将图像传递给由 TensorFlow 和 Keras 制成的 CNN model 时出错

Question

I was making a CNN classifier to identify cat and dog images on my colab notebook, but it is giving me the Graph execution error. Initially, I trained the model with various pictures of them with 500 images each which worked without any problems, but my model was not giving me decent accuracy. So, I increased the pictures to 1500 each which gave me this error.

---

Epoch 1/10
---------------------------------------------------------------------------
UnknownError                              Traceback (most recent call last)
<ipython-input-32-086f2dc31200> in <module>
----> 1 model.fit(x = train_batches, validation_data = valid_batches, epochs = 10, verbose = 2)

1 frames
/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:

UnknownError: Graph execution error:

2 root error(s) found.
  (0) UNKNOWN:  UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f7814555ef0>
Traceback (most recent call last):

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/script_ops.py", line 271, in __call__
ret = func(*args)

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py", line 642, in wrapper
return func(*args, **kwargs)

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 1004, in generator_py_func
values = next(generator_state.get_iterator(iterator_id))

  File "/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py", line 830, in wrapped_generator
    for data in generator_fn():

  File "/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py", line 956, in generator_fn
    yield x[I]

  File "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/iterator.py", line 65, in __getitem__
    return self._get_batches_of_transformed_samples(index_array)

  File "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/iterator.py", line 230, in _get_batches_of_transformed_samples
interpolation=self.interpolation)

  File "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/utils.py", line 114, in load_img
    img = pil_image.open(io.BytesIO(f.read()))

  File "/usr/local/lib/python3.7/dist-packages/PIL/Image.py", line 2896, in open
"cannot identify image file %r" % (filename if filename else fp)

PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f7814555ef0>


 [[{{node PyFunc}}]]
 [[IteratorGetNext]]
 [[IteratorGetNext/_2]]
  (1) UNKNOWN:  UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f7814555ef0>
Traceback (most recent call last):

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/script_ops.py", line 271, in __call__
ret = func(*args)

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py", line 642, in wrapper
return func(*args, **kwargs)

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 1004, in generator_py_func
values = next(generator_state.get_iterator(iterator_id))

  File "/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py", line 830, in wrapped_generator
for data in generator_fn():

  File "/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py", line 956, in generator_fn
yield x[I]

  File "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/iterator.py", line 65, in __getitem__
return self._get_batches_of_transformed_samples(index_array)

  File "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/iterator.py", line 230, in _get_batches_of_transformed_samples
interpolation=self.interpolation)

  File "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/utils.py", line 114, in load_img
img = pil_image.open(io.BytesIO(f.read()))

  File "/usr/local/lib/python3.7/dist-packages/PIL/Image.py", line 2896, in open
"cannot identify image file %r" % (filename if filename else fp)

PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f7814555ef0>


 [[{{node PyFunc}}]]
 [[IteratorGetNext]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_2503]

---

This is my model

model = Sequential([
Conv2D(filters = 32, kernel_size = (3, 3), activation = "relu", padding = "same", input_shape = (224, 224, 3)),
MaxPool2D(pool_size = (2, 2), strides = 3),
Conv2D(filters = 64, kernel_size = (3, 3), activation = "relu", padding = "same"),
MaxPool2D(pool_size = (2, 2), strides = 3),
Flatten(),
Dense(units = 2, activation = "softmax")

])

My Model Summary

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d_2 (Conv2D)           (None, 224, 224, 32)      896       
                                                             
 max_pooling2d_2 (MaxPooling 2D)  (None, 75, 75, 32)       0         
                                                          
                                                             
 conv2d_3 (Conv2D)           (None, 75, 75, 64)        18496     
                                                             
 max_pooling2d_3 (MaxPooling 2D)  (None, 25, 25, 64)       0                                                                      
                                                             
 flatten_1 (Flatten)         (None, 40000)             0         
                                                             
 dense_1 (Dense)             (None, 2)                 80002     
                                                             
=================================================================
Total params: 99,394
Trainable params: 99,394
Non-trainable params: 0
_________________________________________________________________

My Compile and Training code

model.compile(optimizer = Adam(learning_rate = 0.0001), loss = 
"categorical_crossentropy", metrics = ["accuracy"])

model.fit(x = train_batches, validation_data = valid_batches, epochs = 10, verbose = 2)

Any solutions?

Answer 1

You may have a corrupt image in your dataset. Try to find out that image and remove it.

import PIL
from pathlib import Path
from PIL import UnidentifiedImageError

def aggregate_images(
    dataset_root,
    extensions = ["png", "jpg", "jpeg"],
):
    """
    Globs for images in a given data directory and returns them
    """
    dataset_root = Path(dataset_root)
    image_paths = []

    for extension in extensions:
        image_paths.extend(list(dataset_root.glob("**/*.{}".format(extension))))

    return image_paths

path = aggregate_images(/path/to/your/dataset/root)
for img_p in path:
    try:
        img = PIL.Image.open(img_p)
    except PIL.UnidentifiedImageError:
            print(img_p)