word_it = flatten_it(corpus_stream)
ngrams = window_it(word_it, args.n)
else:
sentence_ngrams = (window_it(sentence, args.n)
for sentence in corpus_stream)
ngrams = flatten_it(sentence_ngrams)
ngram_ids = [list(map(lambda w: vocab[w], ngram)) for ngram in ngrams]
ngrams = np.array(ngram_ids)
dataset = hdf5_file.create_dataset(name, data=ngrams, compression="gzip")
dataset.attrs['n'] = args.n
try:
print("storing training set")
training_dataset = ptb_reader.training_set()
store_ngrams(training_dataset, "training")
print("storing test set")
test_dataset = ptb_reader.test_set()
store_ngrams(test_dataset, "test")
print("storing validation set")
validation_dataset = ptb_reader.validation_set()
store_ngrams(validation_dataset, "validation")
hdf5_file.close()
print("done")
except (KeyboardInterrupt, Exception) as e:
os.remove(hdf5_path)
raise e
if __name__ == '__main__':
data_path = os.path.join(os.getenv("HOME"), "data/datasets/ptb/")
corpus = PTBReader(path=data_path, mark_eos=True)
corpus_stats = h5py.File(os.path.join(data_path, "ptb_stats.hdf5"),
mode='r')
vocab = marisa_trie.Trie(corpus_stats["vocabulary"])
batch_size = 4
num_steps = 3
# data = [vocab[word] for word in it.take_it(1000, it.flatten_it(corpus.training_set(1000)))]
data = [
word
for word in it.take_it(it.flatten_it(corpus.training_set(1000)), 1000)
]
data = iter((c for c in it.flatten_it(data)))
print(next(data))
# data = np.array(data)
# Starting from sequential data, batchify arranges the dataset into columns.
# For instance, with the alphabet as the sequence and batch size 4, we'd get
# ┌ a g m s ┐
# │ b h n t │
# │ c i o u │
# │ d j p v │
# │ e k q w │
# └ f l r x ┘.
# These columns are treated as independent by the model, which means that the
# dependence of e. g. 'g' on 'f' can not be learned, but allows more efficient
def run(**kwargs):
arg_dict.from_dict(kwargs)
args = arg_dict.to_namespace()
# ======================================================================================
# Load Corpus & Vocab
# ======================================================================================
corpus = PTBReader(path=args.corpus, mark_eos=args.mark_eos)
corpus_stats = h5py.File(os.path.join(args.corpus, "ptb_stats.hdf5"),
mode='r')
ri_generator = Generator(dim=args.k_dim,
num_active=args.s_active,
symmetric=True)
# vocab = marisa_trie.Trie(corpus_stats["vocabulary"])
index = TrieSignIndex(generator=ri_generator,
vocabulary=corpus_stats["vocabulary"],
pregen_indexes=True)
# for i in range(1000):
# w = index.get_sign(i)
# ri: RandomIndex = index.get_ri(w)
# print(w)
# print(ri)
# print(ri)
# print(index.get_id(w))
# pre-gen indices for vocab, we could do this iteratively ... same thing
# ris = [ri_generator.generate() for _ in range(len(vocab))]
# print(vocab.keys())
# index = TrieSignIndex(generator=ri_generator,vocabulary=vocab)
# TODO could create the NRP model with NCE only and input with random indices could be passed to the model
# dynamically, also for inference and evaluation, we could either work with a dynamic encoding process
# or give it the current ri tensor with all the known ris if we know there are no OOV words (words that might not
# have been seen during training.
# table with random indices for all known symbols
# ri_tensor = RandomIndexTensor.from_ri_list(ris, k=args.k_dim, s=args.s_active)
def corpus_pipeline(corpus_stream,
n_gram_size=args.ngram_size,
epochs=1,
batch_size=args.batch_size,
shuffle=args.shuffle,
flatten=False):
""" Corpus Processing Pipeline.
Transforms the corpus reader -a stream of sentences or words- into a stream of n-gram batches.
