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import numpy as np import scipy.io from matplotlib import pyplot as plt from keras.utils import np_utils from keras.models import Sequential, Input, Model from keras.layers.core import Dense, Dropout, Activation, Reshape, Flatten, Lambda from keras.layers import Conv2D, MaxPooling2D, UpSampling2D from keras.callbacks import EarlyStopping mat = scipy.io.loadmat('Data/train_32x32.mat') mat = mat['X'] b, h, d, n = mat.shape #Convert all RGB-Images to greyscale img_gray = np.zeros(shape =(n, b, h, 1)) def rgb2gray(rgb): return np.dot(rgb[...,:3], [0.299, 0.587, 0.114]) for i in range(n): #Convert to greyscale img = rgb2gray(mat[:,:,:,i]) img = img.reshape(1, 32, 32, 1) img_gray[i,:] = img # Normalize input img_gray = img_gray/255. img_size = Input(shape=(b, h, 1)) x = Conv2D(16, (3, 3), activation='relu', padding='same')(img_size) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3), activation='relu', padding='same')(x) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3), activation='relu', padding='same')(x) encoded = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded) x = UpSampling2D((2, 2))(x) x = Conv2D(8, (3, 3), activation='relu', padding='same')(x) 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(img_size, decoded) autoencoder.compile(optimizer='rmsprop', loss='binary_crossentropy')#, metrics=['binary_accuracy']) # Output summary of network autoencoder.summary() callbacks = EarlyStopping(monitor='val_loss', patience=5) n_epochs = 1000 batch_size = 128 autoencoder.fit( img_gray, img_gray, epochs=n_epochs, batch_size=batch_size, shuffle=True, validation_split=0.2 callbacks=callbacks ) pred = autoencoder.predict(img_gray) n = 5 plt.figure(figsize=(15, 5)) for i in range(n): rand = np.random.randint(len(img_gray)) ax = plt.subplot(2, n, i + 1) plt.imshow(img_gray[i].reshape(32, 32), cmap='gray') ax = plt.subplot(2, n, i + 1 + n) plt.imshow(pred[i].reshape(32, 32), cmap='gray') plt.show() |
