wehub-resource-sync
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
import time
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import MNN.numpy as np
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import MNN
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nn = MNN.nn
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F = MNN.expr
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# open lazy evaluation for train
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F.lazy_eval(True)
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# month_pay=pow(rate/12+1, times)*(rate/12)*total/(pow(rate/12+1,times)-1)
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# Know month_pa, total, times, solve rate
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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one = np.array([0.001])
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one.fix_as_trainable()
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self.rate = one
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def forward(self, times, total):
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r12 = self.rate / 12.0
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r12_1 = r12 + np.array([1.0])
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total_rate = np.power(r12_1, times)
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p0 = (total_rate * r12 * total) / (total_rate-np.array([1.0]))
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return p0
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model = Net()
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opt = MNN.optim.SGD(model, 0.0000000001, 0.9, 0.0005)
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times = np.array([60.0])
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month_diff = np.array([1.0])
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month_diff.fix_as_placeholder()
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month_diff.name = "month_diff"
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total = np.array([630000.0])
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month_comp = model.forward(times, total)
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rates, rates_grad = opt.grad([month_comp], [month_diff], [model.rate])
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lr_rate = np.array([0.0000001])
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lr_rate.fix_as_placeholder()
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lr_rate.name = "lr_rate"
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rates, rates_update = opt.get_update_graph(rates, rates_grad, [lr_rate])
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opt.save_graph("update.mnn", [], rates, rates_update)
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