mvoelk

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

import cv2
import numpy as np

from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, AvgPool2D, Activation
from tensorflow.keras.layers import Layer, BatchNormalization, ZeroPadding2D, Flatten, add
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.models import Model
from tensorflow.keras import initializers

fchollet

"""Downsized version of Xception, without residual connections.
"""
from __future__ import print_function
from __future__ import absolute_import

from keras.models import Model
from keras.layers import Dense
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers import Activation

stuaxo

$ sudo apt-get install dex
$ /usr/bin/dex -c /usr/bin/dex -t ~/.local/share/applications/

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

kdkorthauer

sudo sh -c 'echo "deb http://cran.rstudio.com/bin/linux/ubuntu trusty/" >> /etc/apt/sources.list'
gpg --keyserver keyserver.ubuntu.com --recv-key E084DAB9
gpg -a --export E084DAB9 | sudo apt-key add -
sudo apt-get update
sudo apt-get -y install r-base libapparmor1 libcurl4-gnutls-dev libxml2-dev libssl-dev gdebi-core
sudo apt-get install libcairo2-dev
sudo apt-get install libxt-dev
sudo apt-get install git-core

sudo /bin/dd if=/dev/zero of=/var/swap.1 bs=1M count=1024

fchollet

from keras.models import Sequential
from keras.layers import Dense

x, y = ...
x_val, y_val = ...

# 1-dimensional MSE linear regression in Keras
model = Sequential()
model.add(Dense(1, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='mse')

fchollet

'''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

fchollet

'''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

fchollet

'''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

albertomontesg