Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras
The illusion of multiple cells appears because a cell exists per timestep but in fact, it
is just the same cell reused repeatedly unless the network is unrolled. The underlying
neural networks of RNNs are shared across cells.
The summary in Listing 1.5.2 indicates that using a SimpleRNN requires a fewer
number of parameters. Figure 1.5.3 shows the graphical description of the RNN
MNIST digit classifier. The model is very concise. Table 1.5.1 shows that the
SimpleRNN has the lowest accuracy among the networks presented.
Listing 1.5.2, RNN MNIST digit classifier summary:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
simple_rnn_1 (SimpleRNN) (None, 256) 72960
_________________________________________________________________
dense_1 (Dense) (None, 10) 2570
_________________________________________________________________
activation_1 (Activation) (None, 10) 0
=================================================================
Total params: 75,530
Trainable params: 75,530
Non-trainable params: 0
Figure 1.5.3: The RNN MNIST digit classifier graphical description
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