#coding:utf-8'''卷积计算'''import mxnet as mxfrom mxnet.gluon import nnfrom mxnet import ndarray as nd# 卷积层# 输入输出的数据格式是： batch * channel * height * width# 权重格式：output_channels * in_channels * height * widthw = nd.arange(4).reshape((1,1,2,2))b = nd.array([1])data = nd.arange(9).reshape((1,1,3,3))# 卷积运算out = nd.Convolution(data,w,b,kernel=w.shape[2:],num_filter=w.shape[1])print('input:',data)print('weight:',w)print('bias:',b)print('output:',out)# 窗口移动和边缘填充out = nd.Convolution(data,w,b,kernel=w.shape[2:],        num_filter=w.shape[1],stride=(2,2),pad=(1,1))print('output:',out)# 多通道数据卷积：每个通道会有相应的权重，然后对每个通道做卷积之后，在通道之间求和data = nd.arange(18).reshape((1,2,3,3))w = nd.arange(8).reshape((1,2,2,2))out = nd.Convolution(data,w,b,kernel=w.shape[2:],num_filter=w.shape[0])print('weight = ',w)print('data = ',data)print('output = ',out)# Poolingdata = nd.arange(18).reshape((1,2,3,3))max_pool = nd.Pooling(data=data, pool_type="max", kernel=(2,2))avg_pool = nd.Pooling(data=data, pool_type="avg", kernel=(2,2))print('data = ',data)print('max pool = ',max_pool)print('avg pool = ',avg_pool)