def __init__(self, dataset_dir='../../datasets'):
filename = 'Nottingham.zip'
source = 'http://www-etud.iro.umontreal.ca/~boulanni/Nottingham.zip'
super(Nottingham, self).__init__(filename=filename, source=source, dataset_dir=dataset_dir)
# now the file path has been installed to self.dataset_locations directory
# grab the appropriate filenames
train_filenames = os.path.join(self.dataset_location, 'Nottingham', 'train', '*.mid')
valid_filenames = os.path.join(self.dataset_location, 'Nottingham', 'valid', '*.mid')
test_filenames = os.path.join(self.dataset_location, 'Nottingham', 'test', '*.mid')
# glob the files
train_files = glob.glob(train_filenames)
valid_files = glob.glob(valid_filenames)
test_files = glob.glob(test_filenames)
# grab the datasets from midireading the files
train_datasets = [midiread(f, r=(21, 109), dt=0.3).piano_roll.astype(theano.config.floatX) for f in train_files]
valid_datasets = [midiread(f, r=(21, 109), dt=0.3).piano_roll.astype(theano.config.floatX) for f in valid_files]
test_datasets = [midiread(f, r=(21, 109), dt=0.3).piano_roll.astype(theano.config.floatX) for f in test_files]
# get the data shapes
self.train_shapes = [train.shape for train in train_datasets]
self.valid_shapes = [valid.shape for valid in valid_datasets]
self.test_shapes = [test.shape for test in test_datasets]
# put them into shared variables
log.debug('Putting Nottingham into theano shared variables')
self.train = dataset_shared(numpy.concatenate(train_datasets), name='nottingham_train', borrow=True)
self.valid = dataset_shared(numpy.concatenate(valid_datasets), name='nottingham_valid', borrow=True)
self.test = dataset_shared(numpy.concatenate(test_datasets), name='nottingham_test', borrow=True)
def __init__(self, dataset_dir='../../datasets'):
"""
Parameters
----------
dataset_dir : str
The `dataset_dir` parameter to a ``FileDataset``.
"""
log.debug("Loading MuseData midi dataset...")
filename = 'MuseData.zip'
source = 'http://www-etud.iro.umontreal.ca/~boulanni/MuseData.zip'
super(MuseData, self).__init__(filename=filename,
source=source,
dataset_dir=dataset_dir)
# now the file path has been installed to self.dataset_locations directory
# grab the appropriate filenames
train_filenames = os.path.join(self.dataset_location, 'MuseData',
'train', '*.mid')
valid_filenames = os.path.join(self.dataset_location, 'MuseData',
'valid', '*.mid')
test_filenames = os.path.join(self.dataset_location, 'MuseData',
'test', '*.mid')
# glob the files
train_files = glob.glob(train_filenames)
valid_files = glob.glob(valid_filenames)
test_files = glob.glob(test_filenames)
# grab the datasets from midireading the files
train_datasets = [
midiread(f, r=(21, 109),
dt=0.3).piano_roll.astype(theano.config.floatX)
for f in train_files
]
valid_datasets = [
midiread(f, r=(21, 109),
dt=0.3).piano_roll.astype(theano.config.floatX)
for f in valid_files
]
test_datasets = [
midiread(f, r=(21, 109),
dt=0.3).piano_roll.astype(theano.config.floatX)
for f in test_files
]
# get the data shapes
self.train_shapes = [train.shape for train in train_datasets]
self.valid_shapes = [valid.shape for valid in valid_datasets]
self.test_shapes = [test.shape for test in test_datasets]
# put them into shared variables
log.debug('Putting MuseData into theano shared variables')
self.train = dataset_shared(numpy.concatenate(train_datasets),
name='muse_train',
borrow=True)
self.valid = dataset_shared(numpy.concatenate(valid_datasets),
name='muse_valid',
borrow=True)
self.test = dataset_shared(numpy.concatenate(test_datasets),
name='muse_test',
borrow=True)
