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graph = tf.Graph()

with graph.as_default():
    # input of every mini-batch training data
    tf_train_X = tf.placeholder(tf.float32, name='tf_train_X',
        shape=(batch_size, image_size, image_size, input_depth))
    tf_train_y = tf.placeholder(tf.float32, name='tf_train_y', 
        shape=(batch_size, 1))
    
    # input for every single realtime prediction

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# make directory for tensorboard logging
>>cd /tmp
>>mkdir -p tensorboard/cat-recognizer

# go back to cat-recognizer project
>>cd ~/Documents/Code/cat-recognition-app/

# tell tensorboard to read log from the directory
>>tensorboard --logdir=/tmp/tensorboard/cat-recognizer/

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# encoding: utf-8
from __future__ import print_function
from __future__ import unicode_literals
import os
import json
import boto3
import logging
import gspread
from oauth2client.service_account import ServiceAccountCredentials

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- Procfile
- runtime.txt
- requirements.txt

# get new heroku app
heroku create
$Creating app... done, ⬢ damp-anchorage-60936 # --> 複製ID

heroku git:remote -a damp-anchorage-60936
git add . 

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brew install heroku/brew/heroku

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pip install awscli --upgrade
aws configure --profile personal

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# References
# https://stackoverflow.com/questions/9166400/convert-rgba-png-to-rgb-with-pil

from PIL import Image

def get_rgb_image(file_path, delete_original_image=True):
    """
    Remove image's alpha channel and return the path of the
    transformed image

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git clone https://gist.github.com/<hash>.git
git add your-image.jpg
git commit -m "Add image"
git push origin master

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References
- https://stackoverflow.com/questions/24383700/resize-image-in-the-wiki-of-github-using-markdown

<img src="images/cover.png" alt="Cover" width="50%"/>

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<div class="row">
  <div class="col-md-6">
    <div>
      <img src="{{cur_image_path}}" class="img-responsive">
    </div>
  </div>
</div>