takuseno

from subprocess import Popen, PIPE
from xml.etree.ElementTree import fromstring


def main():
    p = Popen(['nvidia-smi', '-q', '-x'], stdout=PIPE)
    outs, errors = p.communicate()
    xml = fromstring(outs)
    num_gpus = int(xml.getiterator('attached_gpus')[0].text)
    results = []

takuseno

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

mode = 'maml'
seed = 0
plot = True
innerstepsize = 0.02 # stepsize in inner SGD
innerepochs = 1 # number of epochs of each inner SGD
outerstepsize0 = 0.1 if mode == 'reptile' else 0.001 # stepsize of outer optimization, i.e., meta-optimization

takuseno

import pyaudio
import wave
import numpy as np
from socket import socket, AF_INET, SOCK_DGRAM

FORMAT = pyaudio.paInt32
CHANNELS = 1
RATE = 16000
CHUNK = 4096
ADDRESS = '192.168.11.7'

takuseno

import numpy as np
import mlflow
import gorilla
import time


from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
from mlflow.utils.autologging_utils import try_mlflow_log

takuseno

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S


#------------------------------- neural network ------------------------------#
def q_network(obs, action):
    with nn.parameter_scope('critic'):

takuseno

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S
import argparse
import random
import os
import gym

takuseno

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S
import argparse
import random
import os
import gym

takuseno

import numpy as np
import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.functions as F
import nnabla.solvers as S
import random
import argparse
import gym
import os
import cv2