前回はMNISTを単純なautoencoderで学習推論してみたが
今回はcifar10を畳み込みオートエンコーダー（convolutional autoencoder）で学習・推論してみた

ソース

from keras.datasets import cifar10
from keras.layers import Input, Dense, Conv2D, Activation
from keras.layers import MaxPooling2D, UpSampling2D, BatchNormalization
from keras.models import Model
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.optimizers import Adam
import os
import pickle
import numpy as np
import matplotlib.pyplot as plt

batch_size = 32
epochs = 10
saveDir = "./model/"

if not os.path.isdir(saveDir):
    os.makedirs(saveDir)

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

x_val = x_test[:9000]
x_test = x_test[9000:]

def showPic(orig, dec, num=5):
    n = num
    plt.figure(figsize=(10, 4))

    for i in range(n):
        # display original
        ax = plt.subplot(2, n, i+1)
        plt.imshow(orig[i])
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)

        # display reconstruction
        ax = plt.subplot(2, n, i +1 + n)
        plt.imshow(dec[i])
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
    plt.show()

def encoder(input_img):
    x = Conv2D(64, (3, 3), padding='same')(input_img)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(32, (3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((2, 2), padding='same')(x)
    x = Conv2D(16, (3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    encoded = MaxPooling2D((2, 2), padding='same')(x)
    return encoded

def decoder(input_img,encoded):
    x = Conv2D(16, (3, 3), padding='same')(encoded)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(32, (3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(64, (3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = UpSampling2D((2, 2))(x)
    x = Conv2D(3, (3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    decoded = Activation('sigmoid')(x)
    model = Model(input_img, decoded)
    return model

def autoencoder():
    input_img = Input(shape=(32, 32, 3))
    encoded=encoder(input_img)
    autoencoded=decoder(input_img,encoded)
    return autoencoded

# train
model = autoencoder()
model.compile(optimizer='adam', loss='binary_crossentropy')
es_cb = EarlyStopping(monitor='val_loss', patience=2, verbose=1, mode='auto')
chkpt = saveDir + 'AE_Cifar10.{epoch:02d}-{loss:.2f}-{val_loss:.2f}.hdf5'
cp_cb = ModelCheckpoint(filepath = chkpt, \
       monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
history = model.fit(x_train, x_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_val, x_val),
                    callbacks=[es_cb, cp_cb],
                    shuffle=True)

# predict
c10test = model.predict(x_test)
showPic(x_test, c10test)