def load(subset=None, min_occ=1, min_files=1):
"""
Loads the raw text data that constitutes the Microsoft
Sentence Completion Challenge (stored in ./data/).
Processes the data, tokenizes and parses it, and returns
the results.
Returned is a tuple (train_sents, question_groups, answers,
feature_sizes).
The 'train_sents' numpy array of shape (token_count, feature_count).
Features colums are at first textual (orth, lemma, lemma_4),
then syntactic (pos, dependency-type). The [-2] column contains
syntactic-parent-indices, and the [-1] column denotes to which
sentence the token belongs. The 'question_groups' object
is an iterable question groups. Each group consists of 5
sentences (one of which is correct). Each sentence is a
parsed-text-array as described above. The 'answers'
object is a numpy array of shape (question_group_count, )
that contains the indices of the correct sentences in
question groups. The 'feature_sizes' object is a numpy
array contaning the dimensionality of each feature.
:param subset: The number of training files to process.
If None (default), all of the files are processed.
:param min_occ: Miniumum required number of occurences of
a token (word) required for it to be included in the vocabulary.
Default value (1) uses all words that occured in the trainset.
:param min_files: Minumum required number of files a term has to
occur in for it to be included in the vocabulary.
"""
dir = os.path.join("data", "processed")
if not os.path.exists(dir):
os.makedirs(dir)
name_base = "subset_%r-min_occ_%r-min_files_%r" % (subset, min_occ,
min_files)
# look for the cached processed data, return if present
file_name = os.path.join(dir, name_base + ".pkl")
data = util.try_pickle_load(file_name)
if data is not None:
return data
# did not find cached data, will have to process it
# log the loading process also to a file
log_name = os.path.join(dir, name_base + ".log")
log.addHandler(logging.FileHandler(log_name))
# process the data, cache it and return
data = _load(subset, min_occ, min_files)
util.try_pickle_dump(data, file_name)
return data
def main(x_path, y_path):
x = try_pickle_load(x_path)
y = try_pickle_load(y_path)
print "Shape of loaded x data is", x.shape
print "Shape of loaded y data is", y.shape
assert (x.shape[0] == y.shape[0])
test_size = int(x.shape[0] * TEST_SIZE)
train_size = x.shape[0] - test_size
assert (train_size + test_size == x.shape[0])
print "Train size", train_size
print "Test size", test_size
indices = np.arange(x.shape[0])
np.random.shuffle(indices)
train_ind = indices[:train_size]
test_ind = indices[train_size:]
train_set_x = x[train_ind]
test_set_x = x[test_ind]
train_set_y = y[train_ind]
test_set_y = y[test_ind]
folder_name = os.path.split(x_path)[0]
print "Folder to save", folder_name
try_pickle_dump(train_set_x, os.path.join(folder_name, "x_train.bin"))
try_pickle_dump(test_set_x, os.path.join(folder_name, "x_test.bin"))
try_pickle_dump(train_set_y, os.path.join(folder_name, "y_train.bin"))
try_pickle_dump(test_set_y, os.path.join(folder_name, "y_test.bin"))
print "Done"
def main(x_path, y_path):
x = try_pickle_load(x_path)
y = try_pickle_load(y_path)
print "Shape of loaded x data is", x.shape
print "Shape of loaded y data is", y.shape
assert(x.shape[0] == y.shape[0])
test_size = int(x.shape[0] * TEST_SIZE)
train_size = x.shape[0] - test_size
assert(train_size + test_size == x.shape[0])
print "Train size", train_size
print "Test size", test_size
indices = np.arange(x.shape[0])
np.random.shuffle(indices)
train_ind = indices[:train_size]
test_ind = indices[train_size:]
train_set_x = x[train_ind]
test_set_x = x[test_ind]
train_set_y = y[train_ind]
test_set_y = y[test_ind]
folder_name = os.path.split(x_path)[0]
print "Folder to save", folder_name
try_pickle_dump(train_set_x, os.path.join(folder_name, "x_train.bin"))
try_pickle_dump(test_set_x, os.path.join(folder_name, "x_test.bin"))
try_pickle_dump(train_set_y, os.path.join(folder_name, "y_train.bin"))
try_pickle_dump(test_set_y, os.path.join(folder_name, "y_test.bin"))
print "Done"
def faces(fold):
"""
Retrieves a list of face images. Images are numpy arrays
of (img_height, img_width, RGB) shape. The images represent
the clipped and masked face images from the given fold
of the FDDB database.
