我正在尝试使用keras和tensorflow 1.15在GCP上训练模型。 从现在开始，我的代码类似于我可以在colab上执行的代码：

it('should have font-weight 600',async () => {
  const { getByText } = render(<Tabs tabItems={tabItems} selected="test1" />);

  expect(getByText('Test 1').parentElement).toHaveStyleRule(
    'font-weight','600',{ modifier: css`> button` }
  );
});

但是我的数据在存储桶中，而我的代码在VM中。那我该怎么办？我尝试使用“ gs：// BUCKETS”加载数据，但是它不起作用。我该怎么办 ？ 编辑：我添加我的代码以加载数据，我对不起。

# TPUs
import tensorflow as tf
print(tf.__version__)
cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver("tpu-name")
tf.config.experimental_connect_to_cluster(cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
tpu_strategy = tf.distribute.experimental.TPUStrategy(cluster_resolver)
print("Number of accelerators: ",tpu_strategy.num_replicas_in_sync)


import numpy as np


np.random.seed(123)  # for reproducibility
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,activation,flatten
from tensorflow.keras.layers import Convolution2D,MaxPooling2D,Input
from tensorflow.keras import utils
from tensorflow.keras.datasets import mnist,cifar10
from tensorflow.keras.models import Model

# 4. Load data into train and test sets
(X_train,y_train) = load_data(sets="gs://BUCKETS/dogscats/train/",target_size=img_size)
(X_test,y_test) =  load_data(sets="gs://BUCKETS/dogscats/valid/",target_size=img_size)
print(X_train.shape,X_test.shape)

# 5. Preprocess input data
#X_train = X_train.reshape(X_train.shape[0],28,1)
#X_test = X_test.reshape(X_test.shape[0],1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255.0
X_test /= 255.0

print(y_train.shape,y_test.shape)
# 6. Preprocess class labels One hot encoding
Y_train = utils.to_categorical(y_train,2)
Y_test = utils.to_categorical(y_test,2)
print(Y_train.shape,Y_test.shape)

with tpu_strategy.scope():
  model = make_model((img_size,img_size,3))
  # 8. Compile model
  model.compile(loss='categorical_crossentropy',optimizer="sgd",metrics=['accuracy'])

model.summary()

batch_size = 1250 * tpu_strategy.num_replicas_in_sync
# 9. Fit model on training data
model.fit(X_train,Y_train,steps_per_epoch=len(X_train)//batch_size,epochs=5,verbose=1)

EDIT2：如果其他人在同一情况下，请完成@daudnadeem的答案。

我的目标是从存储桶中获取图像，因此代码可以很好地工作并允许获取字节对象。要将其转换为图像，您只需要使用PIL库：

def load_data(sets="dogcats/train/",k = 5000,target_size=250):
  # define location of dataset
  folder = sets
  photos,labels = list(),list()
  # determine class
  output = 0.0
  for i,dog in enumerate(listdir(folder + "dogs/")):
    if i >= k:
      break
    # load image
    photo = load_img(folder + "dogs/" +dog,target_size=(target_size,target_size))
    # convert to numpy array
    photo = img_to_array(photo)
    # store
    photos.append(photo)
    labels.append(output)

  output = 1.0

  for i,cat in enumerate(listdir(folder + "cats/") ):
    if i >= k:
      break
    # load image
    photo = load_img(folder + "cats/"+cat,target_size))
    # convert to numpy array
    photo = img_to_array(photo)
    # store
    photos.append(photo)
    labels.append(output)

  # convert to a numpy arrays
  photos = asarray(photos)
  labels = asarray(labels)
  print(photos.shape,labels.shape)
  photos,labels = shuffle(photos,labels,random_state=0)
  return photos,labels