问题描述
我正在研究 colab 并使用 TPU，但不幸的是它无法正常工作并且模型在拟合时遇到问题..
这是我的代码：
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_address)
tf.config.experimental_connect_to_cluster(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
with strategy.scope():
model = create_model(input_shape=(HEIGHT,WIDTH,CANAL),n_out=N_CLASSES)
for layer in model.layers:
layer.trainable = False
for i in range(-5,0):
model.layers[i].trainable = True
es = EarlyStopping(monitor='val_loss',mode='min',patience=ES_PATIENCE,restore_best_weights=True,verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss',patience=RLROP_PATIENCE,factor=DECAY_DROP,min_lr=1e-6,verbose=1)
callback_list = [es,rlrop]
optimizer = optimizers.Adam(lr=LEARNING_RATE)
model.compile(optimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE),loss = 'categorical_crossentropy',metrics = ['accuracy'])
model.summary()
STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size
STEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size
history_finetunning = model.fit_generator(generator=train_generator,steps_per_epoch=STEP_SIZE_TRAIN,epochs=EPOCHS,validation_data=valid_generator,validation_steps=STEP_SIZE_VALID,verbose =1)
这是错误..
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1915: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`,which supports generators.
warnings.warn('`Model.fit_generator` is deprecated and '
Epoch 1/40
---------------------------------------------------------------------------
UnavailableError Traceback (most recent call last)
<ipython-input-41-1c157bad2449> in <module>()
4 validation_data=valid_generator,5 validation_steps=STEP_SIZE_VALID,----> 6 verbose =1)
14 frames
/usr/local/lib/python3.7/dist-packages/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)
1930 use_multiprocessing=use_multiprocessing,1931 shuffle=shuffle,-> 1932 initial_epoch=initial_epoch)
1933
1934 def evaluate_generator(self,/usr/local/lib/python3.7/dist-packages/keras/engine/training.py in fit(self,x,y,batch_size,validation_split,sample_weight,initial_epoch,validation_batch_size,use_multiprocessing)
1161 logs = tmp_logs # No error,Now safe to assign to logs.
1162 end_step = step + data_handler.step_increment
-> 1163 callbacks.on_train_batch_end(end_step,logs)
1164 if self.stop_training:
1165 break
/usr/local/lib/python3.7/dist-packages/keras/callbacks.py in on_train_batch_end(self,batch,logs)
434 """
435 if self._should_call_train_batch_hooks:
--> 436 self._call_batch_hook(ModeKeys.TRAIN,'end',logs=logs)
437
438 def on_test_batch_begin(self,logs=None):
/usr/local/lib/python3.7/dist-packages/keras/callbacks.py in _call_batch_hook(self,mode,hook,logs)
276 self._call_batch_begin_hook(mode,logs)
277 elif hook == 'end':
--> 278 self._call_batch_end_hook(mode,logs)
279 else:
280 raise ValueError('Unrecognized hook: {}'.format(hook))
/usr/local/lib/python3.7/dist-packages/keras/callbacks.py in _call_batch_end_hook(self,logs)
296 self._batch_times.append(batch_time)
297
--> 298 self._call_batch_hook_helper(hook_name,logs)
299
300 if len(self._batch_times) >= self._num_batches_for_timing_check:
/usr/local/lib/python3.7/dist-packages/keras/callbacks.py in _call_batch_hook_helper(self,hook_name,logs)
336 hook = getattr(callback,hook_name)
337 if getattr(callback,'_supports_tf_logs',False):
--> 338 hook(batch,logs)
339 else:
340 if numpy_logs is None: # Only convert once.
/usr/local/lib/python3.7/dist-packages/keras/callbacks.py in on_train_batch_end(self,logs)
1042
1043 def on_train_batch_end(self,logs=None):
-> 1044 self._batch_update_progbar(batch,logs)
1045
1046 def on_test_batch_end(self,logs=None):
/usr/local/lib/python3.7/dist-packages/keras/callbacks.py in _batch_update_progbar(self,logs)
1106 if self.verbose == 1:
1107 # Only block async when verbose = 1.
-> 1108 logs = tf_utils.sync_to_numpy_or_python_type(logs)
1109 self.progbar.update(self.seen,list(logs.items()),finalize=False)
1110
/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py in sync_to_numpy_or_python_type(tensors)
505 return t # Don't turn ragged or sparse tensors to NumPy.
506
--> 507 return tf.nest.map_structure(_to_single_numpy_or_python_type,tensors)
508
509
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py in map_structure(func,*structure,**kwargs)
865
866 return pack_sequence_as(
--> 867 structure[0],[func(*x) for x in entries],868 expand_composites=expand_composites)
869
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py in <listcomp>(.0)
865
866 return pack_sequence_as(
--> 867 structure[0],868 expand_composites=expand_composites)
869
/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py in _to_single_numpy_or_python_type(t)
501 def _to_single_numpy_or_python_type(t):
502 if isinstance(t,tf.Tensor):
--> 503 x = t.numpy()
504 return x.item() if np.ndim(x) == 0 else x
505 return t # Don't turn ragged or sparse tensors to NumPy.
/usr/local/lib/python3.7/dist-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
/usr/local/lib/python3.7/dist-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
/usr/local/lib/python3.7/dist-packages/six.py in raise_from(value,from_value)
UnavailableError: 3 root error(s) found.
(0) Unavailable: {{function_node __inference_train_function_53748}} Failed to connect to all addresses
Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:cpu:0:
:{"created":"@1626347736.544045826","description":"Failed to pick subchannel","file":"third_party/grpc/src/core/ext/filters/client_channel/client_channel.cc","file_line":5420,"referenced_errors":[{"created":"@1626347735.785465323","description":"Failed to connect to all addresses","file":"third_party/grpc/src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.cc","file_line":398,"grpc_status":14}]}
[[{{node MultideviceIteratorGetNextFromShard}}]]
[[RemoteCall]]
[[IteratorGetNextAsOptional]]
[[cond_11/switch_pred/_107/_76]]
(1) Unavailable: {{function_node __inference_train_function_53748}} Failed to connect to all addresses
Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:cpu:0:
:{"created":"@1626347736.544045826","grpc_status":14}]}
[[{{node MultideviceIteratorGetNextFromShard}}]]
[[RemoteCall]]
[[IteratorGetNextAsOptional]]
[[cluster_train_function/_execute_2_0/_333]]
(2) Unavailable: {{function_node __inference_train_function_53748}} Failed to connect to all addresses
Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:cpu:0:
:{"created":"@1626347736.544045826","grpc_status":14}]}
[[{{node MultideviceIteratorGetNextFromShard}}]]
[[RemoteCall]]
[[IteratorGetNextAsOptional]]
0 successful operations.
6 derived errors ignored.
对来自 tensorflow_datasets 的 tfds 使用相同的 TPU 配置代码并使用 model.fit 方法拟合模型不会引发任何错误，
解决方法
Model.fit_generator 已弃用且不适用于 TPU。
尝试使用 tf.keras.preprocessing.image_dataset_from_directory 或 tf.data.Dataset 并将其与 Keras 预处理 layers 结合起来