python - 如何在 tensorflow 2.1.0 中将 mnist 数据集转换为 tfrecords

Question

我看到了许多类似以下要点的代码，但是，它ModuleNotFoundError: No module named 'tensorflow.examples.tutorials'在 tensorflow 2.1.0 中打印出来。

检查这个问题时，我知道我可以使用以下代码从 keras 加载。

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

但是，结果是一个 3d ndarray，我在尝试使用以下代码创建功能时遇到了一些错误

(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()
X_train_full = X_train_full / 255.0
X_test = X_test / 255.0

path = "train.tfrecord"
writer = tf.io.TFRecordWriter(path, options=None)
option = tf.io.TFRecordOptions(compression_type="GZIP")
feature_internal = {
    "image":tf.train.Features(float_list=tf.train.FloatList(value=[X_train_full])),
    "label":tf.train.Features(float_list=tf.train.FloatList(value=[y_train_full]))
}

它输出

TypeError: array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       ...,

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0. has type <class 'numpy.ndarray'>, but expected one of: numbers.Real

我想知道如何解决这个错误并将 mnist 转换为 tfrecord

标签： pythontensorflowkeras

Answer 1

要将您的数据保存到 TFRecord 文件，工作流程如下： 第 1 步：使用您自己的程序加载数据。

第 2 步：使用 tf.python_io.TFRecordWriter 打开一个 TFRecord 文件。

第 3 步：解析数据并将其保存到 TFRecord 文件。按着这些次序：

1. 使用tf.train.BytesList、 tf.train.FloatList或tf.train.Int64List将您的数据转换为tf.train.Feature。
2. 使用转换后的数据创建一个tf.train.Features。
3. 使用tf.train.Example创建一个示例协议缓冲区。
4. 使用 tf.train.Example.SerializeToString() 将示例序列化为字符串。
5. 使用创建的 TFRecordWriter 将序列化示例写入 TFRecord。

import gzip
import os

import numpy
from six.moves import urllib
import tensorflow as tf


params = {}
params['download_data_location'] = '/dbfs/ml/MNISTDemo/mnistData/'
params['tfrecord_location'] = '/dbfs/ml/MNISTDemo/mnistData/'


def download(directory, filename):
  """Download a file from the MNIST dataset if not already done."""
  filepath = os.path.join(directory, filename)
  if tf.gfile.Exists(filepath):
    return filepath
  if not tf.gfile.Exists(directory):
    tf.gfile.MakeDirs(directory)
  # CVDF mirror of http://yann.lecun.com/exdb/mnist/
  url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'
  temp_file_name, _ = urllib.request.urlretrieve(url)
  tf.gfile.Copy(temp_file_name, filepath)
  with tf.gfile.GFile(filepath) as f:
      size = f.size()
  print('Successfully downloaded', filename, 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(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

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

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

def load_dataset(directory, images_file, labels_file):
  """Download and parse MNIST dataset."""

  images_file = download(directory, images_file)
  labels_file = download(directory, labels_file)

  with tf.gfile.Open(images_file, 'rb') as f:
    images = extract_images(f)

  with tf.gfile.Open(labels_file, 'rb') as f:
    labels = extract_labels(f)

  return images, labels

directory = params['download_data_location']
validation_size=5000
train_images, train_labels = load_dataset(directory, 'train-images-idx3-ubyte', 'train-labels-idx1-ubyte')
test_images, test_labels = load_dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte') 
validation_images = train_images[:validation_size]
validation_labels = train_labels[:validation_size]
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:]

name = "train.tfrecords"
filename = os.path.join(params['tfrecord_location'], name)
tfrecord_writer = tf.python_io.TFRecordWriter(filename)

def _int64_feature(value):
  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def _bytes_feature(value):
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

train_images.shape[0]

num_examples = train_images.shape[0]
images = train_images
labels = train_labels

rows = images.shape[1]
cols = images.shape[2]
depth = images.shape[3]

for index in range(num_examples):
  # 1. Convert your data into tf.train.Feature
  image_raw = images[index].tostring()
  feature = {
    'height': _int64_feature(rows),
    'width': _int64_feature(cols),
    'depth': _int64_feature(depth),
    'label': _int64_feature(int(labels[index])),
    'image_raw': _bytes_feature(image_raw)
  }
  # 2. Create a tf.train.Features
  features = tf.train.Features(feature=feature)
  # 3. Createan example protocol
  example = tf.train.Example(features=features)
  # 4. Serialize the Example to string
  example_to_string = example.SerializeToString()
  # 5. Write to TFRecord
  tfrecord_writer.write(example_to_string)

def convert_and_save_to(images, labels , name, params):
  """Converts a TF dataset to tfrecords."""
  num_examples = images.shape[0]

  rows = images.shape[1]
  cols = images.shape[2]
  depth = images.shape[3]

  filename = os.path.join(params['tfrecord_location'], name + '.tfrecords')
  print('Writing', filename)
  with tf.python_io.TFRecordWriter(filename) as writer:
    for index in range(num_examples):
      image_raw = images[index].tostring()
      feature={
              'label': _int64_feature(int(labels[index])),
              'image_raw': _bytes_feature(image_raw)
              }
      features=tf.train.Features(feature=feature)
      example = tf.train.Example(features=features)
      writer.write(example.SerializeToString())

# Convert to Examples and write the result to TFRecord files.
convert_and_save_to(train_images, train_labels, 'train', params)
convert_and_save_to(test_images, test_labels, 'test', params)