Python base.maybe_download方法代码示例

# 需要导入模块: from tensorflow.contrib.learn.python.learn.datasets import base [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.datasets.base import maybe_download [as 别名]
def maybe_download_dbpedia(data_dir):
  """Download if DBpedia data is not present."""
  train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv')
  test_path = os.path.join(data_dir, 'dbpedia_csv/test.csv')
  if not (gfile.Exists(train_path) and gfile.Exists(test_path)):
    archive_path = base.maybe_download(
        'dbpedia_csv.tar.gz', data_dir, DBPEDIA_URL)
    tfile = tarfile.open(archive_path, 'r:*')
    tfile.extractall(data_dir) 

# 需要导入模块: from tensorflow.contrib.learn.python.learn.datasets import base [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.datasets.base import maybe_download [as 别名]
def _download_and_preprocess_data(data_dir):
    # Conditionally download data
    LDC93S1_BASE = "LDC93S1"
    LDC93S1_BASE_URL = "https://catalog.ldc.upenn.edu/desc/addenda/"
    local_file = base.maybe_download(LDC93S1_BASE + ".wav", data_dir, LDC93S1_BASE_URL + LDC93S1_BASE + ".wav")
    trans_file = base.maybe_download(LDC93S1_BASE + ".txt", data_dir, LDC93S1_BASE_URL + LDC93S1_BASE + ".txt")
    with open(trans_file, "r") as fin:
        transcript = ' '.join(fin.read().strip().lower().split(' ')[2:]).replace('.', '')

    df = pandas.DataFrame(data=[(os.path.abspath(local_file), os.path.getsize(local_file), transcript)],
                          columns=["wav_filename", "wav_filesize", "transcript"])
    df.to_csv(os.path.join(data_dir, "ldc93s1.csv"), index=False) 

# 需要导入模块: from tensorflow.contrib.learn.python.learn.datasets import base [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.datasets.base import maybe_download [as 别名]
def read_data_sets(train_dir,
                   fake_data=False,
                   one_hot=False,
                   dtype=dtypes.float32):
  if fake_data:

    def fake():
      return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)

    train = fake()
    validation = fake()
    test = fake()
    return base.Datasets(train=train, validation=validation, test=test)

  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
  VALIDATION_SIZE = 5000

  local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
                                   SOURCE_URL + TRAIN_IMAGES)
  train_images = extract_images(local_file)

  local_file = base.maybe_download(TRAIN_LABELS, train_dir,
                                   SOURCE_URL + TRAIN_LABELS)
  train_labels = extract_labels(local_file, one_hot=one_hot)

  local_file = base.maybe_download(TEST_IMAGES, train_dir,
                                   SOURCE_URL + TEST_IMAGES)
  test_images = extract_images(local_file)

  local_file = base.maybe_download(TEST_LABELS, train_dir,
                                   SOURCE_URL + TEST_LABELS)
  test_labels = extract_labels(local_file, one_hot=one_hot)

  validation_images = train_images[:VALIDATION_SIZE]
  validation_labels = train_labels[:VALIDATION_SIZE]
  train_images = train_images[VALIDATION_SIZE:]
  train_labels = train_labels[VALIDATION_SIZE:]

  train = DataSet(train_images, train_labels, start_id=0, dtype=dtype)
  validation = DataSet(validation_images,
                       validation_labels,
                       start_id=len(train_images),
                       dtype=dtype)
  test = DataSet(test_images,
                 test_labels,
                 start_id=(len(train_images) + len(validation_images)),
                 dtype=dtype)

  return base.Datasets(train=train, validation=validation, test=test) 

# 需要导入模块: from tensorflow.contrib.learn.python.learn.datasets import base [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.datasets.base import maybe_download [as 别名]
def read_data_sets(train_dir,
                   fake_data=False,
                   one_hot=False,
                   dtype=dtypes.float32,
                   reshape=True,
                   validation_size=5000):
  if fake_data:

    def fake():
      return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)

    train = fake()
    validation = fake()
    test = fake()
    return base.Datasets(train=train, validation=validation, test=test)

  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'

  local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
                                   SOURCE_URL + TRAIN_IMAGES)
  with open(local_file, 'rb') as f:
    train_images = extract_images(f)

  local_file = base.maybe_download(TRAIN_LABELS, train_dir,
                                   SOURCE_URL + TRAIN_LABELS)
  with open(local_file, 'rb') as f:
    train_labels = extract_labels(f, one_hot=one_hot)

  local_file = base.maybe_download(TEST_IMAGES, train_dir,
                                   SOURCE_URL + TEST_IMAGES)
  with open(local_file, 'rb') as f:
    test_images = extract_images(f)

  local_file = base.maybe_download(TEST_LABELS, train_dir,
                                   SOURCE_URL + TEST_LABELS)
  with open(local_file, 'rb') as f:
    test_labels = extract_labels(f, one_hot=one_hot)

