def main(m=5, r=2, window_size=20, batch_size=2):
gen = SimpleGenerator(num=m)
bed = TestBed(m=m, r=r, window_size=window_size, batch_size=batch_size)
vis = Visualizer()
for i in xrange(10):
bed.supply(gen.next())
for i,y in enumerate(gen):
if i % window_size == 0:
# pretrain
avg_cost = bed.pretrain(10, pretraining_lr=0.1)
print(" pretrain cost: {}".format(avg_cost))
# predict
y_pred = bed.predict()
print("{}: y={}, y_pred={}".format(i, y, y_pred))
vis.append(y, y_pred)
# finetune
bed.supply(y)
avg_cost = bed.finetune(10, finetunning_lr=0.1)
# bed.finetune(100, finetunning_lr=0.01)
# bed.finetune(100, finetunning_lr=0.001)
print(" train cost: {}".format(avg_cost))
time.sleep(.1)
def main(m=2, r=2, window_size=20, batch_size=2):
gen = SimpleGenerator(num=m)
bed = TestBed(m=m, r=r, window_size=window_size, batch_size=batch_size)
vis = Visualizer()
for i in xrange(10):
bed.supply(gen.next())
for i, y in enumerate(gen):
if i % window_size == 0:
# pretrain
avg_cost = bed.pretrain(10, pretraining_lr=0.1)
print(" pretrain cost: {}".format(avg_cost))
# predict
y_pred = bed.predict()
print("{}: y={}, y_pred={}".format(i, y, y_pred))
vis.append(y, y_pred)
# finetune
bed.supply(y)
avg_cost = bed.finetune(10, finetunning_lr=0.1)
# bed.finetune(100, finetunning_lr=0.01)
# bed.finetune(100, finetunning_lr=0.001)
print(" train cost: {}".format(avg_cost))
time.sleep(.1)