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diff --git a/exercises/contour_detection.py b/exercises/contour_detection.py
index 64ceb82..4fa3a2e 100644
--- a/exercises/contour_detection.py
+++ b/exercises/contour_detection.py
@@ -29,4 +29,25 @@ def contour_detection(image_path):
     # 7. 使用 cv2.drawContours() 在副本上绘制轮廓。
     # 8. 返回绘制后的图像和轮廓列表。
     # 9. 使用 try...except 处理异常。
-    pass 
\ No newline at end of file
+    try:
+        img=cv2.imread(image_path)
+        if img is None:
+            return None,None
+
+        gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        _, binary=cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
+        contours=cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+
+        if len(contours)==2:
+            contours=contours[0]
+        elif len(contours)==3:
+            contours=contours[1]
+        else:
+            raise ValueError("error")
+        if not isinstance(contours,list):
+            contours=list(contours)
+        img_with_contours=img.copy()
+        cv2.drawContours(img_with_contours,contours,-1,(0,255,0),2)
+        return img_with_contours, contours
+    except Exception as e:
+        return None, None
\ No newline at end of file
diff --git a/exercises/conv.py b/exercises/conv.py
index 0d42079..fc4dd2c 100644
--- a/exercises/conv.py
+++ b/exercises/conv.py
@@ -31,4 +31,43 @@ def conv2d(x, kernel):
     # 5. 提取输入 x 中与当前卷积核对应的区域 (patch)。
     # 6. 计算 patch 和 kernel 的元素乘积之和 (np.sum(patch * kernel))。
     # 7. 将结果存入输出数组 out[i, j]。
-    pass 
\ No newline at end of file
+    # 获取输入和卷积核的形状
+    H,W=x.shape
+    kH,kW=kernel.shape
+    # print('H,W:',H,' ',W)
+    # print('kH,kW:',kH,' ',kW)
+    out_H=H-kH+1
+    out_W=W-kW+1
+    out = np.zeros((out_H,out_W))
+    for i in range(out_H):
+        for j in range(out_W):
+            patch=x[i:i+kH,j:j+kW]
+            out[i,j]=np.sum(patch*kernel)
+    return out
+
+if __name__=='__main__':
+    img1 = np.zeros((5,5))
+    kernel1 = np.random.rand(3,3)
+    result1 = conv2d(img1, kernel1)
+
+    img2 = np.eye(5)
+    kernel2 = np.array([[1,0,0],
+                       [0,1,0],
+                       [0,0,1]])
+    result2 = conv2d(img2, kernel2)
+    expected2 = np.array([[3.,0.,0.],
+                         [0.,3.,0.],
+                         [0.,0.,3.]])
+
+    img3 = np.arange(25).reshape(5,5)
+    kernel3 = np.ones((3,3))
+    result3 = conv2d(img3, kernel3)
+    expected3 = np.array([
+        [ 54,  63,  72],
+        [ 99, 108, 117],
+        [144, 153, 162]
+    ])
+
+    print(result1)
+    print(result2)
+    print(result3)
\ No newline at end of file
diff --git a/exercises/cross_entropy.py b/exercises/cross_entropy.py
index 6a70a16..f6fb95c 100644
--- a/exercises/cross_entropy.py
+++ b/exercises/cross_entropy.py
@@ -33,4 +33,11 @@ def cross_entropy_loss(y_true, y_pred):
     # 4. 计算交叉熵损失：L = - sum(y_true * log(y_pred))。
     #    在 NumPy 中是 -np.sum(y_true * np.log(y_pred))。
     # 5. 计算所有样本的平均损失：L / N。
-    pass 
\ No newline at end of file
+    N = y_pred.shape[0]
+    C = y_pred.shape[1]
+    if y_true.ndim==1 or (y_true.ndim==2 and y_true.shape[1]==1):
+        y_true=np.eye(C)[y_true.reshape(-1)]
+    eps=1e-12
+    y_pred=np.clip(y_pred,eps,1.0-eps)
+    loss=-np.sum(y_true*np.log(y_pred))/N
+    return loss
\ No newline at end of file
diff --git a/exercises/image_processing.py b/exercises/image_processing.py
index e141a89..03d1b29 100644
--- a/exercises/image_processing.py
+++ b/exercises/image_processing.py
@@ -27,4 +27,14 @@ def image_processing_pipeline(image_path):
     # 4. 使用 cv2.GaussianBlur() 进行高斯滤波。
     # 5. 使用 cv2.Canny() 进行边缘检测。
     # 6. 使用 try...except 包裹代码以处理可能的异常。
-    pass 
\ No newline at end of file
+    try:
+        img=cv2.imread(image_path)
+        if img is None:
+            return None
+        gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        blurred=cv2.GaussianBlur(gray,(5,5),1.5)
+        edges=cv2.Canny(blurred,50,150)
+        return edges
+    except Exception as e:
+        return None
\ No newline at end of file
diff --git a/exercises/iou.py b/exercises/iou.py
index a9ed259..5d4488d 100644
--- a/exercises/iou.py
+++ b/exercises/iou.py
@@ -38,4 +38,13 @@ def calculate_iou(box1, box2):