:param fold: int indicating which fold is
desired. In [1, 10] range.
"""
log.info("Retrieving face images for fold %s", str(fold))
# generate file name in which this fold's face images are stored
faces_file_name = os.path.join(
FACE_ONLY_ROOT, "fddb_facesonly_fold_{:02d}.zip".format(fold))
# try to load and return pickled data
face_images = util.try_pickle_load(faces_file_name, zip=False)
if face_images is not None:
return face_images
# resulting face images
# each image is a numpy array of RGB components of
# (img_height, img_width, 3) shape
face_images = []
# go through all the photos in the fold
# and their FDDB elipsis info (face annotations)
for photo_path, (masks, bboxes) in image_face_masks_bboxes(fold).items():
log.info("Processing photo %s", photo_path)
# load photo
log.debug("Loading photo")
photo_RGB = cv2.imread(photo_path, 1)
# for each elipse info get mask and bbox
for mask, bbox in zip(masks, bboxes):
# apply the bounding box
log.debug("Applying mask and bounds")
face_img = np.array(photo_RGB[bbox[0][0]:bbox[1][0],
bbox[0][1]:bbox[1][1], :])
# apply the mask
face_mask = mask[bbox[0][0]:bbox[1][0], bbox[0][1]:bbox[1][1]]
face_img[np.logical_not(face_mask), :] = 0
# store the image
face_images.append(face_img)
# store image data for subsequent usage
if not util.try_pickle_dump(face_images, faces_file_name, zip=False):
raise "Failed to pickle face images"
return face_images
def image_face_masks_bboxes(fold):
"""
Returns a dictionary in which keys are file
paths of images belonging to the fold.
Values are tuples (masks, bboxes) where "masks"
are lists of face-elipse booleam masks for that image
and "bboxes" are bounding box info for that image.
The returned dictionary is ordered the same way
the elisis info file is.
"""
log.info("Retrieving image masks for fold %s", str(fold))
# file name of the cached version
masks_file_name = os.path.join(
FACE_MASK_ROOT, "fddb_face_masks_fold{:02d}.zip".format(fold))
# try to load and return pickled data
masks = util.try_pickle_load(masks_file_name, zip=False)
if masks is not None:
return masks
# there is no pickled version, we need to create the masks
masks_dict = collections.OrderedDict()
for photo_path, elipses in image_elipses(fold).items():
log.info("Processing photo %s", photo_path)
# load photo
log.debug("Loading photo")
photo_RGB = cv2.imread(photo_path, 1)
photo_shape = photo_RGB.shape[:2]
# for each elipse info get mask and bbox, and store them
# first prepare the numpy arrays in which they are stored
masks = np.zeros((len(elipses), photo_shape[0], photo_shape[1]),
dtype=np.bool)
bboxes = np.zeros((len(elipses), 2, 2), dtype=np.int32)
# then out those arrays into the dict
masks_dict[photo_path] = (masks, bboxes)
# and then fill up the arrays with real data
for elipse_ind, elipse in enumerate(elipses):
log.debug("Calculating mask and bounds")
mask, bbox = __elipsis_mask_and_box(photo_shape, elipse)
masks[elipse_ind] = mask
bboxes[elipse_ind] = bbox
# store image data for subsequent usage
if not util.try_pickle_dump(masks_dict, masks_file_name, zip=False):
raise "Failed to pickle face masks"
return masks_dict
def main(show=False):
logger.info("... loading data")
logger.debug("Theano.config.floatX is %s" % theano.config.floatX)
# samples is list of Sample objects
samples = load_dataset(DATASET_PATH)
samples = list(samples)
# use only subset of data TODO remove this
# DATA_TO_USE = 60
# samples = samples[:DATA_TO_USE]
random.seed(23455)
random.shuffle(samples)
train_samples, test_samples = split_samples(samples, 0.1)
del samples
cc = ClassCounter()
x_train = generate_x(train_samples)
x_test = generate_x(test_samples)
y_train = generate_targets(train_samples, cc)
y_test = generate_targets(test_samples, cc)
del train_samples
del test_samples
cc.log_stats()
try_pickle_dump(x_train, OUT_PATH + "x_train.bin")
try_pickle_dump(x_test, OUT_PATH + "x_test.bin")
try_pickle_dump(y_train, OUT_PATH + "y_train.bin")
try_pickle_dump(y_test, OUT_PATH + "y_test.bin")
# print x_train[0][0, 0, 80:90, 80:90]
# print x_test[0][0, 0, 80:90, 80:90]
if show:
n_imgs = 5
