Reads batches of Examples. (deprecated)
tf.contrib.data.read_batch_features(
file_pattern, batch_size, features, reader=tf.data.TFRecordDataset,
reader_args=None, randomize_input=True, num_epochs=None, capacity=10000
)
Example:
serialized_examples = [
features {
feature { key: "age" value { int64_list { value: [ 0 ] } } }
feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } }
},
features {
feature { key: "age" value { int64_list { value: [] } } }
feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
feature { key: "kws" value { bytes_list { value: [ "sports" ] } } }
}
]
We can use arguments:
features: {
"age": FixedLenFeature([], dtype=tf.int64, default_value=-1),
"gender": FixedLenFeature([], dtype=tf.string),
"kws": VarLenFeature(dtype=tf.string),
}
And the expected output is:
{
"age": [[0], [-1]],
"gender": [["f"], ["f"]],
"kws": SparseTensor(
indices=[[0, 0], [0, 1], [1, 0]],
values=["code", "art", "sports"]
dense_shape=[2, 2]),
}
Args |
file_pattern
|
List of files or patterns of file paths containing
Example records. See tf.io.gfile.glob for pattern rules.
|
batch_size
|
An int representing the number of records to combine
in a single batch.
|
features
|
A dict mapping feature keys to FixedLenFeature or
VarLenFeature values. See tf.io.parse_example .
|
reader
|
A function or class that can be
called with a filenames tensor and (optional) reader_args and returns
a Dataset of Example tensors. Defaults to tf.data.TFRecordDataset .
|
reader_args
|
Additional arguments to pass to the reader class.
|
randomize_input
|
Whether the input should be randomized.
|
num_epochs
|
Integer specifying the number of times to read through the
dataset. If None, cycles through the dataset forever.
|
capacity
|
Buffer size of the ShuffleDataset. A large capacity ensures better
shuffling but would increase memory usage and startup time.
|
Returns |
A dict from keys in features to Tensor or SparseTensor objects.
|