I'm doing feature extraction on image with autoencodeur. My image are bit map => pixel value = 0 or 1
I use the following code:
X_train_autoencodeur = X_train.reshape(-1, 96*96)
X_valid_autoencodeur=X_valid.reshape(-1,96*96)
input_img = Input(shape=(96*96,))
encoded = Dense(1024, activation='relu')(input_img)
encoded = Dense(512, activation='relu')(encoded)
encoded = Dense(256, activation='relu')(encoded)
encoded = Dense(128, activation='relu')(encoded)
decoded = Dense(256, activation='relu')(encoded)
decoded = Dense(512, activation='relu')(decoded)
decoded = Dense(1024, activation='relu')(decoded)
decoded = Dense(96*96, activation='sigmoid')(decoded)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(X_train_autoencodeur, X_train_autoencodeur,
epochs=100,
batch_size=256,
shuffle=True,
validation_data=(X_valid_autoencodeur, X_valid_autoencodeur))
Then I plot reconstructed images with
decoded_imgs = autoencoder.predict(X_valid_autoencodeur)
plt.imshow(decoded_imgs[7].reshape(96,96))
After 3 epoch validation and training loss goes to very low value and doesnt change
Reconstructed images are full of black, feature vector are all the same.
I have train the autoencodeur for 100 epoch, should I train for more? Did i made mistake on my code which could explain bad reconstruction?
Just from experience I know that autoencoders normally need a long time to train (more like 1000 epochs) even if you are using Convolutional Neural Networks.
You however, are trying to use a fully connected NN (and a fairly large one) which will take even longer to learn something.
My suggestions are: Try using a CNN and more training epochs.