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# Example dimensions input_dim = 1000 # Number of possible genomic variations encoding_dim = 128 # Dimension of the embedding

autoencoder = Model(inputs=input_layer, outputs=decoder) autoencoder.compile(optimizer='adam', loss='binary_crossentropy') hereditary20181080pmkv top

autoencoder.fit(X_train, X_train, epochs=100, batch_size=256, shuffle=True) # Example dimensions input_dim = 1000 # Number