[cnn][julia]Flux实现卷积神经网络cnn预测手写MNIST

julia_Flux

1.导入Flux.jl和其他所需工具包

using Flux, MLDatasets, Statistics
using Flux: onehotbatch, onecold, logitcrossentropy, params
using MLDatasets: MNIST
using Base.Iterators: partition
using Printf, BSON
using Images
using CUDA,LinearAlgebra,Random
CUDA.allowscalar(false)

# 为学习率、batch、epoch和保存文件的路径设置默认值
Base.@kwdef mutable struct TrainArgs
  lr::Float64 = 3e-3
  epochs::Int = 20
  batch_size = 128
end

TrainArgs

训练集测试集

MNIST 60000训练集 10000测试集

train_data= MLDatasets.MNIST(split=:train)

dataset MNIST:
  metadata  =>    Dict{String, Any} with 3 entries
  split     =>    :train
  features  =>    28×28×60000 Array{Float32, 3}
  targets   =>    60000-element Vector{Int64}

test_data= MLDatasets.MNIST(split=:test)

dataset MNIST:
  metadata  =>    Dict{String, Any} with 3 entries
  split     =>    :test
  features  =>    28×28×10000 Array{Float32, 3}
  targets   =>    10000-element Vector{Int64}

train_data.features

28×28×60000 Array{Float32, 3}:
[:, :, 1] =
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.215686  0.533333  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.67451   0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.886275  0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.831373  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.529412  0.0  0.0  0.0
 ⋮                        ⋮         ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.101961     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.65098   …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.968627     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.498039     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0

[:, :, 2] =
 0.0  0.0  0.0  0.0  0.0       0.0       …  0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0       …  0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0980392  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.501961   0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.988235   0.0  0.0  0.0  0.0
 ⋮                             ⋮         ⋱                  ⋮         
 0.0  0.0  0.0  0.0  0.196078  0.929412     0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0       …  0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0       …  0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0

[:, :, 3] =
 0.0  0.0  0.0  0.0  0.0  0.0       0.0       …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.243137     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.317647  …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                        ⋮                   ⋱                 ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.6  0.6  0.6  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.262745  0.470588  …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.909804  0.705882     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.152941  0.152941     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0       …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       0.0          0.0  0.0  0.0  0.0  0.0  0.0

;;; … 

[:, :, 59998] =
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.45098   0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.941176  0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.988235  0.615686  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.639216  0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.576471  0.992157  0.0  0.0  0.0
 ⋮                        ⋮         ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.376471     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.47451   …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.835294     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.47451      0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0

[:, :, 59999] =
 0.0  0.0  0.0       0.0       0.0        …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0        …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                                        ⋱                 ⋮         
 0.0  0.0  0.752941  0.988235  0.745098      0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.901961  0.756863  0.0352941  …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.105882  0.105882  0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0        …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0       0.0       0.0           0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 60000] =
 0.0  0.0  0.0  0.0  0.0  0.0  …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.101961  0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.898039  0.286275  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.976471  0.756863  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.690196  0.772549  0.0  0.0  0.0
 ⋮                        ⋮    ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0     0.0       0.0       0.0  0.0  0.0

X_train = reshape(train_data.features, 28,28,1,:)
size(X_train)
pic_1 = X_train[:,:,:,1]

28×28×1 Array{Float32, 3}:
[:, :, 1] =
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.215686  0.533333  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.67451   0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.886275  0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.831373  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.529412  0.0  0.0  0.0
 ⋮                        ⋮         ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.101961     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.65098   …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.968627     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.498039     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0

img = pic_1[:,:]

28×28 Matrix{Float32}:
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.215686  0.533333  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.67451   0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.886275  0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.992157  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.831373  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.992157  0.529412  0.0  0.0  0.0
 ⋮                        ⋮         ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.101961     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.65098   …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.968627     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.498039     0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0  0.0

colorview(Gray,img')

train_data.targets

60000-element Vector{Int64}:
 5
 0
 4
 1
 9
 2
 1
 3
 1
 4
 ⋮
 2
 9
 5
 1
 8
 3
 5
 6
 8

Flux.onehotbatch(train_data.targets, 0:9)

