做完MNIST数据集的训练之后,我们想把自己的数据也拿来做一下相关的训练,那么如果调用MNIST数据读取的接口就需要按照他的数据格式来存取数据,首先来看看这个接口(input_data.read_data_set())):
#coding=utf-8 #input_data.py的详解 #学习读取数据文件的方法,以便读取自己需要的数据库文件(二进制文件) """Functions for downloading and reading MNIST data.""" from __future__ import print_function import gzip import os import urllib import numpy import matplotlib.pyplot as plt SOURCE_URL = ‘http://yann.lecun.com/exdb/mnist/‘ def maybe_download(filename, work_directory): """Download the data from Yann‘s website, unless it‘s already here.""" #判断目录文件是否存在,不存在则创建该目录 if not os.path.exists(work_directory): os.mkdir(work_directory) #需要读取的文件路径 filepath = os.path.join(work_directory, filename) if not os.path.exists(filepath): filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath) statinfo = os.stat(filepath) print(‘Succesfully downloaded‘, filename, statinfo.st_size, ‘bytes.‘) return filepath def _read32(bytestream): dt = numpy.dtype(numpy.uint32).newbyteorder(‘>‘) return numpy.frombuffer(bytestream.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 != 2051: raise ValueError( ‘Invalid magic number %d in MNIST image file: %s‘ % (magic, filename)) num_images = _read32(bytestream) print(num_images) rows = _read32(bytestream) print(rows) cols = _read32(bytestream) print(cols) buf = bytestream.read(rows * cols * num_images) print(‘hhh‘) data = numpy.frombuffer(buf, dtype=numpy.uint8) data = data.reshape(num_images, rows, cols, 1) return data #将稠密标签向量变成稀疏的标签矩阵 #eg:若原向量的第i行为3,则对应稀疏矩阵的第i行下标为3的值为1,其余为0 def dense_to_one_hot(labels_dense, num_classes=10): """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_dense.ravel()将整个数组展成一个一维数组 #labels_dense.flat[i]即将labels_dense看成一个一维数组,取其第i个变量 labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1#报错? 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 != 2049: 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, images, 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, columns, 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 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): """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 #若当前训练读取的index>总体的images数时,则读取读取开始的batch_size大小的数据 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] # Start 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, fake_data=False, one_hot=False): class DataSets(object): pass data_sets = DataSets() if fake_data: data_sets.train = DataSet([], [], fake_data=True) data_sets.validation = DataSet([], [], fake_data=True) data_sets.test = DataSet([], [], fake_data=True) return data_sets TRAIN_IMAGES = ‘train-images-idx3-ubyte.gz‘ TRAIN_LABELS = ‘train-labels-idx1-ubyte.gz‘ TEST_IMAGES = ‘t10k-images-idx3-ubyte.gz‘ TEST_LABELS = ‘t10k-labels-idx1-ubyte.gz‘ VALIDATION_SIZE = 5000 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_download(TEST_IMAGES, train_dir) test_images = extract_images(local_file) local_file = maybe_download(TEST_LABELS, train_dir) test_labels = extract_labels(local_file, one_hot=one_hot) validation_images = train_images[:VALIDATION_SIZE] validation_labels = train_labels[:VALIDATION_SIZE] train_images = train_images[VALIDATION_SIZE:] train_labels = train_labels[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 HHH = extract_images(‘D:\\train-images-idx3-ubyte.gz‘) Pic = HHH[1] print(type(HHH[1])) print(numpy.shape(HHH[1])) L = numpy.reshape(Pic, [28, 28]) plt.figure(1) plt.imshow(L)
时间: 2024-10-06 00:06:24