From c07f488910e5f336654514efa88917582b5960e9 Mon Sep 17 00:00:00 2001
From: aketkar18 <anujketkar1118@gmail.com>
Date: Sun, 3 Dec 2023 14:16:26 -0600
Subject: [PATCH] Added LeNet file for CSCE 420 backup

---
 images/.DS_Store | Bin 0 -> 6148 bytes
 lenet.html       |  70 +++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 70 insertions(+)
 create mode 100644 images/.DS_Store
 create mode 100644 lenet.html
diff --git a/images/.DS_Store b/images/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
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diff --git a/lenet.html b/lenet.html
new file mode 100644
index 0000000..4bea0b2
--- /dev/null
+++ b/lenet.html
@@ -0,0 +1,70 @@
+<!DOCTYPE html><html><head><meta charSet="utf-8"/><link rel="apple-touch-icon" sizes="180x180" href="https://cdn.oaistatic.com/_next/static/media/apple-touch-icon.59f2e898.png"/><link rel="icon" type="image/png" sizes="32x32" href="https://cdn.oaistatic.com/_next/static/media/favicon-32x32.be48395e.png"/><link rel="icon" type="image/png" sizes="16x16" href="https://cdn.oaistatic.com/_next/static/media/favicon-16x16.9b8dbb69.png"/><link rel="preconnect" href="https://cdn.oaistatic.com"/><meta name="title" content="ChatGPT: Get instant answers, find inspiration, learn something new"/><meta name="description" content="ChatGPT is a free-to-use AI system. Use it for engaging conversations, gain insights, automate tasks, and witness the future of AI, all in one place."/><meta name="keywords" content="ai chat,ai,chap gpt,chat gbt,chat gpt 3,chat gpt login,chat gpt website,chat gpt,chat gtp,chat openai,chat,chatai,chatbot gpt,chatg,chatgpt login,chatgpt,gpt chat,open ai,openai chat,openai chatgpt,openai"/><meta http-equiv="Content-Type" content="text/html; charset=utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><meta property="og:url" content="https://chat.openai.com"/><meta name="apple-itunes-app" content="app-id=6448311069"/><title>LeNet Model for MNIST</title><meta property="og:site_name" content="ChatGPT"/><meta name="robots" content="noindex,nofollow"/><meta property="og:title" content="LeNet Model for MNIST"/><meta property="og:description" content="Shared by Anuj Ketkar via ChatGPT"/><meta property="og:image" content="https://cdn.oaistatic.com/_next/static/media/chatgpt-share-og.49cbbffe.png"/><meta 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overflow-hidden"><div class="relative flex h-full max-w-full flex-1 flex-col overflow-hidden"><main class="relative h-full w-full flex-1 overflow-auto transition-width"><div role="presentation" class="flex h-full flex-col"><div class="flex-1 overflow-hidden"><div class="h-full overflow-auto dark:bg-gray-800"><div class="flex flex-col pb-9 text-sm"><div class="mx-auto w-full p-4 md:max-w-2xl md:px-0 lg:max-w-[38rem] xl:max-w-3xl"><div class="border-b border-gray-100 pb-4 pt-3 sm:mb-2 sm:pb-6 sm:pt-8"><h1 class="text-3xl font-semibold leading-tight text-gray-700 dark:text-gray-100 sm:text-4xl">LeNet Model for MNIST</h1><div class="pt-3 text-base text-gray-400 sm:pt-4"><span>Anuj Ketkar</span><span class="px-2">•</span>December 1, 2023</div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-2"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#7989FF;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="icon-sm" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M20 21v-2a4 4 0 0 0-4-4H8a4 4 0 0 0-4 4v2"></path><circle cx="12" cy="7" r="4"></circle></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)]"><div class="font-semibold select-none">Anonymous</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa2fe28-08ef-4965-88bb-3c3e2ef20048" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="">Write tensorflow code for the LeNet model for Convolutional Neural Networks (Yann LeCun&#x27;s original model), and train it on the MNIST dataset for handwritten data. The receptive field should be 5x5, there should be 2 convolutional layers, and 25 channels for each layer. When possible, do everything in separate steps through adding (rather than using a constructor that does everything in one line). DO NOT include a SINGLE comment ANYWHERE in the code (I&#x27;m serious not a single one!) .</div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-3"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#E06C2B;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-sm" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 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15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)] agent-turn"><div class="font-semibold select-none">ChatGPT</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="91ebd573-7b7b-45b8-a6f8-0c7d95392214" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="markdown prose w-full break-words dark:prose-invert light"><p>Sure, here is the TensorFlow code for the LeNet model with 2 convolutional layers, each having 25 channels, and a 5x5 receptive field. This code also trains the model on the MNIST dataset:</p><pre><div class="bg-black rounded-md"><div class="flex items-center relative text-gray-200 bg-gray-800 dark:bg-token-surface-primary px-4 py-2 text-xs font-sans justify-between rounded-t-md"><span>python</span><button class="flex gap-1 items-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-sm"><path fill-rule="evenodd" clip-rule="evenodd" d="M12 4C10.8954 4 10 4.89543 10 6H14C14 4.89543 13.1046 4 12 4ZM8.53513 4C9.22675 2.8044 10.5194 2 12 2C13.4806 2 14.7733 2.8044 15.4649 4H17C18.6569 4 20 5.34315 20 7V19C20 20.6569 18.6569 22 17 22H7C5.34315 22 4 20.6569 4 19V7C4 5.34315 5.34315 4 7 4H8.53513ZM8 6H7C6.44772 6 6 6.44772 6 7V19C6 19.5523 6.44772 20 7 20H17C17.5523 20 18 19.5523 18 19V7C18 6.44772 17.5523 6 17 6H16C16 7.10457 15.1046 8 14 8H10C8.89543 8 8 7.10457 8 6Z" fill="currentColor"></path></svg>Copy code</button></div><div class="p-4 overflow-y-auto"><code class="!whitespace-pre hljs language-python"><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
