deep learning - Difference Between keras.layer.Dense(32) and keras.layer.SimpleRNN(32)? -


what difference between keras.layer.dense() , keras.layer.simplernn()? understand neural network , rnn, api intuition not clear.? when see keras.layer.dense(32) understand layer 32 neurons. not clear if simplernn(32) means same. newbie on keras.

  1. how dense() , simplernn differ each other?
  2. is dense() , simplernn() function same @ point of time?
  3. if when , if not difference between simplernn() , dense()?
  4. would great if in visualizing it?

what's happening in https://github.com/fchollet/keras/blob/master/examples/addition_rnn.py

definitely different.

according keras dense dense implements operation: output = activation(dot(input, kernel) + bias), base architecture neural network.

but simplernn, keras simplernn fully-connected rnn output fed input.

the structure of neural network , recurrent neural network different.

to answer question:

  1. the difference between dense() , simplernn differences between traditional neural network , recurrent neural network.
  2. no, define structure each network, work in different way.
  3. then same 1
  4. check resources neural network , recurrent neural network, there lots of them on internet.

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