def __init__(self, binary=False, one_hot=False, concat_train_valid=True,
dataset_dir='../../datasets', sequence_number=0, rng=None):
# instantiate the Dataset class to install the dataset from the url
log.info('Loading MNIST with binary=%s and one_hot=%s', str(binary), str(one_hot))
filename = 'mnist.pkl.gz'
source = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
super(MNIST, self).__init__(filename=filename, source=source, dataset_dir=dataset_dir)
# self.dataset_location now contains the os path to the dataset file
# self.file_type tells how to load the dataset
# load the dataset into memory
if PY3:
pickle_load = partial(pickle.load, encoding='bytes')
else:
pickle_load = partial(pickle.load)
if self.file_type is file_ops.GZ:
(self.train_X, self.train_Y), (self.valid_X, self.valid_Y), (self.test_X, self.test_Y) = pickle_load(
gzip.open(self.dataset_location, 'rb')
)
else:
(self.train_X, self.train_Y), (self.valid_X, self.valid_Y), (self.test_X, self.test_Y) = pickle_load(
open(self.dataset_location, 'r')
)
if concat_train_valid:
log.debug('Concatenating train and valid sets together...')
self.train_X = numpy.concatenate((self.train_X, self.valid_X))
self.train_Y = numpy.concatenate((self.train_Y, self.valid_Y))
# sequence the dataset
if sequence_number is not None:
self.sequence(sequence_number=sequence_number, rng=rng)
# make optional binary
if binary:
_binary_cutoff = 0.5
log.debug('Making MNIST X values binary with cutoff %s', str(_binary_cutoff))
self.train_X = (self.train_X > _binary_cutoff).astype('float32')
self.valid_X = (self.valid_X > _binary_cutoff).astype('float32')
self.test_X = (self.test_X > _binary_cutoff).astype('float32')
# make optional one-hot labels
if one_hot:
self.train_Y = numpy_one_hot(self.train_Y, n_classes=10)
self.valid_Y = numpy_one_hot(self.valid_Y, n_classes=10)
self.test_Y = numpy_one_hot(self.test_Y, n_classes=10)
log.debug("loading datasets into shared variables")
self.train_X = dataset_shared(self.train_X, name='mnist_train_x', borrow=True)
self.train_Y = dataset_shared(self.train_Y, name='mnist_train_y', borrow=True)
self.valid_X = dataset_shared(self.valid_X, name='mnist_valid_x', borrow=True)
self.valid_Y = dataset_shared(self.valid_Y, name='mnist_valid_y', borrow=True)
self.test_X = dataset_shared(self.test_X, name='mnist_test_x', borrow=True)
self.test_Y = dataset_shared(self.test_Y, name='mnist_test_y', borrow=True)
def __init__(self, train_X, train_Y=None, valid_X=None, valid_Y=None, test_X=None, test_Y=None):
log.info('Wrapping matrix from memory')
super(self.__class__, self).__init__()
# make sure the inputs are arrays
train_X = numpy.array(train_X)
self._train_shape = train_X.shape
self.train_X = dataset_shared(train_X, name='memory_train_x', borrow=True)
if train_Y is not None:
try:
self.train_Y = dataset_shared(numpy.array(train_Y), name='memory_train_y', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT train_Y TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if valid_X is not None:
try:
valid_X = numpy.array(valid_X)
self._valid_shape = valid_X.shape
self.valid_X = dataset_shared(valid_X, name='memory_valid_x', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT valid_X TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if valid_Y is not None:
try:
self.valid_Y = dataset_shared(numpy.array(valid_Y), name='memory_valid_y', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT valid_Y TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if test_X is not None:
try:
test_X = numpy.array(test_X)
self._test_shape = test_X.shape