for j in xrange(n_imgs):
pylab.subplot(2, n_imgs, 0 * n_imgs + j + 1)
pylab.axis('off')
pylab.imshow(x_train[0][j, 0, :, :]) # rgb
for j in xrange(n_imgs):
pylab.subplot(2, n_imgs, 1 * n_imgs + j + 1)
pylab.gray()
pylab.axis('off')
pylab.imshow(y_train[j, :, :])
pylab.show()
def main(show=False):
logger.info("... loading data")
logger.debug("Theano.config.floatX is %s" % theano.config.floatX)
# samples is list of Sample objects
samples = load_dataset(DATASET_PATH)
samples = list(samples)
# use only subset of data TODO remove this
# DATA_TO_USE = 60
# samples = samples[:DATA_TO_USE]
random.seed(23455)
random.shuffle(samples)
train_samples, test_samples = split_samples(samples, 0.1)
del samples
cc = ClassCounter()
x_train = generate_x(train_samples)
x_test = generate_x(test_samples)
y_train = generate_targets(train_samples, cc)
y_test = generate_targets(test_samples, cc)
del train_samples
del test_samples
cc.log_stats()
try_pickle_dump(x_train, OUT_PATH + "x_train.bin")
try_pickle_dump(x_test, OUT_PATH + "x_test.bin")
try_pickle_dump(y_train, OUT_PATH + "y_train.bin")
try_pickle_dump(y_test, OUT_PATH + "y_test.bin")
# print x_train[0][0, 0, 80:90, 80:90]
# print x_test[0][0, 0, 80:90, 80:90]
if show:
n_imgs = 5
for j in xrange(n_imgs):
pylab.subplot(2, n_imgs, 0 * n_imgs + j + 1)
pylab.axis('off')
pylab.imshow(x_train[0][j, 0, :, :]) # rgb
for j in xrange(n_imgs):
pylab.subplot(2, n_imgs, 1 * n_imgs + j + 1)
pylab.gray()
pylab.axis('off')
pylab.imshow(y_train[j, :, :])
pylab.show()
def histograms(fold):
"""
Generates YIQ component histograms for
face and not-face parts of the images
of the given fold(s) of the FDDB database.
Returns a tuple (hist_face, hist_noface).
:type fold: int or iterable of ints
:param fold: When int: number of the fold of
the FDDB database. When iterable: a number
of folds for the FDDB database.
"""
if not isinstance(fold, int):
# fold param is an iterable
# get individual fold histograms
hists_face, hists_noface = zip(*[histograms(f) for f in fold])
# sum them up and return
fold_count = len(hists_face)
hist_face = sum(hists_face[1:], hists_face[0]) / fold_count
hist_noface = sum(hists_noface[1:], hists_noface[0]) / fold_count
return (hist_face, hist_noface)
# generate file name in which this fold's histograms are stored
hist_file_name = os.path.join(
HIST_ROOT, "fddb_YIQ_histogram_fold_{:02d}.pkl".format(fold))
# try to load and return pickled histogram data
pickled_hist = util.try_pickle_load(hist_file_name)
if pickled_hist is not None:
return pickled_hist
# failed to load pickled data, create histograms
# prepare histograms
# first dimension indicates Y, I or Q,
# second dimension are bins
hist_face = np.zeros((3, 256), np.int)
hist_noface = np.zeros((3, 256), np.int)
# go through all the photos in the fold
# and their FDDB elipsis info (face annotations)
for photo_path, (masks, bboxes) in image_face_masks_bboxes(fold).items():
log.info("Processing photo %s", photo_path)
# load photo, convert to YIO
log.debug("Loading photo")
photo_RGB = cv2.imread(photo_path, 1)
log.debug("Converting to YIQ")
photo_YIQ = util.rgb_to_yiq(photo_RGB)
# create masks from elipses and OR them into one mask
log.debug("Creating faces mask")
mask_face = masks.any(axis=0)
mask_noface = np.logical_not(mask_face)
# add current image histograms to total histograms
log.debug("Histogramming")
for component_ind in range(3):
hist_face[component_ind, :] += np.histogram(
photo_YIQ[mask_face, component_ind],
__bin_edges[component_ind]
)[0]
hist_noface[component_ind, :] += np.histogram(
photo_YIQ[mask_noface, component_ind],
__bin_edges[component_ind]
)[0]
# normalize histograms
hist_face = hist_face.astype(np.float) / hist_face[1, :].sum()
hist_noface = hist_noface.astype(np.float) / hist_noface[1, :].sum()
# store histogram data for subsequent usage
if not util.try_pickle_dump((hist_face, hist_noface), hist_file_name):
raise "Failed to pickle histograms"
return (hist_face, hist_noface)
def histograms(fold):
"""
Generates YIQ component histograms for
face and not-face parts of the images
of the given fold(s) of the FDDB database.
