我正在尝试使用tensorflow集线器对BERT进行文本分类。该代码在Colab GPU上运行良好，但是当我将其转换为Colab TPU时，它会显示以下“未初始化的层”错误。 以下是Bert层-
class BertLayer(tf.keras.layers.Layer):
def __init__(self, n_fine_tune_layers, **kwargs):
self.n_fine_tune_layers = n_fine_tune_layers
self.trainable = True
self.output_size = 768
super(BertLayer, self).__init__(**kwargs)
def build(self, input_shape):
self.bert = hub.Module(
bert_path,
trainable=True,# did this in place of self.trainable
name="{}_module".format(self.name)
)
trainable_vars = self.bert.variables
trainable_vars = [var for var in trainable_vars if not "/cls/" in var.name]
#print("--------------------------len=",len(trainable_vars))
# Select how many layers to fine tune
trainable_vars = trainable_vars[-self.n_fine_tune_layers:]
# Add to trainable weights
for var in trainable_vars:
self._trainable_weights.append(var)
for var in self.bert.variables:
if var not in self._trainable_weights:
self._non_trainable_weights.append(var)
super(BertLayer, self).build(input_shape)
def call(self, inputs):
inputs = [K.cast(x, dtype="int32") for x in inputs]
input_ids, input_mask, segment_ids = inputs
bert_inputs = dict(
input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids
)
result = self.bert(inputs=bert_inputs, signature="tokens", as_dict=True)[
"pooled_output"
]
return result
def compute_output_shape(self, input_shape):
return (input_shape[0], self.output_size)
以下是我的模特-
print("-----------------------------1")
from tensorflow.keras.layers import Input,Dense
#from tensorflow.keras.callbacks import TensorBoard
#Name="test run-{}".format(1)
#tensorboard=TensorBoard(log_dir="logs/{}".format(Name))
in_id=Input(shape=(max_seq_length,),)
in_mask=Input(shape=(max_seq_length,),)
in_segment=Input(shape=(max_seq_length,),)
print("-----------------------------2")
in_id = tf.keras.layers.Input(shape=(max_seq_length,))
in_mask = tf.keras.layers.Input(shape=(max_seq_length,))
in_segment = tf.keras.layers.Input(shape=(max_seq_length,))
print("-----------------------------3")
bert_inputs=[in_id,in_mask,in_segment]
bert_outputs=BertLayer(n_fine_tune_layers=100)(bert_inputs)
#step=bert_outpu
step=tf.keras.layers.Dropout(rate=0.1)(bert_outputs)
step=tf.keras.layers.Dense(512,activation='relu',kernel_initializer='glorot_normal')(step)
#step=tf.keras.layers.Dense(256,activation='relu')(step)
step=tf.keras.layers.Dropout(rate=dropout)(step)
step=tf.keras.layers.Dense(256,activation='relu',kernel_initializer='glorot_normal')(step)
#dense=tf.keras.layers.Dense(256,activation='relu')(bert_outputs)
step=tf.keras.layers.Dropout(rate=dropout)(step)
pred=tf.keras.layers.Dense(1,activation='sigmoid')(step)
model=tf.keras.Model(inputs=bert_inputs,outputs=pred)
print("-----------------------------4")
model.compile(loss='binary_crossentropy',
optimizer=tf.train.AdamOptimizer(lr),
metrics=[f1,'accuracy'])
print("-----------------------------5")
tpu_model = tf.contrib.tpu.keras_to_tpu_model(model,
strategy=tf.contrib.tpu.TPUDistributionStrategy(tf.contrib.cluster_resolver.TPUClusterResolver(tpu_address)))
print("-----------------------------6")
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
K.set_session(sess)
print("-----------------------------7")
tpu_model.fit([train_input_ids, train_input_masks, train_segment_ids],
train_labels,
epochs=epochs,
batch_size=64)
#validation_data=([val_input_ids, val_input_masks, val_segment_ids],val_labels))
model.evaluate([test_input_ids, test_input_masks, test_segment_ids],test_labels)
以下是错误-
-----------------------------1
-----------------------------2
-----------------------------3
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
I0605 10:36:17.033424 140062933383040 saver.py:1483] Saver not created because there are no variables in the graph to restore
-----------------------------4
-----------------------------5
INFO:tensorflow:Querying Tensorflow master (grpc://10.85.103.202:8470) for TPU system metadata.
