- Sort Score
- Result 10 results
- Languages All
Results 1 - 3 of 3 for create_feed_dict_from_input_data (0.48 sec)
-
tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset_test.py
) rng = np.random.default_rng(seed=14) input_tensor_value = rng.random(size=(2, 2)) sample = {'input_tensor': input_tensor_value} feed_dict = repr_dataset.create_feed_dict_from_input_data( sample, signature_def ) self.assertLen(feed_dict, 1) self.assertIn('input:0', feed_dict) self.assertAllEqual(feed_dict['input:0'], input_tensor_value)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jan 04 07:35:19 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset.py
# Handle this as if the size is unknown. logging.info('Cannot determine the size of the dataset (%s).', ex) return None else: return None def create_feed_dict_from_input_data( input_data: RepresentativeSample, signature_def: meta_graph_pb2.SignatureDef, ) -> Mapping[str, np.ndarray]: """Constructs a feed_dict from input data.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 14.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib.py
): # Create a mapping from input tensor name to the input tensor value. # ex) "Placeholder:0" -> [0, 1, 2] feed_dict = rd.create_feed_dict_from_input_data(sample, signature_def) sess.run(output_tensor_names, feed_dict=feed_dict) def _run_graph_for_calibration_graph_mode( model_dir: str, tags: Collection[str],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 05:32:11 UTC 2024 - 27.4K bytes - Viewed (0)