From b2eb0254672f1c846e498a4c4909bcaf45f77bf1 Mon Sep 17 00:00:00 2001 From: "Daniel J. McDonald" Date: Fri, 3 Nov 2023 14:52:39 -0700 Subject: [PATCH] rebuild for web display --- .../keras-nnet/execute-results/html.json | 16 + .../keras-nnet/figure-html/plot-w-preds-1.png | Bin 0 -> 141151 bytes .../keras-nnet/figure-html/show-boot-1.png | Bin 0 -> 42426 bytes .../figure-html/show-some-clothes-1.png | Bin 0 -> 123252 bytes .../libs/bootstrap/bootstrap-icons.css | 2018 +++++++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 164168 bytes .../libs/bootstrap/bootstrap.min.css | 10 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../keras-nnet/libs/quarto-html/anchor.min.js | 9 + .../keras-nnet/libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 203 ++ .../keras-nnet/libs/quarto-html/quarto.js | 902 ++++++++ .../keras-nnet/libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + schedule/handouts/keras-nnet.html | 744 ------ schedule/handouts/keras-nnet.qmd | 455 ++++ 17 files changed, 3636 insertions(+), 744 deletions(-) create mode 100644 _freeze/schedule/handouts/keras-nnet/execute-results/html.json create mode 100644 _freeze/schedule/handouts/keras-nnet/figure-html/plot-w-preds-1.png create mode 100644 _freeze/schedule/handouts/keras-nnet/figure-html/show-boot-1.png create mode 100644 _freeze/schedule/handouts/keras-nnet/figure-html/show-some-clothes-1.png create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/bootstrap/bootstrap-icons.css create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/bootstrap/bootstrap-icons.woff create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/bootstrap/bootstrap.min.css create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/bootstrap/bootstrap.min.js create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/clipboard/clipboard.min.js create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/quarto-html/anchor.min.js create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/quarto-html/popper.min.js create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/quarto-html/quarto.js create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/quarto-html/tippy.css create mode 100644 _freeze/schedule/handouts/keras-nnet/libs/quarto-html/tippy.umd.min.js delete mode 100644 schedule/handouts/keras-nnet.html create mode 100644 schedule/handouts/keras-nnet.qmd diff --git a/_freeze/schedule/handouts/keras-nnet/execute-results/html.json b/_freeze/schedule/handouts/keras-nnet/execute-results/html.json new file mode 100644 index 0000000..f72b7f5 --- /dev/null +++ b/_freeze/schedule/handouts/keras-nnet/execute-results/html.json @@ -0,0 +1,16 @@ +{ + "hash": "02d65b4839a2eafbf9a4acca86aa1ab9", + "result": { + "markdown": "---\ntitle: \"Keras and Neural Networks\"\nauthor: \"DJM\"\ndate: \"Last updated: 3 November 2023\"\n---\n\n\n\n\n## Installation\n\n_Attribution: this Lab derives mainly from a Vignette in the [R Keras package](https://keras.rstudio.com) under the [MIT License](https://keras.rstudio.com/LICENSE-text.html)._\n\n\nThis proved to be more challenging than I anticipated...\n\nMy setup:\n\n* MacOS M1 Processor\n* R/Rstudio 4.3+\n\nThen, if not already installed, you'll need 2 R packages\n\n\n::: {.cell}\n\n```{.r .cell-code}\ninstall.packages(\"reticulate\")\nremotes::install_github(\"rstudio/tensorflow\")\ninstall.packages(\"keras\")\n```\n:::\n\n\nNow make sure that python is installed on your system. If it isn't (or if you haven't used it in a while, or if it's somewhere R can't find) this may take a while.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nreticulate::install_python()\n```\n:::\n\n\nNow restart R before proceeding. \n\nFinally, install the python `keras` package (which also installs `tensorflow`\nand some other things).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkeras::install_keras()\n```\n:::\n\n\nRestart R again.\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr 1.1.3 ✔ readr 2.1.4\n✔ forcats 1.0.0 ✔ stringr 1.5.0\n✔ ggplot2 3.4.3 ✔ tibble 3.2.1\n✔ lubridate 1.9.2 ✔ tidyr 1.3.0\n✔ purrr 1.0.2 \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\nℹ Use the conflicted package () to force all conflicts to become errors\n```\n:::\n:::\n\n\n\n\n## Thoughts on R vs. Python\n\nI'm doing this in R because it's easier to walk through an R notebook than a \nJupyter notebook (for me). \n\nMost deep learning infrastructure is written in Python. So everything here is\nrunning python under the hood. \n\nOnce configured, it doesn't matter which you use: do what you're comfortable \nwith.