How can I solve this kind of error, I'm using word embedding word2vec: InvalidArgumentError: Graph execution error:

Question

i am trying to train a model using cnn method with tensorflow and keras, but i keep getting the error bellow this code , could anyone help me or give me at least a peace of advice?

#Convolutional Neural Network (CNN) with Word2Vec
    CNN_Word2Vec_model = Sequential([
        Embedding(input_dim =word2Vec_embedding_matrix.shape[0], input_length=max_len, output_dim=word2Vec_embedding_matrix.shape[1],weights=[word2Vec_embedding_matrix], trainable=False),
        SpatialDropout1D(0.5),
        # ... 100 filters with a kernel size of 4 so that each convolution will consider a window of 4 word embeddings
        Conv1D(filters=100, kernel_size=4, padding='same', activation='relu'),
        #**batch normalization layer** normalizes the activations of the previous layer at each batch, 
        #i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1. 
        #It will be added after the activation function between a convolutional and a max-pooling layer.
        BatchNormalization(),
        GlobalMaxPool1D(),
        Dropout(0.2),
        Dense(50, activation = 'relu'),
        Dense(5, activation = 'softmax')
    ])
    
    #Customized the evaluation to analyse the model in terms of accuracy and mean value accuracy
    def mean_pred(y_true, y_pred):
        return K.mean(y_pred)
    CNN_Word2Vec_model.compile(loss="sparse_categorical_crossentropy", optimizer=Adam(0.01), metrics=['accuracy'])
    #CNN_Word2Vec_model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(0.01), metrics=['accuracy', mean_pred, fmeasure, precision, auroc, recall])
    
    CNN_Word2Vec_model.summary()

When I feed data to CNN model with word embedding (word2vec) in the below code then I got the error, so please how can I resolve it.

CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val), callbacks=[early]) 

the full error is below:

InvalidArgumentError          Traceback (most recent call last)
<ipython-input-48-7214f2a862e6> in <module>()
----> 1 CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val), callbacks=[early])
      2 #CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val))

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     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:

InvalidArgumentError: Graph execution error:

Detected at node 'sequential/embedding/embedding_lookup' defined at (most recent call last):
    File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
      "__main__", mod_spec)
    File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
      exec(code, run_globals)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
      app.launch_new_instance()
    File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
      app.start()
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
      self.io_loop.start()
    File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 431, in _run_callback
      callback(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
      return self.dispatch_shell(stream, msg)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
      handler(stream, idents, msg)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
      user_expressions, allow_stdin)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
      res = shell.run_cell(code, store_history=store_history, silent=silent)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
      interactivity=interactivity, compiler=compiler, result=result)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
      if self.run_code(code, result):
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "<ipython-input-47-169eb83cb089>", line 2, in <module>
      CNN_Word2Vec_model_fit = CNN_Word2Vec_model.fit(X_tra, y_tra, batch_size=batch_size2, epochs=num_epochs, validation_data=(X_val, y_val))
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
      tmp_logs = self.train_function(iterator)
Node: 'sequential/embedding/embedding_lookup'
indices[233,187] = 10690 is not in [0, 8262)
     [[{{node sequential/embedding/embedding_lookup}}]] [Op:__inference_train_function_5282]

Answer 1

When I ran into this it was because the shape of my data and the model input were incompatible/ I think it can also be caused by the the training data shape and model output being incompatible. So my recommendation would be to check the shape of your inputs/outputs and make sure they are what you want.