我正在使用Numpy在Python中实现XOR。在[1,1,0]，[0,0,0]，[1,0,1]，[0,1,1]上训练了权重，其中最后一个元素是目标，当我在网络上运行前馈算法时，我得到的结果大致相同，即使它已被训练50 000次随机顺序。错误在哪里？
我试图看到权重正在更新 - 它们是。我也检查了我的代码以寻找错误，但没有找到任何错误。
## Need to generalize it for n layers! it is generalized for n nodes in the 3 layers.
class nn:
# input_nodes = 0
# hidden_nodes = 0
# output_nodes = 0
def __init__(self, input_nodes, hidden_nodes, output_nodes):
self.input_nodes = input_nodes # The number of features!
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
self.weights_ih = np.random.randn(self.hidden_nodes,self.input_nodes )#2x2
self.weights_ho = np.random.randn(self.output_nodes, self.hidden_nodes)#1x2
self.bias_h = np.zeros([self.hidden_nodes, 1])#2x1
self.bias_o = np.zeros([self.output_nodes, 1])#1x1
self.learning_rate = 0.1
def sigmoid(self,x):
res = 1 / (1 + np.exp(-x))
return res
def feedforward(self, input_sample):
# Generating the hidden outputs: input_sample: 1x2 input_sample_T = np.asmatrix(input_sample).T
input_sample_T = np.asmatrix(input_sample).T
hidden = np.matrix(np.dot(self.weights_ih, input_sample_T))
hidden = np.add(hidden,self.bias_h)
sig = lambda t: self.sigmoid(t)
hidden = sig(hidden)
# Now, generate the output for the output layer:
outputs = np.matrix(np.dot(self.weights_ho, hidden ))
outputs = np.add(outputs,self.bias_o)
sig = lambda t: self.sigmoid(t)
outputs = sig(outputs)
output_outputs = np.asmatrix(outputs)
return hidden, output_outputs
def train(self, input_sample, targets):
targets = np.asmatrix(targets).T
hidden, outputs = self.feedforward(input_sample)
# Calculate the errors from the output layer:
# ERROR = Targets - ypred
output_errors = np.subtract(targets,outputs)
# Calculate gradient
# gradient = outputs*(1 - outputs)
desig = lambda t: self.desigmoid(t)
gradients = desig(np.asmatrix(outputs))
gradients = np.multiply(gradients, output_errors)
gradients = np.multiply(gradients, self.learning_rate)
# Calculate deltas
hidden_T = hidden.T
weight_deltas_ho = np.dot(gradients, hidden_T)
# Adjust the weights by the deltas
self.weights_ho = np.add(self.weights_ho,weight_deltas_ho)
# Adjust the bias by it's deltas:
self.bias_h = np.add(self.bias_o, gradients)
# Calculate the error from the hidden node:
hidden_errors = np.dot(self.weights_ho.T,output_errors)
# Calculate the gradient for the hidden layer:
hidden_gradient = desig(np.asmatrix(hidden))
hidden_gradient = np.multiply(hidden_gradient, hidden_errors)
hidden_gradient = np.multiply(hidden_gradient, self.learning_rate)
# Calculate change of weight for input -> hidden (deltas):
input_sample_T = np.matrix(input_sample).T
weight_ih_deltas = np.dot(hidden_gradient, input_sample_T.T)
# Update the input -> Hidden weights:
self.weights_ih = np.add(self.weights_ih, weight_ih_deltas)
# Adjust the bias by it's deltas:
self.bias_h = np.add(self.bias_h, hidden_gradient)
def desigmoid(self, y):
res = y.T*(1-y)
return res
# ********* Main ***********
# The number of nodes in the neural net:
n1 = nn(2,2,1)
matrix_x = [[]] * 4
list_y = []
j=0
for row in df.iterrows():
index, data = row
data = data.tolist()
y_train = [data.pop(2)]
x_train = data
matrix_x[j] = x_train
list_y.append(y_train[0])
j+=1
print(list_y)
print(matrix_x)
def randomize_train(x,y):
for i in range(10000):
rand = randint(0, 3)
print(x[rand],y[rand])
n1.train(x[rand], y[rand])
print(i," ",end='')
# Running the program:
randomize_train(matrix_x, list_y)
h, o = n1.feedforward([1,1])
print(o)
h, o = n1.feedforward([0,1])
print(o)
h, o = n1.feedforward([1,0])
print(o)
h, o = n1.feedforward([0,0])
print(o)
Output:
[[0.09657458]]
[[0.67883337]]
[[0.6458358]]
[[0.68269945]]
谢谢您的帮助！