我昨天买了一台带有M1芯片的Mac mini，因为我的2018款Mac mini在训练NN时总是非常热，而且需要很长时间。现在，我在这款Mac上安装了Tensorflow，一次是在venv中安装的，一次在venv上使用Rosetta进行了仿真。rosetta版本的工作与预期的一样（但比我的旧Mac mini 2018和i5慢？）但我无法让本机版本的代码正确运行，因为我总是收到下面的错误
我也无法在本地安装Matplotlib、sklearn和许多其他软件包。

代码(来自deeplizard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)

任何人都可以指导我解决这个问题吗？

否则我想我会退回 M1 并继续使用我的 2018 Mac mini，尽管我总是担心，因为训练时恒定的 100°C