看的教程是下面這個網址，教程的題目和我的標題前半部分一樣。我只是只是按部就班的操作一遍,加粗的是我自己的閑言碎語。
http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html
1.MNIST數據集
MNIST數據集的官網是Yann LeCun’s website。在這里，我們提供了一份python源代碼用于自動下載和安裝這個數據集。你可以下載這份代碼，然后用下面的代碼導入到你的項目里面，也可以直接復制粘貼到你的代碼文件里面。
以下是教程中調用的模塊input_data.py,教程中的網址我打不開，是從https://github.com/tensorflow/tensorflow下載的，具體是在這個位置https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/mnist。將代碼放置在主目錄下以后，就可以運行教程中的兩行代碼了(不要問我什么是主目錄，我是把它和教程中的那兩行代碼的文件放在一起就可以)

# 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_functionimport collections import gzip import osimport numpy from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtinfrom 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_classeslabels_one_hot = numpy.zeros((num_labels, num_classes))labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1return 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 labelsclass _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 imageslabels: The labelsfake_data: Ignore inages and labels, use fake data.one_hot: Bool, return the labels as one hot vectors (if True) or ints (ifFalse).dtype: Output image dtype. One of [uint8, float32]. `uint8` output hasrange [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 returnednumpy.random.seed(seed1 if seed is None else seed2)dtype = dtypes.as_dtype(dtype).base_dtypeif dtype not in (dtypes.uint8, dtypes.float32):raise TypeError('Invalid image dtype %r, expected uint8 or float32' %dtype)if fake_data:self._num_examples = 10000self.one_hot = one_hotelse: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] == 1images = 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 = imagesself._labels = labelsself._epochs_completed = 0self._index_in_epoch = 0@propertydef images(self):return self._images@propertydef labels(self):return self._labels@propertydef num_examples(self):return self._num_examples@propertydef epochs_completed(self):return self._epochs_completeddef 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] * 784if self.one_hot:fake_label = [1] + [0] * 9else:fake_label = 0return [fake_image for _ in xrange(batch_size)], [fake_label for _ in xrange(batch_size)]start = self._index_in_epoch# Shuffle for the first epochif 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 epochif start + batch_size > self._num_examples:# Finished epochself._epochs_completed += 1# Get the rest examples in this epochrest_num_examples = self._num_examples - startimages_rest_part = self._images[start:self._num_examples]labels_rest_part = self._labels[start:self._num_examples]# Shuffle the dataif shuffle:perm = numpy.arange(self._num_examples)numpy.random.shuffle(perm)self._images = self.images[perm]self._labels = self.labels[perm]# Start next epochstart = 0self._index_in_epoch = batch_size - rest_num_examplesend = self._index_in_epochimages_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_sizeend = self._index_in_epochreturn 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 checksource_url = DEFAULT_SOURCE_URLtrain_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)

教程里的兩行代碼我也給你們貼上

import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

我運行后出現了警告，瞟了一眼應該不影響使用，我就沒仔細看。以下是收到警告的提示信息

"E:\MNIST introduction\venv\Scripts\python.exe" "E:/MNIST introduction/venv/Download and install MNIST dataset automatically.py" Extracting MNIST_data/train-images-idx3-ubyte.gz WARNING:tensorflow:From E:/MNIST introduction/venv/Download and install MNIST dataset automatically.py:3: read_data_sets (from input_data) is deprecated and will be removed in a future version. Instructions for updating: Please use alternatives such as: tensorflow_datasets.load('mnist') WARNING:tensorflow:From E:\MNIST introduction\venv\input_data.py:297: _maybe_download (from input_data) is deprecated and will be removed in a future version. Instructions for updating: Please write your own downloading logic. WARNING:tensorflow:From E:\MNIST introduction\venv\input_data.py:299: _extract_images (from input_data) is deprecated and will be removed in a future version. Instructions for updating: Please use tf.data to implement this functionality. Extracting MNIST_data/train-labels-idx1-ubyte.gz WARNING:tensorflow:From E:\MNIST introduction\venv\input_data.py:304: _extract_labels (from input_data) is deprecated and will be removed in a future version. Extracting MNIST_data/t10k-images-idx3-ubyte.gz Instructions for updating: Please use tf.data to implement this functionality. WARNING:tensorflow:From E:\MNIST introduction\venv\input_data.py:112: _dense_to_one_hot (from input_data) is deprecated and will be removed in a future version. Instructions for updating: Please use tf.one_hot on tensors. Extracting MNIST_data/t10k-labels-idx1-ubyte.gz WARNING:tensorflow:From E:\MNIST introduction\venv\input_data.py:328: _DataSet.__init__ (from input_data) is deprecated and will be removed in a future version. Instructions for updating: Please use alternatives such as official/mnist/_DataSet.py from tensorflow/models.Process finished with exit code 0

總結

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