Training an RNN model

To explain how we optimize the weights (parameters) of an RNN, we first annotate the weights and the data on the network, as follows:

• U denotes the weights connecting the input layer and the hidden layer.
• V denotes the weights between the hidden layer and the output layer. Note here that we use only one recurrent layer for simplicity.
• W denotes the weights of the recurrent layer; that is, the feedback layer.
• x_t denotes the inputs at time step t.
• s_t denotes the hidden state at time step t.
• h_t denotes the outputs at time step t.

Next, we unfold the simple RNN model over three time steps: t − 1, t, and t + 1, as follows:

Figure 13.9: Unfolding a recurrent layer

We describe the mathematical relationships between the layers as follows:

• We let a denote the activation function for the hidden layer. In RNNs, we usually choose tanh or ReLU as the activation function for the hidden layers...