Args:
n_gram_size: the size of the n-gram window
corpus_stream: the stream of sentences of words
epochs: number of epochs we want to iterate over this corpus
batch_size: batch size for the n-gram batch
shuffle: if true, shuffles the n-grams according to a buffer size
flatten: if true sliding windows are applied over a stream of words rather than within each sentence
(n-grams can cross sentence boundaries)
"""
if flatten:
word_it = flatten_it(corpus_stream)
n_grams = window_it(word_it, n_gram_size)
else:
sentence_n_grams = (window_it(sentence, n_gram_size)
for sentence in corpus_stream)
n_grams = flatten_it(sentence_n_grams)
# at this point this is an n_gram iterator
# n_grams = ([vocab[w] for w in ngram] for ngram in n_grams)
n_grams = ([index.get_id(w) for w in ngram] for ngram in n_grams)
if epochs > 1:
n_grams = repeat_it(n_grams, epochs)
if shuffle:
n_grams = shuffle_it(n_grams, args.shuffle_buffer_size)
n_grams = batch_it(n_grams, size=batch_size, padding=False)
return n_grams
# print("counting dataset samples...")
training_len = sum(1 for _ in corpus_pipeline(
corpus.training_set(), batch_size=1, epochs=1, shuffle=False))
validation_len = None
test_len = None
if args.eval_progress:
validation_len = sum(1 for _ in corpus_pipeline(
corpus.validation_set(), batch_size=1, epochs=1, shuffle=False))
test_len = sum(1 for _ in corpus_pipeline(
corpus.test_set(), batch_size=1, epochs=1, shuffle=False))
# print("done")
# print("dset len ", training_len)
# ======================================================================================
# Load Params, Prepare results assets
# ======================================================================================
# Experiment parameter summary
res_param_filename = os.path.join(
args.out_dir, "params_{id}_{run}.csv".format(id=args.id, run=args.run))
with open(res_param_filename, "w") as param_file:
writer = csv.DictWriter(f=param_file, fieldnames=arg_dict.keys())
writer.writeheader()
writer.writerow(arg_dict)
param_file.flush()
# make dir for model checkpoints
if args.save_model:
model_ckpt_dir = os.path.join(
args.out_dir, "model_{id}_{run}".format(id=args.id, run=args.run))
os.makedirs(model_ckpt_dir, exist_ok=True)
model_path = os.path.join(
model_ckpt_dir, "nnlm_{id}_{run}.ckpt".format(id=args.id,
run=args.run))
# start perplexity file
ppl_header = ["id", "run", "epoch", "step", "lr", "dataset", "perplexity"]
ppl_fname = os.path.join(
args.out_dir, "perplexity_{id}_{run}.csv".format(id=args.id,
run=args.run))
ppl_file = open(ppl_fname, "w")
ppl_writer = csv.DictWriter(f=ppl_file, fieldnames=ppl_header)
ppl_writer.writeheader()
# ======================================================================================
# MODEL
# ======================================================================================
# Configure weight initializers based on activation functions
if args.h_act == "relu":
h_act = tx.relu
h_init = tx.he_normal_init()
elif args.h_act == "tanh":
h_act = tx.tanh
h_init = tx.glorot_uniform()
elif args.h_act == "elu":
h_act = tx.elu
h_init = tx.he_normal_init()
elif args.h_act == "selu":
h_act = tf.nn.selu
h_init = tx.glorot_uniform()
# Configure embedding and logit weight initializers
if args.embed_init == "normal":
embed_init = tx.random_normal(mean=0., stddev=args.embed_init_val)
elif args.embed_init == "uniform":
embed_init = tx.random_uniform(minval=-args.embed_init_val,
maxval=args.embed_init_val)
if args.logit_init == "normal":
logit_init = tx.random_normal(mean=0., stddev=args.logit_init_val)
elif args.logit_init == "uniform":
logit_init = tx.random_uniform(minval=-args.logit_init_val,
maxval=args.logit_init_val)
f_init = None
