understanding recurrent neural networks you can even flesh out the function fthe transformation of the input and state into an output will be parameterized by two matricesw and uand bias vector it' similar to the transformation operated by densely connected layer in feedforward network listing more det ailed pseudocode for he rnn state_t for input_t in input_sequenceoutput_t activation(dot(winput_tdot(ustate_tbstate_t output_t to make these notions absolutely unambiguouslet' write naive numpy implementation of the forward pass of the simple rnn listing numpy implement at ion of simple rnn number of timesteps in the input sequence dimensionality of the input feature space import numpy as np timesteps input_features output_features input datarandom noise for the sake of the example dimensionality of the output feature space initial statean all-zero vector inputs np random random((timestepsinput_features)state_t np zeros((output_features,) np random random((output_featuresinput_features) np random random((output_featuresoutput_features) np random random((output_features,)creates random weight matrices input_t is vector of successive_outputs [shape (input_features,for input_t in inputsoutput_t np tanh(np dot(winput_tnp dot(ustate_tbsuccessive_outputs append(output_tstate_t output_t final_output_sequence np concatenate(successive_outputsaxis= the final output is tensor of shape (timestepsoutput_featuresstores this output in list combines the input with the current state (the previous outputto obtain the current output updates the state of the network for the next timestep easy enoughin summaryan rnn is for loop that reuses quantities computed during the previous iteration of the loopnothing more of coursethere are many different rnns fitting this definition that you could build--this example is one of the simplest rnn formulations rnns are characterized by their step functionsuch as the following function in this case (see figure )output_t np tanh(np dot(winput_tnp dot(ustate_tblicensed to