def softmax(self, x): """:param x: 输入数据 :return:返回softmax激活函数值""" return 1/(1+np.exp(-x)) def forward_propagation(self, input_data, weight_matrix, b): """ :param input_data: 输入数据 :param weight_matrix: 权重矩阵 :return: 激活函数后输出的活性值 """ z = np.add(np.dot(weight_matrix, input_data), b) return z, self.softmax(z) def predict(self, input_data, label): print("Begin to predict") #precision = 0 #for i in range(number): z1, a1 = self.forward_propagation(input_data[:].reshape(-1, 1), w1, self.b1) z2, a2 = self.forward_propagation(a1, w2, b2) print("预测值为",np.argmax(a2))转换为c语言

以下是转换为 C 语言后的代码： #include <stdio.h> #include <stdlib.h> #include <math.h> #define ROWS 1 #define COLS 784 #define HIDDEN 256 #define LABELS 10 double sigmoid(double x) { return 1 / (1 + exp(-x)); } void softmax(double *x, int len) { double max = x[0]; for (int i = 1; i < len; i++) { if (x[i] > max) { max = x[i]; } } double sum = 0.0; for (int i = 0; i < len; i++) { x[i] = exp(x[i] - max); sum += x[i]; } for (int i = 0; i < len; i++) { x[i] /= sum; } } void forward_propagation(double *input_data, double *weight_matrix1, double *b1, double *weight_matrix2, double *b2, double *a1, double *a2) { double z1[HIDDEN]; for (int i = 0; i < HIDDEN; i++) { z1[i] = 0.0; for (int j = 0; j < COLS; j++) { z1[i] += weight_matrix1[i * COLS + j] * input_data[j]; } z1[i] += b1[i]; a1[i] = sigmoid(z1[i]); } for (int i = 0; i < LABELS; i++) { a2[i] = 0.0; for (int j = 0; j < HIDDEN; j++) { a2[i] += weight_matrix2[i * HIDDEN + j] * a1[j]; } a2[i] += b2[i]; } softmax(a2, LABELS); } int predict(double *input_data, double *weight_matrix1, double *b1, double *weight_matrix2, double *b2) { double a1[HIDDEN], a2[LABELS]; forward_propagation(input_data, weight_matrix1, b1, weight_matrix2, b2, a1, a2); int prediction = 0; double max = a2[0]; for (int i = 1; i < LABELS; i++) { if (a2[i] > max) { max = a2[i]; prediction = i; } } return prediction; } int main() { double input_data[ROWS * COLS]; double weight_matrix1[HIDDEN * COLS]; double b1[HIDDEN]; double weight_matrix2[LABELS * HIDDEN]; double b2[LABELS]; // 读取数据和模型参数 // ... // 进行预测 int prediction = predict(input_data, weight_matrix1, b1, weight_matrix2, b2); printf("预测值为 %d\n", prediction); return 0; }