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- Add basic files (.gitignore, requirements.txt) - Create some hyperparameters - Create preprocessing functions
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‎.gitignore

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# Created by https://www.toptal.com/developers/gitignore/api/visualstudiocode,macos,windows,linux,git,virtualenv,python
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# Edit at https://www.toptal.com/developers/gitignore?templates=visualstudiocode,macos,windows,linux,git,virtualenv,python
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### Git ###
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# Created by git for backups. To disable backups in Git:
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# $ git config --global mergetool.keepBackup false
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*.orig
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# Created by git when using merge tools for conflicts
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*.BACKUP.*
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*.BASE.*
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*.LOCAL.*
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*.REMOTE.*
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*_BACKUP_*.txt
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*_BASE_*.txt
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*_LOCAL_*.txt
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*_REMOTE_*.txt
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### Linux ###
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*~
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# temporary files which can be created if a process still has a handle open of a deleted file
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.DocumentRevisions-V100
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.fseventsd
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.VolumeIcon.icns
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*.py[cod]
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*$py.class
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lib/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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pytestdebug.log
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# Translations
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*.pot
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*.log
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local_settings.py
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.webassets-cache
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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env/
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ENV/
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venv.bak/
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pythonenv*
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# profiling data
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.prof
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### VirtualEnv ###
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# Virtualenv
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# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
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[Bb]in
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[Ii]nclude
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[Ll]ib
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[Ll]ib64
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[Ll]ocal
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[Ss]cripts
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pyvenv.cfg
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pip-selfcheck.json
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### VisualStudioCode ###
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.vscode/*
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!.vscode/tasks.json
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!.vscode/launch.json
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*.code-workspace
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### VisualStudioCode Patch ###
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# Ignore all local history of files
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.history
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.ionide
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### Windows ###
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# Windows thumbnail cache files
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Thumbs.db
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Thumbs.db:encryptable
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ehthumbs.db
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ehthumbs_vista.db
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# Dump file
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*.stackdump
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# Folder config file
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[Dd]esktop.ini
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# Recycle Bin used on file shares
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$RECYCLE.BIN/
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# Windows Installer files
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*.cab
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*.msi
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*.msix
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*.msm
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*.msp
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# Windows shortcuts
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*.lnk
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# End of https://www.toptal.com/developers/gitignore/api/visualstudiocode,macos,windows,linux,git,virtualenv,python

‎LICENCE

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Copyright 2020 Eddy Decena
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

‎cycle/__init__.py

Whitespace-only changes.

‎cycle/config.py

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# original image size for cycle_gan/horse2zebra dataset
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ORIG_IMG_SIZE = (286, 286)
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# Random Crop
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INPUT_IMG_SIZE = (256, 256, 3)
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BATCH_SIZE = 1
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BUFFER_SIZE = 256

‎cycle/preprocessing.py

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import tensorflow as tf
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# Hyperparameters
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from cycle.config import ORIG_IMG_SIZE
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from cycle.config import INPUT_IMG_SIZE
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def normalize_img(img):
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img = tf.cast(img, tf.float32)
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return (img / 127.5) - 1.0
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def preprocess_train_image(img, label):
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img = tf.image.random_flip_left_right(img)
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img = tf.image.resize(img, [*ORIG_IMG_SIZE])
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img = tf.image.random_crop(img, [*INPUT_IMG_SIZE])
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return normalize_img(img)
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def preprocess_test_image(img, label):
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img = tf.image.resize(img, [INPUT_IMG_SIZE[0], INPUT_IMG_SIZE[1]])
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return normalize_img(img)

‎requirements.txt

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tensorflow==2.3.0
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numpy==1.19.4
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matplotlib==3.3.2
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tensorflow-addons==0.11.2
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tensorflow-datasets==4.1.0

‎train.py

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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import tensorflow_datasets as tfds
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from tensorflow.data.experimental import AUTOTUNE
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# Hyperparameters
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from cycle.config import ORIG_IMG_SIZE
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from cycle.config import INPUT_IMG_SIZE
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from cycle.config import BUFFER_SIZE
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from cycle.config import BATCH_SIZE
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from cycle.preprocessing import preprocess_train_image
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from cycle.preprocessing import preprocess_test_image
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datasets = tfds.load('cycle_gan/horse2zebra', as_supervised=True)
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train_horses, train_zebras = datasets['trainA'], datasets['trainB']
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test_horses, test_zebras = datasets['testA'], datasets['testB']
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# Apply preprocessing
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train_horses = (
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train_horses.map(preprocess_train_image, num_parallel_calls=AUTOTUNE)
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.cache()
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.shuffle(BUFFER_SIZE)
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.batch(BATCH_SIZE)
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)
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train_zebras = (
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train_zebras.map(preprocess_train_image, num_parallel_calls=AUTOTUNE)
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.cache()
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.shuffle(BUFFER_SIZE)
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.batch(BATCH_SIZE)
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)
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test_horses = (
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test_horses.map(preprocess_test_image, num_parallel_calls=AUTOTUNE)
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.cache()
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.shuffle(BUFFER_SIZE)
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.batch(BATCH_SIZE)
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)
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test_zebras = (
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test_zebras.map(preprocess_test_image, num_parallel_calls=AUTOTUNE)
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.cache()
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.shuffle(BUFFER_SIZE)
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.batch(BATCH_SIZE)
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)
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_, ax_train = plt.subplots(4, 2, figsize=(10, 15))
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for i, samples in enumerate(zip(train_horses.take(4), train_zebras.take(4))):
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horse = (((samples[0][0] * 127.5) + 127.5).numpy()).astype(np.uint8)
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zebra = (((samples[1][0] * 127.5) + 127.5).numpy()).astype(np.uint8)
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ax_train[i, 0].imshow(horse)
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ax_train[i, 1].imshow(zebra)
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plt.show()
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_, ax_test = plt.subplots(4, 2, figsize=(10, 15))
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for i, samples in enumerate(zip(test_horses.take(4), test_zebras.take(4))):
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horse = (((samples[0][0] * 127.5) + 127.5).numpy()).astype(np.uint8)
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zebra = (((samples[1][0] * 127.5) + 127.5).numpy()).astype(np.uint8)
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ax_test[i, 0].imshow(horse)
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ax_test[i, 1].imshow(zebra)
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plt.show()
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