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将多张时序图片合成为一段视频import cv2import os# 读取时序图中的第一张图片img = cv2.imread('input/in000001.jpg')# 设置每秒读取多少张图片fps = 25imgInfo = img.shape# 获取图片宽高度信息size = (imgInfo[1], imgInfo[0])fourcc = cv2.VideoWriter_fourcc(*"MJPG")# 定义写入图片的策略videoWrite = cv2.Vide

获取当前目录所在的文件夹路径：import oscur_file_path = os.getcwd()print(cur_file_path)输出结果：获取当前目录所在的文件夹的上层文件夹：import osdata_root = os.path.abspath(os.getcwd() + "/..")print(data_root)输出结果:获取当前目录所在的文件夹的上上层文件夹：import osdata_root = os.path.abspa

In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. Imag

采用3层的全连接网络，首先正向传播一次并计算误差，之后反向传播一次更新参数，最后再次正向传播并计算误差全连接网络的具体结构如下：输入参数(input_param)：初始化第一层权重(w1)：初始化第二层参数(w2):给定预期的输出(output):1. 第一次正向传播及计算误差进行第一次正向传播得到输出(pred)：pred = input_param *w1 *w2其中隐藏层(hidden)在第一次正向传播中各个参数为：hidden =input_pa...