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
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
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
