Mercurial > hg > tvii
annotate docs/matrix.txt @ 56:5867e4a10fae
[doc] link to deeplearn.js
author | Jeff Hammel <k0scist@gmail.com> |
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date | Fri, 29 Sep 2017 10:00:31 -0700 |
parents | 857a606783e1 |
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[documentation] add notes for matrices + vectorization
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37 | 2 X = [x1 x2 ...xm] = A0 |
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3 [| | | ] |
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4 |
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5 Z1 = w'X + b1 |
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6 |
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7 A1 = sigmoid(Z1) |
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8 |
37 | 9 Z2 = W2 A1 + b2 |
10 | |
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11 [---] |
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12 W1 = [---] |
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13 [---] |
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14 |
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15 `W1x1` gives some column vector, where `x1` |
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16 is the first training example. |
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17 |
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18 Y = [ y1 y2 ... ym] |
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19 |
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20 For a two-layer network: |
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21 |
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22 dZ2 = A2 - Y |
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23 |
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24 dW = (1/m) dZ2 A1' |
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25 |
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26 db2 = (1./m)*np.sum(dZ2, axis=1, keepdims=True) |
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27 |
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28 dZ1 = W2' dZ2 * g1 ( Z1 ) |
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29 : W2' dZ2 : an (n1, m) matrix |
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30 : * : element-wise product |
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31 |
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32 dW1 = (1/m) dZ1 X' |
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33 |
857a606783e1
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parents:
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34 db1 = (1/m) np.sum(dZ1, axis=1, keepdims=True) |