10×60000 OneHotMatrix(::Vector{UInt32}) with eltype Bool:
 ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  …  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  1  ⋅  ⋅  1  ⋅  1  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  1  ⋅  1     ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅
 ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  …  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  1  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  1
 ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  1  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅

function loader(data::MNIST=train_data; batchsize::Int=64)
    x4dim = reshape(data.features, 28,28,1,:)
    yhot = Flux.onehotbatch(data.targets, 0:9)
    Flux.DataLoader((x4dim, yhot); batchsize, shuffle=true) |> gpu
end

loader()

x1, y1 = first(loader()) 
x1

28×28×1×64 CuArray{Float32, 4, CUDA.Mem.DeviceBuffer}:
[:, :, 1, 1] =
 0.0  0.0  0.0  0.0  0.0  0.0  0.0       …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0       …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.247059     0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                        ⋮              ⋱                 ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0       …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0       …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 1, 2] =
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                        ⋮         ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 1, 3] =
 0.0  0.0  0.0  0.0  0.0       0.0       …  0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0       …  0.0509804  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.435294   0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.627451   0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.533333   0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.270588   0.0  0.0  0.0  0.0
 ⋮                             ⋮         ⋱                  ⋮         
 0.0  0.0  0.0  0.0  0.27451   0.992157     0.0745098  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.101961  0.937255  …  0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.478431     0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0       …  0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0       0.0          0.0        0.0  0.0  0.0  0.0

;;;; … 

[:, :, 1, 62] =
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                        ⋮                   ⋱                 ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 1, 63] =
 0.0  0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.396078  0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.976471  0.882353  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.988235  0.988235  0.0  0.0
 ⋮                        ⋮              ⋱            ⋮              
 0.0  0.0  0.0  0.0  0.0  0.0  0.345098     0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0       …  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.886275     0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.466667     0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.662745  …  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.439216     0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0          0.0       0.0       0.0  0.0

[:, :, 1, 64] =
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                        ⋮              ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0

y1

10×64 OneHotMatrix(::CuArray{UInt32, 1, CUDA.Mem.DeviceBuffer}) with eltype Bool:
 ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  1  …  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅
 ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  1  ⋅  ⋅  1  1  ⋅  ⋅  ⋅  1
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  …  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 1  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  1  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  1  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅
 ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  1  1  ⋅  ⋅  ⋅  ⋅     ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅
 ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅     ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅

卷积神经网络模型 LeNet，通常用于手写数字识别等任务。让我们来看一下每一层的作用：

第一个卷积层：使用 Conv((5, 5), 1=>6, relu)，对输入图像进行 6 次滤波操作，每个过滤器都是 5x5 大小，生成 6 个输出通道。ReLU 激活函数将输出非线性化；
第一个最大池化层：使用 MaxPool((2, 2))，对输入的单通道或多通道特征图进行 2x2 的最大池化操作，从每个 2x2 的窗口中选出最大值，减少特征图的空间大小和计算复杂度；
第二个卷积层：使用 Conv((5, 5), 6=>16, relu)，输入为第一层的 6 个输出通道，经过 16 次 5x5 的卷积得到 16 个输出通道，ReLU 对其进行非线性化处理；
第二个最大池化层：使用 MaxPool((2, 2))，同样对输入的 16 通道特征图进行 2x2 的最大池化操作，减小特征图大小；
Flatten 层：使用 Flux.flatten，将大小为 (16, 5, 5) 的张量拉伸为一维向量；
全连接层 1：使用 Dense(256 => 120, relu)，输入为拉伸后的一维向量，输出为大小为 120 的特征向量，ReLU 对其进行非线性化处理；
全连接层 2：使用 Dense(120 => 84, relu)，输入为大小为 120 的特征向量，输出为大小为 84 的特征向量，ReLU 对其进行非线性化处理；
输出层：使用 Dense(84 => 10)，输入为大小为 84 的特征向量，输出为大小为 10 的得分向量，每个元素表示样本属于该类别的概率。

28 * 28 * 1 - > 24 * 24 * 6 - > 12 * 12 * 6 - > 8 * 8 * 16 - > 4 * 4 * 16 - > 256

model = Chain(
    Conv((5, 5), 1=>6, relu),
    MaxPool((2, 2)),
    Conv((5, 5), 6=>16, relu),
    MaxPool((2, 2)),
    Flux.flatten,
    Dense(256 => 120, relu),
    Dense(120 => 84, relu), 
    Dense(84 => 10),
) |> gpu

Chain(
  Conv((5, 5), 1 => 6, relu),           [90m# 156 parameters[39m
  MaxPool((2, 2)),
  Conv((5, 5), 6 => 16, relu),          [90m# 2_416 parameters[39m
  MaxPool((2, 2)),
  Flux.flatten,
  Dense(256 => 120, relu),              [90m# 30_840 parameters[39m
  Dense(120 => 84, relu),               [90m# 10_164 parameters[39m
  Dense(84 => 10),                      [90m# 850 parameters[39m
) [90m                  # Total: 10 arrays, [39m44_426 parameters, 2.086 KiB.

