ValueError in CNN+RNN model in keras
It might not be directly possible to shoehorn the output of your CNN directly into an LSTM (at least without being a lot more thorough with your dimensions).



Another approach is to have a look at the Keras wrapper layer: TimeDistributed. You essentially extract features using your Conv layers as usual, but do that over time-steps, not just a random mini-batch. The features the come out then are e.g. over 5 consecutive timeframes -> this would be one single sample for the LSTM. If you perform a minibatch of say 10 samples, this means 10 * 5 = 50 input frames.

Straight from the linked documentation:


  This wrapper applies a layer to every temporal slice of an input.
  
  The input should be at least 3D, and the dimension of index one will
  be considered to be the temporal dimension.
  
  Consider a batch of 32 samples, where each sample is a sequence of 10
  vectors of 16 dimensions. The batch input shape of the layer is then
  (32, 10, 16), and the input_shape, not including the samples
  dimension, is  (10, 16).
  
  You can then use TimeDistributed to apply a Dense layer to each of the
  10 timesteps, independently:


# as the first layer in a model
model = Sequential()
model.add(TimeDistributed(Dense(8), input_shape=(10, 16)))
# now model.output_shape == (None, 10, 8)




As a hint for dimensions, make use of model.summary() on a compiled model in order to inspect the dimensions of each layer through the model. You will likely have to get a pen and paper out to work through the dimensions, as things start to become a little complicated when using such a wrapper layer.

Unfortunately I don't have any links/tutorials to show how this is exactly done in Keras, but hopefully the description above gives you enough info for some useful Bing! searches. Just kiddin... Google searches  ;-)