examples/tutorials/tutorial2.py
def compute_tensor(self, x):
        """
        Build the computation graph here.
        """
        internal_variable = self.encoder.compute_tensor(x)

        decoding_output = self.decoder.compute_tensor(internal_variable)

        classification_output = self.classifier.compute_tensor(internal_variable)

        auto_encoder_cost = AutoEncoderCost(decoding_output, x).get()

        classification_cost = CrossEntropyCost(classification_output, self.target_input).get()

        final_cost = 0.01 * auto_encoder_cost + classification_cost

        error_rate = ErrorRateCost(classification_output, self.target_input).get()

        self.register_monitors(("err", error_rate),
                               ("encoder_cost", auto_encoder_cost),
                               ("classify_cost", classification_cost))

        return final_cost
def compute_tensor(self, x):
        """
        Build the computation graph here.
        """
        internal_variable = self.encoder.compute_tensor(x)

        decoding_output = self.decoder.compute_tensor(internal_variable)

        classification_output = self.classifier.compute_tensor(internal_variable)

        auto_encoder_cost = AutoEncoderCost(decoding_output, x).get()

        classification_cost = CrossEntropyCost(classification_output, self.target_input).get()

        final_cost = 0.01 * auto_encoder_cost + classification_cost

        error_rate = ErrorRateCost(classification_output, self.target_input).get()

        self.register_monitors(("err", error_rate),
                               ("encoder_cost", auto_encoder_cost),
                               ("classify_cost", classification_cost))

        return final_cost
def compute_tensor ( self , x ) : internal_variable = self . encoder . compute_tensor ( x ) decoding_output = self . decoder . compute_tensor ( internal_variable ) classification_output = self . classifier . compute_tensor ( internal_variable ) auto_encoder_cost = AutoEncoderCost ( decoding_output , x ) . get ( ) classification_cost = CrossEntropyCost ( classification_output , self . target_input ) . get ( ) final_cost = 0.01 * auto_encoder_cost + classification_cost error_rate = ErrorRateCost ( classification_output , self . target_input ) . get ( ) self . register_monitors ( ( "err" , error_rate ) , ( "encoder_cost" , auto_encoder_cost ) , ( "classify_cost" , classification_cost ) ) return final_cost
Build the computation graph here.
Build the computation graph here .