+<span class="hljs-keyword">from</span> tensorflow.keras <span class="hljs-keyword">import</span> layers, models
+<span class="hljs-keyword">from</span> tensorflow.keras.datasets <span class="hljs-keyword">import</span> mnist
+
+(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+
+train_images = train_images.reshape((<span class="hljs-number">60000</span>, <span class="hljs-number">28</span>, <span class="hljs-number">28</span>, <span class="hljs-number">1</span>))
+test_images = test_images.reshape((<span class="hljs-number">10000</span>, <span class="hljs-number">28</span>, <span class="hljs-number">28</span>, <span class="hljs-number">1</span>))
+
+train_images, test_images = train_images / <span class="hljs-number">255.0</span>, test_images / <span class="hljs-number">255.0</span>
+
+model = models.Sequential()
+model.add(layers.Conv2D(<span class="hljs-number">25</span>, (<span class="hljs-number">5</span>, <span class="hljs-number">5</span>), activation=<span class="hljs-string">&#x27;relu&#x27;</span>, input_shape=(<span class="hljs-number">28</span>, <span class="hljs-number">28</span>, <span class="hljs-number">1</span>)))
+model.add(layers.MaxPooling2D((<span class="hljs-number">2</span>, <span class="hljs-number">2</span>)))
+model.add(layers.Conv2D(<span class="hljs-number">25</span>, (<span class="hljs-number">5</span>, <span class="hljs-number">5</span>), activation=<span class="hljs-string">&#x27;relu&#x27;</span>))
+model.add(layers.MaxPooling2D((<span class="hljs-number">2</span>, <span class="hljs-number">2</span>)))
+model.add(layers.Flatten())
+model.add(layers.Dense(<span class="hljs-number">100</span>, activation=<span class="hljs-string">&#x27;relu&#x27;</span>))
+model.add(layers.Dense(<span class="hljs-number">10</span>, activation=<span class="hljs-string">&#x27;softmax&#x27;</span>))
+
+model.<span class="hljs-built_in">compile</span>(optimizer=<span class="hljs-string">&#x27;adam&#x27;</span>,
+              loss=<span class="hljs-string">&#x27;sparse_categorical_crossentropy&#x27;</span>,
+              metrics=[<span class="hljs-string">&#x27;accuracy&#x27;</span>])
+
+model.fit(train_images, train_labels, epochs=<span class="hljs-number">5</span>)
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=<span class="hljs-number">2</span>)
+<span class="hljs-built_in">print</span>(<span class="hljs-string">&#x27;\nTest accuracy:&#x27;</span>, test_acc)
+</code></div></div></pre><p>I hope this helps!</p></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-4"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#7989FF;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="icon-sm" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M20 21v-2a4 4 0 0 0-4-4H8a4 4 0 0 0-4 4v2"></path><circle cx="12" cy="7" r="4"></circle></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)]"><div class="font-semibold select-none">Anonymous</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa2e47d-d6a6-418e-9552-4e59d7002538" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="">Now plot the convolutional kernels from the first convolutional layer with the grey colormap. It should be a 5x5 arrangement and do not import anything that was previously imported in the previous code as this is for the same notebook.  Once again, not a single comment should be present. Also make sure it is labeled in the graph.</div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-5"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#E06C2B;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-sm" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 40.5054 26.7133 39.8321 28.4418 38.5772C30.1704 37.3223 31.4556 35.5506 32.1119 33.5179C33.5027 33.2332 34.8167 32.6547 35.9659 31.821C37.115 30.9874 38.0728 29.9178 38.7752 28.684C39.8458 26.8371 40.3023 24.6979 40.0789 22.5748C39.8556 20.4517 38.9639 18.4544 37.5324 16.8707ZM22.4978 37.8849C20.7443 37.8874 19.0459 37.2733 17.6994 36.1501C17.7601 36.117 17.8666 36.0586 17.936 36.0161L25.9004 31.4156C26.1003 31.3019 26.2663 31.137 26.3813 30.9378C26.4964 30.7386 26.5563 30.5124 26.5549 30.2825V19.0542L29.9213 20.998C29.9389 21.0068 29.9541 21.0198 29.9656 21.0359C29.977 21.052 29.9842 21.0707 29.9867 21.0902V30.3889C29.9842 32.375 29.1946 34.2791 27.7909 35.6841C26.3872 37.0892 24.4838 37.8806 22.4978 37.8849ZM6.39227 31.0064C5.51397 29.4888 5.19742 27.7107 5.49804 25.9832C5.55718 26.0187 5.66048 26.0818 5.73461 26.1244L13.699 30.7248C13.8975 30.8408 14.1233 30.902 14.3532 30.902C14.583 30.902 14.8088 30.8408 15.0073 30.7248L24.731 25.1103V28.9979C24.7321 29.0177 24.7283 29.0376 24.7199 29.0556C24.7115 29.0736 24.6988 29.0893 