def _predict(self, X): if not self._initialized: raise NotFittedError() predict_data_feeder = setup_predict_data_feeder(X) preds = [] for data in predict_data_feeder: preds.append( self._session.run(self._model_predictions, feed_dict={self._inp.name: data})) return np.concatenate(preds, axis=0)
def _predict(self, X): if not self._initialized: raise NotFittedError() predict_data_feeder = setup_predict_data_feeder(X) preds = [] for data in predict_data_feeder: preds.append(self._session.run( self._model_predictions, feed_dict={ self._inp.name: data })) return np.concatenate(preds, axis=0)
def _predict(self, X): if not self._initialized: raise NotFittedError() self._graph.add_to_collection("IS_TRAINING", False) predict_data_feeder = setup_predict_data_feeder(X) 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 preds.append(self._session.run(self._model_predictions, feed_dict)) return np.concatenate(preds, axis=0)
def _predict(self, X): if not self._initialized: raise NotFittedError() predict_data_feeder = setup_predict_data_feeder(X) 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 preds.append(self._session.run( self._model_predictions, feed_dict)) return np.concatenate(preds, axis=0)
def _predict(self, X, axis=-1, batch_size=-1): if not self._initialized: raise NotFittedError() 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=-1): if not self._initialized: raise NotFittedError() 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 test_iterable_data(self): X = iter([[1, 2], [3, 4], [5, 6]]) df = setup_predict_data_feeder(X, batch_size=2) self.assertAllClose(six.next(df), [[1, 2], [3, 4]]) self.assertAllClose(six.next(df), [[5, 6]])