Returns a tuple (hist_face, hist_noface).
:type fold: int or iterable of ints
:param fold: When int: number of the fold of
the FDDB database. When iterable: a number
of folds for the FDDB database.
"""
if not isinstance(fold, int):
# fold param is an iterable
# get individual fold histograms
hists_face, hists_noface = zip(*[histograms(f) for f in fold])
# sum them up and return
fold_count = len(hists_face)
hist_face = sum(hists_face[1:], hists_face[0]) / fold_count
hist_noface = sum(hists_noface[1:], hists_noface[0]) / fold_count
return (hist_face, hist_noface)
# generate file name in which this fold's histograms are stored
hist_file_name = os.path.join(
HIST_ROOT, "fddb_YIQ_histogram_fold_{:02d}.pkl".format(fold))
# try to load and return pickled histogram data
pickled_hist = util.try_pickle_load(hist_file_name)
if pickled_hist is not None:
return pickled_hist
# failed to load pickled data, create histograms
# prepare histograms
# first dimension indicates Y, I or Q,
# second dimension are bins
hist_face = np.zeros((3, 256), np.int)
hist_noface = np.zeros((3, 256), np.int)
# go through all the photos in the fold
# and their FDDB elipsis info (face annotations)
for photo_path, (masks, bboxes) in image_face_masks_bboxes(fold).items():
log.info("Processing photo %s", photo_path)
# load photo, convert to YIO
log.debug("Loading photo")
photo_RGB = cv2.imread(photo_path, 1)
log.debug("Converting to YIQ")
photo_YIQ = util.rgb_to_yiq(photo_RGB)
# create masks from elipses and OR them into one mask
log.debug("Creating faces mask")
mask_face = masks.any(axis=0)
mask_noface = np.logical_not(mask_face)
# add current image histograms to total histograms
log.debug("Histogramming")
for component_ind in range(3):
hist_face[component_ind, :] += np.histogram(
photo_YIQ[mask_face, component_ind],
__bin_edges[component_ind])[0]
hist_noface[component_ind, :] += np.histogram(
photo_YIQ[mask_noface, component_ind],
__bin_edges[component_ind])[0]
# normalize histograms
hist_face = hist_face.astype(np.float) / hist_face[1, :].sum()
hist_noface = hist_noface.astype(np.float) / hist_noface[1, :].sum()
# store histogram data for subsequent usage
if not util.try_pickle_dump((hist_face, hist_noface), hist_file_name):
raise "Failed to pickle histograms"
return (hist_face, hist_noface)
def main(conf, gen_func, n_layers, show=False):
"""
conf: dictionary
configuration dictionary, from json file
gen_func: function
function used for generating inputs to network
n_layers: int
number of layers of laplacian pyramid used as input
show: bool
if true, few parsed images will be shown as a result
"""
logger.info("... loading data")
logger.debug("Theano.config.floatX is %s" % theano.config.floatX)
# samples is list of Sample objects
dataset_path = conf['training']['dataset-folder']
samples = load_dataset(dataset_path)
samples = list(samples)
random.seed(conf['training']['shuffle-seed'])
random.shuffle(samples)
validation_size = float(conf['training']['validation-percent']) / 100.0
train_samples, validation_samples = split_samples(samples, validation_size)
del samples
out_folder = conf['training']['out-folder']
write_samples_log(train_samples,
os.path.join(out_folder, "samples_train.log"))
write_samples_log(validation_samples,
os.path.join(out_folder, "samples_validation.log"))
cc = ClassCounter()
x_train = generate_x(train_samples, n_layers, gen_func)
x_validation = generate_x(validation_samples, n_layers, gen_func)
y_train = generate_targets(train_samples, cc)
y_validation = generate_targets(validation_samples, cc)
del train_samples
del validation_samples
