python keras 报错 "stream did not block host until done; was already in an error state"

Question

from keras import layers
from keras import models

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
#这些分类器可以处理 1D 向量，而当前的输出是 3D 张量。 
#首先，我们需要将 3D 输出展平为 1D，然后在上面添加几个 Dense 层。

from keras.datasets import mnist
from keras.utils import np_utils


(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255

train_labels = np_utils.to_categorical(train_labels)
test_labels = np_utils.to_categorical(test_labels)

前面这些没问题，后面这段就报错。

model.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

报错：

---------------------------------------------------------------------------
InternalError                             Traceback (most recent call last)
C:\Users\LOISLU~1\AppData\Local\Temp/ipykernel_12408/247866327.py in <module>
      1 model.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])
----> 2 model.fit(train_images, train_labels, epochs=5, batch_size=64)

D:\QLDownload\AnacondaDon\envs\tensorflow_gpu2021\lib\site-packages\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)
   1106          training_utils.RespectCompiledTrainableState(self):
   1107       # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
-> 1108       data_handler = data_adapter.get_data_handler(
   1109           x=x,
   1110           y=y,

D:\QLDownload\AnacondaDon\envs\tensorflow_gpu2021\lib\site-packages\keras\engine\data_adapter.py in get_data_handler(*args, **kwargs)
   1346   if getattr(kwargs["model"], "_cluster_coordinator", None):
   1347     return _ClusterCoordinatorDataHandler(*args, **kwargs)
-> 1348   return DataHandler(*args, **kwargs)
   1349 
   1350 

D:\QLDownload\AnacondaDon\envs\tensorflow_gpu2021\lib\site-packages\keras\engine\data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)
   1132     else:
   1133       self._steps_per_execution = steps_per_execution
-> 1134       self._steps_per_execution_value = steps_per_execution.numpy().item()
   1135 
   1136     adapter_cls = select_data_adapter(x, y)

D:\QLDownload\AnacondaDon\envs\tensorflow_gpu2021\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py in numpy(self)
    626   def numpy(self):
    627     if context.executing_eagerly():
--> 628       return self.read_value().numpy()
    629     raise NotImplementedError(
    630         "numpy() is only available when eager execution is enabled.")

D:\QLDownload\AnacondaDon\envs\tensorflow_gpu2021\lib\site-packages\tensorflow\python\framework\ops.py in numpy(self)
   1092     """
   1093     # TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.
-> 1094     maybe_arr = self._numpy()  # pylint: disable=protected-access
   1095     return maybe_arr.copy() if isinstance(maybe_arr, np.ndarray) else maybe_arr
   1096 

D:\QLDownload\AnacondaDon\envs\tensorflow_gpu2021\lib\site-packages\tensorflow\python\framework\ops.py in _numpy(self)
   1060       return self._numpy_internal()
   1061     except core._NotOkStatusException as e:  # pylint: disable=protected-access
-> 1062       six.raise_from(core._status_to_exception(e.code, e.message), None)  # pylint: disable=protected-access
   1063 
   1064   @property

D:\QLDownload\AnacondaDon\envs\tensorflow_gpu2021\lib\site-packages\six.py in raise_from(value, from_value)

InternalError: stream did not block host until done; was already in an error state

Answer 1

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