\n\nThere's nothing special about Python (nor R). Consider this quote from Yann \nLeCun, head of AI at Facebook and one of the three fathers of deep learning \n(posted on Facebook on 26 October 2020):\n\n\n![](lecun-python.png)\n\n\n\n## Overview\n\n\nIn this guide, we will train a neural network model to classify images of \nclothing, like sneakers and shirts. \n\n\n\n\nThis guide uses the \n[Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which\ncontains 70,000 grayscale images in 10 categories. The images show individual \narticles of clothing at low resolution (28 by 28 pixels), as seen here:\n\n![](https://s3-eu-central-1.amazonaws.com/zalando-wp-zalando-research-production/2017/08/fashion-mnist-sprite.png)\n\n\nFashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. The MNIST dataset contains \nimages of handwritten digits (0, 1, 2, etc) in an identical format to the \narticles of clothing we'll use here. The original MNIST was curated by Yann \nLeCun, and he maintained a \n[database of performance results](http://yann.lecun.com/exdb/mnist/) for many \nyears.\n\nHere, we use Fashion MNIST for variety, and because it's a slightly more \nchallenging problem than regular MNIST. Both datasets are relatively small \nand are used to verify that an algorithm works as expected. They're good \nstarting points to test and debug code.\n\nWe will use 60,000 images to train the network and 10,000 images to evaluate \nhow accurately the network learned to classify images. You can access the \nFashion MNIST directly from Keras.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfashion_mnist <- dataset_fashion_mnist()\n\nc(train_images, train_labels) %<-% fashion_mnist$train\nc(test_images, test_labels) %<-% fashion_mnist$test\n```\n:::\n\n\nAt this point we have four arrays: The train_images and train_labels arrays \nare the training set — the data the model uses to learn. The model is tested \nagainst the test set: the test_images, and test_labels arrays.\n\nThe images each are 28 x 28 arrays, with pixel values ranging between 0 and 255.\nThe labels are arrays of integers, ranging from 0 to 9. These correspond to the\nclass of clothing the image represents:\n\nDigit | Class\n-------| -------------\n0 | T-shirt/top\n1 | Trouser\n2 | Pullover\n3 | Dress\n4 | Coat\n5 | Sandal\n6 | Shirt\n7 | Sneaker\n8 | Bag\n9 | Ankle boot\n\nEach image is mapped to a single label. Since the class names are not included \nwith the dataset, we'll store them in a vector to use later when plotting the \nimages.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nclass_names <- c(\n \"T-shirt/top\",\n \"Trouser\",\n \"Pullover\",\n \"Dress\",\n \"Coat\",\n \"Sandal\",\n \"Shirt\",\n \"Sneaker\",\n \"Bag\",\n \"Ankle boot\"\n)\n```\n:::\n\n\n\n\n## Explore the data\n\nLet's explore the format of the dataset before training the model. The following\nshows there are 60,000 images in the training set, with each image represented \nas 28 x 28 pixels:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndim(train_images)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 60000 28 28\n```\n:::\n:::\n\n\n\n\nLikewise, there are 60,000 labels in the training set:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndim(train_labels)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 60000\n```\n:::\n:::\n\n\n\n\nEach label is an integer between 0 and 9:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntrain_labels[1:20]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 9 0 0 3 0 2 7 2 5 5 0 9 5 5 7 9 1 0 6 4\n```\n:::\n:::\n\n\n\nThere are 10,000 images in the test set. Again, each image is represented as \n28 x 28 pixels:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndim(test_images)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 10000 28 28\n```\n:::\n:::\n\n\n\n\nAnd the test set contains 10,000 images labels:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndim(test_labels)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 10000\n```\n:::\n:::\n\n\n\n## Preprocess the data\n\nThe data should be preprocessed before training the network. If you inspect the \nfirst image in the training set, you will see that the pixel values fall in the \nrange of 0 to 255:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nimage1 <- as.data.frame(train_images[1, , ])\ncolnames(image1) <- seq_len(ncol(image1))\nimage1$y <- seq_len(nrow(image1))\nimage1 <- pivot_longer(image1, -y, names_to = \"x\")\nimage1$x <- as.integer(image1$x)\n\nggplot(image1, aes(x, y, fill = value)) +\n geom_raster() +\n scale_fill_gradient(low = \"white\", high = \"#053b64\", na.value = NA) +\n scale_y_reverse() +\n theme_void() +\n theme(aspect.ratio = 1)\n```\n\n::: {.cell-output-display}\n![](keras-nnet_files/figure-html/show-boot-1.png){width=672}\n:::\n:::\n\n\n\nWe scale these values to a range of 0 to 1 before feeding to the neural network\nmodel. For this, we simply divide by 255. The main implications here are for \nstarting values, learning rate, and regularization. The defaults like inputs in \n[0, 1].