无效参数错误和无效参数错误：创建LSTM模型时图形执行错误tensorflow

vd2z7a6w  于 2022-11-16  发布在  其他
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我在运行我的代码时遇到多个错误，无法理解是因为数据集或一些体系结构问题。

from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.layers import Dense,concatenate,Activation,Dropout,Input,LSTM,Flatten
from tensorflow.keras import regularizers,initializers,optimizers,Model

tokenizeressay = Tokenizer(oov_token="<OOV>")
# generate word indexes
tokenizeressay.fit_on_texts(X_train.essay)
# generate sequences
tokenisedessayTrain  = tokenizeressay.texts_to_sequences(X_train.essay)
tokenisedessayTest  = tokenizeressay.texts_to_sequences(X_test.essay)
tokenisedessayCV  = tokenizeressay.texts_to_sequences(X_cv.essay)

tokenisedTrainessay = pad_sequences(tokenisedessayTrain,maxlen=350)
tokenisedTestessay = pad_sequences(tokenisedessayTest,maxlen=350)
tokenizedCvessay = pad_sequences(tokenisedessayCV,maxlen=350)

import pickle
with open("C:\\Users\\Administrator\\Downloads\\glove_vectors", 'rb') as f:
    glove = pickle.load(f)

size_glove = 300     # glove vectors are 300 dims
size_vocab = len(list(tokenizeressay.word_counts.keys()))
word_Weights = np.zeros((size_vocab+1, size_glove))
for word, i in tokenizeressay.word_index.items():
    embedding_vector = glove.get(word)
    if embedding_vector is not None:
        # words not found in embedding index will be all-zeros.
        word_Weights[i-1] = embedding_vector
        
print("Max Length on Essay")
print(X_train.essay.apply(lambda x : len(x.split(' '))).max())
print("Word Item Length:")
print(len(tokenizeressay.word_index.items()))
print("Max Length we are taken will be 350")

Max Length on Essay
339
Word Item Length:
49330
Max Length we are taken will be 350

#LSTM and get the LSTM output and Flatten that output.  ----> Accoridng to the assignment
# I have choose 128 Units

essayInput = Input(shape=(350,),dtype='int32',name='essayInput')
embeddedEssay = Embedding(input_dim=(len(tokenizeressay.word_index.items())),output_dim=300,name='embeddedEssay',weights=[word_Weights],trainable=False)(essayInput)
essayLSTM = LSTM(units=128, return_sequences=True)(embeddedEssay)
essayOut = Flatten()(essayLSTM)

from tensorflow.keras.layers import concatenate
concat_layer = concatenate(inputs=[essayOut],name="concat_layer")
from tensorflow.keras.layers import Dropout,BatchNormalization

AfterConcatLayer = Dense(256,activation='relu',kernel_regularizer=regularizers.l2(0.001),kernel_initializer=initializers.he_normal())(concat_layer)
AfterConcatLayer = Dropout(0.5)(AfterConcatLayer)

AfterConcatLayer = Dense(128,activation='relu',kernel_regularizer=regularizers.l2(0.001),kernel_initializer=initializers.he_normal())(AfterConcatLayer)
AfterConcatLayer = Dropout(0.5)(AfterConcatLayer)
AfterConcatLayer = BatchNormalization()(AfterConcatLayer)
AfterConcatLayer = Dense(64,activation='relu',kernel_regularizer=regularizers.l2(0.001),kernel_initializer=initializers.he_normal())(AfterConcatLayer)
AfterConcatLayer = Dropout(0.5)(AfterConcatLayer)
SoftmaxOutput = Dense(2, activation = 'softmax')(AfterConcatLayer)
model1 = Model([essayInput], SoftmaxOutput)
model1.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(lr=0.0006,decay = 1e-4),metrics=[auc])
print(model1.summary())

checkpoint_path ="C:\\Windows_Old\\Learnings\\MachineLearning\\CheckPoints.hdf5"

cp_callback = ModelCheckpoint(filepath=checkpoint_path,save_best_only=True, save_weights_only=True, verbose=1,monitor='val_auc')

#Delete THIS CODE

import os
from keras.utils import to_categorical

if os.path.isfile(checkpoint_path):
    model1.load_weights(checkpoint_path)
#essayInput,SchoolStateInput,TeacherPrefixInput,CleanCategoriesInput,CleanSubCategoriesInput,ProjectGradeInput,PriceProjectNumberInput

model1.fit([tokenisedTrainessay],to_categorical(y_train), epochs=50,verbose=2,batch_size=512,validation_split=0.3,callbacks = [cp_callback])

我正在使用DonorChoose数据集。您可以从这个Link下载相同的数据集
我遇到的错误是：

InvalidArgumentError                      Traceback (most recent call last)
Input In [23], in <cell line: 10>()
      7     model1.load_weights(checkpoint_path)
      8 #essayInput,SchoolStateInput,TeacherPrefixInput,CleanCategoriesInput,CleanSubCategoriesInput,ProjectGradeInput,PriceProjectNumberInput
---> 10 model1.fit([tokenisedTrainessay],to_categorical(y_train), epochs=50,verbose=2,batch_size=512,validation_split=0.3,callbacks = [cp_callback])

File C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     52 try:
     53   ctx.ensure_initialized()
---> 54   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     55                                       inputs, attrs, num_outputs)
     56 except core._NotOkStatusException as e:
     57   if name is not None:

不同的错误：无效参数错误

InvalidArgumentError: Graph execution error:

Detected at node 'model/embeddedEssay/embedding_lookup' defined at (most recent call last):
    File "C:\ProgramData\Anaconda3\lib\runpy.py", line 197, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\ProgramData\Anaconda3\lib\runpy.py", line 87, in _run_code
      exec(code, run_globals)
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
      app.launch_new_instance()
    File "C:\ProgramData\Anaconda3\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
      app.start()
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
      self.io_loop.start()
    File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
      self.asyncio_loop.run_forever()
    File "C:\ProgramData\Anaconda3\lib\asyncio\base_events.py", line 601, in run_forever
      self._run_once()
    File "C:\ProgramData\Anaconda3\lib\asyncio\base_events.py", line 1905, in _run_once
      handle._run()
    File "C:\ProgramData\Anaconda3\lib\asyncio\events.py", line 80, in _run
      self._context.run(self._callback, *self._args)
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 471, in dispatch_queue
      await self.process_one()
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 460, in process_one
      await dispatch(*args)
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 367, in dispatch_shell
      await result
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 662, in execute_request
      reply_content = await reply_content
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 360, in do_execute
      res = shell.run_cell(code, store_history=store_history, silent=silent)
    File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 532, in run_cell
      return super().run_cell(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2863, in run_cell
      result = self._run_cell(
    File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in _run_cell
      return runner(coro)
    File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner
      coro.send(None)
    File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3106, in run_cell_async
      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
    File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3309, in run_ast_nodes
      if await self.run_code(code, result, async_=asy):
    File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3369, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "C:\Users\Administrator\AppData\Local\Temp\2\ipykernel_4140\2952872782.py", line 10, in <cell line: 10>
      model1.fit([tokenisedTrainessay],to_categorical(y_train), epochs=50,verbose=2,batch_size=512,validation_split=0.3,callbacks = [cp_callback])
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1606, in fit
      val_logs = self.evaluate(
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1947, in evaluate
      tmp_logs = self.test_function(iterator)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1727, in test_function
      return step_function(self, iterator)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1713, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1701, in run_step
      outputs = model.test_step(data)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1665, in test_step
      y_pred = self(x, training=False)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 557, in __call__
      return super().__call__(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1097, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\functional.py", line 510, in call
      return self._run_internal_graph(inputs, training=training, mask=mask)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\functional.py", line 667, in _run_internal_graph
      outputs = node.layer(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1097, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\core\embedding.py", line 208, in call
      out = tf.nn.embedding_lookup(self.embeddings, inputs)
Node: 'model/embeddedEssay/embedding_lookup'
indices[413,323] = 49330 is not in [0, 49330)
     [[{{node model/embeddedEssay/embedding_lookup}}]] [Op:__inference_test_function_5617]

这三天我都被困在这了。
我已经尝试增加1的暗淡，但失败了。代码片段附上

essayInput = Input(shape=(350,),dtype='int32',name='essayInput')
embeddedEssay = Embedding(input_dim=(len(tokenizeressay.word_index.items())+1),output_dim=300,name='embeddedEssay',weights=[word_Weights],trainable=False)(essayInput)
essayLSTM = LSTM(units=128, return_sequences=True)(embeddedEssay)
essayOut = Flatten()(essayLSTM)

执行此操作时出现错误。

ValueError                                Traceback (most recent call last)
Input In [25], in <cell line: 5>()
      1 #LSTM and get the LSTM output and Flatten that output.  ----> Accoridng to the assignment
      2 # I have choose 128 Units
      4 essayInput = Input(shape=(350,),dtype='int32',name='essayInput')
----> 5 embeddedEssay = Embedding(input_dim=(len(tokenizeressay.word_index.items())+1),output_dim=300,name='embeddedEssay',weights=[word_Weights],trainable=False)(essayInput)
      6 essayLSTM = LSTM(units=128, return_sequences=True)(embeddedEssay)
      7 essayOut = Flatten()(essayLSTM)

File C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\base_layer.py:1772, in Layer.set_weights(self, weights)
   1770 ref_shape = param.shape
   1771 if not ref_shape.is_compatible_with(weight_shape):
-> 1772     raise ValueError(
   1773         f"Layer {self.name} weight shape {ref_shape} "
   1774         "is not compatible with provided weight "
   1775         f"shape {weight_shape}."
   1776     )
   1777 weight_value_tuples.append((param, weight))
   1778 weight_index += 1

ValueError: Layer embeddedEssay weight shape (49331, 300) is not compatible with provided weight shape (49330, 300).
tensorflow

1条答案

按热度按时间
atmip9wb

atmip9wb1#

如 错误 中 所 述 ，
ValueError ： 层 嵌入 的 Essay 权重 形状 （ 49331 ， 300 ） 与 提供 的 权重 形状 （ 49330 ， 300 ） 不 兼容 。
由于 形状 不 匹配 而 发生 错误 ， 因此 尝试 增加 嵌入 层 的 输入 尺寸 ， 如下 所 示 。

embeddedEssay = Embedding(input_dim=(len(tokenizeressay.word_index.items()))+1,output_dim=300,name='embeddedEssay',weights=[word_Weights],trainable=False)(essayInput)

中 的 每 一 个
为了 避免 错误 ， 还 应 按 如下 方式 增加 词汇 量 。

size_vocab = len(list(tokenizeressay.word_counts.keys()))+1

格式
请 参考 这个 gist 以 获得 完整 的 代码 ， 并 参考 这个 例子 以 获得 关于 错误 的 更多 信息 。 谢谢 !

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