kkweon

"""
Simple policy gradient in Keras

"""
import gym
import numpy as np

from keras import layers
from keras.models import Model
from keras import backend as K

tokestermw

""" 
Play with saving .

Closest:
    https://github.com/tensorflow/tensorflow/issues/616#issuecomment-205620223
"""
import numpy as np

import tensorflow as tf
from tensorflow.python.platform import gfile

beeman

# Stop all containers
docker stop `docker ps -qa`

# Remove all containers
docker rm `docker ps -qa`

# Remove all images
docker rmi -f `docker images -qa `

# Remove all volumes

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

freeman-lab

package thunder.streaming

import org.apache.spark.{SparkConf, Logging}
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.mllib.clustering.KMeansModel

import scala.util.Random.nextDouble

mabdrabo

import pyaudio
import wave
 
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 5
WAVE_OUTPUT_FILENAME = "file.wav"