self.test_X = dataset_shared(test_X, name='memory_test_x', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT test_X TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if test_Y is not None:
try:
self.test_Y = dataset_shared(numpy.array(test_Y), name='memory_test_y', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT test_Y TO NUMPY ARRAY. EXCEPTION: %s", str(e))
def __init__(self, train_X, train_Y=None, valid_X=None, valid_Y=None, test_X=None, test_Y=None):
log.info('Wrapping matrix from memory')
# make sure the inputs are arrays
train_X = numpy.array(train_X)
self._train_shape = train_X.shape
self.train_X = dataset_shared(train_X, name='memory_train_x', borrow=True)
if train_Y is not None:
try:
self.train_Y = dataset_shared(numpy.array(train_Y), name='memory_train_y', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT train_Y TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if valid_X is not None:
try:
valid_X = numpy.array(valid_X)
self._valid_shape = valid_X.shape
self.valid_X = dataset_shared(valid_X, name='memory_valid_x', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT valid_X TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if valid_Y is not None:
try:
self.valid_Y = dataset_shared(numpy.array(valid_Y), name='memory_valid_y', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT valid_Y TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if test_X is not None:
try:
test_X = numpy.array(test_X)
self._test_shape = test_X.shape
self.test_X = dataset_shared(test_X, name='memory_test_x', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT test_X TO NUMPY ARRAY. EXCEPTION: %s", str(e))
if test_Y is not None:
try:
self.test_Y = dataset_shared(numpy.array(test_Y), name='memory_test_y', borrow=True)
except Exception as e:
log.exception("COULD NOT CONVERT test_Y TO NUMPY ARRAY. EXCEPTION: %s", str(e))
def __init__(self,
binary=False,
binary_cutoff=0.5,
one_hot=False,
concat_train_valid=False,
dataset_dir='../../datasets',
sequence_number=0,
rng=None):
"""
Parameters
----------
binary : bool, optional
Flag to binarize the input images.
binary_cutoff : float, optional
If you want to binarize the input images, what threshold value to use.
one_hot : bool, optional
Flag to convert the labels to one-hot encoding rather than their normal integers.
concat_train_valid : bool, optional
Flag to concatenate the training and validation datasets together. This would be the original split.
dataset_dir : str, optional
The `dataset_dir` parameter to a ``FileDataset``.
sequence_number : int, optional
The sequence method to use if we want to put the input images into a specific order. 0 defaults to random.
rng : random, optional
The random number generator to use when sequencing.
"""
# instantiate the Dataset class to install the dataset from the url
log.info('Loading MNIST with binary=%s and one_hot=%s', str(binary),
str(one_hot))
filename = 'mnist.pkl.gz'
source = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
super(MNIST, self).__init__(filename=filename,
source=source,
dataset_dir=dataset_dir)
# self.dataset_location now contains the os path to the dataset file
# self.file_type tells how to load the dataset
# load the dataset into memory
if self.file_type is file_ops.GZ:
(self.train_X,
self.train_Y), (self.valid_X,
self.valid_Y), (self.test_X,
self.test_Y) = pickle.load(
gzip.open(
self.dataset_location,
'rb'))
else:
(self.train_X,
self.train_Y), (self.valid_X,
self.valid_Y), (self.test_X,
self.test_Y) = pickle.load(
open(self.dataset_location,
'r'))
if concat_train_valid:
log.debug('Concatenating train and valid sets together...')
