<s> import logging <EOL> logging . basicConfig ( level = logging . INFO ) <EOL> from deepy import * <EOL> from variational_autoencoder import VariationalAutoEncoder <EOL> LATENT_DIM = <NUM_LIT:2> <EOL> model_path = os . path . join ( os . path . dirname ( __file__ ) , "<STR_LIT>" , "<STR_LIT>" % LATENT_DIM ) <EOL> def create_model ( load = False , sample = False ) : <EOL> model = VariationalAutoEncoder ( input_dim = <NUM_LIT> * <NUM_LIT> ) <EOL> model . stack_encoders ( Dense ( <NUM_LIT> , '<STR_LIT>' , init = GaussianInitializer ( ) , random_bias = True ) ) <EOL> model . stack_reparameterization_layer ( LATENT_DIM ) <EOL> model . stack_decoders ( Dense ( <NUM_LIT> , '<STR_LIT>' , init = GaussianInitializer ( ) , random_bias = True ) , <EOL> Dense ( <NUM_LIT> * <NUM_LIT> , '<STR_LIT>' , init = GaussianInitializer ( ) ) ) <EOL> if load : <EOL> model . load_params ( model_path ) <EOL> return model <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> model = create_model ( ) <EOL> trainer = AdamTrainer ( model , { "<STR_LIT>" : LearningRateAnnealer . learning_rate ( <NUM_LIT> ) } ) <EOL> mnist = MiniBatches ( MnistDataset ( for_autoencoder = True ) , batch_size = <NUM_LIT:100> ) <EOL> trainer . run ( mnist , controllers = [ LearningRateAnnealer ( trainer ) ] ) <EOL> model . save_params ( model_path ) </s>