try_pickle_dump(x_train, os.path.join(out_folder, "x_train.bin"))
try_pickle_dump(x_validation, os.path.join(out_folder, "x_validation.bin"))
try_pickle_dump(y_train, os.path.join(out_folder, "y_train.bin"))
try_pickle_dump(y_validation, os.path.join(out_folder, "y_validation.bin"))
# if test data defined
if 'test' in conf:
logger.info("Found test configuration, generating test data")
test_samples = load_dataset(conf['test']['dataset-folder'])
test_samples = list(test_samples)
write_samples_log(test_samples,
os.path.join(out_folder, "samples_test.log"))
x_test = generate_x(test_samples, n_layers, gen_func)
y_test = generate_targets(test_samples, cc)
try_pickle_dump(x_test, os.path.join(out_folder, "x_test.bin"))
try_pickle_dump(y_test, os.path.join(out_folder, "y_test.bin"))
cc.log_stats()
if show:
# show few parsed samples from train set
n_imgs = 5
for j in xrange(n_imgs):
pylab.subplot(3, n_imgs, 0 * n_imgs + j + 1)
pylab.axis('off')
pylab.imshow(x_train[0][j, 0, :, :]) # Y
for j in xrange(n_imgs):
pylab.subplot(3, n_imgs, 1 * n_imgs + j + 1)
pylab.gray()
pylab.axis('off')
pylab.imshow(x_train[0][j, 3, :, :]) # depth
for j in xrange(n_imgs):
pylab.subplot(3, n_imgs, 2 * n_imgs + j + 1)
pylab.gray()
pylab.axis('off')
pylab.imshow(y_train[j, :, :])
pylab.show()
def eval_model(conf,
train_fn,
test_fn,
n_train_batches,
n_test_batches,
layers,
pre_fn=None,
l_rate_wrapper=None):
"""
Function for trainig and validating models
n_epochs: dictionary
configuration params
train_fn: theano function
training function
test_fn: theano function
validation function
n_train_batches: int
number of batches for training
n_test_batches: int
number of batches for validation
layers: list
list of layers, used to extract best params
pre_fn: function
function to be called before training
l_rate_wrapper: UpdateParameters object
learning rate wrapper object
returns: (best_validation_error, best_iter, best_params)
the best validation error, iteration and parameters
"""
assert (type(conf) is dict)
n_epochs = conf['epochs']
if n_epochs < 0:
n_epochs = maxint
# how often to lower learning rate if no improvement
epochs_check_learn_rate = None
if 'learning-rate-decrease-params' in conf:
lrdp_params = conf['learning-rate-decrease-params']
epochs_check_learn_rate = lrdp_params['no-improvement-epochs']
min_learning_rate = lrdp_params['min-learning-rate']
# file for dumping weights
now = datetime.now()
weights_filename = "network-%d-%d.bin" % (now.hour, now.minute)
logger.info('... training')
# early-stopping parameters
# look as this many iterations regardless
patience = n_train_batches * 20 # skip first 20 epochs
# wait this much longer when a new best is found
patience_increase = 1.1
# a relative improvement of this much is considered significant
improvement_threshold = 0.998
# go through this many minibatche before checking the network
# on the validation set; in this case we check every epoch
validation_frequency = min(n_train_batches, patience / 2)
logger.debug('Validation frequency is %d' % validation_frequency)
set_layers_training_mode(layers, 1)
best_validation_loss = numpy.inf
best_iter = 0
best_epoch = 0 # best epoch for train cost
best_params = []
best_train_cost = numpy.inf
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
# function to be called before every epoch
if pre_fn is not None:
pre_fn()
training_costs = numpy.zeros((n_train_batches), dtype='float32')
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
cost_ij = train_fn(minibatch_index)
training_costs[minibatch_index] = cost_ij
# logger.info('training @ iter = %d, cost %f' % (iter, cost_ij))
stdout.write('.')
