| """ 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 |
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| #include <Python.h> // Must be first | |
| #include <vector> | |
| #include <stdexcept> | |
| #include "PyUtils.h" | |
| using namespace std; | |
| // ===== | |
| // LISTS | |
| // ===== |
| """ | |
| (C) Mathieu Blondel - 2010 | |
| License: BSD 3 clause | |
| Implementation of the collapsed Gibbs sampler for | |
| Latent Dirichlet Allocation, as described in | |
| Finding scientifc topics (Griffiths and Steyvers) | |
| """ |