     # 6. 计算并集面积 union_area = box1_area + box2_area - intersection_area。
     # 7. 计算 IoU = intersection_area / union_area。
     #    注意处理 union_area 为 0 的情况 (除零错误)。
-    pass 
\ No newline at end of file
+    x_left = max(box1[0], box2[0])
+    y_top = max(box1[1], box2[1])
+    x_right = min(box1[2], box2[2])
+    y_bottom = min(box1[3], box2[3])
+    intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top)
+    box1_area=(box1[2]-box1[0])*(box1[3]-box1[1])
+    box2_area=(box2[2]-box2[0])*(box2[3]-box2[1])
+    union_area = box1_area + box2_area - intersection_area
+    IoU = 0 if union_area==0 else intersection_area / union_area
+    return IoU
\ No newline at end of file
diff --git a/exercises/leaky_relu.py b/exercises/leaky_relu.py
index 6ee95a2..804a99f 100644
--- a/exercises/leaky_relu.py
+++ b/exercises/leaky_relu.py
@@ -27,4 +27,4 @@ def leaky_relu(x, alpha=0.01):
     # 1. 可以使用 np.maximum() 函数。
     # 2. 计算 alpha * x。
     # 3. 计算 max(alpha * x, x)。
-    pass 
\ No newline at end of file
+    return np.maximum(alpha * x, x)
diff --git a/exercises/maxpool.py b/exercises/maxpool.py
index aceb96b..1851c3a 100644
--- a/exercises/maxpool.py
+++ b/exercises/maxpool.py
@@ -32,4 +32,17 @@ def maxpool(x, kernel_size, stride):
     # 5. 提取当前池化窗口 window = x[h_start:h_start+kernel_size, w_start:w_start+kernel_size]。
     # 6. 找到窗口中的最大值 np.max(window)。
     # 7. 将最大值存入输出数组 out[i, j]。
-    pass 
\ No newline at end of file
+    H,W=x.shape
+    
+    out_H=(H-kernel_size)//stride+1
+    out_W=(W-kernel_size)//stride+1
+    
+    out=np.zeros((out_H,out_W))
+    for i in range(out_H):
+        for j in range(out_W):
+            h_start=i*stride
+            w_start=j*stride
+            
+            window=x[h_start:h_start+kernel_size,w_start:w_start+kernel_size]
+            out[i,j]=np.max(window)
+    return out
\ No newline at end of file
diff --git a/exercises/nms.py b/exercises/nms.py
index 4ece0bc..711221d 100644
--- a/exercises/nms.py
+++ b/exercises/nms.py
@@ -24,7 +24,16 @@ def calculate_iou(box1, box2):
     # 请在此处编写代码
     # (与 iou.py 中的练习相同，可以复用代码或导入)
     # 提示：计算交集面积和并集面积，然后相除。
-    pass
+    x_left = max(box1[0], box2[0])
+    y_top = max(box1[1], box2[1])
+    x_right = min(box1[2], box2[2])
+    y_bottom = min(box1[3], box2[3])
+    intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top)
+    box1_area=(box1[2]-box1[0])*(box1[3]-box1[1])
+    box2_area=(box2[2]-box2[0])*(box2[3]-box2[1])
+    union_area = box1_area + box2_area - intersection_area
+    IoU = 0 if union_area==0 else intersection_area / union_area
+    return IoU
 
 def nms(boxes, scores, iou_threshold):
     """
@@ -52,4 +61,30 @@ def nms(boxes, scores, iou_threshold):
     #    c. 找到 IoU 小于等于 iou_threshold 的索引 inds。
     #    d. 更新 order，只保留那些 IoU <= threshold 的框的索引 (order = order[inds + 1])。
     # 7. 返回 keep 列表。
-    pass 
\ No newline at end of file
+    if len(boxes)<=0:
+        return boxes
+    areas=[]
+    for box1 in boxes:
+        areas.append((box1[2]-box1[0])*(box1[3]-box1[1]))
+    boxes=np.array(boxes)
+    scores=np.array(scores)
+    order = np.argsort(scores)[::-1].tolist()
+    # print(order)
+    i=order.pop(0)
+    keep=[i]
+    for v in range(len(order)):
+        # print(calculate_iou(boxes[i],boxes[order[v]]))
+        if calculate_iou(boxes[i],boxes[order[v]])<=iou_threshold:
+            keep.append(order[v])
+    return keep
+
+if __name__=='__main__':
+    boxes2 = [
+        [10, 10, 60, 60],    # 得分最高
+        [15, 15, 65, 65],    # 与第一个框IoU≈0.53
+        [50, 50, 100, 100],  # 与第一个框IoU≈0.11
+        [150, 150, 200, 200] # 完全独立
+    ]
+    scores2 = [0.9, 0.85, 0.8, 0.75]
+    result2 = nms(boxes2, scores2, 0.5)
+    print(result2)
diff --git a/exercises/smooth_l1.py b/exercises/smooth_l1.py
index 7847a04..82688ca 100644
--- a/exercises/smooth_l1.py
+++ b/exercises/smooth_l1.py
@@ -30,4 +30,8 @@ def smooth_l1(x, sigma=1.0):
     # 3. 对满足条件的元素应用第一个公式 (0.5 * (sigma * x)**2)。
     # 4. 对不满足条件的元素应用第二个公式 (|x| - 0.5 / sigma2)。
     # 5. 可以使用 np.where() 来根据条件选择应用哪个公式。
-    pass 
\ No newline at end of file
+    sigma2=sigma**2
+    t=1.0/sigma2
+    ax=np.abs(x)
+    loss=np.where(ax<t, 0.5*(sigma*x)**2, ax-0.5/sigma2)
+    return loss
\ No newline at end of file