stdout.flush()
if (iter + 1) % validation_frequency == 0:
stdout.write('\n') # newline after iteration dots
set_layers_training_mode(layers, 0)
# compute zero-one loss on validation set
validation = [test_fn(i) for i in xrange(n_test_batches)]
set_layers_training_mode(layers, 1)
validation_losses = [v[0] for v in validation]
validation_costs = [v[1] for v in validation]
# class accuracies
correct = numpy.zeros((layers[0].n_classes), dtype='int32')
total = numpy.zeros((layers[0].n_classes), dtype='int32')
for v in validation:
correct += v[2]
total += v[3]
validation_class_accuracy = calc_class_accuracy(correct, total)
this_validation_loss = numpy.mean(validation_losses)
logger.info(
'epoch %i, minibatch %i/%i, validation error %f %%', epoch,
minibatch_index + 1, n_train_batches,
this_validation_loss * 100.)
logger.info('validation cost: %f',
numpy.mean(validation_costs))
logger.info('mean class accuracy: %f %%',
validation_class_accuracy * 100.)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
# improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(
patience, 10 * n_train_batches +
int(iter * patience_increase + 1))
logger.info("Patience increased to %d epochs",
int(patience / n_train_batches))
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# save model parameters
best_params = [l.get_weights() for l in layers]
try_pickle_dump(best_params, weights_filename)
logger.info((' epoch %i, minibatch %i/%i,'
'validation error of best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if patience <= iter:
logger.info("Ran out of patience")
done_looping = True
break
train_cost = numpy.mean(training_costs)
logger.info('Average training cost %f', train_cost)
if train_cost < best_train_cost * improvement_threshold:
best_train_cost = train_cost
best_epoch = epoch
# lower learning rate if no improvement
learn_rate = l_rate_wrapper.learning_rate.get_value()
if learn_rate > min_learning_rate and\
(epoch - best_epoch + 1) % epochs_check_learn_rate == 0:
l_rate_wrapper.lower_rate_by_factor(0.5)
logger.info('Optimization complete.')
return best_validation_loss, best_iter, best_params
def eval_model(conf, train_fn, test_fn, n_train_batches, n_test_batches,
layers, pre_fn=None, l_rate_wrapper=None):
"""
Function for trainig and validating models
n_epochs: dictionary
configuration params
train_fn: theano function
training function
test_fn: theano function
validation function
n_train_batches: int
number of batches for training
n_test_batches: int
number of batches for validation
layers: list
list of layers, used to extract best params
pre_fn: function
function to be called before training
l_rate_wrapper: UpdateParameters object
learning rate wrapper object
returns: (best_validation_error, best_iter, best_params)
the best validation error, iteration and parameters
"""
assert(type(conf) is dict)
n_epochs = conf['epochs']
if n_epochs < 0:
n_epochs = maxint
# how often to lower learning rate if no improvement
epochs_check_learn_rate = None
if 'learning-rate-decrease-params' in conf:
lrdp_params = conf['learning-rate-decrease-params']
epochs_check_learn_rate = lrdp_params['no-improvement-epochs']
min_learning_rate = lrdp_params['min-learning-rate']
# file for dumping weights
now = datetime.now()
weights_filename = "network-%d-%d.bin" % (now.hour, now.minute)
logger.info('... training')
# early-stopping parameters
# look as this many iterations regardless
patience = n_train_batches * 20 # skip first 20 epochs
# wait this much longer when a new best is found
patience_increase = 1.1
# a relative improvement of this much is considered significant
improvement_threshold = 0.998
# go through this many minibatche before checking the network
# on the validation set; in this case we check every epoch
validation_frequency = min(n_train_batches, patience / 2)
logger.debug('Validation frequency is %d' % validation_frequency)
set_layers_training_mode(layers, 1)
best_validation_loss = numpy.inf
best_iter = 0
best_epoch = 0 # best epoch for train cost
best_params = []
best_train_cost = numpy.inf
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
# function to be called before every epoch
if pre_fn is not None:
pre_fn()
training_costs = numpy.zeros((n_train_batches), dtype='float32')
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
cost_ij = train_fn(minibatch_index)
training_costs[minibatch_index] = cost_ij
# logger.info('training @ iter = %d, cost %f' % (iter, cost_ij))
stdout.write('.')