#先把x1放进去预测试试看
y1hat = model(x1) 
#行：对应的10个输出
#列：对应一个batch中64个样本

10×64 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
  0.0216377  -0.157416    -0.0696039  …  -0.113559     -0.169696
 -0.0367052  -0.022017     0.0686541     -0.000658836   0.0592685
  0.0315908   0.073026    -0.0919517     -0.0104487    -0.126225
 -0.127688   -0.101221    -0.0616018     -0.0647386    -0.02525
  0.0745182   0.00518273   0.106297       0.1257        0.0266679
  0.0660713  -0.018785    -0.0910264  …  -0.0812425    -0.126134
 -0.395019   -0.343077    -0.303919      -0.334892     -0.37829
 -0.0184298  -0.0638247   -0.0148924     -0.0276188    -0.0715621
  0.219471    0.22844      0.207235       0.126554      0.202388
 -0.172887   -0.0813897   -0.126973      -0.220921     -0.268266

y_hat = hcat(Flux.onecold(y1hat, 0:9), Flux.onecold(y1, 0:9)) |> cpu

64×2 Matrix{Int64}:
 8  6
 8  9
 8  2
 8  0
 8  3
 8  8
 8  6
 8  8
 8  8
 8  6
 ⋮  
 4  4
 8  7
 8  6
 8  4
 8  4
 1  1
 1  6
 8  8
 8  4

size(y_hat)[1]

result = hcat(y_hat,zeros(size(y_hat)[1],1))

64×3 Matrix{Float64}:
 8.0  6.0  0.0
 8.0  9.0  0.0
 8.0  2.0  0.0
 8.0  0.0  0.0
 8.0  3.0  0.0
 8.0  8.0  0.0
 8.0  6.0  0.0
 8.0  8.0  0.0
 8.0  8.0  0.0
 8.0  6.0  0.0
 ⋮         
 4.0  4.0  0.0
 8.0  7.0  0.0
 8.0  6.0  0.0
 8.0  4.0  0.0
 8.0  4.0  0.0
 1.0  1.0  0.0
 1.0  6.0  0.0
 8.0  8.0  0.0
 8.0  4.0  0.0


# 遍历原始矩阵，根据需要设置的第三列
for i in 1:size(y_hat, 1)
    if y_hat[i, 1] == y_hat[i, 2]
      result[i, 3] = 1
    end
end

result

64×3 Matrix{Float64}:
 8.0  6.0  0.0
 8.0  9.0  0.0
 8.0  2.0  0.0
 8.0  0.0  0.0
 8.0  3.0  0.0
 8.0  8.0  1.0
 8.0  6.0  0.0
 8.0  8.0  1.0
 8.0  8.0  1.0
 8.0  6.0  0.0
 ⋮         
 4.0  4.0  1.0
 8.0  7.0  0.0
 8.0  6.0  0.0
 8.0  4.0  0.0
 8.0  4.0  0.0
 1.0  1.0  1.0
 1.0  6.0  0.0
 8.0  8.0  1.0
 8.0  4.0  0.0

check_display = [result[:,1] result[:,2] result[:,3]]
# 预测值 | 真实值 | 是否正确
vscodedisplay(check_display)

using Statistics: mean

function loss_and_accuracy(model, data::MNIST=test_data)
    (x,y) = only(loader(data; batchsize=length(data)))  # make one big batch
    ŷ = model(x)
    loss = Flux.logitcrossentropy(ŷ, y)  # did not include softmax in the model
    acc = round(100 * mean(Flux.onecold(ŷ) .== Flux.onecold(y)); digits=2)
    (; loss, acc, split=data.split)  # return a NamedTuple
end

loss_and_accuracy (generic function with 2 methods)

@show loss_and_accuracy(model);

loss_and_accuracy(model) = (loss = 2.3237605f0, acc = 11.78, split = :test)

