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| """ | |
| Usage: python remove_output.py notebook.ipynb [ > without_output.ipynb ] | |
| Modified from remove_output by Minrk | |
| """ | |
| import sys | |
| import io | |
| import os | |
| from IPython.nbformat.current import read, write |
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| #!/bin/bash | |
| ##################################################### | |
| # Name: Bash CheatSheet for Mac OSX | |
| # | |
| # A little overlook of the Bash basics | |
| # | |
| # Usage: | |
| # | |
| # Author: J. Le Coupanec | |
| # Date: 2014/11/04 |
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| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
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| from keras import applications | |
| from keras.preprocessing.image import ImageDataGenerator | |
| from keras import optimizers | |
| from keras.models import Sequential, Model | |
| from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D | |
| from keras import backend as k | |
| from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping | |
| img_width, img_height = 256, 256 |
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