多輸入通道

多輸出通道：

設卷積核輸入通道，輸出通道是 ci , co，高寬為 kh , kw

為每個輸出通道分別建立 ci * kh * kw 的核陣列。在輸出通道維上連結，卷積核的形狀即為co * ci *kh*kw

from mxnet import autograd,nd
from mxnet import gluon,init
from mxnet.gluon import nn,loss as gloss
from mxnet.gluon import data as gdata

# 二維卷積層
def corr2d(X,K):
    h, w 
 
= K.shape
    Y = nd.zeros((X.shape[0] - h + 1,X.shape[1] - w + 1))
    for i in range(Y.shape[0]):
        for j in range(Y.shape[1]):
            Y[i,j] = (X[i: i+h,j:j+w]*K).sum()
    return Y

# 多通道輸入
def corr2d_multi_in(X,K):
    return nd.add_n(*[corr2d(x,k) for x,k in zip(X,K)])

X = nd.array([[[0,1,2],[3,4,5],[6,7,8]],
             [[
 
1,2,3],[4,5,6],[7,8,9]]])
print(X)
K = nd.array([[[0,1],[2,3]],[[1,2],[3,4]]])

print(corr2d_multi_in(X,K))

# 多通道輸出
# 為每個輸出通道分別建立 ci * kh * kw 的核陣列
# 將他們在輸出通道維上連結，卷積核的形狀即為 co * ci * kh * kw 的核陣列

def corr2d_multi_in_out(X,K):
    return nd.stack(*[corr2d_multi_in(X,k) for k in K])

# 3 通道核 3 * 2 * 2 * 2
 

K = nd.stack(K,K+1,K+2)
print(K)
print(corr2d_multi_in_out(X,K))