data_mining:neural_network:overfitting

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data_mining:neural_network:overfitting [2018/05/10 17:52] – [Weight penalites] phreazerdata_mining:neural_network:overfitting [2018/05/10 17:55] – [Inverted dropout] phreazer
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 Layer $l=3$. Layer $l=3$.
  
-$keep.prob = 0.8$+$keep.prob = 0.8$ // probability that unit will be kept
  
 $d3 = np.random.rand(a3.shape[0], a3.shape[i]) < keep.prob$ $d3 = np.random.rand(a3.shape[0], a3.shape[i]) < keep.prob$
  
-$a3 = np.multiply(a3,d3)$+$a3 = np.multiply(a3,d3)$ // activations in layer 3 $a3 *= d3$
  
 $a3 /= keep.prob$ // e.g. 50 units => 10 units shut off $a3 /= keep.prob$ // e.g. 50 units => 10 units shut off
  • data_mining/neural_network/overfitting.txt
  • Last modified: 2018/05/10 18:03
  • by phreazer