使用keras时获取AssertionError

Question

运行此代码时，出现以下错误，不知道为什么？

AssertionError       Traceback (most recent call last)

我一次又一次尝试，这是我的代码：

def ConvNet(embeddings, max_sequence_length, num_words, embedding_dim, labels_index, trainable=False, extra_conv=True):

    embedding_layer = Embedding(num_words,
                            embedding_dim,
                            weights=[embeddings],
                            input_length=max_sequence_length,
                            trainable=trainable)

    sequence_input = Input(shape=(max_sequence_length,), dtype='int32')
    embedded_sequences = embedding_layer(sequence_input)

    # Yoon Kim model (https://arxiv.org/abs/1408.5882)
    convs = []
    filter_sizes = [3,4,5]

    for filter_size in filter_sizes:
        l_conv = Conv1D(filters=128, kernel_size=filter_size, activation='relu')(embedded_sequences)
        l_pool = MaxPooling1D(pool_size=3)(l_conv)
        convs.append(l_pool)

    l_merge = Concatenate(axis=1)(convs)

    # add a 1D convnet with global maxpooling, instead of Yoon Kim model
    conv = Conv1D(filters=128, kernel_size=3, activation='relu')(embedded_sequences)
    pool = MaxPooling1D(pool_size=3)(conv)
    x = (l_merge)
    if extra_conv==True:
        x = Dropout(0.5)(l_merge)  
    else:
        # Original Yoon Kim model
        x = Dropout(0.5)(pool)
    x = Flatten()(x)
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.5)(x)
    # Finally, we feed the output into a Sigmoid layer.
    # The reason why sigmoid is used is because we are trying to achieve a binary classification(1,0) 
    # for each of the 6 labels, and the sigmoid function will squash the output between the bounds of 0 and 1.
    preds = Dense(labels_index, activation='sigmoid')(x)

    model = Model(sequence_input, preds)
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    model.summary()
    return model


model = ConvNet(train_embedding_weights, MAX_SEQUENCE_LENGTH, len(train_word_index)+1, EMBEDDING_DIM, 
                len(list(label_names)), False)

我得到AssertionError。