import numpy as np
from neural_net import NeuralNet
nn = NeuralNet(learning_rate=1,
layer_dims=[2, 3, 1],
actn_fn=['sigmoid', 'sigmoid'],
initializer='random')
# print(nn)
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]).T
Y = np.array([[0], [1], [1], [0]]).T
nn.layers[0].w = np.array([[0.1, 0.6], [0.2, 0.4], [0.3, 0.7]])
nn.layers[0].b = np.array([[0], [0], [0]])
nn.layers[1].w = np.array([[0.1, 0.4, 0.9]])
nn.layers[1].b = np.array([[0]])
for i in range(5000):
forward_val = nn(X)
# print ("Forward :", forward_val)
print("Error: ", nn.error(Y, nn.layers[-1].activation_fn.prev))
nn.backward(X, Y, forward_val)
y_pred = nn.classify(nn(X).reshape(-1))
print(y_pred)
print(nn.score(Y.reshape(-1), y_pred))