\n\nIt's important that the training set and the testing set are preprocessed in the\nsame way:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntrain_images <- train_images / 255\ntest_images <- test_images / 255\n```\n:::\n\n\n\nDisplay the first 25 images from the training set and display the class name \nabove each image. \n\nVerify that the data is in the correct format and we're ready to build and train\nthe network.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsample_clothes <- map(1:25, ~ expand_grid(x = 1:28, y = 1:28)) |>\n list_rbind(names_to = \"idx\")\nimgs <- train_images[1:25, , ]\nimgs <- apply(imgs, 1, c)\ncn <- class_names[train_labels[1:25] + 1]\nnames(cn) <- 1:25\nsample_clothes$value <- c(imgs)\nrm(imgs)\nggplot(sample_clothes, aes(x, y, fill = value)) +\n geom_raster() +\n scale_fill_gradient(low = \"white\", high = \"#053b64\", na.value = NA) +\n scale_y_reverse() +\n theme_void() +\n facet_wrap(~idx, nrow = 5, ncol = 5, labeller = labeller(idx = cn)) +\n theme()\n```\n\n::: {.cell-output-display}\n![](keras-nnet_files/figure-html/show-some-clothes-1.png){width=672}\n:::\n:::\n\n\n\n## Build the model\n\nBuilding the neural network requires configuring the layers of the model, then compiling the model.\n\n\n### Setup the layers\n\nThe basic building block of a neural network is the layer. Layers extract\nrepresentations from the data fed into them. And, hopefully, these \nrepresentations are more meaningful for the problem at hand.\n\nMost of deep learning consists of chaining together simple layers. Most layers, \nlike `layer_dense()`, have parameters that are learned during training.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmodel <- keras_model_sequential()\nmodel |>\n layer_flatten(input_shape = c(28, 28)) %>% # input\n layer_dense(units = 128, activation = \"relu\") %>% # hidden layer\n layer_dense(units = 10, activation = \"softmax\") # output class\n```\n:::\n\n\nThe first layer in this network, `layer_flatten()`, transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 \npixels. Think of this layer as unstacking rows of pixels in the image and\nlining them up. This layer has no parameters to learn; it only reformats the \ndata.\n\nAfter the pixels are flattened, the network consists of a sequence of two \n'dense' layers. These are densely-connected, or fully-connected, neural layers. \nThe first dense layer has 128 nodes (or neurons). The second (and last) layer \nis a 10-node softmax layer---this returns an array of 10 probability scores that\nsum to 1. Each node contains a score that indicates the probability that the \ncurrent image belongs to one of the 10 digit classes.\n\n### Compile the model\n\nBefore the model is ready for training, it needs a few more settings. These are\nadded during the model's compile step:\n\n* Loss function: This measures how accurate the model is during training. We \nwant to minimize this function to \"steer\" the model in the right direction.\n* Optimizer: This is how the model is updated based on the data it sees and its\nloss function.\n* Metrics: Used to monitor the training and testing steps. The following example\nuses accuracy, the fraction of the images that are correctly classified.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmodel |> compile(\n optimizer = \"adam\",\n loss = \"sparse_categorical_crossentropy\",\n metrics = c(\"accuracy\")\n)\n```\n:::\n\n\n\n### Train the model\n\nTraining the neural network model requires the following steps:\n\n1. Feed the training data to the model — in this example, the `train_images` and `train_labels` arrays.\n1. The model learns to associate images and labels.\n1. We ask the model to make predictions about a test set — in this example, the `test_images` array. We verify that the predictions match the labels from the `test_labels` array.\n\nTo start training, call the `fit()` method — the model is \"fit\" to the training\ndata (takes about a minute):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmodel |> fit(train_images, train_labels, epochs = 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nEpoch 1/5\n1875/1875 - 9s - loss: 0.5468 - accuracy: 0.8102 - 9s/epoch - 5ms/step\nEpoch 2/5\n1875/1875 - 8s - loss: 0.4775 - accuracy: 0.8356 - 8s/epoch - 4ms/step\nEpoch 3/5\n1875/1875 - 8s - loss: 0.4722 - accuracy: 0.8370 - 8s/epoch - 4ms/step\nEpoch 4/5\n1875/1875 - 9s - loss: 0.4732 - accuracy: 0.8406 - 9s/epoch - 5ms/step\nEpoch 5/5\n1875/1875 - 9s - loss: 0.4783 - accuracy: 0.8379 - 9s/epoch - 5ms/step\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntrain_score <- model |> evaluate(train_images, train_labels, verbose = 0)\ntrain_score\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n loss accuracy \n0.4885503 0.8282500 \n```\n:::\n:::\n\n\n\nAs the model trains, the loss and accuracy metrics are displayed. This model \nreaches an accuracy of about 83% on \nthe training data.