我正在尝试使用 EfficientNet 进行迁移学习并收到此错误任何答案请提供详细信息如何解决。错误是当我尝试训练模型时。由于某种原因，它有时无法读取图像。
import pickle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pylab import rcParams
import tensorflow as tf
import keras
from keras import layers, optimizers
get_ipython().run_line_magic('matplotlib', 'inline')
from keras.models import Sequential, load_model
from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import Callback, EarlyStopping, ModelCheckpoint
from sklearn.metrics import confusion_matrix, classification_report
from skimage.transform import resize
import seaborn as sns
import cv2
import os
import glob
import shutil
batch_size = 48
width = 150
height = 150
epochs = 20
NUM_TRAIN = 2000
NUM_TEST = 1000
dropout_rate = 0.2
input_shape = (height, width, 3)
from efficientnet.keras import EfficientNetB0 as Net
from efficientnet.keras import center_crop_and_resize, preprocess_input
conv_base = Net(weights='imagenet', include_top=False, input_shape=input_shape)
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to target height and width.
target_size=(height, width),
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(height, width),
batch_size=batch_size,
class_mode='categorical')
model = Sequential()
model.add(conv_base)
model.add(layers.GlobalMaxPooling2D(name="gap"))
# model.add(layers.Flatten(name="flatten"))
if dropout_rate > 0:
model.add(layers.Dropout(dropout_rate, name="dropout_out"))
# model.add(layers.Dense(256, activation='relu', name="fc1"))
model.add(layers.Dense(2, activation='softmax', name="fc_out"))
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=2e-5),
metrics=['acc'])
history = model.fit_generator(
train_generator,
steps_per_epoch= NUM_TRAIN //batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps= NUM_TEST //batch_size,
verbose=1,
use_multiprocessing=True,
workers=4)
我有这个错误，我知道它与枕头和阅读图像或张量流有关：
Epoch 1/20
WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.
11/41 [=======>......................] - ETA: 15s - loss: 1.9679 - acc: 0.5833
---------------------------------------------------------------------------
UnknownError Traceback (most recent call last)
<ipython-input-21-bbda3a575f01> in <module>
2 optimizer=optimizers.RMSprop(lr=2e-5),
3 metrics=['acc'])
----> 4 history = model.fit_generator(
5 train_generator,
6 steps_per_epoch= NUM_TRAIN //batch_size,
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
322 'in a future version' if date is None else ('after %s' % date),
323 instructions)
--> 324 return func(*args, **kwargs)
325 return tf_decorator.make_decorator(
326 func, new_func, 'deprecated',
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1813 """
1814 _keras_api_gauge.get_cell('fit_generator').set(True)
-> 1815 return self.fit(
1816 generator,
1817 steps_per_epoch=steps_per_epoch,
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
805 # In this case we have created variables on the first call, so we run the
806 # defunned version which is guaranteed to never create variables.
--> 807 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
808 elif self._stateful_fn is not None:
809 # Release the lock early so that multiple threads can perform the call
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2827 with self._lock:
2828 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2830
2831 @property
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs, cancellation_manager)
1841 `args` and `kwargs`.
1842 """
-> 1843 return self._call_flat(
1844 [t for t in nest.flatten((args, kwargs), expand_composites=True)
1845 if isinstance(t, (ops.Tensor,
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1921 and executing_eagerly):
1922 # No tape is watching; skip to running the function.
-> 1923 return self._build_call_outputs(self._inference_function.call(
1924 ctx, args, cancellation_manager=cancellation_manager))
1925 forward_backward = self._select_forward_and_backward_functions(
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
543 with _InterpolateFunctionError(self):
544 if cancellation_manager is None:
--> 545 outputs = execute.execute(
546 str(self.signature.name),
547 num_outputs=self._num_outputs,
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 try:
58 ctx.ensure_initialized()
---> 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
UnknownError: OSError: cannot identify image file <_io.BytesIO object at 0x7fc87d222b30>
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/lib/python3.8/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py", line 679, in get_index
return _SHARED_SEQUENCES[uid][i]
File "/home/zion/.local/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py", line 65, in __getitem__
return self._get_batches_of_transformed_samples(index_array)
File "/home/zion/.local/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py", line 227, in _get_batches_of_transformed_samples
img = load_img(filepaths[j],
File "/home/zion/.local/lib/python3.8/site-packages/keras_preprocessing/image/utils.py", line 114, in load_img
img = pil_image.open(io.BytesIO(f.read()))
File "/usr/local/lib/python3.8/dist-packages/PIL/Image.py", line 2818, in open
raise IOError("cannot identify image file %r" % (filename if filename else fp))
OSError: cannot identify image file <_io.BytesIO object at 0x7fc87d222b30>
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/script_ops.py", line 244, in __call__
ret = func(*args)
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py", line 302, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 827, in generator_py_func
values = next(generator_state.get_iterator(iterator_id))
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/data_adapter.py", line 814, in wrapped_generator
for data in generator_fn():
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py", line 900, in get
six.reraise(*sys.exc_info())
File "/home/zion/.local/lib/python3.8/site-packages/six.py", line 703, in reraise
raise value
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py", line 891, in get
inputs = self.queue.get(block=True, timeout=5).get()
File "/usr/lib/python3.8/multiprocessing/pool.py", line 771, in get
raise self._value
OSError: cannot identify image file <_io.BytesIO object at 0x7fc87d222b30>
[[{{node PyFunc}}]]
[[IteratorGetNext]] [Op:__inference_train_function_30570]
Function call stack:
train_function