Exemplo n.º 1
0
 def predict(self, sequence):
     if LBL:
         targetrepr = sequence[-1:]
         sequence = sequence[:-1]
         (predictrepr, score) = graph.predict(self.embed(sequence), self.embed(targetrepr)[0], self.parameters.score_biases[targetrepr], self.parameters)
         return score
     else:
         (score) = graph.predict(self.embed(sequence), self.parameters)
         return score
Exemplo n.º 2
0
    def validate_errors(self, correct_sequences, noise_sequences):
        """
        Count the errors in this validation batch.
        """

        #            r = graph.train(self.embeds(correct_sequences), self.embeds(noise_sequences), learning_rate * weights[0])
        correct_scores = graph.predict(self.embeds(correct_sequences))
        noise_scores = graph.predict(self.embeds(noise_sequences))

        #        print correct_scores
        #        print noise_scores
        return correct_scores > noise_scores
Exemplo n.º 3
0
    def validate_errors(self, correct_sequences, noise_sequences):
        """
        Count the errors in this validation batch.
        """

#            r = graph.train(self.embeds(correct_sequences), self.embeds(noise_sequences), learning_rate * weights[0])
        correct_scores = graph.predict(self.embeds(correct_sequences))
        noise_scores = graph.predict(self.embeds(noise_sequences))

#        print correct_scores
#        print noise_scores
        return correct_scores > noise_scores