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Public Release v1.0
travis test links fix readme Public Release v1.0
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‎.firebaserc

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{
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"projects": {
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"default": "alzheimersai-2121"
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}
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}

‎.gitignore

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venv/
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*.pyc
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__pycache__/
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instance/
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.pytest_cache/
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.coverage
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htmlcov/
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dist/
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build/
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*.egg-info/
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.vscode/
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page_design/
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.firebase/
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firebase-debug.log
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node_modules/
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functions/node_modules/
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package-lock.json
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*.pth
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static/assets/usr/*
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!.keep
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server/src/templates/usr_data/*
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!.keep
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export.pkl

‎.travis.yml

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matrix:
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include:
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- language: generic
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python: "3.7"
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node_js: "12.12.0"
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services: docker
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env:
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- GCP_PROJECT_ID=alzheimersai-2121
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- IMAGE=gcr.io/alzheimersai-2121/alzheimersai
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- CLOUD_RUN_SERVICE=alzheimersai
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- CLOUD_RUN_REGION=us-central1
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- CLOUDSDK_CORE_DISABLE_PROMPTS=1 # prevent gcloud from prompting
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before_install:
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- nvm install node
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- npm i -g firebase-tools
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- pip install gdown --user
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- python travis_builder.py
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- openssl aes-256-cbc -K $encrypted_98d237b7dbf4_key -iv $encrypted_98d237b7dbf4_iv -in google-key.json.enc -out google-key.json -d
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- curl https://sdk.cloud.google.com | bash > /dev/null
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- source "$HOME/google-cloud-sdk/path.bash.inc"
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- gcloud auth activate-service-account --key-file=google-key.json
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- gcloud auth configure-docker # enable "docker push" to gcr
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- gcloud config set project "${GCP_PROJECT_ID}"
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- gcloud config set builds/use_kaniko True
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- gcloud components install beta # until Cloud Run is generally available (GA)
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install: true
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script:
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- |
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set -ex;
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docker build -t "${IMAGE}:${TRAVIS_COMMIT}" ./server && \
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docker push "${IMAGE}:${TRAVIS_COMMIT}" && \
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gcloud beta run deploy "${CLOUD_RUN_SERVICE}" --memory=1Gi \
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--image="${IMAGE}:${TRAVIS_COMMIT}" \
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--platform=managed \
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--region="${CLOUD_RUN_REGION}" \
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--allow-unauthenticated;
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set +x
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deploy:
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provider: firebase
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token:
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secure: "WWs0UiXF9x2wU8Xbxu3CLcz9uUdVXRYV/RiSzBLg6BC2GgWwoGD2s0dD0s+CCneJua4G/mLfapaZ+4DZIhgiLl87KVNEfR6l99ydQf7VknFaq8cqqB91C53lv6AJUmdra6ny8hT6Bo1Q9vdCAAX0VKbkx/2sYboxNJs10rQ6FCCc+GRVOqTpYI0Mb97Lc1ck704CwrKgkla0KmroIdHJa70TrT7iuDQIz1f5BUOPrY7TvNqgKYLNZXBIUVQkGRrQkQKQH3FHHCSn5AlJxv2ZaM4QPOF45Iqz8ZX4Q452Z8ITtjOcT9vrsUUzf0HpJsgYEVzGa7YRfbv7J5nsQyHK8Jj4RdnnirAvTUhh3dAWeowoFQvmmqYC51Efk2WTl6mT8j9IEkV+GAZKrfbginr6kVtH25dMvffCFyEYzhIUF7qF5cWhJvv8A2YPcbe4CCBVQF4lsb371Xu6F7tAgEw2QTY3/Fi4OUdEABXa96cp1qrhx6+XMV7ERUxGo4qZfAXvhJCl/qVjd2Q8AiSmarwAbeey3egUde2xPzaP7TP5gnd+aa7s69xdUQBW6wermZ5uJ+76Hq1l76/zM899xh4A7Tf+Svinqqhgp+l4nk9Ba6CCvr7xn1h1BZCdeUqfSymEX3mAeSYGdT4MhICmk9Z+9ErPYQoqwN+Uq12qIB1zwlI="

‎AI/NeuralZ-training/.ipynb_checkpoints/Untitled-checkpoint.ipynb

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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎AI/NeuralZ-training/.ipynb_checkpoints/Untitled1-checkpoint.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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"metadata": {},
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"outputs": [],
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"source": [
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"from fastai.vision import *\n",
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"import numpy as np\n",
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"import pandas as pd"
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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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"path = Path('../data/train')\n",
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"path.ls()"
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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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"np.random.seed(42)\n",
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"data = ImageDataBunch.from_folder(path, train=\".\", valid_pct=0.2,\n",
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" ds_tfms=get_transforms(), size=128, num_workers=4).normalize(imagenet_stats)"
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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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"data.classes"
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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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"data.classes, data.c, len(data.train_ds), len(data.valid_ds)\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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"metadata": {},
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"outputs": [],
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"source": [
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"learn = cnn_learner(data, models.resnet101, metrics=error_rate)\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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"metadata": {},
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"outputs": [],
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"source": [
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"learn.fit_one_cycle(4)\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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"metadata": {},
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"outputs": [],
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"source": [
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"learn.save('stage-1')"
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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": "3.7.6"
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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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}

‎AI/NeuralZ-training/.ipynb_checkpoints/Untitled2-checkpoint.ipynb

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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎AI/NeuralZ-training/.ipynb_checkpoints/Untitled3-checkpoint.ipynb

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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎AI/NeuralZ-training/.ipynb_checkpoints/Untitled4-checkpoint.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": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastai.vision import *\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import torch"
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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": 47,
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"metadata": {},
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"outputs": [],
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"source": [
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"path = Path('../data/train')"
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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": 48,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[WindowsPath('../data/train/MildDemented'),\n",
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" WindowsPath('../data/train/models'),\n",
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" WindowsPath('../data/train/ModerateDemented'),\n",
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" WindowsPath('../data/train/NonDemented'),\n",
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" WindowsPath('../data/train/VeryMildDemented')]"
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]
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},
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"execution_count": 48,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"path.ls()"
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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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"np.random.seed(42)\n",
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"data = ImageDataBunch.from_folder(path, train=\".\", valid_pct=0.2,\n",
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" size=128, num_workers=4).normalize(imagenet_stats)"
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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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"data.classes"
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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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"data.classes, data.c, len(data.train_ds), len(data.valid_ds)\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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"metadata": {},
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"outputs": [],
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"source": [
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"learn = cnn_learner(data, models.resnet101, metrics=error_rate)"
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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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"learn.fit_one_cycle(4)\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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"metadata": {},
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"outputs": [],
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"source": [
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"learn.save('stage-1') # Save the first stage of the model\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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"metadata": {},
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"outputs": [],
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"source": []
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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 3.7.7 64-bit ('python36': conda)",
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"language": "python",
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"name": "python37764bitpython36condadac793e125124000a42e65e438db1e77"
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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": "3.7.7"
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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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