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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)]) |
View MCTS.py
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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: |
View Loading MNIST.ipynb
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license: gpl-3.0 |
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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 |
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View 2 - Using pre-trained VGG16.ipynb
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View 1 - Basic Convolutional Neural Network.ipynb
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View gist:5402e0a59117b70227ecdfa3bebeb4c9
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__________________________________________________________________________________________________ | |
Layer (type) Output Shape Param # Connected to | |
================================================================================================== | |
input_1 (InputLayer) (None, None, None, 3 0 | |
__________________________________________________________________________________________________ | |
conv2d_1 (Conv2D) (None, None, None, 3 864 input_1[0][0] | |
__________________________________________________________________________________________________ | |
batch_normalization_1 (BatchNor (None, None, None, 3 96 conv2d_1[0][0] | |
__________________________________________________________________________________________________ | |
activation_1 (Activation) (None, None, None, 3 0 batch_normalization_1[0][0] |
View screenshot.py
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from selenium import webdriver | |
from BeautifulSoup import BeautifulSoup | |
import requests | |
options = webdriver.ChromeOptions() | |
options.add_argument('headless') | |
driver = webdriver.Chrome(chrome_options=options) | |
driver.set_window_size(1280, 1600) | |
filename = 'NOTICE.html' |
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