Lecture 10: Scripts

Author: sadda

scripts/lecture_10/Project.toml

@@ -1,12 +1,15 @@
 [deps]
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+ImageInspector = "b0ce21f1-0238-464b-b95f-8a4068743199"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 BSON = "= 0.2.6"
 Flux = "= 0.11.6"
 MLDatasets = "= 0.5.6"
+Plots = "= 1.10.3"
 julia = "1.5"

scripts/lecture_10/script_init.jl

@@ -10,12 +10,16 @@ using MLDatasets
 using Flux
 using Flux: onehotbatch, onecold, crossentropy
 using Flux.Data: DataLoader
+using Plots
+using ImageInspector
 
 function reshape_data(X::AbstractArray{<:Real, 3})
     s = size(X)
     return reshape(X, s[1], s[2], 1, s[3])
 end
 
+reshape_data(X::AbstractArray{<:Real, 4}) = X
+
 function load_data(dataset; T=Float32, onehot=false, classes=0:9)
     X_train, y_train = dataset.traindata(T)
     X_test, y_test = dataset.testdata(T)
@@ -33,7 +37,7 @@ end
 
 T = Float32
 X_train, y_train, X_test, y_test = load_data(MLDatasets.MNIST; T=T, onehot=true);
-load_data(MLDatasets.CIFAR; T=T, onehot=true);
+load_data(MLDatasets.CIFAR10; T=T, onehot=true);
 
 inds = findall(y_train .== 0)[1:15]
 imageplot(1 .- X_train, inds; nrows=3, size=(800,480))
@@ -71,4 +75,4 @@ train_model!(m, L, X_train, y_train; n_epochs=1)
 
 accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
 
-string(accuracy(X_test, y_test))
+accuracy(X_test, y_test)

scripts/lecture_10/script_sol.jl

@@ -6,6 +6,8 @@ using MLDatasets
 using Flux
 using Flux: onehotbatch, onecold, crossentropy
 using Flux.Data: DataLoader
+using Plots
+using ImageInspector
 
 T = Float32
 X_train, y_train = MLDatasets.MNIST.traindata(T)