- The paper presents Deep Convolutional Generative Adversarial Nets (DCGAN) - a topologically constrained variant of conditional GAN.
- Link to the paper
- Stable to train
| {-# LANGUAGE GADTs #-} | |
| {- | |
| The following code is based on experimental code by Aslan Askerov | |
| based on Ramsey and Pfeffers "Stochastic Lambda Calculus and Monads of | |
| Probability Distributions". Implementation of random n is from | |
| Audebaud and Paulin-Mohring paper, so is the random walk example. | |
| This gist is used here http://madsbuch.com/the-probability-monad/ | |
| The class hierarchy is as follows: |
Free O'Reilly books and convenient script to just download them.
Thanks /u/FallenAege/ and /u/ShPavel/ from this Reddit post
How to use:
download.sh file and put it into a directory where you want the files to be saved.cd into the directory and make sure that it has executable permissions (chmod +x download.sh should do it)./download.sh and wee there it goes. Also if you do not want all the files, just simply comment the ones you do not want.| import numpy as np | |
| from keras.layers import GRU, initializations, K | |
| from collections import OrderedDict | |
| class GRULN(GRU): | |
| '''Gated Recurrent Unit with Layer Normalization | |
| Current impelemtation only works with consume_less = 'gpu' which is already | |
| set. | |
| # Arguments |
type below:
brew update
brew install redis
To have launchd start redis now and restart at login:
brew services start redis
| import gym | |
| import pandas as pd | |
| import numpy as np | |
| import random | |
| # https://gym.openai.com/envs/CartPole-v0 | |
| # Carlos Aguayo - carlos.aguayo@gmail.com | |
| class QLearner(object): |
| # set a proxy | |
| set HTTP_PROXY= | |
| set HTTPS_PROXY=%HTTP_PROXY% | |
| npm config set proxy %HTTP_PROXY% | |
| npm config set https.proxy %HTTPS_PROXY% | |
| npm config set https-proxy %HTTPS_PROXY% | |
| git config --global http.proxy %HTTP_PROXY% | |
| git config --global https.proxy %HTTPS_PROXY% | |
| # unset proxy |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
| import matplotlib.pyplot as plt | |
| def draw_neural_net(ax, left, right, bottom, top, layer_sizes): | |
| ''' | |
| Draw a neural network cartoon using matplotilb. | |
| :usage: | |
| >>> fig = plt.figure(figsize=(12, 12)) | |
| >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2]) | |