你知道为什么这个网络不想学习吗？这个想法是它在前面的层中使用 ReLU 作为激活函数，在最后一层中使用 sigmoid 作为激活函数。当我只使用 sigmoid 时，网络学得很好。为了验证网络，我使用了 MNIST。
def sigmoid( z ):
return 1.0 / (1.0 + np.exp(-z))
def sigmoid_prime(z):
return sigmoid(z)*(1-sigmoid(z))
def RELU(z):
return z*(z>0)
def RELU_Prime(z):
return (z>0)
# x - training data in mnist for example (1,784) vector
# y - training label in mnist for example (1,10) vector
# nabla is gradient for the current x and y
def backprop(self, x, y):
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
# feedforward
activation = x
activations = [x] # list to store all the activations, layer by layer
zs = [] # list to store all the z vectors, layer by layer
index =0
for b, w in zip(self.biases, self.weights):
z = np.dot(w, activation)+b
zs.append(z)
if index == len(self.weights)-1:
activation = sigmoid(z)
#previous layers are RELU
else:
activation = RELU(z)
activations.append(activation)
index +=1
# backward pass
delta = self.cost_derivative(activations[-1], y) *\
sigmoid_prime(zs[-1])
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
for l in range(2, self.num_layers):
z = zs[-l]
sp = RELU_Prime(z)
delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
nabla_b[-l] = delta
nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
return (nabla_b, nabla_w)
- - - - - - - - 编辑 - - - - - - - - - - - - - - -
def cost_derivative(self, output_activations, y):
return (output_activations-y)
--------------- 编辑 2 -----------------
self.weights = [w-(eta/len(mini_batch))*nw
for w, nw in zip(self.weights, nabla_w)]
self.biases = [b-(eta/len(mini_batch))*nb
for b, nb in zip(self.biases, nabla_b)]
η > 0