rocking5566

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten
from tensorflow.keras.optimizers import RMSprop

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )

klynch

{
    "segments": {
        "left": [
            {
                "function": "powerline.segments.shell.mode"
            },
            {
                "function": "powerline.segments.common.net.hostname",
                "priority": 10,
                "args": {

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

chrislongo

# INSTALL INSTRUCTIONS: save as ~/.gdbinit
#
# DESCRIPTION: A user-friendly gdb configuration file.
#
# REVISION : 7.3 (16/04/2010)
#
# CONTRIBUTORS: mammon_, elaine, pusillus, mong, zhang le, l0kit,
#               truthix the cyberpunk, fG!, gln
#
# FEEDBACK: https://www.reverse-engineering.net

andreyvit

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a