\n\n### Evaluate accuracy\n\nNext, compare how the model performs on the test dataset:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntest_score <- model |> evaluate(test_images, test_labels, verbose = 0)\ntest_score\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n loss accuracy \n0.5505691 0.8124000 \n```\n:::\n:::\n\n\nIt turns out, the accuracy on the test data set is a little less than the \naccuracy on the training dataset. \n\n\n### Make predictions\n\nWith the model trained, we can use it to make predictions about some images.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npredictions <- model |> predict(test_images)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n313/313 - 1s - 514ms/epoch - 2ms/step\n```\n:::\n:::\n\n\nHere, the model has predicted the label for each image in the testing set. Let's\ntake a look at the first prediction:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nround(predictions[1, ], 3)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 0.000 0.000 0.000 0.000 0.000 0.035 0.000 0.027 0.000 0.938\n```\n:::\n:::\n\n\n\nA prediction is an array of 10 numbers. These are the posterior probabilities \nfor each of the 10 different articles of clothing. We can see which label has \nthe highest confidence value:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwhich.max(predictions[1, ])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 10\n```\n:::\n:::\n\n\n\nAlternatively, we can also directly get the class prediction:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nclass_pred <- model |>\n predict(test_images) |>\n k_argmax()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n313/313 - 0s - 453ms/epoch - 1ms/step\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nas.vector(class_pred[1:20])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 9 2 1 1 6 1 4 6 5 7 2 5 5 3 4 1 2 6 8 0\n```\n:::\n:::\n\n\n\n\nAs the labels are 0-based, this actually means a predicted label of 9 would correspond to the label \nfound in `class_names[10]`. So the model is most confident that this image is \nan ankle boot. And we can check the test label to see this is correct:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntest_labels[1]\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 9\n```\n:::\n:::\n\n\n\n\nLet's plot several images with their predictions. Correct prediction labels \nare blue and incorrect prediction labels are orange\n\n\n::: {.cell}\n\n```{.r .cell-code}\npar(mfcol = c(5, 5))\npar(mar = c(0, 0, 1.5, 0), xaxs = \"i\", yaxs = \"i\")\nfor (i in 1:25) {\n img <- test_images[i, , ]\n img <- t(apply(img, 2, rev))\n # subtract 1 as labels go from 0 to 9\n predicted_label <- which.max(predictions[i, ]) - 1\n true_label <- test_labels[i]\n color <- ifelse(predicted_label == true_label, \"#0b62a4\", \"#ff9200\")\n image(1:28, 1:28, img,\n col = gray((255:0) / 255),\n xaxt = \"n\", yaxt = \"n\",\n main = paste0(\n class_names[predicted_label + 1], \" (\",\n class_names[true_label + 1], \")\"\n ),\n col.main = color\n )\n}\n```\n\n::: {.cell-output-display}\n![](keras-nnet_files/figure-html/plot-w-preds-1.png){width=672}\n:::\n:::\n\n\n\n## What about random forests?\n\nWhat if we just did random forests instead? Takes a good bit less effort.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(ranger) # faster version of randomForests\ntrain_images <- t(apply(train_images, 1, c)) # flatten\ntest_images <- t(apply(test_images, 1, c))\ntrain_images <- cbind(train_labels, train_images) |> as_tibble()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The `x` argument of `as_tibble.matrix()` must have unique column names if\n`.name_repair` is omitted as of tibble 2.0.0.\nℹ Using compatibility `.name_repair`.\n```\n:::\n\n```{.r .cell-code}\ntest_images <- cbind(test_labels, test_images) |> as_tibble()\nnames(train_images) <- c(\"cl\", paste0(\"x\", 1:(ncol(train_images) - 1)))\nnames(test_images) <- names(train_images)\ntrain_images$cl <- as.factor(train_images$cl)\ntest_images$cl <- as.factor(test_images$cl)\nrf <- ranger(cl ~ ., data = train_images, num.trees = 100)\npreds <- predict(rf, data = test_images)\n```\n:::\n\n\nThe Test Set accuracy from Random Forests is \n88%. \n\nSlightly 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0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function Se(t){return[mt,_t,gt,bt].some((function(e){return t[e]>=0}))}const Ne={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ke(e,{elementContext:"reference"}),a=ke(e,{altBoundary:!0}),l=De(r,n),c=De(a,s,o),h=Se(l),d=Se(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},Ie={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof 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0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

