本文分享了我在silent speech 項目過程中實現(xiàn)的基于嘴唇圖像數(shù)據(jù)集的autoencoder自編碼器。輸入輸出都是$64?64$的嘴唇灰度圖。自編碼器由編碼解碼兩個部分構(gòu)成，同時實現(xiàn)了利用checkpoint在每個epoch運算時，自動保存測試集loss更小的模型。

數(shù)據(jù)集共包含84679張圖片，其中前68728張圖片作為訓(xùn)練集，后15951張圖片作為測試集。

import tensorflow as tf from tensorflow.keras import layers from tensorflow import keras import numpy as np from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras import optimizers from matplotlib import pyplot as plt from tensorflow.keras import Input from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, concatenate, Flatten, Conv2DTranspose, UpSampling2D from tensorflow.keras.models import Modeldef autoencoder_lips():input_img = Input(shape=(64, 64, 1))# encodingconv1 = Conv2D(filters=16, kernel_size=(5, 5), activation='relu', padding='same', name='lip_conv1')(input_img)conv2 = Conv2D(filters=16, kernel_size=(5, 5), activation='relu', padding='same', name='lip_conv2')(conv1)pooling1 = MaxPooling2D(pool_size=(2, 2), name='lip_pooling1')(conv2)conv3 = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same', name='lip_conv3')(pooling1)conv4 = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same', name='lip_conv4')(conv3)pooling2 = MaxPooling2D(pool_size=(2, 2), name='lip_pooling2')(conv4)# decodingconv5 = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same', name='lip_conv5')(pooling2)conv6 = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same', name='lip_conv6')(conv5)upsample1 = UpSampling2D(size=(2, 2), name='lip_upsample1')(conv6)conv7 = Conv2D(filters=16, kernel_size=(5, 5), activation='relu', padding='same', name='lip_conv7')(upsample1)conv8 = Conv2D(filters=16, kernel_size=(5, 5), activation='relu', padding='same', name='lip_conv8')(conv7)upsample2 = UpSampling2D(size=(2, 2), name='lip_upsample2')(conv8)decoded = Conv2D(filters=1, kernel_size=(5, 5), activation='sigmoid', padding='same', name='lip_decoded')(upsample2)autoencoder_lip = Model(input_img, decoded, name='autoencoder_lips')autoencoder_lip.summary()return autoencoder_lipif __name__ == "__main__":X = np.load("lips_all_chapiters.npy")nb_images_chapiter7 = 15951# normalisationX = X/255.0# ch1-ch6X_train = X[:-15951, :]X_test = X[-15951:, :]model = autoencoder_lips()my_optimizer = keras.optimizers.Adam(learning_rate=0.0001, epsilon=1e-8)model.compile(optimizer=my_optimizer, loss='binary_crossentropy')filepath = "autoencoder_lips/autoencoder_lips-{epoch:02d}-{val_loss:.8f}.h5"checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1,save_best_only=True, mode='auto') # only save improved accuracy modelcallbacks_list = [checkpoint]history = model.fit(x=X_train, y=X_train, batch_size=256, epochs=100, callbacks=callbacks_list,validation_data=(X_test, X_test))

訓(xùn)練好的自編碼器最終在測試集上的表現(xiàn)如下 :

第一行是原始輸入的圖像，第二行是自編碼器輸出的對應(yīng)圖像。我們可以發(fā)現(xiàn)雖然自編碼器生成的圖像與原圖像相比損失了清晰度，但是整體上還原了圖像原本的形態(tài)。我們因此可以利用自編碼器的中間層作為壓縮后的圖像信息，或稱features特征。自編碼器往往可以用于壓縮信息，或作為進一步進行學習的中間標簽。

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