SUM(IF())函数损失精度问题

SUM(IF())函数在处理大量数据时可能会出现精度问题。这是因为在计算过程中，IF()函数返回的结果是一个布尔值，而不是一个精确的数值。当使用SUM(IF())函数时，它会将布尔值转换为数值进行求和，这可能导致精度损失。

具体来说，当IF()函数返回TRUE时，它会被转换为1；当IF()函数返回FALSE时，它会被转换为0。然后，SUM()函数会对这些数值进行求和。然而，由于浮点数的精度限制，当数据量很大时，这种转换可能会导致精度损失。

为了解决这个问题，可以考虑使用其他函数或方法来处理大量数据的求和操作。例如，可以使用SUMPRODUCT()函数或者使用循环来逐个累加数据。

相关问题

c++高精度加法函数

以下是一个C++实现高精度加法的函数示例：

#include <iostream>
#include <string>
#include <algorithm>
using namespace std;

string add(string s1, string s2) {
    string result = ""; // 存储结果的字符串
    int carry = 0; // 进位
    int len1 = s1.size(), len2 = s2.size();
    // 将两个字符串反转，方便从低位到高位相加
    reverse(s1.begin(), s1.end());
    reverse(s2.begin(), s2.end());
    // 从低位到高位相加
    for (int i = 0; i < max(len1, len2); i++) {
        int num1 = i < len1 ? s1[i] - '0' : 0;
        int num2 = i < len2 ? s2[i] - '0' : 0;
        int sum = num1 + num2 + carry;
        result += to_string(sum % 10); // 将个位数加入结果字符串
        carry = sum / 10; // 计算进位
    }
    if (carry > 0) { // 如果最高位有进位，加入结果字符串
        result += to_string(carry);
    }
    reverse(result.begin(), result.end()); // 反转结果字符串
    return result;
}

int main() {
    string s1 = "12345678901234567890";
    string s2 = "98765432109876543210";
    cout << add(s1, s2) << endl; // 输出：111111111011111111100
    return 0;
}

yolov3损失函数代码

Yolov3是一种流行的目标检测模型，它的损失函数设计非常特殊。与传统的目标检测模型不同，yolov3的损失函数不是基于交叉熵或类似的损失函数，而是将目标检测问题定义为一种回归问题，通过对坐标和大小进行回归来预测目标框。

在yolov3的损失函数中，主要包含三部分损失函数：置信度损失、分类损失和坐标损失。置信度损失用于衡量预测的目标框与实际目标框的重叠度，分类损失用于衡量预测的目标框中包含的物体类型是否正确，坐标损失则用于衡量目标框的位置和大小的回归精度。

具体的代码实现如下：

def yolo_loss(args, anchors, num_classes, rescore_confidence=False, print_loss=False):
"""
YOLOv3 loss function.
:param args: YOLOv3 output tensor list.
:param anchors: Anchor box list.
:param num_classes: Number of classes.
:param rescore_confidence: Whether to rescore confidence based on IOU between prediction and target.
:param print_loss: Whether to print loss values for debugging purposes.
:return: Total loss tensor.
"""
# Retrieve model input shape.
input_shape = tf.cast(tf.shape(args[0])[1:3] * 32, tf.float32)
# Tuple of scalars representing the grid shape (width, height).
grid_shape = [tf.cast(tf.shape(args[l])[1:3], tf.float32) for l in range(3)]
# Compute scale factors for box width and height.
scales = [input_shape / grid_shape[l] for l in range(3)]
# Anchor box tensor.
anchors_tensor = tf.reshape(tf.constant(anchors, dtype=tf.float32), [1, 1, 1, 3, 2])
# Element-wise compute inverse of anchor box dimensions.
anchor_dims = anchors_tensor[..., ::-1]
# Extract objectness probability and class predictions from output tensor list.
yolo_outputs = args[:3]
# Extract predicted box coordinates and convert to float.
xy_offset, wh, objectness, class_probs = yolo_head(yolo_outputs, anchors, num_classes, input_shape)
# Compute grid offsets.
grid_offset = [tf.range(tf.cast(grid_shape[l], tf.float32), dtype=tf.float32) for l in range(2)]
grid_offset = tf.meshgrid(grid_offset[1], grid_offset[0])
grid_offset = tf.expand_dims(tf.stack(grid_offset, axis=-1), axis=2)
# Compute true box coordinates and weights.
box_xy, box_wh, box_confidence, box_class_probs, true_box = yolo_boxes_and_scores(y_true, anchors, num_classes, input_shape)
# Compute iou between each predicted box and true box.
iou = yolo_box_iou(xy_offset, wh, true_box[..., 0:4], anchor_dims)
# Parse batch size from input tensor.
batch_size = tf.cast(tf.shape(yolo_outputs[0])[0], tf.float32)
# Compute objectness, class and regression losses.
object_mask = tf.reduce_max(iou, axis=-1, keepdims=True) * y_true[..., 4:5]
object_mask = tf.cast((iou >= object_mask) & (y_true[..., 4:5] > 0), tf.float32)
object_mask_neg = tf.cast((iou < object_mask) & (iou >= 0.5), tf.float32)
object_mask_pos = tf.cast((iou >= object_mask) & (y_true[..., 4:5] > 0), tf.float32)
pred_box_xy = xy_offset * object_mask_pos
pred_box_wh = wh * tf.exp(yolo_outputs[2]) * object_mask_pos
pred_box_confidence = (
(object_mask_pos * objectness) + (object_mask_neg * objectness * rescore_confidence) +
((1 - object_mask_pos - object_mask_neg) * objectness_black_box_rescore)
)
pred_box_class_probs = class_probs * object_mask_pos
true_box_xy = y_true[..., 0:2] / scales[0] - grid_offset
true_box_wh = y_true[..., 2:4] / scales[0]
xy_loss_scale = 2.0 - y_true[..., 2:3] * y_true[..., 3:4] / input_shape / input_shape
wh_loss_scale = 2.0 - y_true[..., 2:3] * y_true[..., 3:4] / input_shape / input_shape
confidence_loss_scale = (1 - y_true[..., 4:5]) + (y_true[..., 4:5] * 4.) * (1 - yolo_outputs[2]) + 1e-8
class_loss_scale = y_true[..., 4:5] * 1.
xy_loss = tf.reduce_sum(tf.square(true_box_xy - pred_box_xy) * xy_loss_scale, axis=-1)
wh_loss = tf.reduce_sum(tf.square(tf.sqrt(true_box_wh) - tf.sqrt(pred_box_wh)) * wh_loss_scale, axis=-1)
confidence_loss = tf.reduce_sum(tf.square(true_box[..., 4:5] - pred_box_confidence) * confidence_loss_scale, axis=-1)
class_loss = tf.reduce_sum(tf.square(true_box[..., 5:] - pred_box_class_probs) * class_loss_scale, axis=-1)
# Normalization factos.
num_positives = tf.reduce_sum(object_mask_pos, axis=[1, 2, 3])
# Compute total YOLOv3 loss.
total_loss = (
xy_loss + wh_loss + confidence_loss + class_loss
)
# Optionally print loss values.
if print_loss:
total_loss = tf.Print(
total_loss, [tf.reduce_mean(xy_loss / num_positives),
tf.reduce_mean(wh_loss / num_positives),
tf.reduce_mean(confidence_loss / num_positives),
tf.reduce_mean(class_loss / num_positives)],
message='loss: '
)
return total_loss