def feed_forward(self, x): # aka forward propagation
l_actvtns = x
for l_ix in range(1, self.l_number):
l_values = np.dot(l_actvtns, self.l_weights[l_ix]) \
+ self.l_biases[l_ix]
l_actvtns = aa.fn(self.actvtn_types[l_ix], l_values)
self.l_actvtns[l_ix] = l_actvtns
def prediction(self, x):
l_actvtns = x
for l_ix in range(1, self.l_number):
l_values = np.dot(l_actvtns, self.l_weights[l_ix]) \
+ self.l_biases[l_ix]
l_actvtns = aa.fn(self.actvtn_types[l_ix], l_values)
return l_actvtns