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t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const 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e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu 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mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/_freeze/schedule/handouts/keras-nnet/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of 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deleted file mode 100644 index dd9be31..0000000 --- a/schedule/handouts/keras-nnet.html +++ /dev/null @@ -1,744 +0,0 @@ - - - - - - - - - - - -Keras and Neural Networks - - - - - - - - - - - - - - - - - - - -     - - - - \ No newline at end of file diff --git a/schedule/handouts/keras-nnet.qmd b/schedule/handouts/keras-nnet.qmd new file mode 100644 index 0000000..c22d82e --- /dev/null +++ b/schedule/handouts/keras-nnet.qmd @@ -0,0 +1,455 @@ +--- +title: "Keras and Neural Networks" +author: "DJM" +date: "Last updated: 3 November 2023" +--- + + + +## Installation + +_Attribution: this Lab derives mainly from a Vignette in the [R Keras package](https://keras.rstudio.com) under the [MIT License](https://keras.rstudio.com/LICENSE-text.html)._ + + +This proved to be more challenging than I anticipated... + +My setup: + +* MacOS M1 Processor +* R/Rstudio 4.3+ + +Then, if not already installed, you'll need 2 R packages + +```{r install-base, eval=FALSE} +install.packages("reticulate") +remotes::install_github("rstudio/tensorflow") +install.packages("keras") +``` + +Now make sure that python is installed on your system. If it isn't (or if you haven't used it in a while, or if it's somewhere R can't find) this may take a while. + +```{r check-python, eval=FALSE} +reticulate::install_python() +``` + +Now restart R before proceeding. + +Finally, install the python `keras` package (which also installs `tensorflow` +and some other things). + +```{r check-tensorflow, eval=FALSE} +keras::install_keras() +``` + +Restart R again. + +```{r setup, echo=FALSE, warning=FALSE} +library(tidyverse) +reticulate::use_virtualenv("r-tensorflow", TRUE) +library(tensorflow) +library(keras) +``` + + + +## Thoughts on R vs. Python + +I'm doing this in R because it's easier to walk through an R notebook than a +Jupyter notebook (for me). + +Most deep learning infrastructure is written in Python. So everything here is +running python under the hood. + +Once configured, it doesn't matter which you use: do what you're comfortable +with. + +There's nothing special about Python (nor R). Consider this quote from Yann +LeCun, head of AI at Facebook and one of the three fathers of deep learning +(posted on Facebook on 26 October 2020): + + +![](lecun-python.png) + + + +## Overview + + +In this guide, we will train a neural network model to classify images of +clothing, like sneakers and shirts. + + + + +This guide uses the +[Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which +contains 70,000 grayscale images in 10 categories. The images show individual +articles of clothing at low resolution (28 by 28 pixels), as seen here: + +![](https://s3-eu-central-1.amazonaws.com/zalando-wp-zalando-research-production/2017/08/fashion-mnist-sprite.png) + + +Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. The MNIST dataset contains +images of handwritten digits (0, 1, 2, etc) in an identical format to the +articles of clothing we'll use here. The original MNIST was curated by Yann +LeCun, and he maintained a +[database of performance results](http://yann.lecun.com/exdb/mnist/) for many +years. + +Here, we use Fashion MNIST for variety, and because it's a slightly more +challenging problem than regular MNIST. Both datasets are relatively small +and are used to verify that an algorithm works as expected. They're good +starting points to test and debug code. + +We will use 60,000 images to train the network and 10,000 images to evaluate +how accurately the network learned to classify images. You can access the +Fashion MNIST directly from Keras. + +```{r} + +fashion_mnist <- dataset_fashion_mnist() + +c(train_images, train_labels) %<-% fashion_mnist$train +c(test_images, test_labels) %<-% fashion_mnist$test +``` + +At this point we have four arrays: The train_images and train_labels arrays +are the training set — the data the model uses to learn. The model is tested +against the test set: the test_images, and test_labels arrays. + +The images each are 28 x 28 arrays, with pixel values ranging between 0 and 255. +The labels are arrays of integers, ranging from 0 to 9. These correspond to the +class of clothing the image represents: + +Digit | Class +-------| ------------- +0 | T-shirt/top +1 | Trouser +2 | Pullover +3 | Dress +4 | Coat +5 | Sandal +6 | Shirt +7 | Sneaker +8 | Bag +9 | Ankle boot + +Each image is mapped to a single label. Since the class names are not included +with the dataset, we'll store them in a vector to use later when plotting the +images. + +```{r} +class_names <- c( + "T-shirt/top", + "Trouser", + "Pullover", + "Dress", + "Coat", + "Sandal", + "Shirt", + "Sneaker", + "Bag", + "Ankle boot" +) +``` + + + +## Explore the data + +Let's explore the format of the dataset before training the model. The following +shows there are 60,000 images in the training set, with each image represented +as 28 x 28 pixels: + +```{r dim-train} +dim(train_images) +``` + + + +Likewise, there are 60,000 labels in the training set: + +```{r dim-test} +dim(train_labels) +``` + + + +Each label is an integer between 0 and 9: + +```{r train-labels} +train_labels[1:20] +``` + + +There are 10,000 images in the test set. Again, each image is represented as +28 x 28 pixels: + +```{r test-image} +dim(test_images) +``` + + + +And the test set contains 10,000 images labels: + +```{r test-labels} +dim(test_labels) +``` + + +## Preprocess the data + +The data should be preprocessed before training the network. If you inspect the +first image in the training set, you will see that the pixel values fall in the +range of 0 to 255: + +```{r show-boot} +image1 <- as.data.frame(train_images[1, , ]) +colnames(image1) <- seq_len(ncol(image1)) +image1$y <- seq_len(nrow(image1)) +image1 <- pivot_longer(image1, -y, names_to = "x") +image1$x <- as.integer(image1$x) + +ggplot(image1, aes(x, y, fill = value)) + + geom_raster() + + scale_fill_gradient(low = "white", high = "#053b64", na.value = NA) + + scale_y_reverse() + + theme_void() + + theme(aspect.ratio = 1) +``` + + +We scale these values to a range of 0 to 1 before feeding to the neural network +model. For this, we simply divide by 255. The main implications here are for +starting values, learning rate, and regularization. The defaults like inputs in +[0, 1]. + +It's important that the training set and the testing set are preprocessed in the +same way: + +```{r} +train_images <- train_images / 255 +test_images <- test_images / 255 +``` + + +Display the first 25 images from the