def create_model(self, model_input, vocab_size, num_mixtures=None,
l2_penalty=1e-8, sub_scope="ddcc", original_input=None,
dropout=False, keep_prob=None, noise_level=None,
num_frames=None, **unused_params):
num_supports = FLAGS.num_supports
num_models = FLAGS.divergence_model_count
support_predictions = []
for i in xrange(num_models):
sub_prediction = self.sub_model(model_input,vocab_size, num_mixtures,
l2_penalty, sub_scope+"%d"%i,
dropout, keep_prob, noise_level)
support_predictions.append(sub_prediction)
support_predictions = tf.stack(support_predictions, axis=1)
main_predictions = tf.reduce_mean(support_predictions, axis=1)
return {"predictions": main_predictions, "support_predictions": support_predictions}
multitask_divergence_moe_model.py 文件源码
python
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