Lecture 10: ImageInspector removed

Author: sadda

docs/Project.toml

@@ -10,7 +10,6 @@ DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
 GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
-ImageInspector = "b0ce21f1-0238-464b-b95f-8a4068743199"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

docs/src/lecture_10/exercises.md

@@ -307,7 +307,7 @@ Plot all images which are ``9`` but were classified as ``7``.
 
 To plot all these misclassified images, we find their indices and use the function `imageplot`. Since `y` are stored in the 1:10 format, we need to specify `classes`.
 
-```@example gpuu
+```julia
 using ImageInspector
 
 classes = 0:9
@@ -328,7 +328,7 @@ savefig("miss.svg") # hide
 
 
 
-![](miss.svg)
+# ![](miss.svg)
 
 We see that some of the nines could be recognized as a seven even by humans.
 
@@ -377,7 +377,7 @@ train_or_load!(file_name, m)
 
 Before plotting, we perform a for loop over the digits. Then ```onecold(y_train, classes) .== i``` creates a ```BitArray``` with ones if the condition is satisfied, and zeros if the condition is not satisfied. Then ```findall(???)``` selects all ones, and ```???[1:5]``` finds the first five indices. Since we need to plot the original image, and the images after the second and fourth layer (there is always a convolutional layer before the pooling layer), we save these values into ```z1```, ```z2``` and ```z3```. Then we need to access to desired channels and plot then via the `ImageInspector` package.
 
-```@example gpuu
+```julia
 using ImageInspector
 
 classes = 0:9
@@ -412,15 +412,15 @@ We plot and comment on three selected digits below.
 
 Digit 0
 
-![](Layers_0.svg)
+# ![](Layers_0.svg)
 
 Digit 1
 
-![](Layers_1.svg)
+# ![](Layers_1.svg)
 
 Digit 9
 
-![](Layers_9.svg)
+# ![](Layers_9.svg)
 
 We may observe several things:
 - The functions inside the neural network do the same operations on all samples. The second row is always a black digit on a grey background.

docs/src/lecture_10/nn.md

@@ -1,7 +1,7 @@
 ```@setup nn
 using Plots
 using MLDatasets
-using ImageInspector
+# using ImageInspector
 
 Core.eval(Main, :(using Flux)) # hide
 ENV["DATADEPS_ALWAYS_ACCEPT"] = true
@@ -85,7 +85,7 @@ nothing # hide
 
 We use the `imageplot` function to plot the images. To invert the colours, we need to call it with `1 .- X_train` instead of `X_train`.
 
-```@example nn
+```julia
 using Plots
 using ImageInspector
 
@@ -98,7 +98,7 @@ savefig("mnist_intro2.svg") # hide
 </p></details>
 ```
 
-![](mnist_intro2.svg)
+# ![](mnist_intro2.svg)