记录执行一个使用LeNet神经网络在数据集上进行分类任务的训练过程，并记录每个 epoch 的训练损失、准确率和测试准确率。具体解释如下：

settings 为一个命名元组（named tuple），包含了该模型的相关设置，例如学习率 eta、权重衰减 lambda、批量大小 batchsize 等。
train_log 为空数组，后面将用它来存储每个 epoch 的日志信息。
opt_rule 使用 Adam 优化器和权重衰减规则构成一个优化器链式表达式（opt_group），用于更新神经网络的参数。
opt_state 利用 Flux.setup 函数根据模型的初始权重设置好优化器的状态变量。
进入 for 循环，分别对于每个 epoch 训练，这里用 @time 宏可以显示程序执行时间。循环采用 loader() 函数在 batch 中随机加载数据。grads 包含了所有参数的梯度信息，通过 Flux.update! 函数将梯度传递给优化器进行参数更新。
当 epoch 为单数时，储存当前 epoch 的 train_loss、准确率（accuracy）以及 test_loss、test_acc（测试集上的损失和准确率），并将该 epoch 的信息作为一个特定的命名元组 nt 保存到 train_log 数组中。
训练过程结束，程序退出。
实现了一个基本的分类任务模型，并对其进行训练。在训练过程中，每个 epoch 的信息都被记录和储存，以便于后续的统计和分析。

settings = (;
    eta = 0.001,     # 学习率
    lambda = 1e-2,  # 在使用正则化（Regularization）方法优化神经网络的过程中，通常会添加一个权值衰减项（Weight Decay），它是一种标准正则化方法，旨在防止模型过度拟合并提高泛化性能。该方法通过对网络权重施加额外的约束，使得训练过程中权重逐渐趋向于较小的值。
    batchsize = 128,
    epochs = 30,
)
train_log = []

Any[]

opt_rule = OptimiserChain(WeightDecay(settings.lambda), Adam(settings.eta))  #优化器
opt_state = Flux.setup(opt_rule, model);   #配置优化器和模型的函数。该函数接受两个参数：一个优化器对象和一个模型对象model
using JLD2

for epoch in 1:settings.epochs
    @time for (x,y) in loader(batchsize=settings.batchsize)
        grads = Flux.gradient(m -> Flux.logitcrossentropy(m(x), y), model) #计算梯度
        Flux.update!(opt_state, model, grads[1]) #更新模型参数
    end


    if epoch % 2 == 1
      loss, acc, _ = loss_and_accuracy(model,train_data)
      test_loss, test_acc, _ = loss_and_accuracy(model, test_data)
      @info "logging:" epoch acc test_acc
      nt = (; epoch, loss, acc, test_loss, test_acc)  # make a NamedTuple
      push!(train_log, nt) #在训练集和测试集上进行训练，记录并输出每个 epoch 中训练的 loss 和 accuracy，并将结果以 NamedTuple 的形式保存在一个数组中。
    end

  if epoch % 5 == 0
    JLD2.jldsave("mymodel"; model_state = Flux.state(model) |> cpu) #保存模型
    println("saved to ", "mymodel", " after ", epoch, " epochs")
  end
end

 23.558565 seconds (34.64 M allocations: 2.349 GiB, 3.66% gc time, 75.51% compilation time)


┌ Info: logging:
│   epoch = 1
│   acc = 96.07
│   test_acc = 96.28
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.207501 seconds (1.75 M allocations: 333.873 MiB, 7.44% gc time)
  1.046763 seconds (1.75 M allocations: 333.984 MiB, 5.96% gc time)


┌ Info: logging:
│   epoch = 3
│   acc = 96.86
│   test_acc = 97.23
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.056170 seconds (1.75 M allocations: 333.873 MiB, 6.33% gc time)
  1.022417 seconds (1.74 M allocations: 333.682 MiB, 4.46% gc time)


┌ Info: logging:
│   epoch = 5
│   acc = 97.2
│   test_acc = 97.39
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


saved to mymodel after 5 epochs
  1.310450 seconds (1.76 M allocations: 334.547 MiB, 6.40% gc time)
  1.046871 seconds (1.75 M allocations: 333.978 MiB, 5.59% gc time)


┌ Info: logging:
│   epoch = 7
│   acc = 97.4
│   test_acc = 97.68
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.039215 seconds (1.75 M allocations: 333.876 MiB, 6.44% gc time)
  1.010181 seconds (1.74 M allocations: 333.688 MiB, 4.35% gc time)