training set and display the class name +above each image. + +Verify that the data is in the correct format and we're ready to build and train +the network. + + + +```{r show-some-clothes} +sample_clothes <- map(1:25, ~ expand_grid(x = 1:28, y = 1:28)) |> + list_rbind(names_to = "idx") +imgs <- train_images[1:25, , ] +imgs <- apply(imgs, 1, c) +cn <- class_names[train_labels[1:25] + 1] +names(cn) <- 1:25 +sample_clothes$value <- c(imgs) +rm(imgs) +ggplot(sample_clothes, aes(x, y, fill = value)) + + geom_raster() + + scale_fill_gradient(low = "white", high = "#053b64", na.value = NA) + + scale_y_reverse() + + theme_void() + + facet_wrap(~idx, nrow = 5, ncol = 5, labeller = labeller(idx = cn)) + + theme() +``` + + +## Build the model + +Building the neural network requires configuring the layers of the model, then compiling the model. + + +### Setup the layers + +The basic building block of a neural network is the layer. Layers extract +representations from the data fed into them. And, hopefully, these +representations are more meaningful for the problem at hand. + +Most of deep learning consists of chaining together simple layers. Most layers, +like `layer_dense()`, have parameters that are learned during training. + +```{r build-model} +model <- keras_model_sequential() +model |> + layer_flatten(input_shape = c(28, 28)) %>% # input + layer_dense(units = 128, activation = "relu") %>% # hidden layer + layer_dense(units = 10, activation = "softmax") # output class +``` + +The first layer in this network, `layer_flatten()`, transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 +pixels. Think of this layer as unstacking rows of pixels in the image and +lining them up. This layer has no parameters to learn; it only reformats the +data. + +After the pixels are flattened, the network consists of a sequence of two +'dense' layers. These are densely-connected, or fully-connected, neural layers. +The first dense layer has 128 nodes (or neurons). The second (and last) layer +is a 10-node softmax layer---this returns an array of 10 probability scores that +sum to 1. Each node contains a score that indicates the probability that the +current image belongs to one of the 10 digit classes. + +### Compile the model + +Before the model is ready for training, it needs a few more settings. These are +added during the model's compile step: + +* Loss function: This measures how accurate the model is during training. We +want to minimize this function to "steer" the model in the right direction. +* Optimizer: This is how the model is updated based on the data it sees and its +loss function. +* Metrics: Used to monitor the training and testing steps. The following example +uses accuracy, the fraction of the images that are correctly classified. + + +```{r compile-model} +model |> compile( + optimizer = "adam", + loss = "sparse_categorical_crossentropy", + metrics = c("accuracy") +) +``` + + +### Train the model + +Training the neural network model requires the following steps: + +1. Feed the training data to the model — in this example, the `train_images` and `train_labels` arrays. +1. The model learns to associate images and labels. +1. We ask the model to make predictions about a test set — in this example, the `test_images` array. We verify that the predictions match the labels from the `test_labels` array. + +To start training, call the `fit()` method — the model is "fit" to the training +data (takes about a minute): + +```{r fitit} +model |> fit(train_images, train_labels, epochs = 5) +``` + +```{r train-score} +train_score <- model |> evaluate(train_images, train_labels, verbose = 0) +train_score +``` + + +As the model trains, the loss and accuracy metrics are displayed. This model +reaches an accuracy of about `r unname(round(train_score[2], 2)) * 100`% on +the training data. + +### Evaluate accuracy + +Next, compare how the model performs on the test dataset: + +```{r test-score} +test_score <- model |> evaluate(test_images, test_labels, verbose = 0) +test_score +``` + +It turns out, the accuracy on the test data set is a little less than the +accuracy on the training dataset. + + +### Make predictions + +With the model trained, we can use it to make predictions about some images. + +```{r} +predictions <- model |> predict(test_images) +``` + +Here, the model has predicted the label for each image in the testing set. Let's +take a look at the first prediction: + +```{r pred1} +round(predictions[1, ], 3) +``` + + +A prediction is an array of 10 numbers. These are the posterior probabilities +for each of the 10 different articles of clothing. We can see which label has +the highest confidence value: + +```{r pred-softmax} +which.max(predictions[1, ]) +``` + + +Alternatively, we can also directly get the class prediction: + +```{r} +class_pred <- model |> + predict(test_images) |> + k_argmax() +``` + +```{r top20class-preds} +as.vector(class_pred[1:20]) +``` + + + +As the labels are 0-based, this actually means a predicted label of 9 would correspond to the label +found in `class_names[10]`. So the model is most confident that this image is +an ankle boot. And we can check the test label to see this is correct: + +```{r test-lab1} +test_labels[1] +``` + + + +Let's plot several images with their predictions. Correct prediction labels +are blue and incorrect prediction labels are orange + +```{r plot-w-preds} +par(mfcol = c(5, 5)) +par(mar = c(0, 0, 1.5, 0), xaxs = "i", yaxs = "i") +for (i in 1:25) { + img <- test_images[i, , ] + img <- t(apply(img, 2, rev)) + # subtract 1 as labels go from 0 to 9 + predicted_label <- which.max(predictions[i, ]) - 1 + true_label <- test_labels[i] + color <- ifelse(predicted_label == true_label, "#0b62a4", "#ff9200") + image(1:28, 1:28, img, + col = gray((255:0) / 255), + xaxt = "n", yaxt = "n", + main = paste0( + class_names[predicted_label + 1], " (", + class_names[true_label + 1], ")" + ), + col.main = color + ) +} +``` + + +## What about random forests? + +What if we just did random forests instead? Takes a good bit less effort. + +```{r rf-code, echo=TRUE, eval=TRUE} +library(ranger) # faster version of randomForests +train_images <- t(apply(train_images, 1, c)) # flatten +test_images <- t(apply(test_images, 1, c)) +train_images <- cbind(train_labels, train_images) |> as_tibble() +test_images <- cbind(test_labels, test_images) |> as_tibble() +names(train_images) <- c("cl", paste0("x", 1:(ncol(train_images) - 1))) +names(test_images) <- names(train_images) +train_images$cl <- as.factor(train_images$cl) +test_images$cl <- as.factor(test_images$cl) +rf <- ranger(cl ~ ., data = train_images, num.trees = 100) +preds <- predict(rf, data = test_images) +``` + +The Test Set accuracy from Random Forests is +`r round(mean(preds$predictions == test_labels), 2) * 100`%. + +Slightly better than the Neural Net for my run, but reasonably close. +
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    Keras and Neural Networks

