musyoku

# coding: utf-8
from __future__ import division
import time, chainer, argparse, sys
import fft, config
import numpy as np
from chainer import cuda
import alsaaudio, pyaudio
from multiprocessing import Process, Queue
from model import load_model

musyoku

from __future__ import division
from __future__ import print_function
import argparse, chainer, time, sys
import numpy as np
import chainer.functions as F
from chainer import cuda
from model import Model
from aae.optim import Optimizer, GradientClipping
from aae.utils import onehot, printr, clear_console
from aae.dataset.semi_supervised import Dataset

musyoku

import chainer
import numpy as np


train, test = chainer.datasets.get_mnist()
dataset = train._datasets[0] * 10 - 5
num_data = dataset.shape[0]

def calculate_parameter():
	mean = np.mean(dataset, axis=0)

musyoku

import re

text = "BASIC_LATIN	U+0000	U+007F	(128)\n" \
"LATIN_1_SUPPLEMENT	U+0080	U+00FF	(128)\n" \
"LATIN_EXTENDED_A	U+0100	U+017F	(128)\n" \
"LATIN_EXTENDED_B	U+0180	U+024F	(208)\n" \
"IPA_EXTENSIONS	U+0250	U+02AF	(96)\n" \
"SPACING_MODIFIER_LETTERS	U+02B0	U+02FF	(80)\n" \
"COMBINING_DIACRITICAL_MARKS	U+0300	U+036F	(112)\n" \
"GREEK_AND_COPTIC	U+0370	U+03FF	(135)\n" \

musyoku

# encoding: utf-8
import numpy as np
import chainer
import chainer.links as L
import chainer.functions as F
from chainer import cuda, optimizers
from chainer.optimizer import GradientClipping, WeightDecay

class Model(chainer.Chain):
	def __init__(self):

musyoku

# encoding: utf-8
from __future__ import division
from __future__ import print_function
import numpy as np
import chainer.links as L
from chainer import cuda

def test_linear():
	np.set_printoptions(precision=32)
	xp = cuda.cupy

musyoku

import math
import numpy as np

batchsize = 2
r = 2
out_channels = 3
in_channels = r ** 2 * out_channels
in_height = 3
in_width = 3
out_height = in_height * r

musyoku

# coding: utf-8
import numpy as np
import math

def compute_log_likelihood(x, mean, stddev):
	return math.log(1 / (stddev * math.sqrt(2.0 * math.pi))) - (x - mean) ** 2 / (2.0 * stddev ** 2)

def main():
	prior_mean = 2.5
	prior_stddev = 0.15

musyoku

# -*- coding: utf-8 -*-
import math
import json, os, sys
from args import args
from chainer import cuda
sys.path.append(os.path.split(os.getcwd())[0])
from gan import GAN, DiscriminatorParams, GeneratorParams
from sequential import Sequential
from sequential.layers import Linear, BatchNormalization, Deconvolution2D, Convolution2D
from sequential.functions import Activation, dropout, gaussian_noise, tanh, sigmoid, reshape