diff options
Diffstat (limited to 'tensorflow/python/estimator/inputs/numpy_io.py')
-rw-r--r-- | tensorflow/python/estimator/inputs/numpy_io.py | 83 |
1 files changed, 60 insertions, 23 deletions
diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index c9f37f06e8..3512f66284 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import collections +from six import string_types from tensorflow.python.estimator.inputs.queues import feeding_functions # Key name to pack the target into dict of `features`. See @@ -51,8 +52,9 @@ def numpy_input_fn(x, num_threads=1): """Returns input function that would feed dict of numpy arrays into the model. - This returns a function outputting `features` and `target` based on the dict - of numpy arrays. The dict `features` has the same keys as the `x`. + This returns a function outputting `features` and `targets` based on the dict + of numpy arrays. The dict `features` has the same keys as the `x`. The dict + `targets` has the same keys as the `y` if `y` is a dict. Example: @@ -69,7 +71,7 @@ def numpy_input_fn(x, Args: x: dict of numpy array object. - y: numpy array object. `None` if absent. + y: numpy array object or dict of numpy array object. `None` if absent. batch_size: Integer, size of batches to return. num_epochs: Integer, number of epochs to iterate over data. If `None` will run forever. @@ -81,11 +83,13 @@ def numpy_input_fn(x, such as in prediction and evaluation mode, `num_threads` should be 1. Returns: - Function, that has signature of ()->(dict of `features`, `target`) + Function, that has signature of ()->(dict of `features`, `targets`) Raises: ValueError: if the shape of `y` mismatches the shape of values in `x` (i.e., values in `x` have same shape). + ValueError: if duplicate keys are in both `x` and `y` when `y` is a dict. + ValueError: if x or y is an empty dict. TypeError: `x` is not a dict or `shuffle` is not bool. """ @@ -97,43 +101,76 @@ def numpy_input_fn(x, """Numpy input function.""" if not isinstance(x, dict): raise TypeError('x must be dict; got {}'.format(type(x).__name__)) + if not x: + raise ValueError('x cannot be empty') # Make a shadow copy and also ensure the order of iteration is consistent. - ordered_dict_x = collections.OrderedDict( + ordered_dict_data = collections.OrderedDict( sorted(x.items(), key=lambda t: t[0])) + # Deep copy keys which is a view in python 3 + feature_keys = list(ordered_dict_data.keys()) + + if y is None: + target_keys = None + elif isinstance(y, dict): + if not y: + raise ValueError('y cannot be empty dict, use None instead.') + + ordered_dict_y = collections.OrderedDict( + sorted(y.items(), key=lambda t: t[0])) + target_keys = list(ordered_dict_y.keys()) + + duplicate_keys = set(feature_keys).intersection(set(target_keys)) + if len(duplicate_keys): + raise ValueError('{} duplicate keys are found in both x and y: ' + '{}'.format(len(duplicate_keys), duplicate_keys)) + + ordered_dict_data.update(ordered_dict_y) + else: + target_keys = _get_unique_target_key(ordered_dict_data) + ordered_dict_data[target_keys] = y + + if len(set(v.shape[0] for v in ordered_dict_data.values())) != 1: + shape_dict_of_x = {k: ordered_dict_data[k].shape + for k in feature_keys} + + if target_keys is None: + shape_of_y = None + elif isinstance(target_keys, string_types): + shape_of_y = y.shape + else: + shape_of_y = {k: ordered_dict_data[k].shape + for k in target_keys} - unique_target_key = _get_unique_target_key(ordered_dict_x) - if y is not None: - ordered_dict_x[unique_target_key] = y - - if len(set(v.shape[0] for v in ordered_dict_x.values())) != 1: - shape_dict_of_x = {k: ordered_dict_x[k].shape - for k in ordered_dict_x.keys()} - shape_of_y = None if y is None else y.shape raise ValueError('Length of tensors in x and y is mismatched. All ' 'elements in x and y must have the same length.\n' 'Shapes in x: {}\n' - 'Shape for y: {}\n'.format(shape_dict_of_x, shape_of_y)) + 'Shapes in y: {}\n'.format(shape_dict_of_x, shape_of_y)) queue = feeding_functions._enqueue_data( # pylint: disable=protected-access - ordered_dict_x, + ordered_dict_data, queue_capacity, shuffle=shuffle, num_threads=num_threads, enqueue_size=batch_size, num_epochs=num_epochs) - features = (queue.dequeue_many(batch_size) if num_epochs is None + batch = (queue.dequeue_many(batch_size) if num_epochs is None else queue.dequeue_up_to(batch_size)) - # Remove the first `Tensor` in `features`, which is the row number. - if len(features) > 0: - features.pop(0) + # Remove the first `Tensor` in `batch`, which is the row number. + if len(batch) > 0: + batch.pop(0) - features = dict(zip(ordered_dict_x.keys(), features)) - if y is not None: - target = features.pop(unique_target_key) + features = dict(zip(feature_keys, batch[:len(feature_keys)])) + if target_keys is None: + # TODO(martinwicke), return consistent result + return features + elif isinstance(target_keys, string_types): + target = batch[-1] + return features, target + else: + target = dict(zip(target_keys, batch[-len(target_keys):])) return features, target - return features return input_fn |