24.6829 29.1012L16.6317 33.7497C14.9096 34.7416 12.8643 35.0097 10.9447 34.4954C9.02506 33.9811 7.38785 32.7263 6.39227 31.0064ZM4.29707 13.6194C5.17156 12.0998 6.55279 10.9364 8.19885 10.3327C8.19885 10.4013 8.19491 10.5228 8.19491 10.6071V19.808C8.19351 20.0378 8.25334 20.2638 8.36823 20.4629C8.48312 20.6619 8.64893 20.8267 8.84863 20.9404L18.5723 26.5542L15.206 28.4979C15.1894 28.5089 15.1703 28.5155 15.1505 28.5173C15.1307 28.5191 15.1107 28.516 15.0924 28.5082L7.04046 23.8557C5.32135 22.8601 4.06716 21.2235 3.55289 19.3046C3.03862 17.3858 3.30624 15.3413 4.29707 13.6194ZM31.955 20.0556L22.2312 14.4411L25.5976 12.4981C25.6142 12.4872 25.6333 12.4805 25.6531 12.4787C25.6729 12.4769 25.6928 12.4801 25.7111 12.4879L33.7631 17.1364C34.9967 17.849 36.0017 18.8982 36.6606 20.1613C37.3194 21.4244 37.6047 22.849 37.4832 24.2684C37.3617 25.6878 36.8382 27.0432 35.9743 28.1759C35.1103 29.3086 33.9415 30.1717 32.6047 30.6641C32.6047 30.5947 32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)] agent-turn"><div class="font-semibold select-none">ChatGPT</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="1dcd3d62-070e-4c4f-87b2-0c56e7da63ee" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="markdown prose w-full break-words dark:prose-invert light"><p>Here&#x27;s the code to plot the convolutional kernels from the first convolutional layer of the LeNet model with the grey colormap:</p><pre><div class="bg-black rounded-md"><div class="flex items-center relative text-gray-200 bg-gray-800 dark:bg-token-surface-primary px-4 py-2 text-xs font-sans justify-between rounded-t-md"><span>python</span><button class="flex gap-1 items-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-sm"><path fill-rule="evenodd" clip-rule="evenodd" d="M12 4C10.8954 4 10 4.89543 10 6H14C14 4.89543 13.1046 4 12 4ZM8.53513 4C9.22675 2.8044 10.5194 2 12 2C13.4806 2 14.7733 2.8044 15.4649 4H17C18.6569 4 20 5.34315 20 7V19C20 20.6569 18.6569 22 17 22H7C5.34315 22 4 20.6569 4 19V7C4 5.34315 5.34315 4 7 4H8.53513ZM8 6H7C6.44772 6 6 6.44772 6 7V19C6 19.5523 6.44772 20 7 20H17C17.5523 20 18 19.5523 18 19V7C18 6.44772 17.5523 6 17 6H16C16 7.10457 15.1046 8 14 8H10C8.89543 8 8 7.10457 8 6Z" fill="currentColor"></path></svg>Copy code</button></div><div class="p-4 overflow-y-auto"><code class="!whitespace-pre hljs language-python"><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
+<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
+
+weights = model.layers[<span class="hljs-number">0</span>].get_weights()[<span class="hljs-number">0</span>]
+num_kernels = weights.shape[<span class="hljs-number">3</span>]
+sqrt_num_kernels = <span class="hljs-built_in">int</span>(np.ceil(np.sqrt(num_kernels)))
+
+plt.figure(figsize=(<span class="hljs-number">10</span>, <span class="hljs-number">10</span>))
+<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_kernels):
+    plt.subplot(sqrt_num_kernels, sqrt_num_kernels, i+<span class="hljs-number">1</span>)
+    plt.imshow(weights[:, :, <span class="hljs-number">0</span>, i], cmap=<span class="hljs-string">&#x27;gray&#x27;</span>)
+    plt.title(<span class="hljs-string">f&#x27;Kernel <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&#x27;</span>)
+    plt.axis(<span class="hljs-string">&#x27;off&#x27;</span>)
+
+plt.suptitle(<span class="hljs-string">&#x27;Convolutional Kernels from First Layer&#x27;</span>, fontsize=<span class="hljs-number">16</span>)
+plt.tight_layout()
+plt.show()
+</code></div></div></pre><p>This code will plot the convolutional kernels in a 5x5 arrangement with labels for each kernel.</p></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-6"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#7989FF;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="icon-sm" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M20 21v-2a4 4 0 0 0-4-4H8a4 4 0 0 0-4 4v2"></path><circle cx="12" cy="7" r="4"></circle></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)]"><div class="font-semibold select-none">Anonymous</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa279ad-76fe-4931-9eab-2c71d18f8c9b" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="">Finally, pick an input the category &quot;3&quot; and plot just the feature map. Same rules apply, use the gray colormap and  no comments in the code. </div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-7"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#E06C2B;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-sm" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 40.5054 26.7133 39.8321 28.4418 38.5772C30.1704 37.3223 31.4556 35.5506 32.1119 33.5179C33.5027 33.2332 34.8167 32.6547 35.9659 31.821C37.115 30.9874 38.0728 29.9178 38.7752 28.684C39.8458 26.8371 40.3023 24.6979 40.0789 22.5748C39.8556 20.4517 38.9639 18.4544 37.5324 16.8707ZM22.4978 37.8849C20.7443 37.8874 19.0459 37.2733 17.6994 36.1501C17.7601 36.117 17.8666 36.0586 17.936 36.0161L25.9004 31.4156C26.1003 31.3019 26.2663 31.137 26.3813 30.9378C26.4964 30.7386 26.5563 30.5124 26.5549 30.2825V19.0542L29.9213 20.998C29.9389 21.0068 29.9541 21.0198 29.9656 21.0359C29.977 21.052 29.9842 21.0707 29.9867 21.0902V30.3889C29.9842 32.375 29.1946 34.2791 27.7909 35.6841C26.3872 37.0892 24.4838 37.8806 22.4978 37.8849ZM6.39227 31.0064C5.51397 29.4888 5.19742 27.7107 