┌ Info: logging:
│   epoch = 9
│   acc = 97.51
│   test_acc = 97.76
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.054506 seconds (1.75 M allocations: 333.883 MiB, 6.38% gc time)
saved to mymodel after 10 epochs
  1.010604 seconds (1.74 M allocations: 333.673 MiB, 4.28% gc time)


┌ Info: logging:
│   epoch = 11
│   acc = 97.32
│   test_acc = 97.37
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.073143 seconds (1.75 M allocations: 333.890 MiB, 6.37% gc time)
  1.021915 seconds (1.74 M allocations: 333.682 MiB, 4.27% gc time)


┌ Info: logging:
│   epoch = 13
│   acc = 97.64
│   test_acc = 98.03
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.062875 seconds (1.75 M allocations: 333.876 MiB, 6.31% gc time)
  1.035603 seconds (1.74 M allocations: 333.675 MiB, 4.21% gc time)
saved to mymodel after 15 epochs


┌ Info: logging:
│   epoch = 15
│   acc = 97.59
│   test_acc = 97.91
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.053837 seconds (1.75 M allocations: 333.882 MiB, 6.38% gc time)
  1.025212 seconds (1.74 M allocations: 333.676 MiB, 4.17% gc time)


┌ Info: logging:
│   epoch = 17
│   acc = 97.46
│   test_acc = 97.85
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.079404 seconds (1.75 M allocations: 333.880 MiB, 6.11% gc time)
  1.034417 seconds (1.74 M allocations: 333.682 MiB, 4.37% gc time)


┌ Info: logging:
│   epoch = 19
│   acc = 97.44
│   test_acc = 97.66
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.059716 seconds (1.75 M allocations: 333.880 MiB, 6.29% gc time)
saved to mymodel after 20 epochs
  1.056283 seconds (1.74 M allocations: 333.680 MiB, 4.25% gc time)


┌ Info: logging:
│   epoch = 21
│   acc = 97.74
│   test_acc = 97.94
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.089269 seconds (1.75 M allocations: 333.883 MiB, 6.26% gc time)
  1.012205 seconds (1.74 M allocations: 333.676 MiB, 4.36% gc time)


┌ Info: logging:
│   epoch = 23
│   acc = 97.55
│   test_acc = 97.69
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.059416 seconds (1.75 M allocations: 333.876 MiB, 6.33% gc time)
  1.026613 seconds (1.74 M allocations: 333.679 MiB, 4.77% gc time)
saved to mymodel after 25 epochs


┌ Info: logging:
│   epoch = 25
│   acc = 97.31
│   test_acc = 97.63
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.058952 seconds (1.75 M allocations: 333.879 MiB, 6.39% gc time)
  1.003798 seconds (1.74 M allocations: 333.676 MiB, 3.89% gc time)


┌ Info: logging:
│   epoch = 27
│   acc = 97.19
│   test_acc = 97.44
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.065478 seconds (1.75 M allocations: 333.880 MiB, 6.31% gc time)
  1.011256 seconds (1.74 M allocations: 333.679 MiB, 4.28% gc time)


┌ Info: logging:
│   epoch = 29
│   acc = 97.52
│   test_acc = 97.73
└ @ Main c:\Users\HP\Desktop\模式识别\julia_flux\cnnn.ipynb:15


  1.054528 seconds (1.75 M allocations: 333.890 MiB, 5.99% gc time)
saved to mymodel after 30 epochs

@show train_log;

