katsugeneration

--langdef=Swift 
--langmap=Swift:+.swift 
--regex-swift=/(var|let)[ \t]+([^:=]+).*$/\2/,variable/ 
--regex-swift=/func[ \t]+([^\(\)]+)\([^\(\)]*\)/\1/,function/ 
--regex-swift=/class[ \t]+([^:\{]+).*$/\1/,class/ 
--regex-swift=/protocol[ \t]+([^:\{]+).*$/\1/,protocol/

katsugeneration

# coding:utf-8

import chainer
from chainer import cuda
import chainer.links as L
import chainer.functions as F
from chainer import optimizers
from chainer import serializers

from chainer.functions.activation import sigmoid

katsugeneration

import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab

I = 100
data = np.random.normal(0, 2, I)
y = np.ones(I)
plt.hold(False)
plt.plot(data, y, "o")

katsugeneration

import tensorflow as tf
import numpy as np
import pandas as pd
import math

# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"
IRIS_DATA_SIZE = 4
CLASS_SIZE = 3

katsugeneration

import numpy as np
import tensorflow as tf
import reader
import time

class PTBModel:

    @property
    def optimizer(self):
        return self._optimizer

katsugeneration

import tensorflow as tf
import reader

from datetime import datetime
import os.path
import time
import numpy as np

# Tensorflow の cifar10 サンプルの書き換え
# 学習率を徐々に下げたり、評価時の重みは移動平均を使ったりしているがそれはやっていない

katsugeneration

import numpy as np
from scipy.ndimage import convolve
import tensorflow as tf
from tensorflow import pywrap_tensorflow
from PIL import Image
from matplotlib import pyplot as plt

plt.axis('off')
img = Image.open('009_0001.jpg')
plt.imshow(img)

katsugeneration