-
Notifications
You must be signed in to change notification settings - Fork 0
/
gen_train_test.py
65 lines (51 loc) · 2.75 KB
/
gen_train_test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
# tag::train_generator_imports[]
from dlgo.data.parallel_processor import GoDataProcessor
from dlgo.encoders.oneplane import OnePlaneEncoder
from dlgo.networks import small
from keras.models import Sequential
from keras.layers.core import Dense
from keras.callbacks import ModelCheckpoint # <1>
# <1> With model checkpoints we can store progress for time-consuming experiments
# end::train_generator_imports[]
# tag::train_generator_generator[]
def main():
go_board_rows, go_board_cols = 19, 19
num_classes = go_board_rows * go_board_cols
num_games = 5000
encoder = OnePlaneEncoder((go_board_rows, go_board_cols)) # <1>
processor = GoDataProcessor(encoder=encoder.name()) # <2>
generator = processor.load_go_data('train', num_games, use_generator=True) # <3>
test_generator = processor.load_go_data('test', num_games, use_generator=True)
# <1> First we create an encoder of board size.
# <2> Then we initialize a Go Data processor with it.
# <3> From the processor we create two data generators, for training and testing.
# end::train_generator_generator[]
# tag::train_generator_model[]
input_shape = (encoder.num_planes, go_board_rows, go_board_cols)
network_layers = small.layers(input_shape)
model = Sequential()
for layer in network_layers:
model.add(layer)
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
# end::train_generator_model[]
# tag::train_generator_fit[]
epochs = 5
batch_size = 128
model.fit_generator(generator=generator.generate(batch_size, num_classes), # <1>
epochs=epochs,
steps_per_epoch=generator.get_num_samples() / batch_size, # <2>
validation_data=test_generator.generate(batch_size, num_classes), # <3>
validation_steps=test_generator.get_num_samples() / batch_size, # <4>
callbacks=[ModelCheckpoint('checkpoints\small_model_epoch_{epoch}.h5')]) # <5>
model.evaluate_generator(generator=test_generator.generate(batch_size, num_classes),
steps=test_generator.get_num_samples() / batch_size) # <6>
# <1> We specify a training data generator for our batch size...
# <2> ... and how many training steps per epoch we execute.
# <3> An additional generator is used for validation...
# <4> ... which also needs a number of steps.
# <5> After each epoch we persist a checkpoint of the model.
# <6> For evaluation we also specify a generator and the number of steps.
# end::train_generator_fit[]
if __name__ == '__main__':
main()