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| class CifarData:
def __init__(self,filenames,need_shuffle):
all_data = []
all_labels = []
for filename in filenames:
data,labels = load_data(filename)
for item,label in zip(data,labels):
if label in [0,1]:
all_data.append(item)
all_labels.append(label)
self._data = np.vstack(all_data)
self._labels = np.hstack(all_labels)
print(self._data.shape)
print(self._labels.shape)
self._num_examples = self._data.shape[0]
self._need_shuffle = need_shuffle
self._indicator = 0
if self._need_shuffle:
self._shuffle_data()
def _shuffle_data(self):
# [0,1,2,3,4,5] - > [5,3,2,4,0,1]
p = np.random.permutation(self._num_examples)
self._data = self._data[p]
self._labels = self._labels[p]
def next_batch(self, batch_size):
"""return batch_size examples as a batch."""
end_indicator = self._indicator + batch_size
if end_indicator > self._num_examples:
if self._need_shuffle:
self._shuffle_data()
self._indicator = 0
end_indicator = batch_size
else:
raise Exception("have no more examples")
if end_indicator > self._num_examples:
raise Exception("batch size is larger than all examples")
batch_data = self._data[self._indicator:end_indicator]
batch_labels = self._labels[self._indicator: end_indicator]
train_filenames = [os.path.join(CIFAR_DIR,'data_batch_%d' % i ) for i in range(1,6)]
test_filenames = [os.path.join(CIFAR_DIR,'test_batch')]
train_data = CifarData(train_filenames,True)
|
