# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Functions for downloading and reading MNIST data (deprecated). This module and all its submodules are deprecated. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import gzip import os import numpy from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.framework import dtypes from tensorflow.python.framework import random_seed from tensorflow.python.platform import gfile from tensorflow.python.util.deprecation import deprecated _Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test']) # CVDF mirror of http://yann.lecun.com/exdb/mnist/ DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/' def _read32(bytestream): dt = numpy.dtype(numpy.uint32).newbyteorder('>') return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] @deprecated(None, 'Please use tf.data to implement this functionality.') def _extract_images(f): """Extract the images into a 4D uint8 numpy array [index, y, x, depth]. Args: f: A file object that can be passed into a gzip reader. Returns: data: A 4D uint8 numpy array [index, y, x, depth]. Raises: ValueError: If the bytestream does not start with 2051. """ print('Extracting', f.name) with gzip.GzipFile(fileobj=f) as bytestream: magic = _read32(bytestream) if magic != 2051: raise ValueError('Invalid magic number %d in MNIST image file: %s' % (magic, f.name)) 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, cols, 1) return data @deprecated(None, 'Please use tf.one_hot on tensors.') def _dense_to_one_hot(labels_dense, num_classes): """Convert class labels from scalars to one-hot 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 @deprecated(None, 'Please use tf.data to implement this functionality.') def _extract_labels(f, one_hot=False, num_classes=10): """Extract the labels into a 1D uint8 numpy array [index]. Args: f: A file object that can be passed into a gzip reader. one_hot: Does one hot encoding for the result. num_classes: Number of classes for the one hot encoding. Returns: labels: a 1D uint8 numpy array. Raises: ValueError: If the bystream doesn't start with 2049. """ print('Extracting', f.name) with gzip.GzipFile(fileobj=f) as bytestream: magic = _read32(bytestream) if magic != 2049: raise ValueError('Invalid magic number %d in MNIST label file: %s' % (magic, f.name)) 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, num_classes) return labels class _DataSet(object): """Container class for a _DataSet (deprecated). THIS CLASS IS DEPRECATED. """ @deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py' ' from tensorflow/models.') 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 for convenient deterministic testing. Args: images: The images labels: The labels fake_data: Ignore inages and labels, use fake data. one_hot: Bool, return the labels as one hot vectors (if True) or ints (if False). dtype: Output image dtype. One of [uint8, float32]. `uint8` output has range [0,255]. float32 output has range [0,1]. reshape: Bool. If True returned images are returned flattened to vectors. seed: The random seed to use. """ 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] == 1 images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]) 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 data set.""" 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 _ in xrange(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] @deprecated(None, 'Please write your own downloading logic.') def _maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ if not gfile.Exists(work_directory): gfile.MakeDirs(work_directory) filepath = os.path.join(work_directory, filename) if not gfile.Exists(filepath): urllib.request.urlretrieve(source_url, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath @deprecated(None, 'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')') def read_data_sets(train_dir, fake_data=False, one_hot=False, dtype=dtypes.float32, reshape=True, validation_size=5000, seed=None, source_url=DEFAULT_SOURCE_URL): if fake_data: def fake(): return _DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed) train = fake() validation = fake() test = fake() return _Datasets(train=train, validation=validation, test=test) if not source_url: # empty string check source_url = DEFAULT_SOURCE_URL train_images_file = 'train-images-idx3-ubyte.gz' train_labels_file = 'train-labels-idx1-ubyte.gz' test_images_file = 't10k-images-idx3-ubyte.gz' test_labels_file = 't10k-labels-idx1-ubyte.gz' local_file = _maybe_download(train_images_file, train_dir, source_url + train_images_file) with gfile.Open(local_file, 'rb') as f: train_images = _extract_images(f) local_file = _maybe_download(train_labels_file, train_dir, source_url + train_labels_file) with gfile.Open(local_file, 'rb') as f: train_labels = _extract_labels(f, one_hot=one_hot) local_file = _maybe_download(test_images_file, train_dir, source_url + test_images_file) with gfile.Open(local_file, 'rb') as f: test_images = _extract_images(f) local_file = _maybe_download(test_labels_file, train_dir, source_url + test_labels_file) with gfile.Open(local_file, 'rb') as f: test_labels = _extract_labels(f, one_hot=one_hot) 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] train_images = train_images[validation_size:] train_labels = train_labels[validation_size:] 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 _Datasets(train=train, validation=validation, test=test)