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test_v1.1

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bbig
2023-05-23 17:03:23 +08:00
parent 663edbb5e2
commit 4968e6400e
17 changed files with 1 additions and 210 deletions
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networks/__init__.py
-5
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@@ -1,6 +1 @@
#from .fullyconnected import *
#from .large import *
#from .leaky import *
#from .medium import *
#from .small import *
from .alphago import *
networks/fullyconnected.py
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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[]