Carlos Aguayo carlos-aguayo
carlos-aguayo
/ 1 - Basic Convolutional Neural Network.ipynb
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February 23, 2018 15:23
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carlos-aguayo
/ 2 - Using pre-trained VGG16.ipynb
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February 23, 2018 15:23
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carlos-aguayo
/ 3 - Fine Tuning CNN.ipynb
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February 23, 2018 15:24
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carlos-aguayo
/ pg1497.txt
Created
November 19, 2018 23:30
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| The Project Gutenberg EBook of The Republic, by Plato | |
| This eBook is for the use of anyone anywhere at no cost and with | |
| almost no restrictions whatsoever. You may copy it, give it away or | |
| re-use it under the terms of the Project Gutenberg License included | |
| with this eBook or online at www.gutenberg.org | |
| Title: The Republic |
carlos-aguayo
/ .block
Created
February 23, 2019 17:25
— forked from mbostock/.block
Heatmap (2D Histogram, CSV)
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| license: gpl-3.0 |
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carlos-aguayo
/ MCTS.py
Last active
November 4, 2020 23:58
Run a Monte Carlo Tree Search (MCTS) Simulation
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| # https://github.com/suragnair/alpha-zero-general/blob/5156c7fd1d2f3e5fefe732a4b2e0ffc5b272f819/MCTS.py#L37-L48 | |
| for i in range(self.args.numMCTSSims): # self.args.numMCTSSims, the number of MCTS simulations to compute | |
| self.search(canonicalBoard) # "search" is a MCTS simulations | |
| s = self.game.stringRepresentation(canonicalBoard) | |
| # Count how many times we have visited each node | |
| counts = [self.Nsa[(s, a)] if (s, a) in self.Nsa else 0 for a in range(self.game.getActionSize())] | |
| if temp == 0: |
carlos-aguayo
/ MCTS.py
Last active
November 6, 2020 01:47
Part A - Select the node with the highest Upper Confidence Bound (UCB)
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| # https://github.com/suragnair/alpha-zero-general/blob/5156c7fd1d2f3e5fefe732a4b2e0ffc5b272f819/MCTS.py#L105-L121 | |
| cur_best = -float('inf') | |
| best_act = -1 | |
| # pick the action with the highest upper confidence bound | |
| for a in range(self.game.getActionSize()): | |
| if valids[a]: | |
| if (s, a) in self.Qsa: | |
| u = self.Qsa[(s, a)] + self.args.cpuct * self.Ps[s][a] * math.sqrt(self.Ns[s]) / ( | |
| 1 + self.Nsa[(s, a)]) |
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