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‎Task2.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bb8f5b74-74d7-46d8-ab71-6c57b5538af0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import dataset using kaggle link provided in the repo here.\n",
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"# Note that you have to use google colab for this task.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "32efdd32-13bf-41b7-b42f-0ab5dc84ba34",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Write the code for directory arrangement and file naming convention as you did for Task1. \n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2271cbbf-e534-4425-aabb-e878c1e6ed78",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The directory structure must be maintained as asked in Task1.\n",
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"\n",
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"# /Dataset\n",
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"# /train_set\n",
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"# /class1\n",
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"# /class2\n",
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"# ......\n",
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"# /class21\n",
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"# /class22\n",
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"# /test_set\n",
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"# /class1\n",
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"# /class2\n",
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"# ......\n",
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"# /class21\n",
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"# /class22"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dd278545-9191-444e-aa6b-ba1239cb7de5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Change this code.\n",
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"train_set = \"path/to/your/train_set\"\n",
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"test_set = \"path/to/your/test_set\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "54a23d63-4977-4086-b7a9-358d399f598f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Do not change this code.\n",
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"import os\n",
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"x = os.listdir(train_set)\n",
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"y = os.listdir(test_set)\n",
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"print(f\"Found {len(x)} files in your train_set\")\n",
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"print(x)\n",
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"print(f\"Found {len(y)} files in your test_set\")\n",
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"print(y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ba48388d-6527-487c-8a9c-7c71abc03bed",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Generate one-one image of each class. Write the code here.\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "129fbd0a-b235-430d-90e6-c2399d2a74fa",
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"metadata": {},
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"source": [
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"<h1>Now we arrive at Task2</h1>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f12147c2-c384-4b88-9d8e-496455029b4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Write the code here.\n",
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"\n",
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"# Print the no. of items in each class in train_set. Also the sum of images in train_set and test_set.\n",
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"# Sum of images in train_set: number1\n",
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"# Sum of images in test_set: number2\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0ec54ac8-7a59-442c-a78a-e5f3c9a95d59",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Write image pre-processing code here.\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f749c36d-3ef2-4e29-82e2-4fa0e9ac3452",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Generate one-one image of each class. Write the code here.\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "82d269d5-3bcf-4124-a86b-59da5d2743df",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Write image augmentation code here.\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f0d44d98-f2f9-4497-a9d6-1969c0c51dc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Generate one-one image of each class. Write the code here.\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c9b66f9a-e001-4e49-b95a-8cfbf49369be",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Print the no. of items in each class in train_set. Write the code here.\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9450e74b-d16b-4050-89c9-685f15ea310a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Do we need to handle imbalanced data? Or we handled it already?\n",
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"# Write the code here if not done.\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "08587e16-043d-4b82-9484-e85fb2531458",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Also generate a validation_set for proper validation to be done while evaluating the model.\n",
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"# 80-20 split.\n",
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"# Make the validation split after correct step.\n",
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"# Print the number of images in train_set and validation_set after you do the split."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7bdfa36f-9eec-4799-a279-55f6d7f5acbb",
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"metadata": {},
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"source": [
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"# Run all the cells and the colab file must show clear output of all the cells. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "75da70f5-ff0f-4161-b56d-f178c48291f0",
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"metadata": {},
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"source": [
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"## Write a clean and commented code. Explaining the why and what of the code will help a lot :)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b517fe4f-fb4d-4add-8ef6-387ae38dea93",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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