神经网络

import numpy as np import utils # Forward Neural Network (FNN) class FNN: # layers = 3 and shape = [input dim, xx, class num] def __init__(self, shape, activation='sigmoid') -> None: self._l = len(shape) - 1 # layer number self._w = [] # weight self._b = [] # bias self._z = [i for i in range(self._l)] # wx+b self._a = [i for i in range(self._l)] # activation(wx+b) self._x = None # input feature self._n = None # batch size self._m = None # mean for normalization self._v = None # variance for normalization self._act = activation # activation function for output # He initialization for ly in range(self._l): self._w.append(np.random.randn(shape[ly], shape[ly + 1]) * np.sqrt(2 / shape[ly])) self._b.append(np.zeros((1, shape[ly + 1]))) assert self._l == len(self._w) == len(self._b) def set_normalize(self, m, v): self._m = m s