在 Keras 的 BERT 编码器之上堆叠 LSTM 层
stacking LSTM layer on top of BERT encoder in Keras
我一直在尝试在 Bert 嵌入之上堆叠一个 LSTM 层，但是当我的模型开始训练时，它在最后一批中失败并抛出以下错误消息：
Node: 'model/tf.reshape/Reshape'
Input to reshape is a tensor with 59136 values, but the requested shape has 98304
[[{{node model/tf.reshape/Reshape}}]] [Op:__inference_train_function_70500]
这就是我构建模型的方式，老实说，我无法弄清楚这里出了什么问题：
batch_size = 128
bert_preprocess = hub.load('https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3')
bert_encoder = hub.KerasLayer('https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4', trainable=True)
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
preprocessed_text = bert_preprocess(text_input)
outputs = bert_encoder(preprocessed_text) #shape=(None, 768)
bert_output = outputs['pooled_output']
l = tf.reshape(bert_output, [batch_size, 1, 768])
l = tf.keras.layers.LSTM(32, activation='relu')(l)
l = tf.keras.layers.Dropout(0.1, name='dropout')(l)
l = tf.keras.layers.Dense(8, activation='softmax', name="output")(l)
model = tf.keras.Model(inputs=[text_input], outputs = [l])
print(model.summary())
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
这是完整的输出：
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
text (InputLayer) [(None,)] 0 []
keras_layer (KerasLayer) {'input_mask': (Non 0 ['text[0][0]']
e, 128),
'input_word_ids':
(None, 128),
'input_type_ids':
(None, 128)}
keras_layer_1 (KerasLayer) {'sequence_output': 109482241 ['keras_layer[0][0]',
(None, 128, 768), 'keras_layer[0][1]',
'default': (None, 'keras_layer[0][2]']
768),
'encoder_outputs':
[(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768)],
'pooled_output': (
None, 768)}
tf.reshape (TFOpLambda) (128, 1, 768) 0 ['keras_layer_1[0][13]']
lstm (LSTM) (128, 32) 102528 ['tf.reshape[0][0]']
dropout (Dropout) (128, 32) 0 ['lstm[0][0]']
output (Dense) (128, 8) 264 ['dropout[0][0]']
==================================================================================================
Total params: 109,585,033
Trainable params: 102,792
Non-trainable params: 109,482,241
__________________________________________________________________________________________________
None
WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fc4ff809440> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fc4ff809440> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
18/19 [===========================>..] - ETA: 25s - loss: 1.5747 - accuracy: 0.5456Traceback (most recent call last):
File "bert-test-lstm.py", line 62, in <module>
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/tensorflow/python/eager/execute.py", line 55, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'model/tf.reshape/Reshape' defined at (most recent call last):
File "bert-test-lstm.py", line 62, in <module>
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1021, in train_function
return step_function(self, iterator)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1000, in run_step
outputs = model.train_step(data)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 859, in train_step
y_pred = self(x, training=True)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/functional.py", line 452, in call
inputs, training=training, mask=mask)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/layers/core/tf_op_layer.py", line 226, in _call_wrapper
return self._call_wrapper(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/layers/core/tf_op_layer.py", line 261, in _call_wrapper
result = self.function(*args, **kwargs)
Node: 'model/tf.reshape/Reshape'
Input to reshape is a tensor with 59136 values, but the requested shape has 98304
如果我只是删除 LSTM 并重塑层，代码运行得非常好 - 任何帮助都将不胜感激。
您应该使用 tf.keras.layers.Reshape 以将 bert_output 重塑为 3D 张量并自动考虑批量维度。
简单地改变：
l = tf.reshape(bert_output, [batch_size, 1, 768])
进入：
l = tf.keras.layers.Reshape((1,768))(bert_output)
应该可以。
我一直在尝试在 Bert 嵌入之上堆叠一个 LSTM 层，但是当我的模型开始训练时，它在最后一批中失败并抛出以下错误消息：
Node: 'model/tf.reshape/Reshape'
Input to reshape is a tensor with 59136 values, but the requested shape has 98304
[[{{node model/tf.reshape/Reshape}}]] [Op:__inference_train_function_70500]
这就是我构建模型的方式，老实说，我无法弄清楚这里出了什么问题：
batch_size = 128
bert_preprocess = hub.load('https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3')
bert_encoder = hub.KerasLayer('https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4', trainable=True)
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
preprocessed_text = bert_preprocess(text_input)
outputs = bert_encoder(preprocessed_text) #shape=(None, 768)
bert_output = outputs['pooled_output']
l = tf.reshape(bert_output, [batch_size, 1, 768])
l = tf.keras.layers.LSTM(32, activation='relu')(l)
l = tf.keras.layers.Dropout(0.1, name='dropout')(l)
l = tf.keras.layers.Dense(8, activation='softmax', name="output")(l)
model = tf.keras.Model(inputs=[text_input], outputs = [l])
print(model.summary())
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
这是完整的输出：
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
text (InputLayer) [(None,)] 0 []
keras_layer (KerasLayer) {'input_mask': (Non 0 ['text[0][0]']
e, 128),
'input_word_ids':
(None, 128),
'input_type_ids':
(None, 128)}
keras_layer_1 (KerasLayer) {'sequence_output': 109482241 ['keras_layer[0][0]',
(None, 128, 768), 'keras_layer[0][1]',
'default': (None, 'keras_layer[0][2]']
768),
'encoder_outputs':
[(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768)],
'pooled_output': (
None, 768)}
tf.reshape (TFOpLambda) (128, 1, 768) 0 ['keras_layer_1[0][13]']
lstm (LSTM) (128, 32) 102528 ['tf.reshape[0][0]']
dropout (Dropout) (128, 32) 0 ['lstm[0][0]']
output (Dense) (128, 8) 264 ['dropout[0][0]']
==================================================================================================
Total params: 109,585,033
Trainable params: 102,792
Non-trainable params: 109,482,241
__________________________________________________________________________________________________
None
WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fc4ff809440> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fc4ff809440> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
18/19 [===========================>..] - ETA: 25s - loss: 1.5747 - accuracy: 0.5456Traceback (most recent call last):
File "bert-test-lstm.py", line 62, in <module>
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/tensorflow/python/eager/execute.py", line 55, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'model/tf.reshape/Reshape' defined at (most recent call last):
File "bert-test-lstm.py", line 62, in <module>
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1021, in train_function
return step_function(self, iterator)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1000, in run_step
outputs = model.train_step(data)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 859, in train_step
y_pred = self(x, training=True)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/functional.py", line 452, in call
inputs, training=training, mask=mask)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/layers/core/tf_op_layer.py", line 226, in _call_wrapper
return self._call_wrapper(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/layers/core/tf_op_layer.py", line 261, in _call_wrapper
result = self.function(*args, **kwargs)
Node: 'model/tf.reshape/Reshape'
Input to reshape is a tensor with 59136 values, but the requested shape has 98304
如果我只是删除 LSTM 并重塑层，代码运行得非常好 - 任何帮助都将不胜感激。
您应该使用 tf.keras.layers.Reshape 以将 bert_output 重塑为 3D 张量并自动考虑批量维度。
简单地改变：
l = tf.reshape(bert_output, [batch_size, 1, 768])
进入：
l = tf.keras.layers.Reshape((1,768))(bert_output)
应该可以。