I have a very basic autoencoder model. I am trying to train it on one hot encoded vector.

#make the AutoEncoder Model
# this is the size of our encoded representations
encoding_dim = 64  
# this is our input placeholder
input_vector = Input(shape=(227,))
# "encoded" is the encoded representation of the input
encoded = Dense(encoding_dim, activation='sigmoid')(input_vector)
# "decoded" is the lossy reconstruction of the input
decoded = Dense(227, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_vector, decoded)

Here the encoding dimension is 64. How can I calculate it according to my data ? Does it affect if I change it. I already tried but no obvious influence for now.


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