Is back propagation possible in RNN?

Backpropagation Through Time, or BPTT, is the application of the Backpropagation training algorithm to recurrent neural network applied to sequence data like a time series. A recurrent neural network is shown one input each timestep and predicts one output.

Why Tanh is used in RNN?

A tanh function ensures that the values stay between -1 and 1, thus regulating the output of the neural network. You can see how the same values from above remain between the boundaries allowed by the tanh function. So that’s an RNN.

What is Timestep in neural network?

A time step is a single occurrence of the cell – e.g. on the first time step you produce output1, h0, on the second time step you produce output2 and so on.

How do you find number of parameters in RNN?

Let number of neurons in the layer be n and number of dimension of x be m (not including number of example and time-steps). Therefore, dimension of forget gate will be n too. Therefore, total number of parameters for one equation will be [{n*(n+m)} + n].

Can we use ReLU and tanh frequently in RNN?

You can use the ReLU function in place of the tanh function. Before changing the choice for activation functions, you must know what are the advantages and disadvantages of your choice over others.

Why ReLU is not used in RNN?

RELU can only solve part of the gradient vanishing problem of RNN because the gradient vanishing problem is not only caused by activation function. see above function, the hidden state derivative will depend on both activation and Ws, if Ws’s max eigen value < 1, the long term dependency’s gradient will be vanished.

What are RNN parameters?

Recurrent Neural Networks (RNN) are for handling sequential data. RNNs share parameters across different positions/ index of time/ time steps of the sequence, which makes it possible to generalize well to examples of different sequence length. Usually, there is also a hidden state vector h(t) for each time step t.