS.test), vocab)
model_path = PDPATH('/trained_models/') + FLAGS.model
config = load_configs(model_path)
with tf.Graph().as_default() as graph:
with tf.Session() as session:
test_input = TestData(config = config,
test_data = test_ids,
test_meta = test_meta,
vocab=vocab,
name="TestInput")
with tf.variable_scope("Model"):
mtest = Basic_LSTM_Model(is_training=False, config=config, input_=test_input)
saver = tf.train.Saver(write_version=tf.train.SaverDef.V2)
saved_files = os.listdir(model_path)
for file in saved_files:
if '.meta' in file:
ckpt = file.split(sep='.')[0]
saver.restore(session, os.path.join(model_path,ckpt))
continue
np.set_printoptions(precision=4, suppress=False, linewidth=100)
b = run_test(session=session, model=mtest, model_input=test_input)
print(b)
b = b / np.sum(b, axis=1).reshape([-1,1])
np.set_printoptions(precision=4, suppress=False, linewidth=100)
print(b)
def __init__(self,
data_bundle,
vocabulary=None): #, c_len=None, q_len=None):
(self.contexts, self.questions, self.choices, self.labels,
self.choices_map, self.context_lens, self.qs_lens) = data_bundle
if vocabulary:
self.vocab = vocabulary
else:
self.vocab = rn.get_vocab(self.questions,
self.contexts,
min_frequency=10)
self.vocab_size = len(self.vocab.vocabulary_)
self.labels_idx = sorted(
list(
set([choice for choices in self.choices
for choice in choices])))
print(self.contexts[:10])
print(self.questions[:10])
print(self.labels[:10])
self.transformed_labels_idx = [
x[0] for x in list(self.vocab.transform(self.labels_idx))
]
print(self.transformed_labels_idx)
self.contexts = rn.vocab_transform(self.contexts, self.vocab)
self.questions = rn.vocab_transform(self.questions, self.vocab)
def __init__(self, data_bundle, vocabulary=None):#, c_len=None, q_len=None):
(self.contexts, self.questions, self.choices, self.labels,
self.choices_map, self.context_lens, self.qs_lens) = data_bundle
if vocabulary:
self.vocab = vocabulary
else:
self.vocab = rn.get_vocab(
self.questions, self.contexts, min_frequency=10)
self.vocab_size = len(self.vocab.vocabulary_)
self.labels_idx = sorted(
list(set([choice for choices in self.choices for choice in choices]))
)
self.transformed_labels_idx = [x[0] for x in\
list(self.vocab.transform(self.labels_idx))]
print(self.transformed_labels_idx)
print([x for x in self.questions if '@placeholder' not in x.split(" ")])
self.contexts = rn.vocab_transform(self.contexts, self.vocab)
self.questions = rn.vocab_transform(self.questions, self.vocab)
placeholder_token = ['@placeholder']
placeholder_idx = rn.vocab_transform(placeholder_token,self.vocab)[0][0]
print(placeholder_idx)
print(len([x for x in self.questions if placeholder_idx in x]))
self.placeholder_inds = np.array([list(x).index(placeholder_idx)\
for x in self.questions]).astype(int)
print(self.placeholder_inds.shape)
def vocab_demo():
v = rd.get_vocab('ptb.voc')
items = [
'the', 'dog', 'dogs', 'boy', 'boys', 'is', 'are', 'has', 'have', 'was',
'were'
]
for i in items:
print(i, v.getid(i))
def infer():
args = parse_args()
num_layers = args.num_layers
src_vocab_size = args.vocab_size
tar_vocab_size = args.vocab_size
batch_size = args.batch_size
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
attr_init = args.attr_init
latent_size = 32
if args.enable_ce:
fluid.default_main_program().random_seed = 102
framework.default_startup_program().random_seed = 102
model = VAE(hidden_size,
latent_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
attr_init=attr_init)
beam_size = args.beam_size
trans_res = model.build_graph(mode='sampling', beam_size=beam_size)
# clone from default main program and use it as the validation program
main_program = fluid.default_main_program()
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
dir_name = args.reload_model
print("dir name", dir_name)
dir_name = os.path.join(dir_name, "checkpoint")
fluid.load(main_program, dir_name, exe)
vocab, tar_id2vocab = get_vocab(args.dataset_prefix)
infer_output = np.ones((batch_size, 1), dtype='int64') * BOS_ID
fetch_outs = exe.run(feed={'tar': infer_output},
fetch_list=[trans_res.name],
use_program_cache=False)
with io.open(args.infer_output_file, 'w', encoding='utf-8') as out_file:
for line in fetch_outs[0]:
end_id = -1
if EOS_ID in line:
end_id = np.where(line == EOS_ID)[0][0]
new_line = [tar_id2vocab[e[0]] for e in line[1:end_id]]
out_file.write(space_tok.join(new_line))
out_file.write(line_tok)
def main():
import reader as reader