5.49804 25.9832C5.55718 26.0187 5.66048 26.0818 5.73461 26.1244L13.699 30.7248C13.8975 30.8408 14.1233 30.902 14.3532 30.902C14.583 30.902 14.8088 30.8408 15.0073 30.7248L24.731 25.1103V28.9979C24.7321 29.0177 24.7283 29.0376 24.7199 29.0556C24.7115 29.0736 24.6988 29.0893 24.6829 29.1012L16.6317 33.7497C14.9096 34.7416 12.8643 35.0097 10.9447 34.4954C9.02506 33.9811 7.38785 32.7263 6.39227 31.0064ZM4.29707 13.6194C5.17156 12.0998 6.55279 10.9364 8.19885 10.3327C8.19885 10.4013 8.19491 10.5228 8.19491 10.6071V19.808C8.19351 20.0378 8.25334 20.2638 8.36823 20.4629C8.48312 20.6619 8.64893 20.8267 8.84863 20.9404L18.5723 26.5542L15.206 28.4979C15.1894 28.5089 15.1703 28.5155 15.1505 28.5173C15.1307 28.5191 15.1107 28.516 15.0924 28.5082L7.04046 23.8557C5.32135 22.8601 4.06716 21.2235 3.55289 19.3046C3.03862 17.3858 3.30624 15.3413 4.29707 13.6194ZM31.955 20.0556L22.2312 14.4411L25.5976 12.4981C25.6142 12.4872 25.6333 12.4805 25.6531 12.4787C25.6729 12.4769 25.6928 12.4801 25.7111 12.4879L33.7631 17.1364C34.9967 17.849 36.0017 18.8982 36.6606 20.1613C37.3194 21.4244 37.6047 22.849 37.4832 24.2684C37.3617 25.6878 36.8382 27.0432 35.9743 28.1759C35.1103 29.3086 33.9415 30.1717 32.6047 30.6641C32.6047 30.5947 32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)] agent-turn"><div class="font-semibold select-none">ChatGPT</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="17dba0d8-2623-41c5-82ac-cfa91b95024c" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="markdown prose w-full break-words dark:prose-invert light"><p>Here&#x27;s the code to pick an input image of category &quot;3&quot; and plot the feature map from the first convolutional layer using the grey colormap:</p><pre><div class="bg-black rounded-md"><div class="flex items-center relative text-gray-200 bg-gray-800 dark:bg-token-surface-primary px-4 py-2 text-xs font-sans justify-between rounded-t-md"><span>python</span><button class="flex gap-1 items-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-sm"><path fill-rule="evenodd" clip-rule="evenodd" d="M12 4C10.8954 4 10 4.89543 10 6H14C14 4.89543 13.1046 4 12 4ZM8.53513 4C9.22675 2.8044 10.5194 2 12 2C13.4806 2 14.7733 2.8044 15.4649 4H17C18.6569 4 20 5.34315 20 7V19C20 20.6569 18.6569 22 17 22H7C5.34315 22 4 20.6569 4 19V7C4 5.34315 5.34315 4 7 4H8.53513ZM8 6H7C6.44772 6 6 6.44772 6 7V19C6 19.5523 6.44772 20 7 20H17C17.5523 20 18 19.5523 18 19V7C18 6.44772 17.5523 6 17 6H16C16 7.10457 15.1046 8 14 8H10C8.89543 8 8 7.10457 8 6Z" fill="currentColor"></path></svg>Copy code</button></div><div class="p-4 overflow-y-auto"><code class="!whitespace-pre hljs language-python"><span class="hljs-keyword">import</span> random
+
+<span class="hljs-keyword">def</span> <span class="hljs-title function_">find_first_3</span>():
+    index = random.randint(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(test_labels) - <span class="hljs-number">1</span>)
+    <span class="hljs-keyword">while</span> test_labels[index] != <span class="hljs-number">3</span>:
+        index = random.randint(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(test_labels) - <span class="hljs-number">1</span>)
+    <span class="hljs-keyword">return</span> test_images[index:index+<span class="hljs-number">1</span>]
+
+input_image = find_first_3()
+
+conv1_layer = model.layers[<span class="hljs-number">0</span>]
+feature_map_model = tf.keras.models.Model(inputs=model.inputs, outputs=conv1_layer.output)
+feature_maps = feature_map_model.predict(input_image)
+
+plt.figure(figsize=(<span class="hljs-number">8</span>, <span class="hljs-number">8</span>))
+<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">25</span>):
+    plt.subplot(<span class="hljs-number">5</span>, <span class="hljs-number">5</span>, i+<span class="hljs-number">1</span>)
+    plt.imshow(feature_maps[<span class="hljs-number">0</span>, :, :, i], cmap=<span class="hljs-string">&#x27;gray&#x27;</span>)
+    plt.title(<span class="hljs-string">f&#x27;Feature Map <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>&#x27;</span>)
+    plt.axis(<span class="hljs-string">&#x27;off&#x27;</span>)
+
+plt.suptitle(<span class="hljs-string">&#x27;Feature Maps from First Convolutional Layer (Input Category 3)&#x27;</span>, fontsize=<span class="hljs-number">16</span>)
+plt.tight_layout()
+plt.show()
+</code></div></div></pre><p>This code selects an input image with the label &quot;3,&quot; passes it through the first convolutional layer, and then plots the feature maps in grey colormap.</p></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-8"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#7989FF;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="icon-sm" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M20 21v-2a4 4 0 0 0-4-4H8a4 4 0 0 0-4 4v2"></path><circle cx="12" cy="7" r="4"></circle></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)]"><div class="font-semibold select-none">Anonymous</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa214ae-8efb-421e-a98c-d51f455047b1" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="">Thank you for all your help!