def get_data(data_dir, cached_data, vocab_file):
""" Loads cached data (as sequences of word ids) if it exists, otherwise it
creates the dataset from the raw IMDB text files and caches the
processed dataset.
Args:
data_dir: (str) The IMDB root directory containing the "train"
and "test" subdirectories.
cached_data: (str) The path to the pickle file contianing the
cached data
vocab_file: (str) The file that contains the vocabulary, one
token per line in order from most frequent to
least frequent.
Returns:
(dict)
"""
if os.path.exists(cached_data):
print("LOADING CACHED DATA")
data = pickle2obj(cached_data)
else:
print("PROCESSING RAW DATA")
data = load_data(data_dir=data_dir,
vocab_file=vocab_file,
valid_ratio=0.3,
seed=45)
print("CACHING DATA")
obj2pickle(data, cached_data)
return data
def extract_word2vec_embeddings(file,
n_words,
embed_size,
id2word,
datadir=None):
""" Tries to load pretrained word2vec weights from a file. If it does
not exist, then it trains from scratch and caches the trained
embeddings to that file.
Returns a numpy array of the trained embeddings according to the
word order from `id2word`
"""
if not os.path.isfile(file):
print("Training word2vec embeddings from scratch")
embeddings = create_word2vec_vectors(datadir, embed_size=embed_size)
print("Caching word2vec embeddings")
obj2pickle(embeddings, file)
else:
print("Loading cached word2vec embedings")
embeddings = pickle2obj(file)
# Reorder the embeddings
weights = initialize_embeddings(n_words, embed_size)
for id, word in enumerate(id2word):
vector = embeddings.get(word, None)
if vector is not None:
weights[id] = vector
return weights
def merge_train_extra(pickles_dir, shuffle=True):
""" Merges the train and extra datasets. Optionally shuffles them as well.
then saves the merged data as two pickle files:
X_train_extra_cropped64.pickle
Y_train_extra.pickle
Args:
pickles_dir: (str) directory containing the picle files
shuffle: (bool) Should it shuffle the data (default is True)
"""
print("#" * 60)
print((" " * 34) + "MERGE TRAIN AND EXTRA DATA")
print("#" * 60)
# OPEN TRAIN
X_train = pickle2obj(os.path.join(pickles_dir, "X_train_cropped64.pickle"))
Y_train = pickle2obj(os.path.join(pickles_dir, "Y_train.pickle"))
# OPEN EXTRA
X_extra = pickle2obj(os.path.join(pickles_dir, "X_extra_cropped64.pickle"))
Y_extra = pickle2obj(os.path.join(pickles_dir, "Y_extra.pickle"))
# CONCATENATE
X_merged = np.append(X_train, X_extra, axis=0)
Y_merged = {}
for key in Y_train.keys():
Y_merged[key] = np.append(Y_train[key], Y_extra[key], axis=0)
# SHUFFLE
if shuffle:
random_indices = np.random.permutation(Y_merged["N"].shape[0])
X_merged = X_merged[random_indices]
for key in Y_merged.keys():
Y_merged[key] = Y_merged[key][random_indices]
# SAVE AS:
obj2pickle(X_merged,
file=os.path.join(pickles_dir,
"X_train_extra_cropped64.pickle"))
obj2pickle(Y_merged,
file=os.path.join(pickles_dir, "Y_train_extra.pickle"))
# FEEDBACK
print()
print("X")
print("Train Shape : ", X_train.shape)
print("Extra Shape : ", X_extra.shape)
print("Merged Shape: ", X_merged.shape)
print()
print("Y")
for key in Y_merged.keys():
print("{} : {}".format(key.ljust(10, " "), Y_merged[key].shape))
def create_increased_representation_data(pickles_dir):
print("-" * 60)
print((" " * 35) + "INCREASING REPRESENTATION")
print("-" * 60)
# LOAD DATA
X = pickle2obj(os.path.join(pickles_dir, "X_train_extra_cropped64.pickle"))
Y = pickle2obj(os.path.join(pickles_dir, "Y_train_extra.pickle"))
# INCREASE REPRESENTATION
X, Y = increase_representation(X, Y, min_samples=5000)
# SAVE PICKLES
obj2pickle(X,
os.path.join(pickles_dir, "X_aug_train_extra_cropped64.pickle"),
verbose=True)
obj2pickle(Y,
os.path.join(pickles_dir, "Y_aug_train_extra.pickle"),
verbose=True)
# EXPLORATORY PRINTOUT
explore_data(X=X, labels=Y)
def process_the_data(data, data_dir, out_dir, limit=None):
"""Cleans the data labels, creates images array of 64x64 images, and
saves the data to pickle files.