train_log = Any[(epoch = 1, loss = 0.13786168f0, acc = 96.07, test_loss = 0.12124871f0, test_acc = 96.28), (epoch = 3, loss = 0.10716068f0, acc = 96.86, test_loss = 0.0937544f0, test_acc = 97.23), (epoch = 5, loss = 0.10237007f0, acc = 97.2, test_loss = 0.09357353f0, test_acc = 97.39), (epoch = 7, loss = 0.09652825f0, acc = 97.4, test_loss = 0.08653756f0, test_acc = 97.68), (epoch = 9, loss = 0.08729916f0, acc = 97.51, test_loss = 0.08031578f0, test_acc = 97.76), (epoch = 11, loss = 0.09641348f0, acc = 97.32, test_loss = 0.091933824f0, test_acc = 97.37), (epoch = 13, loss = 0.08514932f0, acc = 97.64, test_loss = 0.07468332f0, test_acc = 98.03), (epoch = 15, loss = 0.09086585f0, acc = 97.59, test_loss = 0.08101739f0, test_acc = 97.91), (epoch = 17, loss = 0.09147936f0, acc = 97.46, test_loss = 0.081585675f0, test_acc = 97.85), (epoch = 19, loss = 0.09023266f0, acc = 97.44, test_loss = 0.080876224f0, test_acc = 97.66), (epoch = 21, loss = 0.08312355f0, acc = 97.74, test_loss = 0.07422219f0, test_acc = 97.94), (epoch = 23, loss = 0.086933106f0, acc = 97.55, test_loss = 0.079941735f0, test_acc = 97.69), (epoch = 25, loss = 0.09566926f0, acc = 97.31, test_loss = 0.087120935f0, test_acc = 97.63), (epoch = 27, loss = 0.10115273f0, acc = 97.19, test_loss = 0.093344204f0, test_acc = 97.44), (epoch = 29, loss = 0.08723685f0, acc = 97.52, test_loss = 0.081499815f0, test_acc = 97.73)]

y1hat = model(x1)
y_hat_new = hcat(Flux.onecold(y1hat, 0:9), Flux.onecold(y1, 0:9)) |> cpu
result_new = hcat(y_hat_new,zeros(size(y_hat_new)[1],1))
for i in 1:size(y_hat_new, 1)
  if y_hat_new[i, 1] == y_hat_new[i, 2]
    result_new[i, 3] = 1
  end
end
result_new

64×3 Matrix{Float64}:
 6.0  6.0  1.0
 9.0  9.0  1.0
 2.0  2.0  1.0
 0.0  0.0  1.0
 3.0  3.0  1.0
 8.0  8.0  1.0
 6.0  6.0  1.0
 8.0  8.0  1.0
 8.0  8.0  1.0
 6.0  6.0  1.0
 ⋮         
 4.0  4.0  1.0
 7.0  7.0  1.0
 6.0  6.0  1.0
 4.0  4.0  1.0
 4.0  4.0  1.0
 1.0  1.0  1.0
 6.0  6.0  1.0
 8.0  8.0  1.0
 4.0  4.0  1.0

check_display_new = [result_new[:,1] result_new[:,2] result_new[:,3]]
# 预测值 | 真实值 | 是否正确
vscodedisplay(check_display_new)

using ImageCore, ImageInTerminal
xtest, ytest = only(loader(test_data, batchsize=length(test_data))); #得到测试所有样本 图片+label

size(xtest)

(28, 28, 1, 10000)

index = 17
get_image = xtest[:,:,1,index] .|> Gray |> transpose |> cpu
colorview(Gray,get_image)

y_label = Flux.onecold(ytest, 0:9)|> cpu
y_label[index]

查找分类最不确定的图像。
首先，在概率的每一列中，寻找概率最大的一个。
然后，在所有图像中寻找最低的该概率，并确定其索引。

ptest = softmax(model(xtest))
max_p = maximum(ptest; dims=1)
_, i = findmin(vec(max_p))

(0.19307606f0, 1018)

xtest[:,:,1,i] .|> Gray |> transpose |> cpu

using JLD2
loaded_state = JLD2.load("mymodel", "model_state"); #加载模型

model2 = Flux.@autosize (28, 28, 1, 1) Chain(
    Conv((5, 5), 1=>6, relu),
    MaxPool((2, 2)),
    Conv((5, 5), _=>16, relu),
    MaxPool((2, 2)),
    Flux.flatten,
    Dense(_ => 120, relu),
    Dense(_ => 84, relu), 
    Dense(_ => 10),
)
model2 = Flux.loadmodel!(model2, loaded_state) |>cpu

Chain(
  Conv((5, 5), 1 => 6, relu),           [90m# 156 parameters[39m
  MaxPool((2, 2)),
  Conv((5, 5), 6 => 16, relu),          [90m# 2_416 parameters[39m
  MaxPool((2, 2)),
  Flux.flatten,
  Dense(256 => 120, relu),              [90m# 30_840 parameters[39m
  Dense(120 => 84, relu),               [90m# 10_164 parameters[39m
  Dense(84 => 10),                      [90m# 850 parameters[39m
) [90m                  # Total: 10 arrays, [39m44_426 parameters, 174.867 KiB.