</div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" style="--avatar-color:#E06C2B" data-testid="conversation-turn-9"><div class="px-4 py-2 justify-center text-base md:gap-6 m-auto"><div class="flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] } group final-completion"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5"><div class="gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full"><div style="background-color:#E06C2B;width:24px;height:24px" class="relative p-1 rounded-sm h-9 w-9 text-white flex items-center justify-center"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-sm" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 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21.0198 29.9656 21.0359C29.977 21.052 29.9842 21.0707 29.9867 21.0902V30.3889C29.9842 32.375 29.1946 34.2791 27.7909 35.6841C26.3872 37.0892 24.4838 37.8806 22.4978 37.8849ZM6.39227 31.0064C5.51397 29.4888 5.19742 27.7107 5.49804 25.9832C5.55718 26.0187 5.66048 26.0818 5.73461 26.1244L13.699 30.7248C13.8975 30.8408 14.1233 30.902 14.3532 30.902C14.583 30.902 14.8088 30.8408 15.0073 30.7248L24.731 25.1103V28.9979C24.7321 29.0177 24.7283 29.0376 24.7199 29.0556C24.7115 29.0736 24.6988 29.0893 24.6829 29.1012L16.6317 33.7497C14.9096 34.7416 12.8643 35.0097 10.9447 34.4954C9.02506 33.9811 7.38785 32.7263 6.39227 31.0064ZM4.29707 13.6194C5.17156 12.0998 6.55279 10.9364 8.19885 10.3327C8.19885 10.4013 8.19491 10.5228 8.19491 10.6071V19.808C8.19351 20.0378 8.25334 20.2638 8.36823 20.4629C8.48312 20.6619 8.64893 20.8267 8.84863 20.9404L18.5723 26.5542L15.206 28.4979C15.1894 28.5089 15.1703 28.5155 15.1505 28.5173C15.1307 28.5191 15.1107 28.516 15.0924 28.5082L7.04046 23.8557C5.32135 22.8601 4.06716 21.2235 3.55289 19.3046C3.03862 17.3858 3.30624 15.3413 4.29707 13.6194ZM31.955 20.0556L22.2312 14.4411L25.5976 12.4981C25.6142 12.4872 25.6333 12.4805 25.6531 12.4787C25.6729 12.4769 25.6928 12.4801 25.7111 12.4879L33.7631 17.1364C34.9967 17.849 36.0017 18.8982 36.6606 20.1613C37.3194 21.4244 37.6047 22.849 37.4832 24.2684C37.3617 25.6878 36.8382 27.0432 35.9743 28.1759C35.1103 29.3086 33.9415 30.1717 32.6047 30.6641C32.6047 30.5947 32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="relative flex w-full flex-col lg:w-[calc(100%-115px)] agent-turn"><div class="font-semibold select-none">ChatGPT</div><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4" class="min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto"><div class="markdown prose w-full break-words dark:prose-invert light"><p>You&#x27;re welcome! If you have any more questions or need further assistance in the future, feel free to reach out. Have a great day!</p></div></div></div></div></div></div></div></div></div></div></div><div class="w-full pt-2 md:pt-0 dark:border-white/20 md:border-transparent md:dark:border-transparent md:w-[calc(100%-.5rem)]"><div class="relative flex w-full flex-1 items-center justify-center gap-2 pt-3 empty:hidden"></div><div class="relative px-2 py-2 text-center text-xs text-gray-600 dark:text-gray-300 md:px-[60px]"><div class="flex justify-center gap-3 text-gray-500"><button>Report content</button><span>|</span><a href="https://openai.com/policies/terms-of-use" target="_blank" rel="noreferrer">Terms of use</a><span>|</span><a href="https://openai.com/policies/privacy-policy" target="_blank" rel="noreferrer">Privacy policy</a></div></div></div></div><div class="group fixed bottom-3 right-3 z-10 hidden gap-1 lg:flex"><div class="group relative" data-headlessui-state=""><button class="flex items-center justify-center rounded-full border border-gray-200 bg-gray-50 text-gray-600 dark:border-white/10 dark:bg-white/10 dark:text-gray-200" id="headlessui-menu-button-:Rd4qhdm:" type="button" aria-haspopup="true" aria-expanded="false" data-headlessui-state=""><div class="flex h-6 w-6 items-center justify-center text-xs">?</div></button></div></div></main></div></div><div class="absolute left-0 right-0 top-0 z-[2]"></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"sharedConversationId":"101324b2-a1ed-4ee4-9fba-b5c103559413","serverResponse":{"type":"data","data":{"title":"LeNet Model for MNIST","create_time":1701402585.796802,"update_time":1701402596,"mapping":{"e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4":{"id":"e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4","message":{"id":"e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4","author":{"role":"assistant","metadata":{}},"create_time":1701402573.895302,"content":{"content_type":"text","parts":["You're welcome! If you have any more questions or need further assistance in the future, feel free to reach out. Have a great day!"]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa214ae-8efb-421e-a98c-d51f455047b1","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa214ae-8efb-421e-a98c-d51f455047b1","children":[]},"aaa214ae-8efb-421e-a98c-d51f455047b1":{"id":"aaa214ae-8efb-421e-a98c-d51f455047b1","message":{"id":"aaa214ae-8efb-421e-a98c-d51f455047b1","author":{"role":"user","metadata":{}},"create_time":1701402573.261102,"content":{"content_type":"text","parts":["Thank you for all your help!"]