I0605 10:36:17.748405 140062933383040 tpu_system_metadata.py:59] Querying Tensorflow master (grpc://10.85.103.202:8470) for TPU system metadata.
INFO:tensorflow:Found TPU system:
I0605 10:36:17.768394 140062933383040 tpu_system_metadata.py:120] Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
I0605 10:36:17.770817 140062933383040 tpu_system_metadata.py:121] *** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
I0605 10:36:17.773086 140062933383040 tpu_system_metadata.py:122] *** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
I0605 10:36:17.775260 140062933383040 tpu_system_metadata.py:124] *** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 8086897810259541316)
I0605 10:36:17.779561 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 8086897810259541316)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 8309407237260141527)
I0605 10:36:17.782429 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 8309407237260141527)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 7140089854169573112)
I0605 10:36:17.785550 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 7140089854169573112)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 17762152438583970348)
I0605 10:36:17.789351 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 17762152438583970348)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 12631201787268957494)
I0605 10:36:17.793601 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 12631201787268957494)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 8708359633115695081)
I0605 10:36:17.796261 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 8708359633115695081)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 601478800410838022)
I0605 10:36:17.800481 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 601478800410838022)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 16793071921697081555)
I0605 10:36:17.804739 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 16793071921697081555)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 16730824918382181321)
I0605 10:36:17.807698 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 16730824918382181321)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 11133990522845180639)
I0605 10:36:17.810022 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 11133990522845180639)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 18001585464951191022)
I0605 10:36:17.812952 140062933383040 tpu_system_metadata.py:126] *** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 18001585464951191022)
WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.
W0605 10:36:17.816158 140062933383040 experimental.py:63] tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1333 try:
-> 1334 return fn(*args)
1335 except errors.OpError as e:
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1318 return self._call_tf_sessionrun(
-> 1319 options, feed_dict, fetch_list, target_list, run_metadata)
1320
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1406 self._session, options, feed_dict, fetch_list, target_list,
-> 1407 run_metadata)
1408
InvalidArgumentError: In ReadVariableOp the following variables were found uninitialized: bert_layer_12_module/bert/embeddings/LayerNorm/beta, bert_layer_12_module/bert/embeddings/LayerNorm/gamma, bert_layer_12_module/bert/embeddings/position_embeddings, bert_layer_12_module/bert/embeddings/token_type_embeddings, bert_layer_12_module/bert/embeddings/word_embeddings, bert_layer_12_module/bert/encoder/layer_0/attention/output/LayerNorm/beta, bert_layer_12_module/bert/encoder/layer_0/attention/output/LayerNorm/gamma, bert_layer_12_module/bert/encoder/layer_0/attention/output/dense/bias, bert_layer_12_module/bert/encoder/layer_0/attention/output/dense/kernel, bert_layer_12_module/bert/encoder/layer_0/attention/self/key/bias, bert_layer_12_module/bert/encoder/layer_0/attention/self/key/kernel, bert_layer_12_module/bert/encoder/layer_0/attention/self/query/bias, bert_layer_12_module/bert/encoder/layer_0/attention/self/query/kernel, bert_layer_12_module/bert/encoder/layer_0/attention/self/value/bias, bert_layer_12_module/bert/encoder/layer_0/attention/self/value/kernel, bert_layer_12_module/bert/encoder/layer_0/intermediate/dense/bias, bert_layer_12_module/bert/encoder/layer_0/intermediate/dense/kernel, bert_layer_12_module/bert/encoder/layer_0/output/LayerNorm/beta, bert_layer_12_module/bert/encoder/layer_0/output/LayerNorm/gamma, bert_layer_12_module/bert/encoder/layer_0/output/dense/bias, bert_layer_12_module/bert/encoder/layer_0/output/dense/kernel, bert_layer_12_m...