stdout.flush()
if (iter + 1) % validation_frequency == 0:
stdout.write('\n') # newline after iteration dots
set_layers_training_mode(layers, 0)
# compute zero-one loss on validation set
validation = [test_fn(i) for i in xrange(n_test_batches)]
set_layers_training_mode(layers, 1)
validation_losses = [v[0] for v in validation]
validation_costs = [v[1] for v in validation]
# class accuracies
correct = numpy.zeros((layers[0].n_classes), dtype='int32')
total = numpy.zeros((layers[0].n_classes), dtype='int32')
for v in validation:
correct += v[2]
total += v[3]
validation_class_accuracy = calc_class_accuracy(correct, total)
this_validation_loss = numpy.mean(validation_losses)
logger.info('epoch %i, minibatch %i/%i, validation error %f %%',
epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.)
logger.info('validation cost: %f',
numpy.mean(validation_costs))
logger.info('mean class accuracy: %f %%',
validation_class_accuracy * 100.)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
# improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience,
10 * n_train_batches + int(iter * patience_increase + 1))
logger.info("Patience increased to %d epochs",
int(patience / n_train_batches))
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# save model parameters
best_params = [l.get_weights() for l in layers]
try_pickle_dump(best_params, weights_filename)
logger.info((' epoch %i, minibatch %i/%i,'
'validation error of best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if patience <= iter:
logger.info("Ran out of patience")
done_looping = True
break
train_cost = numpy.mean(training_costs)
logger.info('Average training cost %f', train_cost)
if train_cost < best_train_cost * improvement_threshold:
best_train_cost = train_cost
best_epoch = epoch
# lower learning rate if no improvement
learn_rate = l_rate_wrapper.learning_rate.get_value()
if learn_rate > min_learning_rate and\
(epoch - best_epoch + 1) % epochs_check_learn_rate == 0:
l_rate_wrapper.lower_rate_by_factor(0.5)
logger.info('Optimization complete.')
return best_validation_loss, best_iter, best_params
def load_ngrams(n,
features_use,
tree,
subset=None,
min_occ=1,
min_files=1,
remove_subst_tokens=False):
"""
Loads the dataset for microsot sentence completion challenge, processed
into ngrams.
The raw dataset is loadaed and processed using the 'load' function,
to which 'subset', 'min_occ' and 'min_files' are forwared.
The resulting dataset is then processed into ngrams using the
'ngrams' function, to which 'n' and 'tree' parameter are forwarded.
This is then cached on the disk for subsequent usage.
The resulting ngrams are pruned from unwanted features as indicated
by the 'features_use parameter'.
Returns a tuple (sents, q_groups, answers, feature_sizes).
This reflects the returned value by the 'load' function, except that
'sents' and 'g_groups' are now not just features extracted from text,
but ngrams built from those features.
