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export PORT=8080 && npm install
npm start

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gcloud app create
gcloud app deploy

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git clone https://github.com/GoogleCloudPlatform/ruby-docs-samples
cd ruby-docs-samples/appengine/hello_world

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bundle install
bundle exec ruby app.rb -p 8080

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# Copyright 2015 Google, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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# Copyright 2015 Google, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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gcloud app create
gcloud app deploy

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# -*- coding: utf-8 -*-


from __future__ import absolute_import, division, print_function, unicode_literals

# tensorflow와 tf.keras를 임포트합니다
import tensorflow as tf
from tensorflow import keras

# 헬퍼(helper) 라이브러리를 임포트합니다

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# -*- coding: utf-8 -*-


import tensorflow as tf

import numpy as np
import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()

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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# 미리 섞여진 fashoin-mnist의 학습 데이터와 테스트 데이터 로드
# (학습 이미지, 학습 레이블), (테스트 이미지, 테스트 레이블)
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()

print("x_train shape:", x_train.shape, "y_train shape:", y_train.shape)