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怖くない誤差逆伝播法 Chainerを添えて
- 3. Komachi Lab Neural Net 1. forward 2. Loss Loss 3. 4. (AdaGrad ) 5. 1 3 SGD
- 4. Komachi Lab Neural Net 2. Loss Loss 3. 4 W Loss Loss
- 5. Komachi Lab Neural Net 1. forward 2. Loss Loss 3. 4. (AdaGrad ) 5. 1 5 SGD W1 W2 predict input
- 6. Komachi Lab Neural Net 1. forward 2. Loss Loss 3. 4. (AdaGrad ) 5. 1 6 SGD Loss func correct W1 W2 predict input
- 7. Komachi Lab Neural Net 1. forward 2. Loss Loss 3. 4. (AdaGrad ) 5. 1 7 SGD gW1 gW2 Loss func correct W1 W2 predict input
- 8. Komachi Lab Neural Net 1. forward 2. Loss Loss 3. 4. (AdaGrad ) 5. 1 8 SGD gW1 gW2 Loss func correct W1 W2 predict input
- 10. Komachi Lab 1. forward 2. Loss Loss 3. 4. (AdaGrad ) 5. 1 10 gW1 gW2 Loss func correct W1 W2 predict input
- 11. Komachi Lab ✤ 11
- 12. Komachi Lab 12
- 13. Komachi Lab ✤ W1 W2 ✤ x, h, y, t ✤ Loss 13 (i train set index) correct W1 W2 predict input :x hidden:h :y :t
- 14. Komachi Lab ✤ 14 W1, W2 correct W1 W2 predict input :x hidden:h :y :t
- 15. Komachi Lab ✤ i 15 forward correct W1 W2 predict input :x hidden:h :y :t
- 18. Komachi Lab 18 (Chainer )
- 19. Komachi Lab 19
- 21. Komachi Lab Chainer ✤ PFN DNN http://chainer.org/ ✤ Define-by-Run Define-and-Run ✤ TensorFlow, Theano, Torch, Keras, Caffe, etc... 21
- 24. Komachi Lab 24 loss.backward() loss backward
- 25. Komachi Lab MeanSquaredError 25 x0, x1 y, t backward gy 1 backward forward inputs gy backward gy
- 26. Komachi Lab Linear 26 grad_outputs Mean Squared Error backward gW backward
- 27. Komachi Lab Linear 27 grad_outputs Linear backward
- 28. Komachi Lab on Chainer ✤ ✤ ✤ ✤ W 28 tanh forward backward chainer.functions.activation.tanh forward Chain backward Chain Chainer
- 30. Komachi Lab LSTM 30 forward a, i, f, o Linear backward gy