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- import matplotlib.pyplot as plt
- from keras.datasets import mnist
- import numpy as np
- from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
- from keras.models import Model
- from keras import backend as K
- (x_train, _), (x_test, _) = mnist.load_data()
- x_train = x_train.astype('float32') / 255.
- x_test = x_test.astype('float32') / 255.
- x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format
- x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format
- noise_factor = 0.5
- x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
- x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
- x_train_noisy = np.clip(x_train_noisy, 0., 1.)
- x_test_noisy = np.clip(x_test_noisy, 0., 1.)
- n = 10
- plt.figure(figsize=(20, 2))
- for i in range(1, n+1):
- ax = plt.subplot(1, n, i)
- plt.imshow(x_test_noisy[i].reshape(28, 28))
- plt.gray()
- ax.get_xaxis().set_visible(False)
- ax.get_yaxis().set_visible(False)
- plt.show()
- input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
- # use Conv2D, MaxPooling2D - twice
- # use Conv2D, UpSampling2D - twice
- x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
- x = MaxPooling2D((2, 2), padding='same')(x)
- x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
- encoded = MaxPooling2D((2, 2), padding='same')(x)
- # at this point the representation is (7, 7, 32)
- x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
- x = UpSampling2D((2, 2))(x)
- x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
- x = UpSampling2D((2, 2))(x)
- decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
- autoencoder = Model(input_img, decoded)
- autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
- autoencoder.summary()
- autoencoder.fit(x_train_noisy, x_train,
- epochs=10,
- batch_size=128,
- shuffle=True,
- validation_data=(x_test_noisy, x_test))
- decoded_imgs = autoencoder.predict(x_test)
- import matplotlib.pyplot as plt
- n = 10
- plt.figure(figsize=(20, 4))
- for i in range(1, n+1):
- # display original
- ax = plt.subplot(2, n, i)
- plt.imshow(x_test[i].reshape(28, 28))
- plt.gray()
- ax.get_xaxis().set_visible(False)
- ax.get_yaxis().set_visible(False)
- # display reconstruction
- ax = plt.subplot(2, n, i + n)
- plt.imshow(decoded_imgs[i].reshape(28, 28))
- plt.gray()
- ax.get_xaxis().set_visible(False)
- ax.get_yaxis().set_visible(False)
- plt.show()
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