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我正在尝试按照TensorFlow 指南站点中教程中的步骤训练 AlexNet CNN 模型。但是，本教程使用以下代码加载训练数据

mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

对我来说，我编写了一个脚本来将我的数据集示例写入 TFRecord 文件，然后在训练期间，尝试将这些记录读回并将其输入 alexnet 网络。请参见下面的代码：

#FUNCTION TO GET ALL DATASET DATA 
def _read_multiple_images(filenames, perform_shuffle=False, repeat_count=1, 
batch_size=1, available_record=39209, num_of_epochs=1):
    def _read_one_image(serialized):
        #Specify the fatures you want to extract
        features = {'image/shape': tf.FixedLenFeature([], tf.string),
            'image/class/label': tf.FixedLenFeature([], tf.int64),
            'image/class/text': tf.FixedLenFeature([], tf.string),
            'image/filename': tf.FixedLenFeature([], tf.string),
            'image/encoded': tf.FixedLenFeature([], tf.string)} 
        parsed_example = tf.parse_single_example(serialized, 
        features=features)

        #Finese extracted data
        image_raw = tf.decode_raw(parsed_example['image/encoded'], tf.uint8)
        shape = tf.decode_raw(parsed_example['image/shape'], tf.int32)
        label = tf.cast(parsed_example['image/class/label'], dtype=tf.int32)
        reshaped_img = tf.reshape(image_raw, shape)
        casted_img =  tf.cast(reshaped_img, tf.float32)
        label_tensor= [label]
        image_tensor = [casted_img]
        return label_tensor, image_tensor

complete_labels = np.array([])
complete_images = np.array([])

dataset = tf.data.TFRecordDataset(filenames=filenames)
dataset = dataset.map(_read_one_image)
dataset = dataset.repeat(repeat_count)      #Repeats dataset this # times
dataset = dataset.batch(batch_size)         #Batch size to use
iterator = dataset.make_initializable_iterator()
labels_tensor, images_tensor = iterator.get_next() #Get batch data
no_of_rounds = int(math.ceil(available_record/batch_size));

#Create tf session, get nest set of batches, and evelauate them in batches
sess = tf.Session()
 count=1
for _ in range(num_of_epochs):
  sess.run(iterator.initializer)

  while True:
    try:
      evaluated_label, evaluated_image = sess.run([labels_tensor, 
       images_tensor])

      #convert evaluated tensors to np array 
      label_np_array = np.asarray(evaluated_label, dtype=np.uint8)
      image_np_array = np.asarray(evaluated_image, dtype=np.uint8)

      #squeeze np array to make dimesnsions appropriate
      squeezed_label_np_array = label_np_array.squeeze()
      squeezed_image_np_array = image_np_array.squeeze()

      #add current batch to total
      complete_labels = np.append(complete_labels, squeezed_label_np_array)
      complete_images = np.append(complete_images, squeezed_image_np_array)
      except tf.errors.OutOfRangeError:
      print("End of Dataset Reached")
      break
    count=count+1

sess.close()
return complete_labels, complete_images

我的主要问题是，在将我的数据集（227x227x3）中的所有 39209 图像恢复为 np 数组时，我可以将其提供给我的 TF 估计器。我的电脑内存不足。

train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": 
complete_images},y=complete_labels,batch_size=100,num_epochs=1, 
shuffle=True)
dataset_classifier.train(input_fn=train_input_fn,num_epochs=1,hooks=
[logging_hook])

有没有一种方法可以分批从我的 TF 记录中获取我的图像和标签，并将其分批提供给我的 TF.Estimator，而不必像本教程中指定的那样将它们全部加载到 np 数组中

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1 回答 1

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如果您可以将数据tf.data.Dataset作为Estimator. 您可以简单地Dataset直接在您的输入函数中构建，如下所示：

def train_input_fn():
  dataset = tf.data.TFRecordDataset(filenames=filenames)
  dataset = dataset.map(_read_one_image)
  dataset = dataset.repeat(1)  # Because `num_epochs=1`.
  dataset = dataset.batch(100)  # Because `batch_size=1`.

  dataset = dataset.prefetch(1)  # To improve performance by overlapping execution.

  iterator = dataset.make_one_shot_iterator()  # NOTE: Use a "one-shot" iterator.
  labels_tensor, images_tensor = iterator.get_next()

  return {"x": images_tensor}, labels_tensor

dataset_classifier.train(
    input_fn=train_input_fn, num_epochs=1, hooks=[logging_hook])

这应该比构建 NumPy 数组更有效，因为它避免了一次将整个数据集具体化到内存中。您还可以使用性能增强Dataset.prefetch()和并行版本Dataset.map()来提高训练速度。

于 2017-12-11T19:26:04.563 回答