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Data.py
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Data.py
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"""
Data.py
Class Data for do next batch
20181105
"""
import numpy as np
class Data(object):
def __init__(self, images, labels):
self._num_examples = images.shape[0]
self._images = images
self._labels = labels
self._steps_completed = 0
self._index_in_step = 0
@property
def images(self):
return self._images
@property
def labels(self):
return self._labels
@property
def num_examples(self):
return self._num_examples
@property
def steps_completed(self):
return self._steps_completed
def next_batch(self, batch_size, shuffle=True):
"""Return the next `batch_size` examples from this data set."""
"go through all the data"
start = self._index_in_step
# 对第一个step进行打乱
if self._steps_completed == 0 and start == 0 and shuffle:
# 返回一个array对象且间隔为1
perm0 = np.arange(self._num_examples)
# 打乱列表
np.random.shuffle(perm0)
self._images = self.images[perm0]
self._labels = self.labels[perm0]
# 进入下一个step之前，有余下数据的处理
if start + batch_size > self._num_examples:
if start + batch_size < 2 * self._num_examples:
# 完成一个step的标志位
self._steps_completed += 1
# 得到该step余下的数据
rest_num_examples = self._num_examples - start
images_rest_part = self._images[start:self._num_examples]
labels_rest_part = self._labels[start:self._num_examples]
# 对数据进行打乱
if shuffle:
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._images = self._images[perm]
self._labels = self._labels[perm]
# 开始下一个step，并凑齐一个batch
start = 0
self._index_in_step = batch_size - rest_num_examples
end = self._index_in_step
images_new_part = self._images[start:end]
labels_new_part = self._labels[start:end]
return np.concatenate((images_rest_part, images_new_part), axis=0), np.concatenate(
(labels_rest_part, labels_new_part), axis=0)
else:
reuse_times = np.int(np.floor((start + batch_size) / self._num_examples) - 1)
self._steps_completed += reuse_times + 1
images_rest_part = self._images[start:self._num_examples]
labels_rest_part = self._labels[start:self._num_examples]
batch_images = images_rest_part
batch_labels = labels_rest_part
for ind_resuse in range(reuse_times):
if shuffle:
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._images = self._images[perm]
self._labels = self._labels[perm]
batch_images = np.concatenate((batch_images, self._images), axis=0)
batch_labels = np.concatenate((batch_labels, self._labels), axis=0)
if (start + batch_size) % self._num_examples == 0:
self._index_in_step = 0
return batch_images, batch_labels
else:
if shuffle:
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._images = self._images[perm]
self._labels = self._labels[perm]
self._index_in_step = (start + batch_size) % self._num_examples
end = self._index_in_step
batch_images = np.concatenate((batch_images, self._images[0:end]), axis=0)
batch_labels = np.concatenate((batch_labels, self._labels[0:end]), axis=0)
return batch_images, batch_labels
else:
self._index_in_step += batch_size
end = self._index_in_step
return self._images[start:end], self._labels[start:end]
class Data3(object):
def __init__(self, images, labels1, labels2):
self._num_examples = images.shape[0]
self._images = images
self._labels1 = labels1
self._labels2 = labels2
self._steps_completed = 0
self._index_in_step = 0
@property
def images(self):
return self._images
@property
def labels1(self):
return self._labels1
@property
def labels2(self):
return self._labels2
@property
def num_examples(self):
return self._num_examples
@property
def steps_completed(self):
return self._steps_completed
def next_batch(self, batch_size, shuffle=True):
"""Return the next `batch_size` examples from this data set."""
"go through all the data"
start = self._index_in_step
# 对第一个step进行打乱
if self._steps_completed == 0 and start == 0 and shuffle:
# 返回一个array对象且间隔为1
perm0 = np.arange(self._num_examples)
# 打乱列表
np.random.shuffle(perm0)
self._images = self.images[perm0]
self._labels1 = self.labels1[perm0]
self._labels2 = self.labels2[perm0]
# 进入下一个step之前，有余下数据的处理
if start + batch_size > self._num_examples:
if start + batch_size < 2 * self._num_examples:
# 完成一个step的标志位
self._steps_completed += 1
# 得到该step余下的数据
rest_num_examples = self._num_examples - start
images_rest_part = self._images[start:self._num_examples]
labels_rest_part1 = self._labels1[start:self._num_examples]
labels_rest_part2 = self._labels2[start:self._num_examples]
# 对数据进行打乱
if shuffle:
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._images = self._images[perm]
self._labels1 = self._labels1[perm]
self._labels2 = self._labels2[perm]
# 开始下一个step，并凑齐一个batch
start = 0
self._index_in_step = batch_size - rest_num_examples
end = self._index_in_step
images_new_part = self._images[start:end]
labels_new_part1 = self._labels1[start:end]
labels_new_part2 = self._labels2[start:end]
return np.concatenate((images_rest_part, images_new_part), axis=0), \
np.concatenate((labels_rest_part1, labels_new_part1), axis=0), \
np.concatenate((labels_rest_part2, labels_new_part2), axis=0)
else:
reuse_times = np.int(np.floor((start + batch_size) / self._num_examples) - 1)
self._steps_completed += reuse_times + 1
images_rest_part = self._images[start:self._num_examples]
labels_rest_part1 = self._labels1[start:self._num_examples]
labels_rest_part2 = self._labels2[start:self._num_examples]
batch_images = images_rest_part
batch_labels1 = labels_rest_part1
batch_labels2 = labels_rest_part2
for ind_resuse in range(reuse_times):
if shuffle:
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._images = self._images[perm]
self._labels1 = self._labels1[perm]
self._labels2 = self._labels2[perm]
batch_images = np.concatenate((batch_images, self._images), axis=0)
batch_labels1 = np.concatenate((batch_labels1, self._labels1), axis=0)
batch_labels2 = np.concatenate((batch_labels2, self._labels2), axis=0)
if (start + batch_size) % self._num_examples == 0:
self._index_in_step = 0
return batch_images, batch_labels1, batch_labels2
else:
if shuffle:
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._images = self._images[perm]
self._labels1 = self._labels1[perm]
self._labels2 = self._labels2[perm]
self._index_in_step = (start + batch_size) % self._num_examples
end = self._index_in_step
batch_images = np.concatenate((batch_images, self._images[0:end]), axis=0)
batch_labels1 = np.concatenate((batch_labels1, self._labels1[0:end]), axis=0)
batch_labels2 = np.concatenate((batch_labels2, self._labels2[0:end]), axis=0)
return batch_images, batch_labels1, batch_labels2
else:
self._index_in_step += batch_size
end = self._index_in_step
return self._images[start:end], self._labels1[start:end], self._labels2[start:end]