尝试在 Colab 中拟合模型时出错，但在 Jupyter 笔记本中工作正常

Question

I tried to fit the model with my train and test data (about 7GB) in Colab as when using Jupyter notebook with my local machine it takes too long to train. However when I tried to use Colab it gives me the below error but it works fine in Jupyter.

    Epoch 1/20
---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-60-677f43e317d7> in <module>()
      6   epochs=20,
      7   steps_per_epoch=len(training_set),
----> 8   validation_steps=len(testing_set)
      9 )

1 frames
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     53     ctx.ensure_initialized()
     54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55                                         inputs, attrs, num_outputs)
     56   except core._NotOkStatusException as e:
     57     if name is not None:

UnimplementedError: Graph execution error:

And I have changed the Runtime type to GPU in colab and I'm currently not using the Colab pro version, datasets are stored in the Google drive. I'm a bit confused as the code was working fine without any issue in Jupyter notebook.

You can view the colab file using the below GitHub link.https://github.com/ArchieVon/DL/blob/main/ResNet_Test1.ipynb

Answer 1

Changing the IMAGE_SIZE from list to tuple resolves the issue. Please find the working code below.

from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow.keras.models import Sequential
import numpy as np
from glob import glob

IMAGE_SIZE = (224, 224, 3)

train_path = '/content/dogs_vs_cats/train'
valid_path = '/content/dogs_vs_cats/test'


resnet = ResNet50(input_shape=IMAGE_SIZE, weights='imagenet', include_top=False)
for layer in resnet.layers:
    layer.trainable = False


folders = glob('/content/dogs_vs_cats/train/*')
len(folders) 

x = Flatten()(resnet.output)
prediction = Dense(len(folders), activation='softmax')(x)

model = Model(inputs=resnet.input, outputs=prediction)
#model.summary()


model.compile(
  loss='categorical_crossentropy',
  optimizer='adam',
  metrics=['accuracy']
)

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory(train_path,
                                                 target_size = (224, 224),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')
testing_set = test_datagen.flow_from_directory(valid_path,
                                            target_size = (224, 224),
                                            batch_size = 32,
                                            class_mode = 'categorical')


len(testing_set)
len(training_set)


# fit the model
# Run the cell. It will take some time to execute
r = model.fit(
  training_set,
  validation_data=testing_set,
  epochs=1,
  steps_per_epoch=len(training_set),
  validation_steps=len(testing_set)
)

The output is as follows:

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94773248/94765736 [==============================] - 0s 0us/step
94781440/94765736 [==============================] - 0s 0us/step
Found 20000 images belonging to 2 classes.
Found 5000 images belonging to 2 classes.
625/625 [==============================] - ETA: 0s - loss: 0.9511 - accuracy: 0.6004

Let us know if the issue still persists. Thanks!