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    Author
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    DJM

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    October 30, 2023

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    Installation

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    Attribution: this Lab derives mainly from a Vignette in the R Keras package under the MIT License.

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    This proved to be more challenging than I anticipated…

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    My setup:

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    • MacOS M1 Processor
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    • R/Rstudio 4.3+
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    Then, if not already installed, you’ll need 2 R packages

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    install.packages("reticulate")
-remotes::install_github("rstudio/tensorflow")
-install.packages("keras")
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    Now make sure that python is installed on your system. If it isn’t (or if you haven’t used it in a while, or if it’s somewhere R can’t find) this may take a while.

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    reticulate::install_python()
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    Now restart R before proceeding.

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    Finally, install the python keras package (which also installs tensorflow and some other things).

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    keras::install_keras()
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    Restart R again.

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    ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
-✔ dplyr     1.1.3     ✔ readr     2.1.4
-✔ forcats   1.0.0     ✔ stringr   1.5.0
-✔ ggplot2   3.4.3     ✔ tibble    3.2.1
-✔ lubridate 1.9.2     ✔ tidyr     1.3.0
-✔ purrr     1.0.2     
-── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
-✖ dplyr::filter() masks stats::filter()
-✖ dplyr::lag()    masks stats::lag()
-ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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    Thoughts on R vs. Python

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    I’m doing this in R because it’s easier to walk through an R notebook than a Jupyter notebook (for me).

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    Most deep learning infrastructure is written in Python. So everything here is running python under the hood.

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    Once configured, it doesn’t matter which you use: do what you’re comfortable with.

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    There’s nothing special about Python (nor R). Consider this quote from Yann LeCun, head of AI at Facebook and one of the three fathers of deep learning (posted on Facebook on 26 October 2020):

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    Overview

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    In this guide, we will train a neural network model to classify images of clothing, like sneakers and shirts.

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    This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:

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    Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc) in an identical format to the articles of clothing we’ll use here. The original MNIST was curated by Yann LeCun, and he maintained a database of performance results for many years.

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    Here, we use Fashion MNIST for variety, and because it’s a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They’re good starting points to test and debug code.

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    We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from Keras.

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    fashion_mnist <- dataset_fashion_mnist()
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-c(train_images, train_labels) %<-% fashion_mnist$train
-c(test_images, test_labels) %<-% fashion_mnist$test
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    At this point we have four arrays: The train_images and train_labels arrays are the training set — the data the model uses to learn. The model is tested against the test set: the test_images, and test_labels arrays.

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    The images each are 28 x 28 arrays, with pixel values ranging between 0 and 255. The labels are arrays of integers, ranging from 0 to 9. These correspond to the class of clothing the image represents:

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    DigitClass
    0T-shirt/top
    1Trouser
    2Pullover
    3Dress
    4Coat
    5Sandal
    6Shirt
    7Sneaker
    8Bag
    9Ankle boot
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    Each image is mapped to a single label. Since the class names are not included with the dataset, we’ll store them in a vector to use later when plotting the images.

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    class_names <- c(
-  "T-shirt/top",
-  "Trouser",
-  "Pullover",
-  "Dress",
-  "Coat",
-  "Sandal",
-  "Shirt",
-  "Sneaker",
-  "Bag",
-  "Ankle boot"
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    Explore the data

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    Let’s explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:

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    dim(train_images)
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    [1] 60000    28    28
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    Likewise, there are 60,000 labels in the training set:

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    dim(train_labels)
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    [1] 60000
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    Each label is an integer between 0 and 9:

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    train_labels[1:20]
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     [1] 9 0 0 3 0 2 7 2 5 5 0 9 5 5 7 9 1 0 6 4
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    There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:

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    dim(test_images)
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    [1] 10000    28    28
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    And the test set contains 10,000 images labels:

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    dim(test_labels)
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    [1] 10000
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    Preprocess the data

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    The data should be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:

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    image1 <- as.data.frame(train_images[1, , ])
-colnames(image1) <- seq_len(ncol(image1))
-image1$y <- seq_len(nrow(image1))
-image1 <- pivot_longer(image1, -y, names_to = "x")
-image1$x <- as.integer(image1$x)
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-ggplot(image1, aes(x, y, fill = value)) +
-  geom_raster() +
-  scale_fill_gradient(low = "white", high = "#053b64", na.value = NA) +
-  scale_y_reverse() +
-  theme_void() +
-  theme(aspect.ratio = 1)
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    We scale these values to a range of 0 to 1 before feeding to the neural network model. For this, we simply divide by 255. The main implications here are for starting values, learning rate, and regularization. The defaults like inputs in [0, 1].

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    It’s important that the training set and the testing set are preprocessed in the same way:

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    train_images <- train_images / 255
-test_images <- test_images / 255
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    Display the first 25 images from the training set and display the class name above each image.

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    Verify that the data is in the correct format and we’re ready to build and train the network.

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    sample_clothes <- map(1:25, ~ expand_grid(x = 1:28, y = 1:28)) |>
-  list_rbind(names_to = "idx")
-imgs <- train_images[1:25, , ]
-imgs <- apply(imgs, 1, c)
-cn <- class_names[train_labels[1:25] + 1]
-names(cn) <- 1:25
-sample_clothes$value <- c(imgs)
-rm(imgs)
-ggplot(sample_clothes, aes(x, y, fill = value)) +
-  geom_raster() +
-  scale_fill_gradient(low = "white", high = "#053b64", na.value = NA) +
-  scale_y_reverse() +
-  theme_void() +
-  facet_wrap(~idx, nrow = 5, ncol = 5, labeller = labeller(idx = cn)) +
-  theme()
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    Build the model

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    Building the neural network requires configuring the layers of the model, then compiling the model.

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    Setup the layers

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    The basic building block of a neural network is the layer. Layers extract representations from the data fed into them. And, hopefully, these representations are more meaningful for the problem at hand.

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    Most of deep learning consists of chaining together simple layers. Most layers, like layer_dense(), have parameters that are learned during training.

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    model <- keras_model_sequential()
-model |>
-  layer_flatten(input_shape = c(28, 28)) %>% # input
-  layer_dense(units = 128, activation = "relu") %>% # hidden layer
-  layer_dense(units = 10, activation = "softmax") # output class
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    The first layer in this network, layer_flatten(), transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels. Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.

