input_dim for Dense Layer after LSTM layers Keras
You do not need to specify the input_dim for the later layers, the model can infer the shape of those input layers from the output shape of the previous layer.

In addition, the input_dim values currently specified don't match the output dimensions of the prior layers- for example, the output dimension of the LSTM layer with return_sequences = False will be (200,). The network should fail to compile with an expected input size error with this code.

You can fix this by correcting or removing the input_dim values from the final three layers.