"""
features_use = np.array(features_use, dtype=bool)
log.info("Loading %d-grams, %s, features_use: %s", n,
"tree" if tree else "linear",
"".join([str(int(i)) for i in features_use]))
dir = os.path.join("data", "processed")
if not os.path.exists(dir):
os.makedirs(dir)
name_base = "%s-%d_grams-subset_%r-min_occ_%r-min_files_%r" % (
"tree" if tree else "linear", n, subset, min_occ, min_files)
# tree-grams can all be seen as a feature-subset of 4 grams
if tree and n < 4:
ngrams_all = load_ngrams(4, np.ones(features_use.size,
dtype=bool), tree, subset, min_occ,
min_files, remove_subst_tokens)
else:
# look for the cached 4-grams with all the features
file_name = os.path.join(dir, name_base + ".pkl")
ngrams_all = util.try_pickle_load(file_name)
# it is possible that sentences are split
# in order to avoid Python bug with storing large arrays
if ngrams_all is not None and isinstance(ngrams_all[0], list):
sents = np.vstack(ngrams_all[0])
ngrams_all = (sents, ) + ngrams_all[1:]
# if unable to load cached data, create it
if ngrams_all is None:
# load data
tokens, q_groups, answers, ftr_sizes = load(subset, min_occ, min_files)
# tokens that should be present in ngrams
# the purpose is to remove ngrams containing tokens that are
# substitutes for removed ones
invalid_tokens = None
if remove_subst_tokens:
invalid_tokens = dict(zip(range(3), ftr_sizes[:3] - 1))
log.info("Invalid tokens: %r", invalid_tokens)
# define a function for generating ngrams, and process
# trainset and questions
_ngrams = lambda tokens: ngrams(n, tree, tokens, invalid_tokens)
sent_ngrams = _ngrams(tokens)
q_ngrams = [map(_ngrams, qg) for qg in q_groups]
# store the processed data for subsequent usage
# split sent ngrams to avoid Py bug with pickling large arrays
util.try_pickle_dump(
(np.vsplit(sent_ngrams,
np.arange(1, 10) *
(len(sent_ngrams) / 10)), q_ngrams, answers, ftr_sizes),
file_name)
ngrams_all = (sent_ngrams, q_ngrams, answers, ftr_sizes)
# remove unwanted features from ngrams_all
used_ftr = np.arange(ngrams_all[0].shape[1])[np.tile(features_use, n)]
sents = ngrams_all[0][:, used_ftr]
q_groups = [[q[:, used_ftr] for q in qg] for qg in ngrams_all[1]]
return (sents, q_groups) + ngrams_all[2:]
def main(conf, gen_func, n_layers, show=False):
"""
conf: dictionary
configuration dictionary, from json file
gen_func: function
function used for generating inputs to network
n_layers: int
number of layers of laplacian pyramid used as input
show: bool
if true, few parsed images will be shown as a result
"""
logger.info("... loading data")
logger.debug("Theano.config.floatX is %s" % theano.config.floatX)
# samples is list of Sample objects
dataset_path = conf["training"]["dataset-folder"]
samples = load_dataset(dataset_path)
samples = list(samples)
if "data-subset" in conf["training"]:
# use only subset of data
data_to_use = conf["training"]["data-subset"]
logger.info("Using only subset of %d samples", data_to_use)
samples = samples[:data_to_use]
random.seed(conf["training"]["shuffle-seed"])
random.shuffle(samples)
out_folder = conf["training"]["out-folder"]
# if test data defined
if "test-percent" in conf["training"]:
logger.info("Found test configuration, generating test data")
test_size = float(conf["training"]["test-percent"]) / 100.0
samples, test_samples = split_samples(samples, test_size)
write_samples_log(test_samples, os.path.join(out_folder, "samples_test.log"))
x_test = generate_x(test_samples, n_layers, gen_func)
y_test = generate_targets(test_samples)
try_pickle_dump(x_test, os.path.join(out_folder, "x_test.bin"))
try_pickle_dump(y_test, os.path.join(out_folder, "y_test.bin"))
else:
logger.info("No test set configuration present")
validation_size = float(conf["training"]["validation-percent"]) / 100.0
train_samples, validation_samples = split_samples(samples, validation_size)
del samples
write_samples_log(train_samples, os.path.join(out_folder, "samples_train.log"))
write_samples_log(validation_samples, os.path.join(out_folder, "samples_validation.log"))
x_train = generate_x(train_samples, n_layers, gen_func)
x_validation = generate_x(validation_samples, n_layers, gen_func)
y_train = generate_targets(train_samples)
y_validation = generate_targets(validation_samples)
del train_samples
del validation_samples
try_pickle_dump(x_train, os.path.join(out_folder, "x_train.bin"))
try_pickle_dump(x_validation, os.path.join(out_folder, "x_validation.bin"))
try_pickle_dump(y_train, os.path.join(out_folder, "y_train.bin"))
try_pickle_dump(y_validation, os.path.join(out_folder, "y_validation.bin"))
if show:
# show few parsed samples from train set
n_imgs = 5
for j in xrange(n_imgs):
pylab.subplot(2, n_imgs, 0 * n_imgs + j + 1)
pylab.axis("off")
pylab.imshow(x_train[0][j, 0, :, :]) # rgb
for j in xrange(n_imgs):
pylab.subplot(2, n_imgs, 1 * n_imgs + j + 1)
pylab.gray()
pylab.axis("off")
pylab.imshow(y_train[j, :, :])
pylab.show()
