75 lines
1.5 KiB
Plaintext
75 lines
1.5 KiB
Plaintext
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib as mpl\n",
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Different Train/Validation/Test Splits\n",
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"\n",
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"## Index\n",
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"0. train prop\n",
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"1. val prop\n",
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"2. test prop\n",
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"3. top-1 accuracy\n",
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"4. top-5 accuracy\n",
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"5. last val loss\n",
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"6. last val accuracy"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"split_results = np.array([\n",
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" [50, 5, 45, 8.14, 20.7, 5.77, 8.29],\n",
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" [50, 25, 25, 7.91, 20.9, 5.59, 8.41],\n",
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" [70, 15, 15, 15.81, 34.62, 4.92, 16.82],\n",
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" [80, 10, 10, 20.94, 44.1, 4.39, 24.45],\n",
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" [90, 5, 5, 20.62, 45.19, 4.17, 30.29]\n",
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"])"
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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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"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",
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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": "3.9.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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