if args.use_f_predict:
if args.f_init == "normal":
f_init = tx.random_normal(mean=0., stddev=args.f_init_val)
elif args.f_init == "uniform":
f_init = tx.random_uniform(minval=-args.f_init_val,
maxval=args.f_init_val)
model = NRP(ctx_size=args.ngram_size - 1,
sign_index=index,
k_dim=args.k_dim,
s_active=args.s_active,
embed_dim=args.embed_dim,
h_dim=args.h_dim,
embed_init=embed_init,
logit_init=logit_init,
num_h=args.num_h,
h_activation=h_act,
h_init=h_init,
use_dropout=args.dropout,
embed_dropout=args.embed_dropout,
keep_prob=args.keep_prob,
l2_loss=args.l2_loss,
l2_loss_coef=args.l2_loss_coef,
f_init=f_init,
embed_share=args.embed_share,
logit_bias=args.logit_bias,
use_nce=args.nce,
nce_samples=args.nce_samples,
nce_noise_amount=0.04)
model_runner = tx.ModelRunner(model)
# Input params can be changed during training by setting their value
# lr_param = tx.InputParam(init_value=args.lr)
lr_param = tensorx.train.EvalStepDecayParam(
value=args.lr,
improvement_threshold=args.eval_threshold,
less_is_better=True,
decay_rate=args.lr_decay_rate,
decay_threshold=args.lr_decay_threshold)
if args.optimizer == "sgd":
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=lr_param.tensor)
elif args.optimizer == "adam":
optimizer = tf.train.AdamOptimizer(learning_rate=lr_param.tensor,
beta1=args.optimizer_beta1,
beta2=args.optimizer_beta2,
epsilon=args.optimizer_epsilon)
elif args.optimizer == "ams":
optimizer = tx.AMSGrad(learning_rate=lr_param.tensor,
beta1=args.optimizer_beta1,
beta2=args.optimizer_beta2,
epsilon=args.optimizer_epsilon)
def clip_grad_global(grads):
grads, _ = tf.clip_by_global_norm(grads, 12)
return grads
def clip_grad_local(grad):
return tf.clip_by_norm(grad, args.clip_value)
if args.clip_grads:
if args.clip_local:
clip_fn = clip_grad_local
else:
clip_fn = clip_grad_global
if args.clip_grads:
model_runner.config_optimizer(optimizer,
optimizer_params=lr_param,
gradient_op=clip_fn,
global_gradient_op=not args.clip_local)
else:
model_runner.config_optimizer(optimizer, optimizer_params=lr_param)
# ======================================================================================
# EVALUATION
# ======================================================================================
def eval_model(runner,
dataset_it,
len_dataset=None,
display_progress=False):
if display_progress:
pb = tqdm(total=len_dataset, ncols=60, position=1)
batches_processed = 0
sum_loss = 0
for batch in dataset_it:
batch = np.array(batch, dtype=np.int64)
ctx = batch[:, :-1]
target = batch[:, -1:]
mean_loss = runner.eval(ctx, target)
sum_loss += mean_loss
if display_progress:
pb.update(args.batch_size)
batches_processed += 1
if display_progress:
pb.close()
return np.exp(sum_loss / batches_processed)
def evaluation(runner: tx.ModelRunner,
progress_bar,
cur_epoch,
step,
display_progress=False):
ppl_validation = eval_model(
runner,
corpus_pipeline(corpus.validation_set(), epochs=1, shuffle=False),
validation_len, display_progress)
res_row = {
"id": args.id,
"run": args.run,
"epoch": cur_epoch,
"step": step,
"lr": lr_param.value,
"dataset": "validation",
"perplexity": ppl_validation
}
ppl_writer.writerow(res_row)
if args.eval_test:
# pb.write("[Eval Test Set]")
ppl_test = eval_model(
runner,
corpus_pipeline(corpus.test_set(), epochs=1, shuffle=False),
test_len, display_progress)
res_row = {
"id": args.id,
"run": args.run,
"epoch": cur_epoch,
"step": step,
"lr": lr_param.value,
"dataset": "test",
"perplexity": ppl_test
}
ppl_writer.writerow(res_row)
ppl_file.flush()
if args.eval_test:
progress_bar.set_postfix({"test PPL ": ppl_test})
# pb.write("valid. ppl = {}".format(ppl_validation))
return ppl_validation