Args:
data: (str) "train", "test", "extra"
data_dir: (str) the base directory where the train, test, and extra
direcotories will all be found.
out_dir: (str) the directory where you want to save the pickle files.
limit = (None or int) Only proccess a subset of the data
Returns:
"""
print("#" * 60)
print((" " * 47) + "{} DATA".format(data.upper()))
print("#" * 60)
# CREATE CLEANED DATA
mat_file = os.path.join(data_dir, data, "digitStruct.mat")
images_dir = os.path.join(data_dir, data)
X, Y = data_cleaning(matfile=mat_file,
images_dir=images_dir,
dataset=data,
limit=limit)
# SAVE THE DATA
obj2pickle(Y,
file=os.path.join(out_dir, "Y_{}.pickle".format(data)),
verbose=True)
obj2pickle(X,
file=os.path.join(out_dir,
"X_{}_cropped64.pickle".format(data)),
verbose=True)
# EXPLORATORY PRINTOUT
explore_data(X=X, labels=Y)
def save_pickle(obj, f):
print("Saving: ", f)
obj2pickle(obj, file=f)
print("--DONE")
def train_n_steps(model,
data,
hyper,
evals,
n_steps,
batch_size=128,
print_every=100,
eval_every=1000):
# TODO: Start epoch timer at last epoch time from evals (but take into
# acount eval time)
# TODO: Do not use globals, feed paths dictionary or object.
epoch_loss = 0
snapshot_count = len(evals["loss"])
start_step = evals["step"][-1] if snapshot_count > 0 else 0
start_step += 1
epoch_timer = Timer()
step_timer = Timer()
eval_timer = Timer()
for step in range(1, n_steps + 1):
step_timer.start()
X, Y = create_random_batch(data["xtrain"],
data["ytrain"],
batchsize=batch_size,
maxlen=hyper["SAMPLE_LENGTH"])
loss = train_step(model, X, Y)
epoch_loss += loss
# PRINTOUTS
if step % print_every == 0:
progress = 100 * float(step) / n_steps
print_train_feedback(start_step + step,
loss=loss,
progress=progress,
elapsed_time=epoch_timer.elapsed(),
avg_time_ms=step_timer.elapsed() / batch_size)
# EVALUATIONS AND SNAPSHOTS
if (step % eval_every == 0):
epoch_time = epoch_timer.elapsed()
print("=" * 60)
snapshot_count += 1
epoch_loss /= eval_every
# EVALUATE - on train and validation data
eval_timer.start()
train_acc = evaluate_model(model,
data["xtrain"],
data["ytrain"],
seq_maxlen=100)
eval_time = eval_timer.elapsed()
valid_acc = evaluate_model(model,
data["xvalid"],
data["yvalid"],
seq_maxlen=100)
print_epoch_feedback(train_acc, valid_acc, epoch_loss)
# UPDATE EVALS
update_evals(evals,
loss=epoch_loss,
train_acc=train_acc,
valid_acc=valid_acc,
train_time=epoch_time,
eval_time=eval_time,
alpha=model.alpha,
step=start_step + step)
# SAVE SNAPSHOTS - of model parameters, and evals dict
epoch_snapshot(model,
snapshot_count,
accuracy=valid_acc,
name=MODEL_NAME,
dir=SNAPSHOTS_DIR)
obj2pickle(evals, EVALS_FILE)
save_hyper_params(hyper, HYPERPARAMS_FILE)
# RESET
epoch_loss = 0
epoch_timer.start()
print("=" * 60)
print("DONE")
# TRAIN
################################################################################
# Load evaluations dictionary
evals = get_evals_dict(EVALS_FILE)
# Calculate important steps
steps_per_epoch = int(np.ceil(n_samples / hyper["BATCH_SIZE"]))
n_steps = n_epochs * steps_per_epoch
# TRAIN - and handle early termination through keyboard interrupt
try:
print("#" * 60)
print(MODEL_NAME.upper())
print("#" * 60)
train_n_steps(model,
data,
evals,
n_steps=n_steps,
batch_size=hyper["BATCH_SIZE"],
print_every=int(steps_per_epoch / 5),
eval_every=steps_per_epoch)
except KeyboardInterrupt:
print("\nEARLY TERMINATION INITIATED - saving evals and hypers")
obj2pickle(evals, EVALS_FILE)
save_hyper_params(hyper, HYPERPARAMS_FILE)
# TODO: Pad the data with zeroes at the end not begining.
# TODO: Create plots and save them in each epoch
# TODO: Custome weight initializations - See https://discuss.pytorch.org/t/weight-initilzation/157/9
# TODO: Time in training printout is based on epoch not total time since train_n_steps called
# LIMIT TRAIN DATA - eg during development and debugging
limit = opts.data_size
data.train.limit_samples(n=limit, verbose=verbose)
# PORTION OF THE TRAINING DATA USED FOR EVALUATION
data.set_train_eval_data(n=1024,
random=False,
random_transforms=True,
batchsize=128,
verbose=verbose)
# CREATE TENSORFLOW GRAPH
print("USING MODEL: ", opts.model)
models = {"a": model_a, "b": model_b, "c": model_c, "d": model_d}
graph = create_graph(logit_func=models[opts.model], settings=opts)
# RUN TENSORFLOW TRAINING SESSION
run_session(graph=graph,
data=data,
paths=paths,
alpha=opts.alpha,
epochs=opts.epochs)
# SAVE SETTINGS TO TEXT AND PICKLE FILES
save_dict_as_text_file(d=vars(opts), f=paths.settings_text_file)
obj2pickle(opts, paths.settings_pickle_file, verbose=verbose)
# CREATE A COMPARISONS FILE
comparisons_file(opts, paths)
def save_dict_pickle(self, f, verbose=False):
short_path = limit_string(f, front=10, tail=31)
verbose_print("Saving Evals to " + short_path, verbose, end="")
obj2pickle(self.stuff, file=f)
verbose_print_done(verbose)