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"17dba0d8-2623-41c5-82ac-cfa91b95024c","children":["e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4"]},"17dba0d8-2623-41c5-82ac-cfa91b95024c":{"id":"17dba0d8-2623-41c5-82ac-cfa91b95024c","message":{"id":"17dba0d8-2623-41c5-82ac-cfa91b95024c","author":{"role":"assistant","metadata":{}},"create_time":1701402143.183437,"content":{"content_type":"text","parts":["Here's the code to pick an input image of category \"3\" and plot the feature map from the first convolutional layer using the grey colormap:\n\n```python\nimport random\n\ndef find_first_3():\n    index = random.randint(0, len(test_labels) - 1)\n    while test_labels[index] != 3:\n        index = random.randint(0, len(test_labels) - 1)\n    return test_images[index:index+1]\n\ninput_image = find_first_3()\n\nconv1_layer = model.layers[0]\nfeature_map_model = tf.keras.models.Model(inputs=model.inputs, outputs=conv1_layer.output)\nfeature_maps = feature_map_model.predict(input_image)\n\nplt.figure(figsize=(8, 8))\nfor i in range(25):\n    plt.subplot(5, 5, i+1)\n    plt.imshow(feature_maps[0, :, :, i], cmap='gray')\n    plt.title(f'Feature Map {i+1}')\n    plt.axis('off')\n\nplt.suptitle('Feature Maps from First Convolutional Layer (Input Category 3)', fontsize=16)\nplt.tight_layout()\nplt.show()\n```\n\nThis code selects an input image with the label \"3,\" passes it through the first convolutional layer, and then plots the feature maps in grey colormap."]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","children":["aaa214ae-8efb-421e-a98c-d51f455047b1"]},"aaa279ad-76fe-4931-9eab-2c71d18f8c9b":{"id":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","message":{"id":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","author":{"role":"user","metadata":{}},"create_time":1701402139.974324,"content":{"content_type":"text","parts":["Finally, pick an input the category \"3\" and plot just the feature map. Same rules apply, use the gray colormap and  no comments in the code. "]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"1dcd3d62-070e-4c4f-87b2-0c56e7da63ee","children":["17dba0d8-2623-41c5-82ac-cfa91b95024c"]},"1dcd3d62-070e-4c4f-87b2-0c56e7da63ee":{"id":"1dcd3d62-070e-4c4f-87b2-0c56e7da63ee","message":{"id":"1dcd3d62-070e-4c4f-87b2-0c56e7da63ee","author":{"role":"assistant","metadata":{}},"create_time":1701401940.632094,"content":{"content_type":"text","parts":["Here's the code to plot the convolutional kernels from the first convolutional layer of the LeNet model with the grey colormap:\n\n```python\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nweights = model.layers[0].get_weights()[0]\nnum_kernels = weights.shape[3]\nsqrt_num_kernels = int(np.ceil(np.sqrt(num_kernels)))\n\nplt.figure(figsize=(10, 10))\nfor i in range(num_kernels):\n    plt.subplot(sqrt_num_kernels, sqrt_num_kernels, i+1)\n    plt.imshow(weights[:, :, 0, i], cmap='gray')\n    plt.title(f'Kernel {i+1}')\n    plt.axis('off')\n\nplt.suptitle('Convolutional Kernels from First Layer', fontsize=16)\nplt.tight_layout()\nplt.show()\n```\n\nThis code will plot the convolutional kernels in a 5x5 arrangement with labels for each kernel."]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa2e47d-d6a6-418e-9552-4e59d7002538","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa2e47d-d6a6-418e-9552-4e59d7002538","children":["aaa279ad-76fe-4931-9eab-2c71d18f8c9b"]},"aaa2e47d-d6a6-418e-9552-4e59d7002538":{"id":"aaa2e47d-d6a6-418e-9552-4e59d7002538","message":{"id":"aaa2e47d-d6a6-418e-9552-4e59d7002538","author":{"role":"user","metadata":{}},"create_time":1701401938.062347,"content":{"content_type":"text","parts":["Now plot the convolutional kernels from the first convolutional layer with the grey colormap. It should be a 5x5 arrangement and do not import anything that was previously imported in the previous code as this is for the same notebook.  Once again, not a single comment should be present. Also make sure it is labeled in the graph."]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"91ebd573-7b7b-45b8-a6f8-0c7d95392214","children":["1dcd3d62-070e-4c4f-87b2-0c56e7da63ee"]},"91ebd573-7b7b-45b8-a6f8-0c7d95392214":{"id":"91ebd573-7b7b-45b8-a6f8-0c7d95392214","message":{"id":"91ebd573-7b7b-45b8-a6f8-0c7d95392214","author":{"role":"assistant","metadata":{}},"create_time":1701401319.719922,"content":{"content_type":"text","parts":["Sure, here is the TensorFlow code for the LeNet model with 2 convolutional layers, each having 25 channels, and a 5x5 receptive field. This code also trains the model on the MNIST dataset:\n\n```python\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models\nfrom tensorflow.keras.datasets import mnist\n\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n\ntrain_images = train_images.reshape((60000, 28, 28, 1))\ntest_images = test_images.reshape((10000, 28, 28, 1))\n\ntrain_images, test_images = train_images / 255.0, test_images / 255.0\n\nmodel = models.Sequential()\nmodel.add(layers.Conv2D(25, (5, 5), activation='relu', input_shape=(28, 28, 1)))\nmodel.add(layers.MaxPooling2D((2, 2)))\nmodel.add(layers.Conv2D(25, (5, 5), activation='relu'))\nmodel.add(layers.MaxPooling2D((2, 2)))\nmodel.add(layers.Flatten())\nmodel.add(layers.Dense(100, activation='relu'))\nmodel.add(layers.Dense(10, activation='softmax'))\n\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nmodel.fit(train_images, train_labels, epochs=5)\n\ntest_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\nprint('\\nTest accuracy:', test_acc)\n```\n\nI hope this helps!"]