from neuralnet import NeuralNet import numpy as np nn = NeuralNet([2, 4, 1]) x = np.array([[1, 2]]).T print(x.shape) o = nn.forward(x)
from neuralnet import NeuralNet
import numpy as np
def loss(a, y):
return np.mean(np.square(a - y)) / 2.0
topology = [3, 5, 4, 1]
nn = NeuralNet(topology)
train_set = np.array([[1, 2, 3], [-1, -2, -2.5], [-3, 2, 4], [5, 2, -1]]).T
labels = np.array([[1, 0, 0, 1]]).T
print(train_set.shape)
print(labels.shape)
a = nn.forward(train_set[:, 0])
delta_out = a - labels[0, 0]
backprop_grad = nn.backprop(delta_out)
print(backprop_grad)
num_grad = []
for i in range(len(topology) - 1):
t = np.zeros((topology[i] + 1, topology[i + 1]), dtype=float)
num_grad.append(t)
for i in range(len(topology) - 1):
for j in range(topology[i] + 1):
for k in range(topology[i + 1]):
a1 = nn.forward(train_set[:, 0])
l1 = loss(a1, labels[0])
nn.theta[i][j, k] += 0.001
a2 = nn.forward(train_set[:, 0])
