To install
cd ~
This guide is unmaintained and was created for a specific workshop in 2017. It remains as a legacy reference. Use at your own risk.
Workshop Instructor:
This workshop is distributed under a CC BY-SA 4.0 license.
lsusb
in the terminal. You should get an output similar to this:Bus 002 Device 002: ID 8087:8000 Intel Corp.
Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub
Bus 001 Device 002: ID 8087:8008 Intel Corp.
Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub
Bus 004 Device 001: ID 1d6b:0003 Linux Foundation 3.0 root hub
Bus 003 Device 005: ID 0bda:0129 Realtek Semiconductor Corp. RTS5129 Card Reader Controller
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |
If a project has to have multiple git repos (e.g. Bitbucket and Github) then it's better that they remain in sync.
Usually this would involve pushing each branch to each repo in turn, but actually Git allows pushing to multiple repos in one go.
If in doubt about what git is doing when you run these commands, just
from __future__ import absolute_import | |
from __future__ import print_function | |
import numpy as np | |
np.random.seed(1337) # for reproducibility | |
import random | |
from keras.datasets import mnist | |
from keras.models import Sequential | |
from keras.layers.core import * | |
from keras.optimizers import SGD, RMSprop |
import sys,os | |
import curses | |
def draw_menu(stdscr): | |
k = 0 | |
cursor_x = 0 | |
cursor_y = 0 | |
# Clear and refresh the screen for a blank canvas | |
stdscr.clear() |
from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation, Flatten | |
from keras.layers.convolutional import Convolution2D, MaxPooling2D | |
from keras.layers.normalization import BatchNormalization | |
#AlexNet with batch normalization in Keras | |
#input image is 224x224 | |
model = Sequential() | |
model.add(Convolution2D(64, 3, 11, 11, border_mode='full')) |
# Assume we are in your home directory | |
cd ~/ | |
# Clone the repo from GitLab using the `--mirror` option | |
$ git clone --mirror git@your-gitlab-site.com:mario/my-repo.git | |
# Change into newly created repo directory | |
$ cd ~/my-repo.git | |
# Push to GitHub using the `--mirror` option. The `--no-verify` option skips any hooks. |