适用于Apple M1的Tensorflow;没有按预期工作.当尝试训练我的Keras模型时，总是会出现PIL错误

Question

我昨天买了一台带有M1芯片的Mac mini，因为我2018年的Mac mini在训练NN时总是非常热，而且花了很长时间。现在，我在这款Mac上安装了Tensorflow，一次是在venv中原生的，另一次是用venv中的Rosetta模拟的。rosetta版本的运行与预期一样(但比我的旧Mac mini 2018 i5慢？(，但我无法使本机版本中的代码正确运行，因为我总是收到下面的错误
我也无法以本机方式安装Matplotlib、sklearn和许多其他软件包。

代码(来自深蜥蜴的课程：

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import itertools
import os
import shutil
import random
import glob
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
os.chdir('data/dogs-vs-cats')
if os.path.isdir('train/dog') is False:
os.makedirs('train/dog')
os.makedirs('train/cat')
os.makedirs('valid/dog')
os.makedirs('valid/cat')
os.makedirs('test/dog')
os.makedirs('test/cat')
for i in random.sample(glob.glob('cat*'), 500):
shutil.move(i, 'train/cat')
for i in random.sample(glob.glob('dog*'), 500):
shutil.move(i, 'train/dog')
for i in random.sample(glob.glob('cat*'), 100):
shutil.move(i, 'valid/cat')
for i in random.sample(glob.glob('dog*'), 100):
shutil.move(i, 'valid/dog')
for i in random.sample(glob.glob('cat*'), 50):
shutil.move(i, 'test/cat')
for i in random.sample(glob.glob('dog*'), 50):
shutil.move(i, 'test/dog')
os.chdir('../../')
train_path = 'data/dogs-vs-cats/train'
valid_path = 'data/dogs-vs-cats/valid'
test_path = 'data/dogs-vs-cats/test'
train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) 
.flow_from_directory(directory=train_path, target_size=(224,224), classes=['cat', 'dog'], batch_size=10)
valid_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) 
.flow_from_directory(directory=valid_path, target_size=(224,224), classes=['cat', 'dog'], batch_size=10)
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) 
.flow_from_directory(directory=test_path, target_size=(224,224), classes=['cat', 'dog'], batch_size=10, shuffle=False)
assert train_batches.n == 1000
assert valid_batches.n == 200
assert test_batches.n == 100
imgs, labels = next(train_batches)
print(labels)

vgg16_model = tf.keras.applications.vgg16.VGG16()
vgg16_model.summary()
model = Sequential()
for layer in vgg16_model.layers[:-1]:   #loop through vgg16 and copy the layers to new model, except the last one!
model.add(layer)
model.summary()
for layer in model.layers:  #iterate over all new layers in sequential model and set them to be not trainable
layer.trainable = False
model.add(Dense(units=2, activation='softmax'))
model.summary()
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=train_batches, validation_data=valid_batches, epochs=5, verbose=2)

终端输出：

Found 1000 images belonging to 2 classes.
Found 200 images belonging to 2 classes.
Found 100 images belonging to 2 classes.
Traceback (most recent call last):
File "main.py", line 56, in <module>
imgs, labels = next(train_batches)
File "/Users/florianraab/tensorflow_macos_venv/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py", line 104, in __next__
return self.next(*args, **kwargs)
File "/Users/florianraab/tensorflow_macos_venv/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py", line 116, in next
return self._get_batches_of_transformed_samples(index_array)
File "/Users/florianraab/tensorflow_macos_venv/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py", line 227, in _get_batches_of_transformed_samples
img = load_img(filepaths[j],
File "/Users/florianraab/tensorflow_macos_venv/lib/python3.8/site-packages/keras_preprocessing/image/utils.py", line 111, in load_img
raise ImportError('Could not import PIL.Image. '
ImportError: Could not import PIL.Image. The use of `load_img` requires PIL.

尝试安装枕头后

pip3 install pillow

我得到以下输出：

Requirement already satisfied: pillow in /Users/florianraab/tensorflow_macos_venv/lib/python3.8/site-packages (8.0.1)

有人能告诉我这个问题的解决办法吗？

否则，我想我会归还M1，继续使用我的2018年Mac mini，尽管我总是很担心，因为在训练时温度恒定在100°C

Answer 1

我希望你已经找到了答案，但如果没有，这里有一些提示可能会有所帮助：

1. M1的优化TensorFlow设置起来并不太困难，但边缘仍然有点粗糙。也许稍后返回并重新获得M1是个好主意，除非你愿意在一些深度学习冒险中忍受类似开发者预览的环境。

2. 如果您查看导致最低级别问题的实际utils.py文件，您会注意到异常是由以下导入之一的失败引发的：

   from PIL import ImageEnhance
   from PIL import Image as pil_image
   

   确保它们正常工作。如果没有，请考虑卸载并重新安装Pillow。请确保您在终端上的罗塞塔设置是一致的。这部分应该可以与M1配合使用。

3. 在此之后，如果您的例程利用了affine_transform功能(即用于图像增强(，则可能会出现SciPy错误。如果发生这种情况，运气会很差；截至12/20，SciPy还不能与M1 MacBook兼容，因为它需要Fortran编译。

   在短期内，如果您确信apply_affine_transform不会对模型的学习过程产生影响，则可以将其临时更改为非SciPy检查身份函数。否则，您可以尝试消除keras.Image.apply_affine_transform对SciPy的依赖，尽管我不建议这样做(我无法付出我愿意付出的努力(。