from trainer import Configs
from PDPATH import PDPATH
ptb_vocab = get_vocab('ptb.voc')
raw_test_data = reader.make_test(PDPATH('/RNN/test_data/coffee.txt'), ptb_vocab)
test_input = TestData(config=Configs(),
test_data=raw_test_data,
vocab=ptb_vocab,
name="TestInput")
def __init__(self, config, data_path=None, vocabulary=None, name=None):
self.batch_size = batch_size = config.batch_size
self.num_steps = num_steps = config.num_steps
raw_context, raw_questions, raw_choices, raw_labels, self.choices_map = \
read.load_data(data_path)
all_choices = read.build_choices(raw_choices)
self.epoch_size = ((len(raw_context) // batch_size) - 1) // num_steps
# build vocab for train data
if not vocabulary:
self.vocabulary = read.get_vocab(raw_questions,\
raw_context,min_frequency=500)
else:
self.vocabulary = vocabulary
raw_choices = [" ".join(x) for x in raw_choices]
self.all_choices = read.vocab_transform(all_choices, self.vocabulary)
self.questions = read.vocab_transform(raw_questions, self.vocabulary)
self.context = read.vocab_transform(raw_context, self.vocabulary)
self.labels = read.vocab_transform(raw_labels, self.vocabulary)
self.choices = read.vocab_transform([" ".join(x) for x in raw_choices],
self.vocabulary)
def __init__(self, data_bundle, vocabulary=None):
(contexts, questions, self.choices, self.labels, self.choices_map,
self.context_lens, self.qs_lens) = data_bundle
if vocabulary:
self.vocab = vocabulary
else:
self.vocab = rn.get_vocab(questions,
contexts,
min_frequency=FLAGS.min_freq)
self.vocab_size = len(self.vocab.vocabulary_)
self.labels_idx = sorted(
list(
set([choice for choices in self.choices
for choice in choices])))
contexts = rn.vocab_transform(contexts, self.vocab)
self.contexts = rn.pad_eval(contexts, FLAGS.context_steps)
questions = rn.vocab_transform(questions, self.vocab)
self.questions = rn.pad_eval(questions, FLAGS.question_steps)
level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename=datetime.now().strftime('mylogfile_%H_%M_%d_%m_%Y.log'),
filemode='w')
if __name__ == "__main__":
# tf.reset_default_graph()
options = get_params()
root = "/Users/liuhongbing/Documents/tensorflow/data/snli_1.0/"
train = [l.strip().split('\t') for l in open(root + 'snli_1.0_train.txt')]
dev = [l.strip().split('\t') for l in open(root + 'snli_1.0_dev.txt')]
test = [l.strip().split('\t') for l in open(root + 'snli_1.0_test.txt')]
vocab = get_vocab(train)
print("vocab (incr. maxfeatures accordingly):", len(vocab))
X_train, Y_train, Z_train = load_data(train, vocab)
X_dev, Y_dev, Z_dev = load_data(dev, vocab)
X_test, Y_test, Z_test = load_data(test, vocab)
print('Build model...')
model = build_model(options)
config_str = getConfig(options)
MODEL_ARCH = root + "/Attention_neural/arch_att" + config_str + ".yaml"
MODEL_WGHT = root + "/Attention_neural/weights_att" + config_str + ".weights"
MAXLEN = options.xmaxlen
X_train = pad_sequences(X_train,
with open('POS/noun_inanim.txt', 'r') as file:
all = file.readlines()
iNs = all[0][:-2].split(sep=',')
iNp = all[1].split(sep=',')
with open('POS/verb_trans.txt', 'r') as file:
all = file.readlines()
tVs = all[0][:-2].split(sep=',')
tVp = all[1].split(sep=',')
with open('POS/verb_intrans.txt', 'r') as file:
all = file.readlines()
iVs = all[0][:-2].split(sep=',')
iVp = all[1].split(sep=',')
with open('ptb_adjs.txt', 'r') as file:
A = file.readline().split(sep=',')
sample = random.randint(20, 50)
print('Here are some singular samples:')
print(
' Anim: {}\n Inanim: {}\n Trans: {}\n Intrans: {}\n Adj: {}\n'.format(
aNs[sample], iNs[sample], tVs[sample], iVs[sample], A[sample]))
print('And ere are some plural samples:')
print(
' Anim: {}\n Inanim: {}\n Trans: {}\n Intrans: {}\n Adj: {}\n'.format(
aNp[sample], iNp[sample], tVp[sample], iVp[sample], A[sample + 1]))
V = rd.get_vocab('big_ptb.voc')
from reader import load_data
from reader import get_vocab
from reader import vocab_transform
from reader import batch_iter
contexts, questions, choices, labels, choices_map, context_lens, qs_lens =\
load_data(data_path="wdw/test")
# # 2. Fit vocabulary with questions and context.
vocab = get_vocab(contexts, questions)
# # 3. Transform context and questions
contexts = vocab_transform(contexts, vocab)
questions = vocab_transform(questions, vocab)
# 4. Give to batch_iter
readers = batch_iter(contexts, questions, choices, labels, choices_map,
context_lens, qs_lens)
# for q, c, ch, lab, ch_map, c_lens, q_lens in readers:
# print(c.shape)
# break