self.train_X = numpy.concatenate((self.train_X, self.valid_X))
self.train_Y = numpy.concatenate((self.train_Y, self.valid_Y))
# sequence the dataset
if sequence_number is not None:
self.sequence(sequence_number=sequence_number, rng=rng)
# make optional binary
if binary:
log.debug('Making MNIST X values binary with cutoff %s',
str(binary_cutoff))
self.train_X = binarize(self.train_X, binary_cutoff)
self.valid_X = binarize(self.valid_X, binary_cutoff)
self.test_X = binarize(self.test_X, binary_cutoff)
# make optional one-hot labels
if one_hot:
self.train_Y = numpy_one_hot(self.train_Y, n_classes=10)
self.valid_Y = numpy_one_hot(self.valid_Y, n_classes=10)
self.test_Y = numpy_one_hot(self.test_Y, n_classes=10)
log.debug("loading datasets into shared variables")
self.train_X = dataset_shared(self.train_X,
name='mnist_train_x',
borrow=True)
self.train_Y = dataset_shared(self.train_Y,
name='mnist_train_y',
borrow=True)
self.valid_X = dataset_shared(self.valid_X,
name='mnist_valid_x',
borrow=True)
self.valid_Y = dataset_shared(self.valid_Y,
name='mnist_valid_y',
borrow=True)
self.test_X = dataset_shared(self.test_X,
name='mnist_test_x',
borrow=True)
self.test_Y = dataset_shared(self.test_Y,
name='mnist_test_y',
borrow=True)
def __init__(self, binary=False, binary_cutoff=0.5, one_hot=False, concat_train_valid=False,
dataset_dir='../../datasets', sequence_number=0, rng=None):
"""
Parameters
----------
binary : bool, optional
Flag to binarize the input images.
binary_cutoff : float, optional
If you want to binarize the input images, what threshold value to use.
one_hot : bool, optional
Flag to convert the labels to one-hot encoding rather than their normal integers.
concat_train_valid : bool, optional
Flag to concatenate the training and validation datasets together. This would be the original split.
dataset_dir : str, optional
The `dataset_dir` parameter to a ``FileDataset``.
sequence_number : int, optional
The sequence method to use if we want to put the input images into a specific order. 0 defaults to random.
rng : random, optional
The random number generator to use when sequencing.
"""
# instantiate the Dataset class to install the dataset from the url
log.info('Loading MNIST with binary=%s and one_hot=%s', str(binary), str(one_hot))
filename = 'mnist.pkl.gz'
source = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
super(MNIST, self).__init__(filename=filename, source=source, dataset_dir=dataset_dir)
# self.dataset_location now contains the os path to the dataset file
# self.file_type tells how to load the dataset
# load the dataset into memory
if self.file_type is file_ops.GZ:
(self.train_X, self.train_Y), (self.valid_X, self.valid_Y), (self.test_X, self.test_Y) = pickle.load(
gzip.open(self.dataset_location, 'rb')
)
else:
(self.train_X, self.train_Y), (self.valid_X, self.valid_Y), (self.test_X, self.test_Y) = pickle.load(
open(self.dataset_location, 'r')
)
if concat_train_valid:
log.debug('Concatenating train and valid sets together...')
self.train_X = numpy.concatenate((self.train_X, self.valid_X))
self.train_Y = numpy.concatenate((self.train_Y, self.valid_Y))
# sequence the dataset
if sequence_number is not None:
self.sequence(sequence_number=sequence_number, rng=rng)
# make optional binary
if binary:
log.debug('Making MNIST X values binary with cutoff %s', str(binary_cutoff))
self.train_X = binarize(self.train_X, binary_cutoff)
self.valid_X = binarize(self.valid_X, binary_cutoff)
self.test_X = binarize(self.test_X, binary_cutoff)
# make optional one-hot labels
if one_hot:
self.train_Y = numpy_one_hot(self.train_Y, n_classes=10)
self.valid_Y = numpy_one_hot(self.valid_Y, n_classes=10)
self.test_Y = numpy_one_hot(self.test_Y, n_classes=10)
log.debug("loading datasets into shared variables")
self.train_X = dataset_shared(self.train_X, name='mnist_train_x', borrow=True)
self.train_Y = dataset_shared(self.train_Y, name='mnist_train_y', borrow=True)
self.valid_X = dataset_shared(self.valid_X, name='mnist_valid_x', borrow=True)
self.valid_Y = dataset_shared(self.valid_Y, name='mnist_valid_y', borrow=True)
self.test_X = dataset_shared(self.test_X, name='mnist_test_x', borrow=True)
self.test_Y = dataset_shared(self.test_Y, name='mnist_test_y', borrow=True)
def __init__(self, source_dir, size_key, one_hot=False, train_split=0.9, valid_split=0.1):
print("Getting dataset %s" % size_key)
# grab the datasets from the preprocessed files
datasets = [(numpy.load(f), get_label(f)) for f in find_processed_files(source_dir, size_key)]
# make sure they are all the correct dimensionality
datasets = [(data.shape, data, label) for data, label in datasets
if data.shape[1] == sizes[size_key] and label is not None]
print("Found %d examples" % len(datasets))