[[{{node ReadVariables_14728137872467799544/_1}}]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-40-fdb59f59a0ef> in <module>()
82 print("-----------------------------5")
83 tpu_model = tf.contrib.tpu.keras_to_tpu_model(model,
---> 84 strategy=tf.contrib.tpu.TPUDistributionStrategy(tf.contrib.cluster_resolver.TPUClusterResolver(tpu_address)))
85
86 print("-----------------------------6")
/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/framework/python/framework/experimental.py in new_func(*args, **kwargs)
62 'any time, and without warning.',
63 decorator_utils.get_qualified_name(func), func.__module__)
---> 64 return func(*args, **kwargs)
65 new_func.__doc__ = _add_experimental_function_notice_to_docstring(
66 func.__doc__)
/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/python/tpu/keras_support.py in tpu_model(model, strategy)
2219 else:
2220 optimizer_config = None
-> 2221 model_weights = model.get_weights()
2222 else:
2223 model_weights = None
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in get_weights(self)
390 for layer in self.layers:
391 weights += layer.weights
--> 392 return backend.batch_get_value(weights)
393
394 def set_weights(self, weights):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py in batch_get_value(tensors)
2817 raise RuntimeError('Cannot get value inside Tensorflow graph function.')
2818 if tensors:
-> 2819 return get_session().run(tensors)
2820 else:
2821 return []
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1150 if final_fetches or final_targets or (handle and feed_dict_tensor):
1151 results = self._do_run(handle, final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata)
1153 else:
1154 results = []
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1326 if handle is None:
1327 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata)
1329 else:
1330 return self._do_call(_prun_fn, handle, feeds, fetches)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1346 pass
1347 message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message)
1349
1350 def _extend_graph(self):
InvalidArgumentError: In ReadVariableOp the following variables were found uninitialized: bert_layer_12_module/bert/embeddings/LayerNorm/beta, bert_layer_12_module/bert/embeddings/LayerNorm/gamma, bert_layer_12_module/bert/embeddings/position_embeddings, bert_layer_12_module/bert/embeddings/token_type_embeddings, bert_layer_12_module/bert/embeddings/word_embeddings, bert_layer_12_module/bert/encoder/layer_0/attention/output/LayerNorm/beta, bert_layer_12_module/bert/encoder/layer_0/attention/output/LayerNorm/gamma, bert_layer_12_module/bert/encoder/layer_0/attention/output/dense/bias, bert_layer_12_module/bert/encoder/layer_0/attention/output/dense/kernel, bert_layer_12_module/bert/encoder/layer_0/attention/self/key/bias, bert_layer_12_module/bert/encoder/layer_0/attention/self/key/kernel, bert_layer_12_module/bert/encoder/layer_0/attention/self/query/bias, bert_layer_12_module/bert/encoder/layer_0/attention/self/query/kernel, bert_layer_12_module/bert/encoder/layer_0/attention/self/value/bias, bert_layer_12_module/bert/encoder/layer_0/attention/self/value/kernel, bert_layer_12_module/bert/encoder/layer_0/intermediate/dense/bias, bert_layer_12_module/bert/encoder/layer_0/intermediate/dense/kernel, bert_layer_12_module/bert/encoder/layer_0/output/LayerNorm/beta, bert_layer_12_module/bert/encoder/layer_0/output/LayerNorm/gamma, bert_layer_12_module/bert/encoder/layer_0/output/dense/bias, bert_layer_12_module/bert/encoder/layer_0/output/dense/kernel, bert_layer_12_m...
[[{{node ReadVariables_14728137872467799544/_1}}]]
请帮助我解决此错误。
答案 0 :(得分：0)
TensorFlow Hub的最新版本支持创建Keras layer directly，这使此任务更加容易。一个警告是，您必须具有可写的Google Cloud Storage bucket才能缓存BERT文件，因为Cloud TPU无法访问本地文件系统。您可以将MetadataStore环境变量设置为指向该存储桶。
您更新的代码应如下所示：
TFHUB_CACHE_DIR我使用了一个由1和0构成的假数据集，以表明该模型可以编译和运行而不会崩溃。实际上，您需要对数据进行预处理，以使其与预训练数据格式相匹配。有关更多详细说明，请参见this BERT model的文档。在此示例中，我任意选择了TFHub中的一个预训练BERT。请参阅TensorFlow 2.x BERTs here的完整集合。