def Dtanh(y):
return 1-y*y
def linear(x):
return x
def Dlinear(y):
return 1
nn = NeuralNetwork(1, 1)
nn.add(5, [tanh, Dtanh])
nn.add(5, [tanh, Dtanh])
nn.init([linear, Dlinear])
n = 25000
cost = []
for i in range(n):
index = np.random.randint(0, N)
x = data[index][0]
y = data[index][1]
c = nn.cost(x, y)
cost.append(c)
nn.train(x, y)
if i % 100 == 0:
from nn import NeuralNetwork import numpy as np # lst = ['a', 'b', 'c'] # pool = cycle(lst) # for item in pool: # print(item) X = np.array([[0, 81, 75, 0]]) y = np.array([[1]]) nn = NeuralNetwork(X,y) nn.add(7) nn.fit(12,100,0.5,0.001)
