Recurrent Neural Network(RNN Part-2)

 

Different RNN Models

Many to one

We take only the LAST output of RNN 
  •  Sentence Classification (Toxic Comment Classification) 
  •  Image Classification 
  •  Anomaly Detection 

Many to Many

  • We take all the output of RNN 
  • POS tagging of sentence 
  • Video Frame Classification 
  • Next Word Prediction

RNN Example 

  • Task - C Class POS Tagging
  • Input - 1 x T x F ○ T = 4 : Timesteps ○ F = Number of features (?) 
  • Target - 1 x T x C ○ C = Number of classes (?)

RNN Limitations

Vanishing / Exploding Gradient Problem 
  • In forward pass output of one Timestep (t) is fed to the next Timestep (t+1) 
  • At any Timestep (t) is the trainable weights are same 
  • At any Timestep (t) is the input is different 
  • In backward pass (Backpropagation through time) we move from (t+1) to t 
  • Due to chain rule of gradients are multiplied  
  • What if gradient at one Timestep (t) is very small - Vanishing Gradient 
  • What if gradient at one Timestep (t) is very large - Exploding Gradient

Long Short Term Memory LSTM 

Input gate: contribution of input 
Output gate: contribution to output 
Reset gate: what to forget 
Proposal gate: what to remember ? Proposes new state

Equation Require 2 Weight Metrics for W and U for EACH of the functions 


RNN/LSTM Implementation Guide 

RNN Equation Input Data Shape : 1 x T x F (T= Timesteps F = Number of Features) 
  • For a single Hidden Layer RNN with D Nodes (POS Tagging problem) 
  • X = Array of shape [1 , T, F] 
  • Y = Array of shape [1 , T, C] 
  • W1 = Array of shape [F , D], U1 = Array of shape [D , D] 
  • W2 = Array of shape [D , C], 
  • For t in 1...T ○ Y(t) = MATMUL(X(t),W1) + MATMUL(Y(t-1),U1) 
  • Y2 = MATMUL(Y,W2) # Y = Array of shape [1 , T , D] Target Data Shape : 1 x T x C (C = Number of Classes) usually one hot encoded

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