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August 11, 2019 21:00
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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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