3.6 KiB
3.6 KiB
import numpy as np
from copy import deepcopy
import matplotlib.pyplot as plt
x = np.array([[1,1]])
y = np.array([[0]])
from copy import deepcopy
import numpy as np
def feed_forward(inputs, outputs, weights):
pre_hidden = np.dot(inputs,weights[0])+ weights[1]
hidden = 1/(1+np.exp(-pre_hidden))
out = np.dot(hidden, weights[2]) + weights[3]
mean_squared_error = np.mean(np.square(out - outputs))
return mean_squared_error
def update_weights(inputs, outputs, weights, lr):
original_weights = deepcopy(weights)
temp_weights = deepcopy(weights)
updated_weights = deepcopy(weights)
original_loss = feed_forward(inputs, outputs, original_weights)
for i, layer in enumerate(original_weights):
for index, weight in np.ndenumerate(layer):
temp_weights = deepcopy(weights)
temp_weights[i][index] += 0.0001
_loss_plus = feed_forward(inputs, outputs, temp_weights)
grad = (_loss_plus - original_loss)/(0.0001)
updated_weights[i][index] -= grad*lr
return updated_weights, original_loss