-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathtrain.py
executable file
·173 lines (134 loc) · 5.16 KB
/
train.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
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
#!/usr/bin/env python
import numpy
import theano
import pickle
import itertools
import scipy.sparse
import h5py
import math
from keras.models import Sequential
try :
# old imports (v0.3.1)
from keras.layers import Dense, Dropout, Activation
from keras.layers import Convolution2D, Reshape, MaxPooling2D, Flatten
except ImportError :
# new keras imports (v0.3.3)
from keras.layers.core import Dense, Dropout, Activation, Reshape, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.callbacks import EarlyStopping, Callback
from keras.optimizers import SGD
from numpy import array
import os, sys, time, random
BATCH_SIZE = 2000
rng = numpy.random
DATA_FOLDER = 'data'
def floatX(x):
return numpy.asarray(x, dtype=theano.config.floatX)
def load_data(dir = DATA_FOLDER):
for fn in os.listdir(dir):
if not fn.endswith('.hdf5'):
continue
fn = os.path.join(dir, fn)
try:
yield h5py.File(fn, 'r')
except:
print 'could not read', fn
def get_data(series=['x', 'm']):
data = [[] for s in series]
for f in load_data():
try:
for i, s in enumerate(series):
data[i].append(f[s].value)
except:
raise
print 'failed reading from', f
def stack(vectors):
if len(vectors[0].shape) > 1:
return numpy.vstack(vectors)
else:
return numpy.hstack(vectors)
data = [stack(d) for d in data]
# #test_size = 10000.0 / len(data[0]) # does not work for small data sets (<10k entries)
# test_size = 0.05 # let's make it fixed 5% instead
# print 'Splitting', len(data[0]), 'entries into train/test set'
# data = train_test_split(*data, test_size=test_size)
#
# print data[0].shape[0], 'train set', data[1].shape[0], 'test set'
return data
def show_board( board ) :
for row in xrange(8):
print ' '.join('%2d' % x for x in board[(row*8):((row+1)*8)])
print
def make_model(data = None) :
global MODEL_DATA
MODEL_SIZE = [1024, 1024, 1024, 1024, 1024, 1024, 1024]
# MODEL_SIZE = [8192, 8192, 4096, 2048, 2048, 1024, 1024]
# MODEL_SIZE = [4096, 4096, 2048, 2048, 1024, 512, 256]
# MODEL_SIZE = [512, 512, 512, 512, 512, 512, 512]
# MODEL_SIZE = [256, 256, 256, 256, 256, 256, 256]
MODEL_SIZE = [4096, 2048, 1024, 1024] # 45M @ AWS
MODEL_SIZE = [4096, 4096, 2048, 1024] # 45M (1999-2001)
MODEL_SIZE = [3072, 2048, 2048, 1024] # 19M @ work (1999-2000)
MODEL_SIZE = [2048, 1024, 1024, 1024] # 5M, 1.011 @ E250
MODEL_SIZE = [2048, 2048, 1024, 1024] # 5M, 0.7122 @ E350
MODEL_SIZE = [2048, 2048, 2048, 1024] # 5M, 0.7673 @ E100, 0.6638 @ E150
MODEL_SIZE = [3072, 2048, 2048, 1024] # 5M, 0.6818 @ E100, 0.6153 @ E125
# MODEL_SIZE = [8192, 4096, 2048, 1024] # 19M @ work (1999-2000)
MODEL_SIZE = [2048, 2048, 1024, 1024] # 287k 10moves, 0.8181 @ E100, 0.8054 @ E200
MODEL_SIZE = [3072, 2048, 2048, 1024] # 287k 10moves, 0.8174 @ E100
MODEL_SIZE = [1024, 1024, 1024, 1024] # 287k 10moves
CONVOLUTION = min( 64, MODEL_SIZE[0] / 64 ) # 64 for 4096 first layer, 32 for 2048 layer
if data :
MODEL_DATA = data
else :
MODEL_DATA = 'new_%s.model' % ('_'.join(['%d' % i for i in MODEL_SIZE]))
MODEL_DATA = 'conv%d_%s.model' % (CONVOLUTION, '_'.join(['%d' % i for i in MODEL_SIZE]))
model = Sequential()
# model.add(Reshape( dims = (1, 8, 8), input_shape = (64,)))
model.add(Reshape( (1, 8, 8), input_shape = (64,)))
model.add(Convolution2D( CONVOLUTION, 3, 3, border_mode='valid'))
model.add(Activation('relu'))
# model.add(Convolution2D(8, 3, 3))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
for i in MODEL_SIZE :
model.add(Dense( i, init='uniform', activation='relu'))
model.add(Dense( 4, init='uniform', activation='relu'))
# model.add(Dense(MODEL_SIZE[0], input_dim = 64, init='uniform', activation='relu' ))
## model.add(Dropout(0.2))
# for i in MODEL_SIZE[1:] :
# model.add(Dense( i, init='uniform', activation='relu'))
## model.add(Dropout(0.2))
# model.add(Dense(4, init='uniform', activation='relu'))
if os.path.isfile( MODEL_DATA ) : # saved model exists, load it
model.load_weights( MODEL_DATA )
return model
def train():
X, m = get_data(['x', 'm'])
# X_train, X_test, m_train, m_test = get_data(['x', 'm'])
# for board in X_train[:2] :
# show_board( board )
model = make_model()
print 'compiling...'
# sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
# model.compile(loss='squared_hinge', optimizer='adadelta')
model.compile(loss='mean_squared_error', optimizer='adadelta')
early_stopping = EarlyStopping( monitor = 'loss', patience = 50 ) # monitor='val_loss', verbose=0, mode='auto'
#print 'fitting...'
history = model.fit( X, m, nb_epoch = 100, batch_size = BATCH_SIZE) #, callbacks = [early_stopping]) #, validation_split=0.05) #, verbose=2) #, show_accuracy = True )
# print 'evaluating...'
# score = model.evaluate(X_test, m_test, batch_size = BATCH_SIZE )
# print 'score:', score
model.save_weights( MODEL_DATA, overwrite = True )
#print X_train[:10]
# print m_train[:20]
# print model.predict( X_train[:20], batch_size = 5 )
print m[:20]
print model.predict( X[:20], batch_size = 5 )
# print m_test[:20]
# print model.predict( X_test[:20], batch_size = 5 )
# with open( MODEL_DATA + '.history', 'w') as fout :
# print >>fout, history.losses
if __name__ == '__main__':
train()