  if not 0 <= validation_size <= len(train_images):
    raise ValueError(
        'Validation size should be between 0 and {}. Received: {}.'
        .format(len(train_images), validation_size))

  validation_images = train_images[:validation_size]
  validation_labels = train_labels[:validation_size]
  train_images = train_images[validation_size:]
  train_labels = train_labels[validation_size:]

  train = DataSet(train_images, train_labels, dtype=dtype, reshape=reshape)
  validation = DataSet(validation_images,
                       validation_labels,
                       dtype=dtype,
                       reshape=reshape)
  test = DataSet(test_images, test_labels, dtype=dtype, reshape=reshape)

  return base.Datasets(train=train, validation=validation, test=test) 

# 需要导入模块: from tensorflow.contrib.learn.python.learn.datasets import base [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.datasets.base import maybe_download [as 别名]
def import_mnist():
    """
    This import mnist and saves the data as an object of our DataSet class
    :return:
    """
    SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
    TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
    TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
    TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
    TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
    VALIDATION_SIZE = 0
    ONE_HOT = True
    TRAIN_DIR = 'MNIST_data'


    local_file = base.maybe_download(TRAIN_IMAGES, TRAIN_DIR,
                                     SOURCE_URL + TRAIN_IMAGES)
    train_images = extract_images(open(local_file, 'rb'))

    local_file = base.maybe_download(TRAIN_LABELS, TRAIN_DIR,
                                     SOURCE_URL + TRAIN_LABELS)
    train_labels = extract_labels(open(local_file, 'rb'), one_hot=ONE_HOT)

    local_file = base.maybe_download(TEST_IMAGES, TRAIN_DIR,
                                     SOURCE_URL + TEST_IMAGES)
    test_images = extract_images(open(local_file, 'rb'))

    local_file = base.maybe_download(TEST_LABELS, TRAIN_DIR,
                                     SOURCE_URL + TEST_LABELS)
    test_labels = extract_labels(open(local_file, 'rb'), one_hot=ONE_HOT)

    validation_images = train_images[:VALIDATION_SIZE]
    validation_labels = train_labels[:VALIDATION_SIZE]
    train_images = train_images[VALIDATION_SIZE:]
    train_labels = train_labels[VALIDATION_SIZE:]

    ## Process images
    train_images = process_mnist(train_images)
    validation_images = process_mnist(validation_images)
    test_images = process_mnist(test_images)

    ## Standardize data
    train_mean, train_std = get_data_info(train_images)
#    train_images = standardize_data(train_images, train_mean, train_std)
#    validation_images = standardize_data(validation_images, train_mean, train_std)
#    test_images = standardize_data(test_images, train_mean, train_std)

    data = DataSet(train_images, train_labels)
    test = DataSet(test_images, test_labels)
    val = DataSet(validation_images, validation_labels)

    return data, test, val 

# 需要导入模块: from tensorflow.contrib.learn.python.learn.datasets import base [as 别名]
# 或者: from tensorflow.contrib.learn.python.learn.datasets.base import maybe_download [as 别名]
def read_data_sets(train_dir,
                   fake_data=False,
                   one_hot=False,
                   dtype=dtypes.float32,
                   reshape=True,
                   validation_size=0):
  if fake_data:

    def fake():
      return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)

    train = fake()
    validation = fake()
    test = fake()
    return base.Datasets(train=train, validation=validation, test=test)


  gz_file_name = 'cifar-10-python.tar.gz'

  local_file = base.maybe_download(gz_file_name, train_dir,
                                   SOURCE_URL + gz_file_name)

  train_images = []
  train_labels = []
  for i in range(1, 6):
    with open(os.path.join(train_dir, 'cifar-10-batches-py', 'data_batch_%d'%i)) as f:
      batch = numpy.load(f)
      tmp_images = batch['data'].reshape([-1, 3, 32, 32])
      train_images.append(tmp_images.transpose([0, 2, 3, 1]))
      train_labels += batch['labels']
  train_images = numpy.concatenate(train_images)
  train_labels = numpy.array(train_labels)

  if not 0 <= validation_size <= len(train_images):
    raise ValueError(
        'Validation size should be between 0 and {}. Received: {}.'
        .format(len(train_images), validation_size))

  validation_images = train_images[:validation_size]
  validation_labels = train_labels[:validation_size]
  train_images = train_images[validation_size:]
  train_labels = train_labels[validation_size:]

  train = DataSet(train_images, train_labels, dtype=dtype, reshape=reshape)
  validation = DataSet(validation_images,
                       validation_labels,
                       dtype=dtype,
                       reshape=reshape)
  #test = DataSet(test_images, test_labels, dtype=dtype, reshape=reshape)
  test = None

  return base.Datasets(train=train, validation=validation, test=test)