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    After the pixels are flattened, the network consists of a sequence of two ‘dense’ layers. These are densely-connected, or fully-connected, neural layers. The first dense layer has 128 nodes (or neurons). The second (and last) layer is a 10-node softmax layer—this returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 digit classes.

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    Compile the model

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    Before the model is ready for training, it needs a few more settings. These are added during the model’s compile step:

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    • Loss function: This measures how accurate the model is during training. We want to minimize this function to “steer” the model in the right direction.
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    • Optimizer: This is how the model is updated based on the data it sees and its loss function.
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    • Metrics: Used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified.
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    model |> compile(
-  optimizer = "adam",
-  loss = "sparse_categorical_crossentropy",
-  metrics = c("accuracy")
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    Train the model

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    Training the neural network model requires the following steps:

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    1. Feed the training data to the model — in this example, the train_images and train_labels arrays.
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    2. The model learns to associate images and labels.
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    3. We ask the model to make predictions about a test set — in this example, the test_images array. We verify that the predictions match the labels from the test_labels array.
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    To start training, call the fit() method — the model is “fit” to the training data (takes about a minute):

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    model |> fit(train_images, train_labels, epochs = 5)
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    Epoch 1/5
-1875/1875 - 9s - loss: 0.5450 - accuracy: 0.8106 - 9s/epoch - 5ms/step
-Epoch 2/5
-1875/1875 - 11s - loss: 0.4763 - accuracy: 0.8361 - 11s/epoch - 6ms/step
-Epoch 3/5
-1875/1875 - 11s - loss: 0.4670 - accuracy: 0.8391 - 11s/epoch - 6ms/step
-Epoch 4/5
-1875/1875 - 10s - loss: 0.4695 - accuracy: 0.8401 - 10s/epoch - 5ms/step
-Epoch 5/5
-1875/1875 - 9s - loss: 0.4722 - accuracy: 0.8410 - 9s/epoch - 5ms/step
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    train_score <- model |> evaluate(train_images, train_labels, verbose = 0)
-train_score
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         loss  accuracy 
-0.4682855 0.8405667
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    As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 84% on the training data.

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    Evaluate accuracy

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    Next, compare how the model performs on the test dataset:

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    test_score <- model |> evaluate(test_images, test_labels, verbose = 0)
-test_score
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        loss accuracy 
-0.534751 0.820100
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    It turns out, the accuracy on the test data set is a little less than the accuracy on the training dataset.

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    Make predictions

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    With the model trained, we can use it to make predictions about some images.

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    predictions <- model |> predict(test_images)
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    313/313 - 1s - 528ms/epoch - 2ms/step
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    Here, the model has predicted the label for each image in the testing set. Let’s take a look at the first prediction:

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     [1] 0.000 0.000 0.000 0.000 0.000 0.012 0.000 0.008 0.000 0.980
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    A prediction is an array of 10 numbers. These are the posterior probabilities for each of the 10 different articles of clothing. We can see which label has the highest confidence value:

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    which.max(predictions[1, ])
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    [1] 10
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    Alternatively, we can also directly get the class prediction:

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-  predict(test_images) |>
-  k_argmax()
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    313/313 - 0s - 486ms/epoch - 2ms/step
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    as.vector(class_pred[1:20])
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     [1] 9 2 1 1 6 1 4 6 5 7 4 5 5 3 4 1 2 4 8 0
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    As the labels are 0-based, this actually means a predicted label of 9 would correspond to the label found in class_names[10]. So the model is most confident that this image is an ankle boot. And we can check the test label to see this is correct:

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    test_labels[1]
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    [1] 9
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    Let’s plot several images with their predictions. Correct prediction labels are blue and incorrect prediction labels are orange

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    par(mfcol = c(5, 5))
-par(mar = c(0, 0, 1.5, 0), xaxs = "i", yaxs = "i")
-for (i in 1:25) {
-  img <- test_images[i, , ]
-  img <- t(apply(img, 2, rev))
-  # subtract 1 as labels go from 0 to 9
-  predicted_label <- which.max(predictions[i, ]) - 1
-  true_label <- test_labels[i]
-  color <- ifelse(predicted_label == true_label, "#0b62a4", "#ff9200")
-  image(1:28, 1:28, img,
-    col = gray((255:0) / 255),
-    xaxt = "n", yaxt = "n",
-    main = paste0(
-      class_names[predicted_label + 1], " (",
-      class_names[true_label + 1], ")"
-    ),
-    col.main = color
-  )
-}
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    What about random forests?

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    What if we just did random forests instead? Takes a good bit less effort.

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    library(ranger) # faster version of randomForests
-train_images <- t(apply(train_images, 1, c)) # flatten
-test_images <- t(apply(test_images, 1, c))
-train_images <- cbind(train_labels, train_images) |> as_tibble()
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    Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
-`.name_repair` is omitted as of tibble 2.0.0.
-ℹ Using compatibility `.name_repair`.
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    test_images <- cbind(test_labels, test_images) |> as_tibble()
-names(train_images) <- c("cl", paste0("x", 1:(ncol(train_images) - 1)))
-names(test_images) <- names(train_images)
-train_images$cl <- as.factor(train_images$cl)
-test_images$cl <- as.factor(test_images$cl)
-rf <- ranger(cl ~ ., data = train_images, num.trees = 100)
-preds <- predict(rf, data = test_images)
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    The Test Set accuracy from Random Forests is 88%.

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    Slightly better than the Neural Net for my run, but reasonably close.

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