这里是knet的conv4 但是我看不太懂，能不能简要介绍下原理，或者过程。
这里是tensorflow.nn.conv2d
对于julia部分，源码核心是这样的：
权重是相同的如下：
testw
3×3×3 Array{Float32,3}:
[:, :, 1] =
0.480015 0.408547 -0.0651456
0.310477 0.0502024 -0.403383
-0.0508717 -0.285228 -0.418516
[:, :, 2] =
0.550379 0.440075 -0.081387
0.345739 0.0406322 -0.453501
-0.0586349 -0.33067 -0.48503
[:, :, 3] =
0.429471 0.373467 -0.0613601
0.27477 0.0386808 -0.367223
-0.0574682 -0.26225 -0.350097
数据部分如下：
julia>partappledata
5×5×3 Array{Float64,3}:
[:, :, 1] =
41.063 41.063 41.063 41.063 41.063
41.063 41.063 41.063 41.063 41.063
41.063 41.063 41.063 41.063 41.063
42.063 42.063 42.063 42.063 42.063
42.063 42.063 42.063 42.063 42.063
[:, :, 2] =
55.221 55.221 55.221 55.221 55.221
55.221 55.221 55.221 55.221 55.221
55.221 55.221 55.221 55.221 55.221
56.221 56.221 56.221 56.221 56.221
56.221 56.221 56.221 56.221 56.221
[:, :, 3] =
60.343 60.343 60.343 60.343 60.343
60.343 60.343 60.343 60.343 60.343
60.343 60.343 60.343 60.343 60.343
61.343 61.343 61.343 61.343 61.343
62.343 62.343 62.343 62.343 62.343
julia 卷积实现如下
using Pkg; for p in ("Knet","ArgParse"); haskey(Pkg.installed(),p) || Pkg.add(p); end
using LinearAlgebra
using CUDAdrv
using Knet, MAT, ArgParse, CuArrays,Statistics
to_host(ypred)=arraytype(ypred)
arraytype=Array{Float64}
data=zeros(5,5,3,1)
data[:,:,:,1]=partappledata[:,:,:];
v = param(testw;atype=KnetArray{Float64})
x = param(partappledata;atype=KnetArray{Float64})
y = conv4(v,x;padding=1)
ydata=to_host(y)
julia>ydata[:,:,1,1]
5×5 Array{Float64,2}:
194.694 120.055 120.055 120.055 -4.38531
140.102 0.368195 0.368195 0.368195 -115.278
142.784 2.84226 2.84226 2.84226 -114.264
144.647 3.42023 3.42023 3.42023 -115.046
0.798015 -130.909 -130.909 -130.909 -171.574
在tensorflow中
import tensorflow as tf
import numpy as np
sess=tf.Session()
immatdata[:,:,:] =
array([[[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ]],
[[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ]],
[[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ],
[ 41.06299973, 55.22100067, 60.3430481 ]],
[[ 42.06299973, 56.22100067, 61.3430481 ],
[ 42.06299973, 56.22100067, 61.3430481 ],
[ 42.06299973, 56.22100067, 61.3430481 ],
[ 42.06299973, 56.22100067, 61.3430481 ],
[ 42.06299973, 56.22100067, 61.3430481 ]],
[[ 42.06299973, 56.22100067, 62.3430481 ],
[ 42.06299973, 56.22100067, 62.3430481 ],
[ 42.06299973, 56.22100067, 62.3430481 ],
[ 42.06299973, 56.22100067, 62.3430481 ],
[ 42.06299973, 56.22100067, 62.3430481 ]]], dtype=float32)
wdata=
array([[[ 0.4800154 , 0.55037946, 0.42947057],
[ 0.4085474 , 0.44007453, 0.373467 ],
[-0.06514555, -0.08138704, -0.06136011]],
[[ 0.31047726, 0.34573907, 0.27476987],
[ 0.05020237, 0.04063221, 0.03868078],
[-0.40338343, -0.45350131, -0.36722335]],
[[-0.05087169, -0.05863491, -0.05746817],
[-0.28522751, -0.33066967, -0.26224968],
[-0.41851634, -0.4850302 , -0.35009676]]], dtype=float32)
w=wdata
data=np.zeros([1,5,5,3])
data[:,:,:,:]=immatdata[:,:,:]
part_conv=tf.nn.conv2d(data,w,strides=[1,1,1,1],padding="SAME")
part_conv_data=sess.run(part_conv)
in[58]:part_conv_data[0,:,:,0]
Out[58]:
array([[-168.01942444, -128.39241028, -128.39241028, -128.39241028,
0.46919155],
[-115.27775574, 0.36819839, 0.36819839, 0.36819839,
140.10180664],
[-117.4095459 , -1.93058014, -1.93058014, -1.93058014,
139.05670166],
[-119.1164856 , -2.76397705, -2.76397705, -2.76397705,
139.79747009],
[ -4.79425049, 122.31194305, 122.31194305, 122.31194305,
198.7494812 ]], dtype=float32)
我想要让tensorflow和julia的结果一致。因为我正在将一个julia版本的卷积转化为tensorflow版，实验需要。我猜测有两个问题，一个是卷积方式，一个是精度问题。
如果是卷积方式不同，那可以调整，但是看起来在tensorflow和julia中的有部分是重复，更像是对称。我猜测可能是卷积与相关卷积的区别。但是，我找的函数tensorflow.nn.convolution注释是这样写的，但是结果没有区别。
转回Julia，我原来想是不是julia中conv4的mode参数不同导致的，但是mode=1 or 0 没有区别。文档解释是mode=0 对应 convolution； mode=1对应cross-correlation.