扫二维码与项目经理沟通
我们在微信上24小时期待你的声音
解答本文疑问/技术咨询/运营咨询/技术建议/互联网交流
Numpy实现卷积神经网络的方法?针对这个问题,这篇文章详细介绍了相对应的分析和解答,希望可以帮助更多想解决这个问题的小伙伴找到更简单易行的方法。
创新互联建站主营宁晋网站建设的网络公司,主营网站建设方案,重庆APP开发,宁晋h5微信小程序开发搭建,宁晋网站营销推广欢迎宁晋等地区企业咨询import numpy as np import sys def conv_(img, conv_filter): filter_size = conv_filter.shape[1] result = np.zeros((img.shape)) # 循环遍历图像以应用卷积运算 for r in np.uint16(np.arange(filter_size/2.0, img.shape[0]-filter_size/2.0+1)): for c in np.uint16(np.arange(filter_size/2.0, img.shape[1]-filter_size/2.0+1)): # 卷积的区域 curr_region = img[r-np.uint16(np.floor(filter_size/2.0)):r+np.uint16(np.ceil(filter_size/2.0)), c-np.uint16(np.floor(filter_size/2.0)):c+np.uint16(np.ceil(filter_size/2.0))] # 卷积操作 curr_result = curr_region * conv_filter conv_sum = np.sum(curr_result) # 将求和保存到特征图中 result[r, c] = conv_sum # 裁剪结果矩阵的异常值 final_result = result[np.uint16(filter_size/2.0):result.shape[0]-np.uint16(filter_size/2.0), np.uint16(filter_size/2.0):result.shape[1]-np.uint16(filter_size/2.0)] return final_result def conv(img, conv_filter): # 检查图像通道的数量是否与过滤器深度匹配 if len(img.shape) > 2 or len(conv_filter.shape) > 3: if img.shape[-1] != conv_filter.shape[-1]: print("错误:图像和过滤器中的通道数必须匹配") sys.exit() # 检查过滤器是否是方阵 if conv_filter.shape[1] != conv_filter.shape[2]: print('错误:过滤器必须是方阵') sys.exit() # 检查过滤器大小是否是奇数 if conv_filter.shape[1] % 2 == 0: print('错误:过滤器大小必须是奇数') sys.exit() # 定义一个空的特征图,用于保存过滤器与图像的卷积输出 feature_maps = np.zeros((img.shape[0] - conv_filter.shape[1] + 1, img.shape[1] - conv_filter.shape[1] + 1, conv_filter.shape[0])) # 卷积操作 for filter_num in range(conv_filter.shape[0]): print("Filter ", filter_num + 1) curr_filter = conv_filter[filter_num, :] # 检查单个过滤器是否有多个通道。如果有,那么每个通道将对图像进行卷积。所有卷积的结果加起来得到一个特征图。 if len(curr_filter.shape) > 2: conv_map = conv_(img[:, :, 0], curr_filter[:, :, 0]) for ch_num in range(1, curr_filter.shape[-1]): conv_map = conv_map + conv_(img[:, :, ch_num], curr_filter[:, :, ch_num]) else: conv_map = conv_(img, curr_filter) feature_maps[:, :, filter_num] = conv_map return feature_maps def pooling(feature_map, size=2, stride=2): # 定义池化操作的输出 pool_out = np.zeros((np.uint16((feature_map.shape[0] - size + 1) / stride + 1), np.uint16((feature_map.shape[1] - size + 1) / stride + 1), feature_map.shape[-1])) for map_num in range(feature_map.shape[-1]): r2 = 0 for r in np.arange(0, feature_map.shape[0] - size + 1, stride): c2 = 0 for c in np.arange(0, feature_map.shape[1] - size + 1, stride): pool_out[r2, c2, map_num] = np.max([feature_map[r: r+size, c: c+size, map_num]]) c2 = c2 + 1 r2 = r2 + 1 return pool_out
我们在微信上24小时期待你的声音
解答本文疑问/技术咨询/运营咨询/技术建议/互联网交流