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andrewgiessel / gist:6071508
Last active December 20, 2015 04:29
save and load wrappers for pickle
import cPickle as pickle
def save(obj, filename):
"""Simple wrapper to pickle an object on disk
:param: obj, any pickable object
:param: filename, string representation of the file to save to
"""
with open(filename, 'wb') as f:
@honnibal
honnibal / theano_mlp_small.py
Last active March 1, 2023 15:10
Stripped-down example of Multi-layer Perceptron MLP in Theano
"""A stripped-down MLP example, using Theano.
Based on the tutorial here: http://deeplearning.net/tutorial/mlp.html
This example trims away some complexities, and makes it easier to see how Theano works.
Design changes:
* Model compiled in a distinct function, so that symbolic variables are not in run-time scope.
* No classes. Network shown by chained function calls.
@karpathy
karpathy / min-char-rnn.py
Last active August 10, 2025 18:47
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
@mikhailov-work
mikhailov-work / turbo_colormap.py
Created August 8, 2019 23:31
Turbo Colormap Look-up Table
# Copyright 2019 Google LLC.
# SPDX-License-Identifier: Apache-2.0
# Author: Anton Mikhailov
turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913