TensorFlow 2.0 - 实现简单 CNN 的初学者

Question

我刚刚完成了 Coursera 的深度学习专业，我正在尝试使用 TensorFlow 2.0 和我自己收集的数据来实现一个 CNN。我遵循了来自 tensorflow.org 的指南和文档，并且能够设置一个管道来加载我的图像。但是，当我运行模型时，我一直遇到与内存/资源相关的问题。

我的模型应该进行 30 个类别的多标签分类。以下是我的代码：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D

#Import helper modules
import os
import numpy as np
import matplotlib.pyplot as plt
import pathlib
import os
import pandas as pd
import IPython.display as display
from PIL import Image

AUTOTUNE = tf.data.experimental.AUTOTUNE

#Define path to directories
train_dir = pathlib.Path.cwd() / 'train'
validation_dir = pathlib.Path.cwd() / 'validation'
test_dir = pathlib.Path.cwd() / 'test'

#Read csv file containing filename and label
train_csv = pd.read_csv(pathlib.Path.cwd() / 'train.csv')
validation_csv = pd.read_csv(pathlib.Path.cwd() / 'validation.csv')

#Define total number of training and validation set
total_train = train_csv.shape[0]
total_val = validation_csv.shape[0]
print(f'Total training images: {total_train}')
print(f'Total validation images: {total_val}')

以下代码来自https://www.tensorflow.org/tutorials/load_data/images

CLASS_NAMES = np.array([item.name for item in train_dir.glob('*')])

#set up variables
BATCH_SIZE = 128
TRAIN_STEPS_PER_EPOCH = np.ceil(total_train/BATCH_SIZE)
VAL_STEPS_PER_EPOCH = np.ceil(total_val/BATCH_SIZE)
IMG_HEIGHT = 150
IMG_WIDTH = 150

#using td.data.Dataset
train_list_ds = tf.data.Dataset.list_files(str(train_dir/'*/*'))
valid_list_ds = tf.data.Dataset.list_files(str(validation_dir/'*/*'))

def get_label(file_path):
  # convert the path to a list of path components
  parts = tf.strings.split(file_path, os.path.sep)
  # The second to last is the class-directory
  return parts[-2] == CLASS_NAMES


def decode_img(img):
  # convert the compressed string to a 3D uint8 tensor
  img = tf.image.decode_jpeg(img, channels=3)
  # Use `convert_image_dtype` to convert to floats in the [0,1] range.
  img = tf.image.convert_image_dtype(img, tf.float32)
  # resize the image to the desired size.
  return tf.image.resize(img, [IMG_HEIGHT, IMG_WIDTH])

def process_path(file_path):
  label = get_label(file_path)
  # load the raw data from the file as a string
  img = tf.io.read_file(file_path)
  img = decode_img(img)
  return img, label

def show_batch(image_batch, label_batch):
  plt.figure(figsize=(10,10))
  for n in range(25):
      ax = plt.subplot(5,5,n+1)
      plt.imshow(image_batch[n])
      plt.title(CLASS_NAMES[label_batch[n]==1][0].title())
      plt.axis('off')

# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.
train_labeled_ds = train_list_ds.map(process_path, num_parallel_calls=AUTOTUNE)
valid_labeled_ds = valid_list_ds.map(process_path, num_parallel_calls=AUTOTUNE)

def prepare_for_training(ds, cache=True, shuffle_buffer_size=1000):
  # use `.cache(filename)` to cache preprocessing work for datasets that don't
  # fit in memory.
  if cache:
    if isinstance(cache, str):
      ds = ds.cache(cache)
    else:
      ds = ds.cache()

  ds = ds.shuffle(buffer_size=shuffle_buffer_size)

  # Repeat forever
  ds = ds.repeat()

  ds = ds.batch(BATCH_SIZE)

  # `prefetch` lets the dataset fetch batches in the background while the model
  # is training.
  ds = ds.prefetch(buffer_size=AUTOTUNE)

  return ds

train_ds = prepare_for_training(train_labeled_ds)
x_train, y_train = next(iter(train_ds))
valid_ds = prepare_for_training(valid_labeled_ds)

模型来自教程：https://www.tensorflow.org/tutorials/images/classification

model = Sequential([
    Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
    MaxPooling2D(),
    Conv2D(32, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(64, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(30, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.summary()

model.fit(train_ds.repeat(), 
          epochs=15, batch_size= BATCH_SIZE, 
          validation_data=valid_ds.repeat(), steps_per_epoch= TRAIN_STEPS_PER_EPOCH,        
          validation_steps=VAL_STEPS_PER_EPOCH)

一开始我遇到输入用完数据，所以我将输入数据集更改为 train_ds.repeat() 而不是 x=x_train, y =y_train.