# ======================================================================================
# TRAINING LOOP
# ======================================================================================
# print("Starting TensorFlow Session")
# preparing evaluation steps
# I use ceil because I make sure we have padded batches at the end
epoch_step = 0
global_step = 0
current_epoch = 0
patience = 0
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
sess = tf.Session(config=cfg)
model_runner.set_session(sess)
model_runner.init_vars()
progress = tqdm(total=training_len * args.epochs,
position=args.pid + 1,
disable=not args.display_progress)
training_data = corpus_pipeline(corpus.training_set(),
batch_size=args.batch_size,
epochs=args.epochs,
shuffle=args.shuffle)
evaluations = []
try:
for ngram_batch in training_data:
epoch = progress.n // training_len + 1
# Start New Epoch
if epoch != current_epoch:
current_epoch = epoch
epoch_step = 0
if args.display_progress:
progress.set_postfix({"epoch": current_epoch})
# ================================================
# EVALUATION
# ================================================
if epoch_step == 0:
current_eval = evaluation(model_runner,
progress,
epoch,
global_step,
display_progress=args.eval_progress)
evaluations.append(current_eval)
lr_param.update(current_eval)
# print(lr_param.eval_history)
# print("improvement ", lr_param.eval_improvement())
if global_step > 0:
if args.early_stop and epoch > 1:
if lr_param.eval_improvement(
) < lr_param.improvement_threshold:
if patience >= 3:
break
patience += 1
else:
patience = 0
# ================================================
# TRAIN MODEL
# ================================================
ngram_batch = np.array(ngram_batch, dtype=np.int64)
ctx_ids = ngram_batch[:, :-1]
word_ids = ngram_batch[:, -1:]
model_runner.train(ctx_ids, word_ids)
progress.update(args.batch_size)
epoch_step += 1
global_step += 1
# if not early stop, evaluate last state of the model
if not args.early_stop or patience < 3:
current_eval = evaluation(model_runner, progress, epoch,
epoch_step)
evaluations.append(current_eval)
ppl_file.close()
if args.save_model:
model_runner.save_model(model_name=model_path,
step=global_step,
write_state=False)
model_runner.close_session()
progress.close()
tf.reset_default_graph()
# return the best validation evaluation
return min(evaluations)
except Exception as e:
traceback.print_exc()
os.remove(ppl_file.name)
os.remove(param_file.name)
raise e
import os
from deepsign.data.corpora.ptb import PTBReader
from collections import Counter
from deepsign.data.iterators import window_it
home = os.getenv("HOME")
ptb_dir = home + "/data/gold_standards/ptb"
reader = PTBReader(ptb_dir)
training_set = reader.training_set()
vocab = Counter()
for sentence in training_set:
words = sentence
ngrams = window_it(words, 4)
for ngram in ngrams:
print(ngram)
vocab.update(words)
print("total words:", sum(vocab.values()))
print("total unique words:", len(vocab.keys()))
print("100 most common:")
for w in vocab.most_common(100):
print(w)
sentence_n_grams = (itx.window_it(sentence, n_gram_size)
for sentence in corpus_stream)
n_grams = itx.flatten_it(sentence_n_grams)
# at this point this is an n_gram iterator
n_grams = ([vocab[w] for w in ngram] for ngram in n_grams)
if shuffle:
n_grams = itx.shuffle_it(n_grams, BATCH_SIZE * 1000)
n_grams = itx.batch_it(n_grams, size=batch_size, padding=False)
return n_grams
epochs = 2
train_data = itx.repeat_apply(lambda corpus: pipeline(corpus.training_set(1)),
ptb,
n=epochs,
enum=True)
data_it = itx.bptt_it(itx.flatten_it(ptb.training_set()),
seq_len=20,
seq_prob=0.95,
min_seq_len=5,
batch_size=80)
for data in data_it:
shape = np.shape(data)
if shape[-1] <= 5:
print(shape)