kastnerkyle

# Author: Kyle Kastner
# License: BSD 3-Clause
# For a reference on parallel processing in Python see tutorial by David Beazley
# http://www.slideshare.net/dabeaz/an-introduction-to-python-concurrency
# Loosely based on IBM example
# http://www.ibm.com/developerworks/aix/library/au-threadingpython/
try:
    import Queue
except ImportError:
    import queue as Queue

kastnerkyle

# Authors: Kyle Kastner, Francesco Visin
# License: BSD 3-Clause
try:
    import Queue
except ImportError:
    import queue as Queue
import threading
import time
from matplotlib.path import Path
from PIL import Image

twiecki

FlorianMuellerklein

import lasagne
from lasagne.nonlinearities import rectify, softmax
from lasagne.layers import InputLayer, DenseLayer, DropoutLayer, batch_norm, BatchNormLayer
from lasagne.layers import ElemwiseSumLayer, NonlinearityLayer, GlobalPoolLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.init import HeNormal

def ResNet_FullPre_Wide(input_var=None, n=3, k=2):
    '''
    Adapted from https://github.com/Lasagne/Recipes/tree/master/papers/deep_residual_learning.

karpathy

""" 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

bayesian_neural_network.ipynb

udibr

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

justheuristic

ericyue

import datetime
import pytz
from tensorflow.contrib.session_bundle import exporter
from tensorflow.python.client import timeline
import glob
import json
import time
import math
import numpy as np
import os

braingineer