我遇到的下一个问题是

100/741 [===>..........................] - ETA: 18:07 - loss: 3.7188 - accuracy: 0.04942020-06-23 14:52:29.232604: E tensorflow/core/lib/jpeg/jpeg_mem.cc:323] Premature end of JPEG data. Stopped at line 910/1000
Traceback (most recent call last):
  File "product_detection.py", line 123, in <module>
    model.fit(train_ds.repeat(), epochs=15, batch_size= BATCH_SIZE, validation_data=valid_ds.repeat(), steps_per_epoch= TRAIN_STEPS_PER_EPOCH, validation_steps=VAL_STEPS_PER_EPOCH)
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapper
    return method(self, *args, **kwargs)
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py", line 848, in fit
    tmp_logs = train_function(iterator)
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\def_function.py", line 580, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\def_function.py", line 611, in _call
    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 2420, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 1665, in _filtered_call
    self.captured_inputs)
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 1746, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 598, in call
    ctx=ctx)
  File "C:\Users\sl199\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 60, in quick_execute
    inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  Invalid JPEG data or crop window, data size 101360
         [[{{node DecodeJpeg}}]]
         [[IteratorGetNext]]
  (1) Invalid argument:  Invalid JPEG data or crop window, data size 101360
         [[{{node DecodeJpeg}}]]
         [[IteratorGetNext]]
         [[IteratorGetNext/_2]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_1113]

Function call stack:
train_function -> train_function

2020-06-23 14:52:29.411288: W tensorflow/core/kernels/data/cache_dataset_ops.cc:794] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

此时我完全迷失了，我怀疑我复制的 prepare_for_training 函数不适合我的应用程序，但我还不够了解，无法进行更改。它明确表示它适用于包含 1000 多张图像的数据集，而我正在使用 90k 训练和 10k 验证数据集。我尝试更改 batch_size 但问题仍然存在。

我正在使用带有 GTX 1050 Ti 的 TensorFlow-gpu。我可以要求有关如何进行此操作的指针吗？提前谢谢你。

编辑 1：将我的 batch_size 更改为 10 并出现此错误

3537/9484 [==========>...................] - ETA: 12:31 - loss: 3.7519 - accuracy: 0.02602020-06-23 16:10:13.148245: W tensorflow/core/framework/op_kernel.cc:1753] OP_REQUIRES failed at cast_op.cc:109 : Resource exhausted: OOM when allocating tensor with shape[943,943,3] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
2020-06-23 16:10:13.159005: W tensorflow/core/framework/op_kernel.cc:1753] OP_REQUIRES failed at cast_op.cc:109 : Resource exhausted: OOM when allocating tensor with shape[678,678,3] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
 Resou3538/9484 [==========>...................] - ETA: 12:31 - loss: 3.7519 - accuracy: 0.0260rce exhausted: OOM when allocating tensor with shape[956,956,3] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu

tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
  (0) Resource exhausted:  OOM when allocating tensor with shape[943,943,3] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
         [[{{node convert_image/Cast}}]]
         [[IteratorGetNext]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

         [[IteratorGetNext/_2]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

  (1) Resource exhausted:  OOM when allocating tensor with shape[943,943,3] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
         [[{{node convert_image/Cast}}]]
         [[IteratorGetNext]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

Answer 1

根据这部分的输出：

tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  Invalid JPEG data or crop window, data size 101360
         [[{{node DecodeJpeg}}]]
         [[IteratorGetNext]]
  (1) Invalid argument:  Invalid JPEG data or crop window, data size 101360
         [[{{node DecodeJpeg}}]]
         [[IteratorGetNext]]
         [[IteratorGetNext/_2]]

如果您的某些数据已损坏，请检查您的数据并删除损坏的条目，这是您可以检查HERE 和THERE 的常见错误