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","children":["aaa2e47d-d6a6-418e-9552-4e59d7002538"]},"aaa2fe28-08ef-4965-88bb-3c3e2ef20048":{"id":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","message":{"id":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","author":{"role":"user","metadata":{}},"create_time":1701401315.905569,"content":{"content_type":"text","parts":["Write tensorflow code for the LeNet model for Convolutional Neural Networks (Yann LeCun's original model), and train it on the MNIST dataset for handwritten data. The receptive field should be 5x5, there should be 2 convolutional layers, and 25 channels for each layer. When possible, do everything in separate steps through adding (rather than using a constructor that does everything in one line). DO NOT include a SINGLE comment ANYWHERE in the code (I'm serious not a single one!) ."]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"9bd98659-a170-4718-af8c-a8eec8ea3285","children":["91ebd573-7b7b-45b8-a6f8-0c7d95392214"]},"9bd98659-a170-4718-af8c-a8eec8ea3285":{"id":"9bd98659-a170-4718-af8c-a8eec8ea3285","message":{"id":"9bd98659-a170-4718-af8c-a8eec8ea3285","author":{"role":"system","metadata":{}},"content":{"content_type":"text","parts":[""]},"status":"finished_successfully","end_turn":true,"weight":0,"metadata":{"shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa11d2a-ca53-43bb-b51c-0c5fcbf18ce0","children":["aaa2fe28-08ef-4965-88bb-3c3e2ef20048"]},"aaa11d2a-ca53-43bb-b51c-0c5fcbf18ce0":{"id":"aaa11d2a-ca53-43bb-b51c-0c5fcbf18ce0","children":["9bd98659-a170-4718-af8c-a8eec8ea3285"]}},"moderation_results":[],"current_node":"e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4","conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413","author_name":"Anuj Ketkar","is_public":true,"linear_conversation":[{"id":"aaa11d2a-ca53-43bb-b51c-0c5fcbf18ce0","children":["9bd98659-a170-4718-af8c-a8eec8ea3285"]},{"id":"9bd98659-a170-4718-af8c-a8eec8ea3285","message":{"id":"9bd98659-a170-4718-af8c-a8eec8ea3285","author":{"role":"system","metadata":{}},"content":{"content_type":"text","parts":[""]},"status":"finished_successfully","end_turn":true,"weight":0,"metadata":{"shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa11d2a-ca53-43bb-b51c-0c5fcbf18ce0","children":["aaa2fe28-08ef-4965-88bb-3c3e2ef20048"]},{"id":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","message":{"id":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","author":{"role":"user","metadata":{}},"create_time":1701401315.905569,"content":{"content_type":"text","parts":["Write tensorflow code for the LeNet model for Convolutional Neural Networks (Yann LeCun's original model), and train it on the MNIST dataset for handwritten data. The receptive field should be 5x5, there should be 2 convolutional layers, and 25 channels for each layer. When possible, do everything in separate steps through adding (rather than using a constructor that does everything in one line). DO NOT include a SINGLE comment ANYWHERE in the code (I'm serious not a single one!) ."]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"9bd98659-a170-4718-af8c-a8eec8ea3285","children":["91ebd573-7b7b-45b8-a6f8-0c7d95392214"]},{"id":"91ebd573-7b7b-45b8-a6f8-0c7d95392214","message":{"id":"91ebd573-7b7b-45b8-a6f8-0c7d95392214","author":{"role":"assistant","metadata":{}},"create_time":1701401319.719922,"content":{"content_type":"text","parts":["Sure, here is the TensorFlow code for the LeNet model with 2 convolutional layers, each having 25 channels, and a 5x5 receptive field. This code also trains the model on the MNIST dataset:\n\n```python\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models\nfrom tensorflow.keras.datasets import mnist\n\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n\ntrain_images = train_images.reshape((60000, 28, 28, 1))\ntest_images = test_images.reshape((10000, 28, 28, 1))\n\ntrain_images, test_images = train_images / 255.0, test_images / 255.0\n\nmodel = models.Sequential()\nmodel.add(layers.Conv2D(25, (5, 5), activation='relu', input_shape=(28, 28, 1)))\nmodel.add(layers.MaxPooling2D((2, 2)))\nmodel.add(layers.Conv2D(25, (5, 5), activation='relu'))\nmodel.add(layers.MaxPooling2D((2, 2)))\nmodel.add(layers.Flatten())\nmodel.add(layers.Dense(100, activation='relu'))\nmodel.add(layers.Dense(10, activation='softmax'))\n\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nmodel.fit(train_images, train_labels, epochs=5)\n\ntest_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\nprint('\\nTest accuracy:', test_acc)\n```\n\nI hope this helps!"]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa2fe28-08ef-4965-88bb-3c3e2ef20048","children":["aaa2e47d-d6a6-418e-9552-4e59d7002538"]},{"id":"aaa2e47d-d6a6-418e-9552-4e59d7002538","message":{"id":"aaa2e47d-d6a6-418e-9552-4e59d7002538","author":{"role":"user","metadata":{}},"create_time":1701401938.062347,"content":{"content_type":"text","parts":["Now plot the convolutional kernels from the first convolutional layer with the grey colormap. It should be a 5x5 arrangement and do not import anything that was previously imported in the previous code as this is for the same notebook.  Once again, not a single comment should be present. Also make sure it is labeled in the graph."]