@show model2(cpu(x1)) ≈ cpu(model(x1))

model2(cpu(x1)) ≈ cpu(model(x1)) = true



true

using Images

# 加载图像并将其转换为 28x28 矩阵
img = load("4.png")
img_28_28 = imresize(img,(28,28))
img_gray = Gray.(Gray.(img_28_28) .> 0.5)
input_img = map(Float32, img_gray')

28×28 Matrix{Float32}:
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0  1.0  1.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                        ⋮              ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0

colorview(Gray,input_img')

input = reshape(input_img, 28,28,1,:) |>cpu

28×28×1×1 Array{Float32, 4}:
[:, :, 1, 1] =
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  1.0  1.0  1.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 ⋮                        ⋮              ⋱                      ⋮         
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  0.0

colorview(Gray,input) #实际输入模型

load_result = model2(input) |>cpu

10×1 Matrix{Float32}:
 -4.2211957
 -0.56267506
 -0.40410087
 -3.0764287
  9.327405
 -1.5239846
  0.65510744
  0.44357768
 -2.260195
  1.5781431

output = softmax(load_result) |>cpu

10×1 Matrix{Float32}:
 1.3047671f-6
 5.063005f-5
 5.9330312f-5
 4.0992018f-6
 0.9991155
 1.9360517f-5
 0.0001711137
 0.00013849005
 9.272249f-6
 0.00043067936

Flux.onecold(output, 0:9) #得到预测标签

1-element Vector{Int64}:
 4

julia图像切割

using Images,ImageFiltering, ImageView, ImageMorphology,ImageSegmentation
# 读取图像
img = load("qq_hand.jpg")

binary_img = map(Float32,1 * (Gray.(img) .> 0.5))   #图片二值化 膨胀腐蚀（预处理）
carplate_img_binary = Gray.(Gray.(binary_img) .< 0.5)
carplate_img_binary_c = closing(closing(closing(carplate_img_binary)))
carplate_img_binary_e = erode(erode(carplate_img_binary_c))

input_img = map(Float32, carplate_img_binary_e)
column = sum(input_img, dims=1)  # 沿着列的方向将矩阵
raw = sum(input_img, dims=2)
# 查找第一个非零元素的索引
raw_first_index = findfirst(raw .!= 0)[1]
# 查找最后一个非零元素的索引
raw_last_index = findlast(raw .!= 0)[1]
column_index = findall(column .!= 0 )
column_index_list = Float32[]
push!(column_index_list,column_index[1][2])
for i in 2:length(column_index)-1
  if (column_index[i-1][2] + 1 != column_index[i][2]) || (column_index[i+1][2] - 1 != column_index[i][2])
    push!(column_index_list,column_index[i][2])
  end
end
push!(column_index_list,column_index[length(column_index)][2])
cut_column = reshape(column_index_list,(2,10))
test_pic = []
for i in 1:size(cut_column)[2]
  push!(test_pic,input_img[raw_first_index:raw_last_index, Int32(cut_column[:,i][1]):Int32(cut_column[:,i][2])])
end

column

1×2145 Matrix{Float32}:
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  0.0

using Plots
plot(1:length(raw),raw[:])

using Plots
plot(1:length(column),column[:])

using Images #处理成能放入网络预测的28*28*1的矩阵
result = []
fillcolor = fill(RGB{Float32}(0), (1, 1))[1]
for i in 1:length(test_pic)
  if size(test_pic[i])[1] > 4 * size(test_pic[i])[2]
    z1 = zeros(Float32,size(test_pic[i])[1],2 * size(test_pic[i])[2])
    test_pic[i] = hcat(z1,hcat(test_pic[i],z1))
  end
  test_pic[i] = imresize(test_pic[i],(28,28),Pad=true,padcolor=fillcolor,stretch=false)
  input = reshape(test_pic[i]', 28,28,1,:) |>cpu
  load_result = model2(input) |>cpu #放入预测
  output = softmax(load_result) |>cpu
  push!(result,Flux.onecold(output, 0:9))
end

colorview(Gray,test_pic[4])

result

10-element Vector{Any}:
 [1]
 [4]
 [5]
 [0]
 [5]
 [3]
 [3]
 [4]
 [3]
 [2]

img

posted @ 2023-06-07 16:05 J1nWan 阅读(171) 评论(0) 编辑收藏举报

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