Tensorflow Keras 嵌入层错误：层权重形状不兼容

Question

Can anyone recommend the best path for me to fix this type of error? I can't figure out what I've done wrong with my dimensions. I have a pretrained embedding that originates in a Word2Vec gensim model, which I want to use to initialize the CNN with. Sorry for the relatively simple question, but very new to both Keras and Tensorflow

#CNN architecture

num_classes = num_labels

#Training params
batch_size = 8 
num_epochs = 25

#Model parameters
num_filters = 64  
weight_decay = 1e-4
kernel_size = 7 #this is the size of the window during convolution...making match the window size in Word2Vec...unsure if needed

print("training CNN ...")

model = Sequential()

#------------------------
FIXED_LENGTH=embedding_matrix.shape[1]
#------------------------

print('Vocab size:', vocab_size)
print('Output_Dim size:', w2v.vector_size)
print('Weights:', pd.Series([embedding_matrix]).shape)
print('Weights underlying shape:', embedding_matrix.shape)
print("Input Length:", FIXED_LENGTH)

#Model add word2vec embedding

model.add(Embedding(vocab_size+1, 
                      output_dim=w2v.vector_size, 
                      weights=[embedding_matrix], 
                      input_length=FIXED_LENGTH, 
                      trainable=False))
model.add(Conv1D(num_filters, kernel_size=kernel_size, activation='relu', padding='same'))
model.add(MaxPooling1D(2))
model.add(Conv1D(num_filters, 7, activation='relu', padding='same'))
model.add(GlobalMaxPooling1D())
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Dense(num_classes, activation='softmax'))  #multi-label (k-hot encoding)

adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='sparse_categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
model.summary()

#define callbacks
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.01, patience=4, verbose=1)
callbacks_list = [early_stopping]

print('Batch size:', batch_size)
print('Num of Epochs:', num_epochs)
print('X Train Size:', x_train_pad.shape)
print('Y Train Size:', y_train.shape)

hist = model.fit(x_train_pad, 
                 y_train, 
                 batch_size=batch_size, 
                 epochs=num_epochs, 
                 callbacks=callbacks_list, 
                 validation_split=0.1, 
                 shuffle=True, 
                 verbose=2)

Output is:

training CNN ...
Vocab size: 32186
Output_Dim size: 100
Weights: (1,)
Weights underlying shape: (32186, 100)
Input Length: 100
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-326-36db7b551866> in <module>()
     31                       weights=[embedding_matrix],
     32                       input_length=FIXED_LENGTH,
---> 33                       trainable=False))
     34 model.add(Conv1D(num_filters, kernel_size=kernel_size, activation='relu', padding='same'))
     35 model.add(MaxPooling1D(2))

c:\users\tt\anaconda3b\lib\site-packages\tensorflow_core\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

c:\users\tt\anaconda3b\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in add(self, layer)
    176           # and create the node connecting the current layer
    177           # to the input layer we just created.
--> 178           layer(x)
    179           set_inputs = True
    180 

c:\users\tt\anaconda3b\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
    815           # Build layer if applicable (if the `build` method has been
    816           # overridden).
--> 817           self._maybe_build(inputs)
    818           cast_inputs = self._maybe_cast_inputs(inputs)
    819 

c:\users\tt\anaconda3b\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in _maybe_build(self, inputs)
   2146     # Optionally load weight values specified at layer instantiation.
   2147     if getattr(self, '_initial_weights', None) is not None:
-> 2148       self.set_weights(self._initial_weights)
   2149       self._initial_weights = None
   2150 

c:\users\tt\anaconda3b\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in set_weights(self, weights)
   1334         raise ValueError('Layer weight shape ' + str(ref_shape) +
   1335                          ' not compatible with '
-> 1336                          'provided weight shape ' + str(w.shape))
   1337       weight_value_tuples.append((p, w))
   1338     backend.batch_set_value(weight_value_tuples)

ValueError: Layer weight shape (32187, 100) not compatible with provided weight shape (32186, 100)

Answer 1

The answer was the encoded sentences contains values higher than were encoded in the lexicon build stage. There should be an index in your lexicon for every value of your training and test set. If not, you have to clean the sentences before sending them to the CNN.

Answer 2

Can you change vocab_size+1 argument in the Embedded layer to vocab_size . I think its the +1 that is causing problem