def _predict(self, X, axis=-1, batch_size=None):
if not self._initialized:
raise _sklearn.NotFittedError()
# Use the batch size for fitting if the user did not specify one.
if batch_size is None:
batch_size = self.batch_size
self._graph.add_to_collection("IS_TRAINING", False)
predict_data_feeder = setup_predict_data_feeder(
X, batch_size=batch_size)
preds = []
dropouts = self._graph.get_collection(DROPOUTS)
feed_dict = {prob: 1.0 for prob in dropouts}
for data in predict_data_feeder:
feed_dict[self._inp] = data
predictions_for_batch = self._session.run(
self._model_predictions,
feed_dict)
if self.n_classes > 1 and axis != -1:
preds.append(predictions_for_batch.argmax(axis=axis))
else:
preds.append(predictions_for_batch)
return np.concatenate(preds, axis=0)
def _predict(self, X, axis=-1, batch_size=None):
if not self._initialized:
raise _sklearn.NotFittedError()
# Use the batch size for fitting if the user did not specify one.
if batch_size is None:
batch_size = self.batch_size
self._graph.add_to_collection('IS_TRAINING', False)
predict_data_feeder = setup_predict_data_feeder(X, batch_size=batch_size)
preds = []
dropouts = self._graph.get_collection(DROPOUTS)
feed_dict = {prob: 1.0 for prob in dropouts}
for data in predict_data_feeder:
feed_dict[self._inp] = data
predictions_for_batch = self._session.run(self._model_predictions,
feed_dict)
if self.n_classes > 1 and axis != -1:
preds.append(predictions_for_batch.argmax(axis=axis))
else:
preds.append(predictions_for_batch)
return np.concatenate(preds, axis=0)
