Convolutional autoencoder – Orbifold Consulting

    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()

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()

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