def train():
for i in range(500):
print('now iter {} load pickled dataset...'.format(i))
Xs = []
ys = []
names = [name for idx, name in enumerate( glob.glob('../dataset/*.pkl') )]
random.shuffle( names )
for idx, name in enumerate(names):
try:
X,y = pickle.loads(open(name,'rb').read() )
except EOFError as e:
continue
if idx%100 == 0:
print('now scan iter', idx)
if idx >= 15000:
break
Xs.append( X )
ys.append( y )
Xs = np.array( Xs )
ys = np.array( ys )
model.fit(Xs, ys, epochs=1 )
print('now iter {} '.format(i))
model.save_weights('models/{:09d}.h5'.format(i))
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