Python config.OMNIGLOT Examples

The following are 8 code examples of config.OMNIGLOT(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module config , or try the search function .
Example #1
Source File: datasets.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test
Example #2
Source File: datasets.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test
Example #3
Source File: datasets.py    From hands-detection with MIT License 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test
Example #4
Source File: datasets.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test
Example #5
Source File: datasets.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test
Example #6
Source File: datasets.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test
Example #7
Source File: datasets.py    From models with Apache License 2.0 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test
Example #8
Source File: datasets.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def read_omniglot(binarize=False):
  """Reads in Omniglot images.

  Args:
    binarize: whether to use the fixed binarization

  Returns:
    x_train: training images
    x_valid: validation images
    x_test: test images

  """
  n_validation=1345

  def reshape_data(data):
    return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')

  omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))

  train_data = reshape_data(omni_raw['data'].T.astype('float32'))
  test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))

  # Binarize the data with a fixed seed
  if binarize:
    np.random.seed(5)
    train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
    test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)

  shuffle_seed = 123
  permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
  train_data = train_data[permutation]

  x_train = train_data[:-n_validation]
  x_valid = train_data[-n_validation:]
  x_test = test_data

  return x_train, x_valid, x_test