<s> import logging , os <EOL> logging . basicConfig ( level = logging . INFO ) <EOL> import theano . tensor as T <EOL> from deepy . dataset import MnistDataset , MiniBatches <EOL> from deepy . networks import ComputationalGraph <EOL> from deepy . layers import Dense , Softmax , NeuralLayer , Chain <EOL> from deepy . trainers import MomentumTrainer , LearningRateAnnealer <EOL> from deepy . utils import AutoEncoderCost , CrossEntropyCost , ErrorRateCost <EOL> model_path = os . path . join ( os . path . dirname ( __file__ ) , "<STR_LIT>" , "<STR_LIT>" ) <EOL> class MyJointTrainingModel ( NeuralLayer ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , internal_layer_size = <NUM_LIT:100> ) : <EOL> super ( MyJointTrainingModel , self ) . __init__ ( "<STR_LIT>" ) <EOL> self . internal_layer_size = internal_layer_size <EOL> def prepare ( self ) : <EOL> """<STR_LIT>""" <EOL> self . output_dim = <NUM_LIT:10> <EOL> self . encoder = Chain ( self . input_dim ) . stack ( Dense ( self . internal_layer_size , '<STR_LIT>' ) ) <EOL> self . decoder = Chain ( self . internal_layer_size ) . stack ( Dense ( self . input_dim ) ) <EOL> self . classifier = Chain ( self . internal_layer_size ) . stack ( Dense ( <NUM_LIT:50> , '<STR_LIT>' ) , <EOL> Dense ( self . output_dim ) , <EOL> Softmax ( ) ) <EOL> self . register_inner_layers ( self . encoder , self . decoder , self . classifier ) <EOL> self . target_input = T . ivector ( '<STR_LIT:target>' ) <EOL> self . register_external_inputs ( self . target_input ) <EOL> def compute_tensor ( self , x ) : <EOL> """<STR_LIT>""" <EOL> internal_variable = self . encoder . compute_tensor ( x ) <EOL> decoding_output = self . decoder . compute_tensor ( internal_variable ) <EOL> classification_output = self . classifier . compute_tensor ( internal_variable ) <EOL> auto_encoder_cost = AutoEncoderCost ( decoding_output , x ) . get ( ) <EOL> classification_cost = CrossEntropyCost ( classification_output , self . target_input ) . get ( ) <EOL> final_cost = <NUM_LIT> * auto_encoder_cost + classification_cost <EOL> error_rate = ErrorRateCost ( classification_output , self . target_input ) . get ( ) <EOL> self . register_monitors ( ( "<STR_LIT>" , error_rate ) , <EOL> ( "<STR_LIT>" , auto_encoder_cost ) , <EOL> ( "<STR_LIT>" , classification_cost ) ) <EOL> return final_cost <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> model = ComputationalGraph ( input_dim = <NUM_LIT> * <NUM_LIT> ) <EOL> mnist = MiniBatches ( MnistDataset ( ) , batch_size = <NUM_LIT:20> ) <EOL> trainer = MomentumTrainer ( model , { '<STR_LIT>' : <NUM_LIT> } ) <EOL> trainer . run ( mnist , controllers = [ LearningRateAnnealer ( trainer ) ] ) <EOL> model . save_params ( model_path ) </s>