# shuffle!
random.seed(1)
random.shuffle(datasets)
# shapes
shapes = [shape for shape, _, _ in datasets]
# data
dataset = [data for _, data, _ in datasets]
# labels
labels = numpy.asarray([label for _, _, label in datasets], dtype='int8')
# make the labels into one-hot vectors
if one_hot:
labels = numpy_one_hot(labels, n_classes=5)
train_len = int(math.floor(train_split * len(dataset)))
valid_len = int(math.floor(valid_split * len(dataset)))
valid_stop = train_len + valid_len
print("# train: %d examples" % train_len)
train_datasets = dataset[:train_len]
train_labels = labels[:train_len]
if valid_len > 0:
valid_datasets = dataset[train_len:valid_stop]
valid_labels = labels[train_len:valid_stop]
else:
valid_datasets = []
if train_len + valid_len < len(dataset):
test_datasets = dataset[valid_stop:]
test_labels = labels[valid_stop:]
else:
test_datasets = []
# median_train_len = int(numpy.median(numpy.asarray(shapes[:train_len]), axis=0)[0])
min_train_len = int(numpy.min(numpy.asarray(shapes[:train_len]), axis=0)[0])
max_train_len = int(numpy.max(numpy.asarray(shapes[:train_len]), axis=0)[0])
# train = numpy.array([data[:min_train_len] for data in train_datasets], dtype='float32')
train = numpy.array(
[numpy.pad(data, [(0, max_train_len-data.shape[0]), (0, 0)], mode='constant') for data in train_datasets],
dtype='float32'
)
self.train_shape = train.shape
self.train = (dataset_shared(train, borrow=True), dataset_shared(train_labels, borrow=True))
if len(valid_datasets) > 0:
min_valid_len = int(numpy.min(numpy.asarray(shapes[train_len:valid_stop]), axis=0)[0])
max_valid_len = int(numpy.max(numpy.asarray(shapes[train_len:valid_stop]), axis=0)[0])
# valid = numpy.array([data[:min_valid_len] for data in valid_datasets], dtype='float32')
valid = numpy.array(
[numpy.pad(data, [(0, max_valid_len-data.shape[0]), (0, 0)], mode='constant') for data in
valid_datasets],
dtype='float32'
)
self.valid_shape = valid.shape
self.valid = (dataset_shared(valid, borrow=True), dataset_shared(valid_labels, borrow=True))
else:
self.valid = None, None
self.valid_shape = None
if len(test_datasets) > 0:
min_test_len = int(numpy.min(numpy.asarray(shapes[valid_stop:]), axis=0)[0])
max_test_len = int(numpy.max(numpy.asarray(shapes[valid_stop:]), axis=0)[0])
# valid = numpy.array([data[:min_test_len] for data in test_datasets], dtype='float32')
test = numpy.array(
[numpy.pad(data, [(0, max_test_len - data.shape[0]), (0, 0)], mode='constant') for data in
test_datasets],
dtype='float32'
)
self.test_shape = test.shape
self.test = (dataset_shared(test, borrow=True), dataset_shared(test_labels, borrow=True))
else:
self.test = None, None
self.test_shape = None
print("Train shape: %s" % str(self.train_shape))
print("Valid shape: %s" % str(self.valid_shape))
print("Test shape: %s" % str(self.test_shape))
print("Dataset %s initialized!" % size_key)
