def __init__(self, filenames):
"""Create a `SequenceFileDataset`.
`SequenceFileDataset` allows a user to read data from a hadoop sequence
file. A sequence file consists of (key value) pairs sequentially. At
the moment, `org.apache.hadoop.io.Text` is the only serialization type
being supported, and there is no compression support.
For example:
```python
tf.compat.v1.enable_eager_execution()
dataset = tf.contrib.hadoop.SequenceFileDataset("/foo/bar.seq")
# Prints the (key, value) pairs inside a hadoop sequence file.
for key, value in dataset:
print(key, value)
```
Args:
filenames: A `tf.string` tensor containing one or more filenames.
"""
self._filenames = ops.convert_to_tensor(filenames,
dtype=dtypes.string,
name="filenames")
variant_tensor = gen_dataset_ops.sequence_file_dataset(
self._filenames,
structure.get_flat_tensor_types(self._element_structure))
super(SequenceFileDataset, self).__init__(variant_tensor)
def __init__(self, filenames):
"""Create a `SequenceFileDataset`.
`SequenceFileDataset` allows a user to read data from a hadoop sequence
file. A sequence file consists of (key value) pairs sequentially. At
the moment, `org.apache.hadoop.io.Text` is the only serialization type
being supported, and there is no compression support.
For example:
```python
tf.compat.v1.enable_eager_execution()
dataset = tf.contrib.hadoop.SequenceFileDataset("/foo/bar.seq")
# Prints the (key, value) pairs inside a hadoop sequence file.
for key, value in dataset:
print(key, value)
```
Args:
filenames: A `tf.string` tensor containing one or more filenames.
"""
self._filenames = ops.convert_to_tensor(
filenames, dtype=dtypes.string, name="filenames")
variant_tensor = gen_dataset_ops.sequence_file_dataset(
self._filenames, self._element_structure._flat_types) # pylint: disable=protected-access
super(SequenceFileDataset, self).__init__(variant_tensor)
def __init__(self, filenames):
"""Create a `SequenceFileDataset`.
`SequenceFileDataset` allows a user to read data from a hadoop sequence
file. A sequence file consists of (key value) pairs sequentially. At
the moment, `org.apache.hadoop.io.Text` is the only serialization type
being supported, and there is no compression support.
For example:
```python
dataset = tf.contrib.hadoop.SequenceFileDataset("/foo/bar.seq")
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
# Prints the (key, value) pairs inside a hadoop sequence file.
while True:
try:
print(sess.run(next_element))
except tf.errors.OutOfRangeError:
break
```
Args:
filenames: A `tf.string` tensor containing one or more filenames.
"""
self._filenames = ops.convert_to_tensor(filenames,
dtype=dtypes.string,
name="filenames")
variant_tensor = gen_dataset_ops.sequence_file_dataset(
self._filenames, self._element_structure._flat_types) # pylint: disable=protected-access
super(SequenceFileDataset, self).__init__(variant_tensor)
def _as_variant_tensor(self):
return gen_dataset_ops.sequence_file_dataset(
self._filenames, self._element_structure._flat_types) # pylint: disable=protected-access
def _as_variant_tensor(self):
return gen_dataset_ops.sequence_file_dataset(
self._filenames, nest.flatten(self.output_types))
def _as_variant_tensor(self):
return gen_dataset_ops.sequence_file_dataset(
self._filenames, nest.flatten(self.output_types))