NeuralNetwork.py 文件源码

def update(self, inputs):
        # input activations
        for i in range(self.input_size - 1):
            self.ai[i] = inputs[i]

        # hidden activations
        for j in range(self.hidden_size):
            sum = 0.0
            for i in range(self.input_size):
                sum = sum + self.ai[i] * self.Wi[i][j]
            self.ah[j] = math.tanh(sum)

        # output activations
        for k in range(self.output_size):
            sum = 0.0
            for j in range(self.hidden_size):
                sum = sum + self.ah[j] * self.Wo[j][k]
            self.ao[k] = sigmoid(sum)

        return self.ao[:]