Python ReLu激活函数无法工作

我的第一个神经网络使用的是sigmoid激活函数，运行良好。现在我想切换到更高级的激活功能RELU。但是有了ReLu，我的NN根本不起作用。90%的错误，使用sigmoid时有4%的错误。我在代码中找不到bug。帮帮我

class NeuralNetwork:
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate = 0.1):
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes
        self.learning_rate = learning_rate

        self.weights_ih = np.random.normal(0.0, pow(input_nodes, -0.5), (hidden_nodes, input_nodes))
        self.weights_ho = np.random.normal(0.0, pow(hidden_nodes, -0.5), (output_nodes, hidden_nodes))
        self.bias_h = np.random.normal(0.0, pow(1, -0.5), (hidden_nodes, 1))
        self.bias_o = np.random.normal(0.0, pow(1, -0.5), (output_nodes, 1))

    def activation_function(self, x):
        return x * (x > 0)

    def activation_function_d(self, x):
        return 1 * (x >= 0)

    def train(self, inputs_list, targets_list):
        inputs = np.array(inputs_list, ndmin=2).T
        targets = np.array(targets_list, ndmin=2).T

        # Feedforward
        hidden_inputs = np.dot(self.weights_ih, inputs) + self.bias_h
        hidden = self.activation_function(hidden_inputs)
        output_inputs = np.dot(self.weights_ho, hidden) + self.bias_o
        outputs = self.activation_function(output_inputs)

        # Calculate errors
        output_errors = targets - outputs
        hidden_errors = np.dot(self.weights_ho.T, output_errors)

        # Calculate gradients
        output_gradient = output_errors * self.activation_function_d(output_inputs) * self.learning_rate
        hidden_gradient = hidden_errors * self.activation_function_d(hidden_inputs) * self.learning_rate

        # Calculate deltas
        output_deltas = np.dot(output_gradient, hidden.T)
        hidden_deltas = np.dot(hidden_gradient, inputs.T)

        # Adjust weights and biases by deltas and gradients
        self.weights_ho += output_deltas
        self.weights_ih += hidden_deltas
        self.bias_o     += output_gradient
        self.bias_h     += hidden_gradient

    def predict(self, inputs_list):
        inputs = np.array(inputs_list, ndmin=2).T
        hidden = self.activation_function(np.dot(self.weights_ih, inputs) + self.bias_h)
        outputs = self.activation_function(np.dot(self.weights_ho, hidden) + self.bias_o)
        return outputs.flatten().tolist()

培训代码：

with open('mnist_train.csv') as train_file:
    for str in train_file:
        data = [int(char) for char in str.split(',')]
        inputs = data[1:]
        targets = [1 if i == data[0] else 0 for i in range(10)]
        nn.train(inputs, targets)

---

最后一层应始终在二进制情况下使用sigmoid，无论您尝试执行什么操作

sigmoid函数用于估计示例在给定类别中的概率，示例的预测是示例具有最高概率的类别

总之，改变这一点：

def predict(self, inputs_list):
    inputs = np.array(inputs_list, ndmin=2).T
    hidden = self.activation_function(np.dot(self.weights_ih, inputs) + self.bias_h)
    outputs = self.activation_function(np.dot(self.weights_ho, hidden) + self.bias_o)
    return outputs.flatten().tolist()

对此

def predict(self, inputs_list):
    inputs = np.array(inputs_list, ndmin=2).T
    hidden = self.activation_function(np.dot(self.weights_ih, inputs) + self.bias_h)
    outputs = sigmoid(np.dot(self.weights_ho, hidden) + self.bias_o) // create a sigmoid function
    return outputs.flatten().tolist()

在培训中：

    # Feedforward
    hidden_inputs = np.dot(self.weights_ih, inputs) + self.bias_h
    hidden = self.activation_function(hidden_inputs)
    output_inputs = np.dot(self.weights_ho, hidden) + self.bias_o
    outputs = self.activation_function(output_inputs)

致：

及

到

---

为什么在计算梯度时在隐藏层上使用sigmoid，在输出层上使用relu？如果我使用sigmoid，那么我应该将目标规格化为0，1，对吗？如果我使用ReLu，我应该将输入标准化到某个范围吗？尝试相同的范围，根据我的经验，它工作得很好，尽管我认为ReLu不需要标准化到特定的范围。我在输出层用sigmoid替换了ReLu，但得到了30%的错误，在第二次训练后，我得到了50%的错误。然后再次切换到sigmoid，得到6%的错误。你能看看我的密码吗？不管怎样，我都会和你在一起。

    # Feedforward
    hidden_inputs = np.dot(self.weights_ih, inputs) + self.bias_h
    hidden = self.activation_function(hidden_inputs)
    output_inputs = np.dot(self.weights_ho, hidden) + self.bias_o
    outputs = sigmoid(output_inputs)

    # Calculate gradients
    output_gradient = output_errors * self.activation_function_d(output_inputs) * self.learning_rate
    hidden_gradient = hidden_errors * self.activation_function_d(hidden_inputs) * self.learning_rate

       # Calculate gradients
    output_gradient = output_errors * sigmoid_d(output_inputs) * self.learning_rate
    hidden_gradient = hidden_errors * self.activation_function_d(hidden_inputs) * self.learning_rate