Download Mnist dataset in TensorFlow

#coding: Utf-8 from __future__ import absolute_import from __future__ Import division from __future__ import print_function Import gzip import OS import numpy from six.moves import urllib from six.moves import xrange # pylint:disable=redefined -builtin "" "TensorFlow input_data.py Load mnist DataSet" "#SOURCE_URL = ' http://yann.lecun.com/exdb/mnist/' Source_url = ' mnist/' #离线下载数据集保存到mnist文件夹下 def maybe_download (filename, work_directory): "" "Download the data from Yann ' s website,
  Unless it ' s already here. "" "
  If not os.path.exists (work_directory): Os.mkdir (work_directory) filepath = os.path.join (work_directory, filename) If not os.path.exists (filepath): filepath, _ = Urllib.request.urlretrieve (Source_url + filename, filepath) statin
  FO = os.stat (filepath) print (' successfully downloaded ', filename, statinfo.st_size, ' bytes. ') Return filepath def _read32 (bytestream): dt = Numpy.dtype (Numpy.uint32). Newbyteorder (' > ') return Numpy.frombuffer (b Ytestream.read (4), DTYPE=DT) [0] def extract_images (filename): "" "extract the images into a 4D uint8 numpy array [index, y, x, depth]." " Print (' extracting ', filename) with gzip.open (filename) as Bytestream:magic = _read32 (ByteStream) if magic! = 20
    51:raise valueerror (' Invalid magic number%d in MNIST image file:%s '% (magic, filename)) Num_images = _read32 (bytestream) rows = _read32 (bytestream) cols = _read32 (bytestream) buf = Bytestream.read ( Rows * cols * num_images) data = Numpy.frombuffer (buf, dtype=numpy.uint8) data = Data.reshape (num_images, rows, CO LS, 1) return Data def dense_to_one_hot (Labels_dense, num_classes=10): "" "Convert class labels from scalars to One-h
  OT vectors. "" " Num_labels = labels_dense.shape[0] Index_offset = Numpy.arange (num_labels) * num_classes labels_one_hot = Numpy.zeros (
  (Num_labels, num_classes)) Labels_one_hot.flat[index_offset + labels_dense.ravel ()] = 1 return labels_one_hot def Extract_labeLS (filename, one_hot=false): "" "Extract the labels into a 1D uint8 numpy array [index]." "" Print (' extracting ', filename) with gzip.open (filename) as Bytestream:magic = _read32 (ByteStream) if magic! = 20
    49:raise valueerror (' Invalid magic number%d in MNIST label file:%s '% (magic, filename)) Num_items = _read32 (bytestream) buf = Bytestream.read (num_items) labels = Numpy.frombuffer (buf, Dtype=numpy.uint8 ) if One_hot:return dense_to_one_hot (labels) return labels Class DataSet (object): Def __init__ (self, imag  ES, labels, fake_data=false): If Fake_data:self._num_examples = 10000 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, column s, depth] # to [num examples, rows*columns] (Assuming depth = = 1) assert images.shape[3] = = 1 images = Images.reshape (images.shape[0],
      IMAGES.SHAPE[1] * images.shape[2]) # Convert from [0, 255], [0.0, 1.0]. Images = Images.astype (numpy.float32) images = numpy.multiply (images, 1.0/255.0) self._images = Images sel F._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_si
    Ze, Fake_data=false): "" "Return the Next ' batch_size ' examples from this data set." " If fake_data:fake_image = [1.0 for _ in Xrange (784)] Fake_label = 0 return [Fake_image for _ in Xrange ( Batch_size)], [Fake_label for _ in Xrange (batch_size)] Start = Self._index_in_epoch Self._index_in_epoch + = batch_size if Self._index_in_epoch > Self._num_examples:
      # finished Epoch self._epochs_completed + = 1 # Shuffle the data perm = Numpy.arange (self._num_examples) Numpy.random.shuffle (perm) self._images = self._images[perm] Self._labels = self._labels[perm] # S
    Tart Next Epoch start = 0 Self._index_in_epoch = batch_size assert batch_size <= self._num_examples End = Self._index_in_epoch return self._images[start:end], Self._labels[start:end] def read_data_sets (Train_dir, FA Ke_data=false, One_hot=false): Class DataSets (object): Pass data_sets = DataSets () if FAKE_DATA:DATA_SETS.T Rain = DataSet ([], [], fake_data=true) data_sets.validation = DataSet ([], [], fake_data=true) data_sets.test = Dat ASet ([], [], fake_data=true) return data_sets train_images = ' train-images-idx3-ubyte.gz ' #mnist文件夹下四个文件 train_la BELs = ' Train-labels-idx1-ubyte.gz ' test_images = ' t10k-images-idx3-ubyte.gz ' test_labels = ' t10k-labels-idx1-ubyte.gz ' VALIDATION_SIZE = 50 xx local_file = Maybe_download (train_images, train_dir) train_images = Extract_images (local_file) Local_file = maybe _download (Train_labels, train_dir) train_labels = Extract_labels (Local_file, one_hot=one_hot) Local_file = maybe_downl Oad (Test_images, train_dir) test_images = Extract_images (local_file) local_file = Maybe_download (TEST_LABELS, Train_di r) Test_labels = Extract_labels (Local_file, one_hot=one_hot) validation_images = train_images[:validation_size] Vali Dation_labels = train_labels[:validation_size] Train_images = train_images[validation_size:] Train_labels = Train_labe Ls[validation_size:] Data_sets.train = DataSet (Train_images, train_labels) data_sets.validation = DataSet (Validation_
 Images, validation_labels) data_sets.test = DataSet (test_images, test_labels) return data_sets