diff options
Diffstat (limited to 'tensorflow/python/keras/engine/training_utils.py')
| -rw-r--r-- | tensorflow/python/keras/engine/training_utils.py | 138 |
1 files changed, 136 insertions, 2 deletions
diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index 728a2b493b..dbbc87daf9 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -19,9 +19,11 @@ from __future__ import division from __future__ import print_function import copy +import math import numpy as np +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context from tensorflow.python.framework import tensor_util @@ -31,6 +33,135 @@ from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.ops import math_ops +def _map_nested(data, func): + """Maps each nested element using func.""" + if isinstance(data, list): + return [_map_nested(nested_data, func) for nested_data in data] + elif isinstance(data, tuple): + return tuple(_map_nested(nested_data, func) for nested_data in data) + elif isinstance(data, dict): + return { + k: _map_nested(nested_data, func) for k, nested_data in data.items() + } + else: + return func(data) + + +def _nested_all(data, cond_func): + """Checks if all elements in a nested structure satisfy cond_func.""" + if isinstance(data, (tuple, list)): + return all([_nested_all(nested_data, cond_func) for nested_data in data]) + elif isinstance(data, dict): + return all( + [_nested_all(nested_data, cond_func) for nested_data in data.values()]) + else: + return cond_func(data) + + +def _nested_any(data, cond_func): + """Checks if any nested_elements in a nested structure satisfy cond_func.""" + if isinstance(data, (tuple, list)): + return any([_nested_any(nested_data, cond_func) for nested_data in data]) + elif isinstance(data, dict): + return any( + [_nested_any(nested_data, cond_func) for nested_data in data.values()]) + else: + return cond_func(data) + + +def _convert_lists_to_tuples(data): + """Converts all lists to tuples, since Datasets expect tuples.""" + if isinstance(data, (tuple, list)): + return tuple(_convert_lists_to_tuples(nested_data) for nested_data in data) + elif isinstance(data, dict): + return { + k: _convert_lists_to_tuples(nested_data) + for k, nested_data in data.items() + } + else: + return data + + +def _get_batch_axis_size(data): + """Returns batch axis shape for nested data.""" + if isinstance(data, (tuple, list)): + return _get_batch_axis_size(data[0]) + elif isinstance(data, dict): + return _get_batch_axis_size(list(data.values())) + else: + return int(data.shape[0]) + + +def convert_to_iterator(x=None, + y=None, + sample_weights=None, + batch_size=None, + steps_per_epoch=None, + epochs=1, + shuffle=False): + """Converts NumPy arrays or EagerTensors to an EagerIterator. + + Combines all provided data into a single EagerIterator. + + Arguments: + x: NumPy array or EagerTensor, or list of Numpy arrays or EagerTensors + representing inputs to a model. + y: Optional. NumPy array or EagerTensor, or list of Numpy arrays or + EagerTensors representing targets of a model. + sample_weights: Optional NumPy array or EagerTensor representing sample + weights. + batch_size: Used to batch data and calculate how many steps EagerIterator + should take per epoch. + steps_per_epoch: If provided, how many steps EagerIterator should take per + epoch. + epochs: Epochs to repeat iterator for. + shuffle: Whether to shuffle data after each epoch. + + Raises: + ValueError: if steps_per_epoch cannot be calculated from the data + provided. + + Returns: + (Iterator, steps_per_epoch). + + """ + if isinstance(x, iterator_ops.EagerIterator): + return x, steps_per_epoch + + if not _nested_any(sample_weights, lambda x: x is None): + data = (x, y, sample_weights) + elif not _nested_any(y, lambda x: x is None): + data = (x, y) + else: + # always wrap in a tuple, so we know y, sample_weights weren't set + # even when x has multiple elements + data = (x,) + + data = _convert_lists_to_tuples(data) + if steps_per_epoch is None and batch_size is not None: + num_samples = _get_batch_axis_size(data) + steps_per_epoch = int(math.ceil(num_samples / batch_size)) + + if steps_per_epoch is None: + raise ValueError('Could not determine steps_per_epoch.' + 'Please provide either batch_size or' + 'steps_per_epoch.') + + # TODO(omalleyt) for NumPy arrays in graph mode + # placeholder ops should be used + # this is only ideal for eager mode + dataset = dataset_ops.Dataset.from_tensor_slices(data) + + if batch_size is not None: + dataset = dataset.batch(batch_size) + if shuffle: + dataset = dataset.shuffle(buffer_size=10000) + dataset = dataset.repeat(epochs) + iterator = dataset.make_one_shot_iterator() + + return iterator, steps_per_epoch + + def check_num_samples(ins, batch_size=None, steps=None, @@ -128,8 +259,8 @@ def standardize_input_data(data, except KeyError as e: raise ValueError('No data provided for "' + e.args[0] + '". Need data ' 'for each key in: ' + str(names)) - elif isinstance(data, list): - if isinstance(data[0], list): + elif isinstance(data, (list, tuple)): + if isinstance(data[0], (list, tuple)): data = [np.asarray(d) for d in data] elif len(names) == 1 and isinstance(data[0], (float, int)): data = [np.asarray(data)] @@ -482,6 +613,9 @@ def standardize_weights(y, Raises: ValueError: In case of invalid user-provided arguments. """ + # Iterator may return sample_weight as 1-tuple + if isinstance(sample_weight, tuple): + sample_weight = sample_weight[0] if sample_weight_mode is not None: if sample_weight_mode != 'temporal': raise ValueError('"sample_weight_mode ' |