| from sklearn.datasets import load_iris | |
| from sklearn.ensemble import RandomForestClassifier | |
| import pandas as pd | |
| import numpy as np | |
| iris = load_iris() | |
| df = pd.DataFrame(iris.data, columns=iris.feature_names) | |
| df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75 | |
| df['species'] = pd.Factor(iris.target, iris.target_names) | |
| df.head() |
This guide documents how we set up an easy workflow for using the IPython Notebook on our compute cluster managed with Sun Grid Engine (SGE).
Summary: We provide a script to the cluster users that runs qrsh to schedule an ipython notebook job using SSL and password protection.
| import asyncio | |
| import aiohttp | |
| import bs4 | |
| import tqdm | |
| @asyncio.coroutine | |
| def get(*args, **kwargs): | |
| response = yield from aiohttp.request('GET', *args, **kwargs) | |
| return (yield from response.read_and_close(decode=True)) |
| """ | |
| Learning Task: | |
| Given a sequence, predict a label based on the first value of the sequence | |
| Explanation of stateful LSTM and setup: | |
| http://philipperemy.github.io/keras-stateful-lstm/ | |
| Exmple: | |
| given a sequence [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], predict 1 | |
| given a sequence [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], predict 0 |
Installation:
A quick installation does not require root access, as shown:
(wget -O - pi.dk/3 || curl pi.dk/3/ || fetch -o - http://pi.dk/3) | bash
For other installation options see http://git.savannah.gnu.org/cgit/parallel.git/tree/README
| """ | |
| Simple example to demostrate the embedding visualization for word embeddings in tensorflow / tensorboard | |
| https://www.tensorflow.org/how_tos/embedding_viz/ | |
| """ | |
| import tensorflow as tf | |
| import os | |
| assert tf.__version__ == '1.0.0-rc0' # if code breaks, check tensorflow version | |
| from tensorflow.contrib.tensorboard.plugins import projector |
