Dimension problem in keras neural networks
Ok I was able to recreate the error. The TimeDistributed layer applies the dense layer to each of the time slices. In this model you will have 100 different time slices as defined by sum_txt_length and the RepeatVector layer. These time slices will be propagated through the network all the way to the TimeDistributed layer. Thus, your $y$ vector must have dimensions $(None, 100, 10000)$.

You can use model.summary() to get the expected dimensions after each layer.

You will need to fix the output layer to match the label dimensions. I believe you will need to add a Flatten() layer before the densely connected layer, and do not use the TimeDistributed layer at the output. That way your output will be of size $vocab_size$, thus predicting the next vocabulary word given an input.