wehub-resource-sync
43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
import time
|
|
import MNN.numpy as np
|
|
import MNN
|
|
nn = MNN.nn
|
|
F = MNN.expr
|
|
|
|
# open lazy evaluation for train
|
|
F.lazy_eval(True)
|
|
|
|
# month_pay=pow(rate/12+1, times)*(rate/12)*total/(pow(rate/12+1,times)-1)
|
|
# Know month_pa, total, times, solve rate
|
|
class Net(nn.Module):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
one = np.array([0.001])
|
|
one.fix_as_trainable()
|
|
self.rate = one
|
|
|
|
def forward(self, times, total):
|
|
r12 = self.rate / 12.0
|
|
r12_1 = r12 + np.array([1.0])
|
|
total_rate = np.power(r12_1, times)
|
|
p0 = (total_rate * r12 * total) / (total_rate-np.array([1.0]))
|
|
return p0
|
|
|
|
model = Net()
|
|
opt = MNN.optim.SGD(model, 0.0000000001, 0.9, 0.0005)
|
|
|
|
for iter in range(0, 1000):
|
|
times = np.array([60.0])
|
|
month_pay = np.array([12439.12])
|
|
total = np.array([630000.0])
|
|
month_comp = model.forward(times, total)
|
|
diff = month_pay - month_comp
|
|
loss = diff * diff
|
|
opt.step(loss)
|
|
|
|
times = np.array([60.0])
|
|
month_pay = np.array([12439.12])
|
|
total = np.array([630000.0])
|
|
month_comp = model.forward(times, total)
|
|
print("rate:", model.rate, " ; month_comp: ", month_comp)
|