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# 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 |
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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") |
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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 |
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import numpy as np | |
import tensorflow as tf | |
import reader | |
import time | |
class PTBModel: | |
@property | |
def optimizer(self): | |
return self._optimizer |
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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 |
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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) |
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