本文整理汇总了Python中domain_adaptation.datasets.mnist_m方法的典型用法代码示例。如果您正苦于以下问题：Python datasets.mnist_m方法的具体用法？Python datasets.mnist_m怎么用？Python datasets.mnist_m使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类domain_adaptation.datasets的用法示例。
在下文中一共展示了datasets.mnist_m方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_dataset
# 需要导入模块: from domain_adaptation import datasets [as 别名]
# 或者: from domain_adaptation.datasets import mnist_m [as 别名]
def get_dataset(dataset_name,
split_name,
dataset_dir,
file_pattern=None,
reader=None):
"""Given a dataset name and a split_name returns a Dataset.
Args:
dataset_name: String, the name of the dataset.
split_name: A train/test split name.
dataset_dir: The directory where the dataset files are stored.
file_pattern: The file pattern to use for matching the dataset source files.
reader: The subclass of tf.ReaderBase. If left as `None`, then the default
reader defined by each dataset is used.
Returns:
A tf-slim `Dataset` class.
Raises:
ValueError: if `dataset_name` isn't recognized.
"""
dataset_name_to_module = {'mnist': mnist, 'mnist_m': mnist_m}
if dataset_name not in dataset_name_to_module:
raise ValueError('Name of dataset unknown %s.' % dataset_name)
return dataset_name_to_module[dataset_name].get_split(split_name, dataset_dir,
file_pattern, reader) 示例2: provide_batch
# 需要导入模块: from domain_adaptation import datasets [as 别名]
# 或者: from domain_adaptation.datasets import mnist_m [as 别名]
def provide_batch(dataset_name, split_name, dataset_dir, num_readers,
batch_size, num_preprocessing_threads):
"""Provides a batch of images and corresponding labels.
Args:
dataset_name: String, the name of the dataset.
split_name: A train/test split name.
dataset_dir: The directory where the dataset files are stored.
num_readers: The number of readers used by DatasetDataProvider.
batch_size: The size of the batch requested.
num_preprocessing_threads: The number of preprocessing threads for
tf.train.batch.
file_pattern: The file pattern to use for matching the dataset source files.
reader: The subclass of tf.ReaderBase. If left as `None`, then the default
reader defined by each dataset is used.
Returns:
A batch of
images: tensor of [batch_size, height, width, channels].
labels: dictionary of labels.
"""
dataset = get_dataset(dataset_name, split_name, dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=num_readers,
common_queue_capacity=20 * batch_size,
common_queue_min=10 * batch_size)
[image, label] = provider.get(['image', 'label'])
# Convert images to float32
image = tf.image.convert_image_dtype(image, tf.float32)
image -= 0.5
image *= 2
# Load the data.
labels = {}
images, labels['classes'] = tf.train.batch(
[image, label],
batch_size=batch_size,
num_threads=num_preprocessing_threads,
capacity=5 * batch_size)
labels['classes'] = slim.one_hot_encoding(labels['classes'],
dataset.num_classes)
# Convert mnist to RGB and 32x32 so that it can match mnist_m.
if dataset_name == 'mnist':
images = tf.image.grayscale_to_rgb(images)
images = tf.image.resize_images(images, [32, 32])
return images, labels