#include #include #include "../common/common.h" #define CONFIG_BIN "./trainer_config.bin" int main() { // Initalize Paddle char* argv[] = {"--use_gpu=False"}; CHECK(paddle_init(1, (char**)argv)); // Reading config binary file. It is generated by `convert_protobin.sh` long size; void* buf = read_config(CONFIG_BIN, &size); // Create a gradient machine for inference. paddle_gradient_machine machine; CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size)); CHECK(paddle_gradient_machine_randomize_param(machine)); // Loading parameter. Uncomment the following line and change the directory. // CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, // "./some_where_to_params")); paddle_arguments in_args = paddle_arguments_create_none(); // There is only one input of this network. CHECK(paddle_arguments_resize(in_args, 1)); // Create input matrix. paddle_matrix mat = paddle_matrix_create(/* sample_num */ 10, /* size */ 784, /* useGPU */ false); srand(time(0)); std::vector input; input.resize(784 * 10); for (int i = 0; i < input.size(); ++i) { input[i] = rand() / ((float)RAND_MAX); } // Set value for the input matrix CHECK(paddle_matrix_set_value(mat, input.data())); CHECK(paddle_arguments_set_value(in_args, 0, mat)); paddle_arguments out_args = paddle_arguments_create_none(); CHECK(paddle_gradient_machine_forward(machine, in_args, out_args, /* isTrain */ false)); paddle_matrix prob = paddle_matrix_create_none(); CHECK(paddle_arguments_get_value(out_args, 0, prob)); std::std::vector result; int height; int width; CHECK(paddle_matrix_get_shape(prob, &height, &width); result.resize(height * width); CHECK(paddle_matrix_get_value(prob, result.data())); printf("Prob: "); for (int i = 0; i < height * width; ++i) { printf("%.2f ", result[i]); } printf("\n"); CHECK(paddle_matrix_destroy(prob)); CHECK(paddle_arguments_destroy(out_args)); CHECK(paddle_matrix_destroy(mat)); CHECK(paddle_arguments_destroy(in_args)); CHECK(paddle_gradient_machine_destroy(machine)); return 0; }