深度学习之二：用Tensorflow实现卷积神经网络(CNN)
目录
1.踩过的坑(tensorflow)
2.tensorboard
3.代码实现（python3.5）
4.运行结果以及分析
class DataSet(object):
"""Container class for a dataset (deprecated).
THIS CLASS IS DEPRECATED. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for general migration instructions.
"""
def __init__(self,
images,
labels,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
seed=None):
"""Construct a DataSet.
one_hot arg is used only if fake_data is true. `dtype` can be either
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
`[0, 1]`. Seed arg provides forconvenient deterministic testing.
"""
seed1, seed2 = random_seed.get_seed(seed)
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seed1 if seed is None else seed2)
dtype = dtypes.as_dtype(dtype).base_dtype
if dtype not in (dtypes.uint8, dtypes.float32):
raise TypeError(
'Invalid image dtype %r,expected uint8 or float32' % dtype)
if fake_data:
self._num_examples = 10000
self.one_hot = one_hot
else:
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape:%s' % (images.shape, labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 3
images =images.reshape(images.shape[0],
images.shape[1] * images.shape[2] * images.shape[3])
if dtype == dtypes.float32:
# Convert from [0, 255] ->[0.0, 1.0].
images =images.astype(numpy.float32)
images = numpy.multiply(images,1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 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 epochs_completed(self):
return self._epochs_completed
def next_batch(self, batch_size, fake_data=False, shuffle=True):
"""Return the next `batch_size` examples from this dataset."""
if fake_data:
fake_image = [1] * 784
if self.one_hot:
fake_label = [1] [0] * 9
else:
fake_label = 0
return [fake_image for _ in xrange(batch_size)], [
fake_label for _ inxrange(batch_size)
]
start = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
perm0 = numpy.arange(self._num_examples)
numpy.random.shuffle(perm0)
self._images = self.images[perm0]
self._labels = self.labels[perm0]
# Go to the next epoch
if start batch_size > self._num_examples:
# Finished epoch
self._epochs_completed = 1
# Get the rest examples in this epoch
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]
# Shuffle the data
if shuffle:
perm =numpy.arange(self._num_examples)
numpy.random.shuffle(perm)
self._images = self.images[perm]
self._labels =self.labels[perm]
# Start next epoch
start = 0
self._index_in_epoch = batch_size - rest_num_examples
end = self._index_in_epoch
images_new_part = self._images[start:end]
labels_new_part = self._labels[start:end]
return numpy.concatenate(
(images_rest_part,images_new_part), axis=0), numpy.concatenate(
(labels_rest_part,labels_new_part), axis=0)
else:
self._index_in_epoch = batch_size
end = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
validation_size=5000,
seed=None):
train_images,train_labels,test_images,test_labels =load_CIFAR10(train_dir)
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]
validation_labels = dense_to_one_hot(validation_labels, 10)
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:]
train_labels = dense_to_one_hot(train_labels, 10)
test_labels = dense_to_one_hot(test_labels, 10)
options = dict(dtype=dtype, reshape=reshape, seed=seed)
train = DataSet(train_images, train_labels, **options)
validation = DataSet(validation_images, validation_labels, **options)
test = DataSet(test_images, test_labels, **options)
return base.Datasets(train=train, validation=validation, test=test)