bbig/tugo
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

test_v1.1

Browse Source
This commit is contained in:
bbig
2023-05-23 17:03:23 +08:00
parent 663edbb5e2
commit 4968e6400e
17 changed files with 1 additions and 210 deletions
Download Patch File Download Diff File Expand all files Collapse all files
encoders/__init__.py
-6
View File
@@ -1,6 +0,0 @@
from tugo.encoders.base import *
#from tugo.encoders.alphago import *
#from tugo.encoders.betago import *
from tugo.encoders.oneplane import *
from tugo.encoders.sevenplane import *
from tugo.encoders.simple import *
encoders/alphago.py
-5
View File
@@ -134,8 +134,3 @@ class AlphaGoEncoder(Encoder):
def shape(self):
return self.num_planes, self.board_height, self.board_width
def create(board_size):
return AlphaGoEncoder(board_size)
encoders/alphago_test.py
-26
View File
@@ -1,26 +0,0 @@
import unittest
from tugo.agent.helpers import is_point_an_eye
from tugo.goboard_fast import Board, GameState, Move
from tugo.gotypes import Player, Point
from tugo.encoders.alphago import AlphaGoEncoder
class AlphaGoEncoderTest(unittest.TestCase):
def test_encoder(self):
alphago = AlphaGoEncoder()
start = GameState.new_game(19)
next_state = start.apply_move(Move.play(Point(16, 16)))
alphago.encode(next_state)
self.assertEquals(alphago.name(), 'alphago')
self.assertEquals(alphago.board_height, 19)
self.assertEquals(alphago.board_width, 19)
self.assertEquals(alphago.num_planes, 49)
self.assertEquals(alphago.shape(), (49, 19, 19))
if __name__ == '__main__':
unittest.main()
encoders/base.py
-8
View File
@@ -1,12 +1,4 @@
# tag::importlib[]
import importlib
# end::importlib[]
__all__ = [
'Encoder',
'get_encoder_by_name',
]
# tag::base_encoder[]
class Encoder:
encoders/oneplane.py
+1 -2
View File
@@ -1,9 +1,8 @@
# tag::oneplane_imports[]
import numpy as np
from tugo.encoders.base import Encoder
from tugo.goboard import Point
# end::oneplane_imports[]
# tag::oneplane_encoder[]
go_sgf/__init__.py
View File
gosgf/sgf.py → go_sgf/sgf.py
View File
gosgf/sgf_grammar.py → go_sgf/sgf_grammar.py
View File
gosgf/sgf_properties.py → go_sgf/sgf_properties.py
View File
gosgf/__init__.py
-1
View File
@@ -1 +0,0 @@
from .sgf import *
networks/__init__.py
-5
View File
@@ -1,6 +1 @@
#from .fullyconnected import *
#from .large import *
#from .leaky import *
#from .medium import *
#from .small import *
from .alphago import *
networks/fullyconnected.py
-14
View File
@@ -1,14 +0,0 @@
from __future__ import absolute_import
from keras.layers.core import Dense, Activation, Flatten
def layers(input_shape):
return [
Dense(128, input_shape=input_shape),
Activation('relu'),
Dense(128, input_shape=input_shape),
Activation('relu'),
Flatten(),
Dense(128),
Activation('relu'),
]
networks/large.py
-39
View File
@@ -1,39 +0,0 @@
from __future__ import absolute_import
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.convolutional import Conv2D, ZeroPadding2D
def layers(input_shape):
return [
ZeroPadding2D((3, 3), input_shape=input_shape, data_format='channels_first'),
Conv2D(64, (7, 7), padding='valid', data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(64, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(64, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(48, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(48, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(32, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(32, (5, 5), data_format='channels_first'),
Activation('relu'),
Flatten(),
Dense(1024),
Activation('relu'),
]
networks/leaky.py
-40
View File
@@ -1,40 +0,0 @@
from __future__ import absolute_import
from keras.layers import LeakyReLU
from keras.layers.core import Dense, Flatten
from keras.layers.convolutional import Conv2D, ZeroPadding2D
def layers(input_shape):
return [
ZeroPadding2D((3, 3), input_shape=input_shape, data_format='channels_first'),
Conv2D(64, (7, 7), padding='valid', data_format='channels_first'),
LeakyReLU(),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(64, (5, 5), data_format='channels_first'),
LeakyReLU(),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(64, (5, 5), data_format='channels_first'),
LeakyReLU(),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(48, (5, 5), data_format='channels_first'),
LeakyReLU(),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(48, (5, 5), data_format='channels_first'),
LeakyReLU(),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(32, (5, 5), data_format='channels_first'),
LeakyReLU(),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(32, (5, 5), data_format='channels_first'),
LeakyReLU(),
Flatten(),
Dense(1024),
LeakyReLU(),
]
networks/medium.py
-31
View File
@@ -1,31 +0,0 @@
from __future__ import absolute_import
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.convolutional import Conv2D, ZeroPadding2D
def layers(input_shape):
return [
ZeroPadding2D((2, 2), input_shape=input_shape, data_format='channels_first'),
Conv2D(64, (5, 5), padding='valid', data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((2, 2), data_format='channels_first'),
Conv2D(64, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((1, 1), data_format='channels_first'),
Conv2D(64, (3, 3), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((1, 1), data_format='channels_first'),
Conv2D(64, (3, 3), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D((1, 1), data_format='channels_first'),
Conv2D(64, (3, 3), data_format='channels_first'),
Activation('relu'),
Flatten(),
Dense(512),
Activation('relu'),
]
networks/small.py
-33
View File
@@ -1,33 +0,0 @@
from __future__ import absolute_import
# tag::small_network[]
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.convolutional import Conv2D, ZeroPadding2D
def layers(input_shape):
return [
ZeroPadding2D(padding=3, input_shape=input_shape, data_format='channels_first'), # <1>
Conv2D(48, (7, 7), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D(padding=2, data_format='channels_first'), # <2>
Conv2D(32, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D(padding=2, data_format='channels_first'),
Conv2D(32, (5, 5), data_format='channels_first'),
Activation('relu'),
ZeroPadding2D(padding=2, data_format='channels_first'),
Conv2D(32, (5, 5), data_format='channels_first'),
Activation('relu'),
Flatten(),
Dense(512),
Activation('relu'),
]
# <1> We use zero padding layers to enlarge input images.
# <2> By using `channels_first` we specify that the input plane dimension for our features comes first.
# end::small_network[]
policies/__init__.py
View File