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"91ebd573-7b7b-45b8-a6f8-0c7d95392214","children":["1dcd3d62-070e-4c4f-87b2-0c56e7da63ee"]},{"id":"1dcd3d62-070e-4c4f-87b2-0c56e7da63ee","message":{"id":"1dcd3d62-070e-4c4f-87b2-0c56e7da63ee","author":{"role":"assistant","metadata":{}},"create_time":1701401940.632094,"content":{"content_type":"text","parts":["Here's the code to plot the convolutional kernels from the first convolutional layer of the LeNet model with the grey colormap:\n\n```python\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nweights = model.layers[0].get_weights()[0]\nnum_kernels = weights.shape[3]\nsqrt_num_kernels = int(np.ceil(np.sqrt(num_kernels)))\n\nplt.figure(figsize=(10, 10))\nfor i in range(num_kernels):\n    plt.subplot(sqrt_num_kernels, sqrt_num_kernels, i+1)\n    plt.imshow(weights[:, :, 0, i], cmap='gray')\n    plt.title(f'Kernel {i+1}')\n    plt.axis('off')\n\nplt.suptitle('Convolutional Kernels from First Layer', fontsize=16)\nplt.tight_layout()\nplt.show()\n```\n\nThis code will plot the convolutional kernels in a 5x5 arrangement with labels for each kernel."]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa2e47d-d6a6-418e-9552-4e59d7002538","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa2e47d-d6a6-418e-9552-4e59d7002538","children":["aaa279ad-76fe-4931-9eab-2c71d18f8c9b"]},{"id":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","message":{"id":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","author":{"role":"user","metadata":{}},"create_time":1701402139.974324,"content":{"content_type":"text","parts":["Finally, pick an input the category \"3\" and plot just the feature map. Same rules apply, use the gray colormap and  no comments in the code. "]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"1dcd3d62-070e-4c4f-87b2-0c56e7da63ee","children":["17dba0d8-2623-41c5-82ac-cfa91b95024c"]},{"id":"17dba0d8-2623-41c5-82ac-cfa91b95024c","message":{"id":"17dba0d8-2623-41c5-82ac-cfa91b95024c","author":{"role":"assistant","metadata":{}},"create_time":1701402143.183437,"content":{"content_type":"text","parts":["Here's the code to pick an input image of category \"3\" and plot the feature map from the first convolutional layer using the grey colormap:\n\n```python\nimport random\n\ndef find_first_3():\n    index = random.randint(0, len(test_labels) - 1)\n    while test_labels[index] != 3:\n        index = random.randint(0, len(test_labels) - 1)\n    return test_images[index:index+1]\n\ninput_image = find_first_3()\n\nconv1_layer = model.layers[0]\nfeature_map_model = tf.keras.models.Model(inputs=model.inputs, outputs=conv1_layer.output)\nfeature_maps = feature_map_model.predict(input_image)\n\nplt.figure(figsize=(8, 8))\nfor i in range(25):\n    plt.subplot(5, 5, i+1)\n    plt.imshow(feature_maps[0, :, :, i], cmap='gray')\n    plt.title(f'Feature Map {i+1}')\n    plt.axis('off')\n\nplt.suptitle('Feature Maps from First Convolutional Layer (Input Category 3)', fontsize=16)\nplt.tight_layout()\nplt.show()\n```\n\nThis code selects an input image with the label \"3,\" passes it through the first convolutional layer, and then plots the feature maps in grey colormap."]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa279ad-76fe-4931-9eab-2c71d18f8c9b","children":["aaa214ae-8efb-421e-a98c-d51f455047b1"]},{"id":"aaa214ae-8efb-421e-a98c-d51f455047b1","message":{"id":"aaa214ae-8efb-421e-a98c-d51f455047b1","author":{"role":"user","metadata":{}},"create_time":1701402573.261102,"content":{"content_type":"text","parts":["Thank you for all your help!"]},"status":"finished_successfully","weight":1,"metadata":{"timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"17dba0d8-2623-41c5-82ac-cfa91b95024c","children":["e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4"]},{"id":"e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4","message":{"id":"e78d7fcb-5f19-4391-87bf-8fdfd5f0d3f4","author":{"role":"assistant","metadata":{}},"create_time":1701402573.895302,"content":{"content_type":"text","parts":["You're welcome! If you have any more questions or need further assistance in the future, feel free to reach out. Have a great day!"]},"status":"finished_successfully","end_turn":true,"weight":1,"metadata":{"finish_details":{"type":"stop","stop_tokens":[100260]},"is_complete":true,"model_slug":"text-davinci-002-render-sha","parent_id":"aaa214ae-8efb-421e-a98c-d51f455047b1","timestamp_":"absolute","shared_conversation_id":"101324b2-a1ed-4ee4-9fba-b5c103559413"},"recipient":"all"},"parent":"aaa214ae-8efb-421e-a98c-d51f455047b1","children":[]}],"has_user_editable_context":false,"continue_conversation_url":"https://chat.openai.com/share/101324b2-a1ed-4ee4-9fba-b5c103559413/continue","model":{"slug":"text-davinci-002-render-sha","max_tokens":8191,"title":"Default (GPT-3.5)","description":"Our fastest model, great for most everyday tasks.","tags":["gpt3.5"]},"moderation_state":{"has_been_moderated":false,"has_been_blocked":false,"has_been_accepted":false,"has_been_auto_blocked":false,"has_been_auto_moderated":false}}},"continueMode":false,"moderationMode":false,"plugins":null,"chatPageProps":{},"isGizmoLive":true},"__N_SSP":true},"page":"/share/[[...shareParams]]","query":{"shareParams":["101324b2-a1ed-4ee4-9fba-b5c103559413"]},"buildId":"abEtXhMojc3RX4SePKVbV","assetPrefix":"https://cdn.oaistatic.com","isFallback":false,"gssp":true,"scriptLoader":[]}</script></body></html>
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