From feb8759764e70dbc9e1abb7312109ccd004adb16 Mon Sep 17 00:00:00 2001
From: Precreator <1689487228@qq.com>
Date: Tue, 1 Jul 2025 11:22:55 +0800
Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90=E4=BA=86test15=2016,?=
 =?UTF-8?q?=E5=9F=BA=E6=9C=AC=E5=AE=8C=E6=88=90=E4=BA=86=E7=BA=BF=E6=80=A7?=
 =?UTF-8?q?=E5=B1=82=E7=9A=84=E4=BB=A3=E7=A0=81=E9=83=A8=E5=88=86?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 cc/operators/nn.h | 33 +++++++++++++++++++++++++++++++--
 1 file changed, 31 insertions(+), 2 deletions(-)

diff --git a/cc/operators/nn.h b/cc/operators/nn.h
index e4824e8..ec07c1c 100644
--- a/cc/operators/nn.h
+++ b/cc/operators/nn.h
@@ -135,15 +135,43 @@ public:
         auto features = this->objects[0];
         auto bias = this->objects[1];
         auto outNode = std::make_shared<tensor::Tensor>(features->data->shape);
+        auto batch_size = features->data->shape[0];
+        auto num_features = features->data->shape[1];
+        for (size_t i = 0; i < batch_size; ++i) {
+            for (size_t j = 0; j < num_features; ++j) {
+                // 计算索引：batch_size行，num_features列的二维张量
+                size_t idx = i * num_features + j;
+                // 每个样本的特征向量加上偏置向量
+                outNode->data[idx] = features->data->data[idx] + bias->data->data[j];
+            }
+        }
         // for循环写加法总会写吧🤔
         // 补全这里的代码
         return outNode;
     }
     std::vector<std::shared_ptr<tensor::Tensor>> backward(std::shared_ptr<tensor::Tensor> gradient) override {
         // assertion needed
-        auto g_bias = std::make_shared<tensor::Tensor>(this->objects[1]->data->shape);
+        auto g_bias = std::make_shared<tensor::Tensor>(this->objects[1]->data->shape);  
+        // 从张量形状获取维度信息
+        auto batch_size = gradient->shape[0];
+        auto num_features = gradient->shape[1];  // 从shape中获取num_features
         // 补全这里的代码
-        
+         auto batch_size = gradient->shape[0];
+        auto num_features = gradient->shape[1];
+    
+    // 初始化偏置梯度为零
+    for (size_t j = 0; j < num_features; ++j)
+     {
+        g_bias->data[j] = 0.0f;
+    }
+    
+    // 计算偏置的梯度：对每个特征维度，将所有样本的梯度累加
+    for (size_t i = 0; i < batch_size; ++i) {
+        for (size_t j = 0; j < num_features; ++j) {
+            // 累加每个样本对该特征维度的梯度贡献
+            g_bias->data[j] += gradient->data[i * num_features + j];
+        }
+    }
         return {gradient, g_bias};
     }
     std::vector<float> get_data() {
@@ -155,6 +183,7 @@ public:
 class Linear: public FunctionNode {
 public:
     Linear(std::shared_ptr<Node> a, std::shared_ptr<Node> b) : FunctionNode(a, b) {
+        this->data=this->forward(); 
         // 这段代码就一行，参考下别的类是怎么写的呢？
         // 在这里补全
     }