質問編集履歴
2
ソースコードの詳細
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### 該当のソースコード
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import numpy as np
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import matplotlib.pyplot as plt
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(x_train, y_train),(x_test, y_test) = mnist.load_data()
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from tensorflow.python import keras
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras.models import Model, Sequential
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from tensorflow.python.keras.layers import Conv2D, Dense, Input, MaxPooling2D, UpSampling2D, Lambda
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from tensorflow.python.keras.preprocessing.image import load_img, img_to_array, array_to_img, ImageDataGenerator
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ls drive/My\ Drive/poke64
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(x_train, y_train),(x_test, y_test) =?????????????????????????
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/////////CNNで扱いやすい形に変形
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x_train = x_train.reshape(-1,28,28,1)
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x_test = x_test.reshape(-1,28,28,1)
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////////特徴量の正規化
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x_train = x_train/255.
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x_test = x_test/255.
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/////////////マスキングノイズ
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def make_masking_noise_data(data_x,percent=0.1):
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size = data_x.shape
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masking = np.random.binomial(n=1, p=percent,size=size)
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return data_x*masking
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x_train_masked = make_masking_noise_data(x_train)
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x_test_masked = make_masking_noise_data(x_test)
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/////////////ガウシアンノイズ
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def make_gaussian_noise_data(data_x, scale=0.8):
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gaussian_data_x = data_x + np.random.normal(loc=0, scale=scale, size=data_x.shape)
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gaussian_data_x = np.clip(gaussian_data_x, 0, 1)
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return gaussian_data_x
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x_train_gauss = make_gaussian_noise_data(x_train)
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x_test_gauss = make_gaussian_noise_data(x_test)
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/////////ノイズをかけた画像の表示
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from IPython.display import display_png
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display_png(array_to_img(x_train[0]))
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display_png(array_to_img(x_train_gauss[0]))
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display_png(array_to_img(x_train_masked[0]))
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### 試したこと
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誤字
title
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@@ -13,7 +13,10 @@
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### 該当のソースコード
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from tensorflow.python.keras.datasets import mnist
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(x_train, y_train),(x_test, y_test) = mnist.load_data()
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これの自分で用意したデータ版のやり方
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### 試したこと
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