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Fix more docstrings to be compatible with the doctest format.

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PiperOrigin-RevId: 267424383
This commit is contained in:
Yash Katariya 2019-09-05 12:10:55 -07:00 committed by TensorFlower Gardener
parent eee9afd6db
commit 6016e8009f
4 changed files with 45 additions and 68 deletions

34
tensorflow/python/ops/init_ops.py
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@ -173,12 +173,8 @@ class Constant(Initializer):
of the `value` list, even reshaped, as shown in the two commented lines
below the `value` list initialization.
```
>>> value = [0, 1, 2, 3, 4, 5, 6, 7]
>>> # value = np.array(value)
>>> # value = value.reshape([2, 4])
>>> init = tf.compat.v1.constant_initializer(value)
>>>
>>> # fitting shape
>>> with tf.compat.v1.Session():
... x = tf.compat.v1.get_variable('x', shape=[2, 4], initializer=init)
@ -186,30 +182,28 @@ class Constant(Initializer):
... print(x.eval())
[[0. 1. 2. 3.]
[4. 5. 6. 7.]]
>>>
>>> # Larger shape
>>> with tf.compat.v1.Session():
... x = tf.compat.v1.get_variable('x', shape=[3, 4], initializer=init)
... x.initializer.run()
... print(x.eval())
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 7. 7. 7. 7.]]
>>>
... y = tf.compat.v1.get_variable('y', shape=[3, 4], initializer=init)
... y.initializer.run()
... print(y.eval())
[[0. 1. 2. 3.]
[4. 5. 6. 7.]
[7. 7. 7. 7.]]
>>> # Smaller shape
>>> with tf.compat.v1.Session():
... x = tf.compat.v1.get_variable('x', shape=[2, 3], initializer=init)
... z = tf.compat.v1.get_variable('z', shape=[2, 3], initializer=init)
Traceback (most recent call last):
...
ValueError: Too many elements provided. Needed at most 6, but received 8
>>>
>>> # Shape verification
>>> init_verify = tf.compat.v1.constant_initializer(value,
verify_shape=True)
>>> init_verify = tf.compat.v1.constant_initializer(value, verify_shape=True)
>>> with tf.compat.v1.Session():
... x = tf.compat.v1.get_variable('x', shape=[3, 4],
... initializer=init_verify)
... u = tf.compat.v1.get_variable('u', shape=[3, 4],
... initializer=init_verify)
Traceback (most recent call last):
...
TypeError: Expected Tensor's shape: (3, 4), got (8,).
>>>
```
"""
@deprecated_args(None,

16
tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py
View File

@ -59,16 +59,12 @@ def batch_gather_with_default(params,
`result.ragged_rank = max(indices.ragged_rank, params.ragged_rank)`.
#### Example:
```python
>>> params = tf.ragged.constant([
['a', 'b', 'c'],
['d'],
[],
['e']])
>>> indices = tf.ragged.constant([[1, 2, -1], [], [], [0, 10]])
>>> batch_gather_with_default(params, indices, 'FOO')
[['b', 'c', 'FOO'], [], [], ['e', 'FOO']]
```
>>> params = tf.ragged.constant([['a', 'b', 'c'], ['d'], [], ['e']])
>>> indices = tf.ragged.constant([[1, 2, -1], [], [], [0, 10]])
>>> batch_gather_with_default(params, indices, 'FOO')
<tf.RaggedTensor [[b'b', b'c', b'FOO'], [], [], [b'e', b'FOO']]>
"""
with ops.name_scope(name, 'RaggedBatchGatherWithDefault'):
params = ragged_tensor.convert_to_tensor_or_ragged_tensor(

44
tensorflow/python/ops/ragged/ragged_string_ops.py
View File

@ -112,11 +112,11 @@ def unicode_encode(input,
A `N` dimensional `string` tensor with shape `[D1...DN]`.
#### Example:
```python
>>> input = [[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]]
>>> unicode_encode(input, 'UTF-8')
['G\xc3\xb6\xc3\xb6dnight', '\xf0\x9f\x98\x8a']
```
>>> input = [[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]]
>>> unicode_encode(input, 'UTF-8')
['G\xc3\xb6\xc3\xb6dnight', '\xf0\x9f\x98\x8a']
"""
with ops.name_scope(name, "UnicodeEncode", [input]):
input_tensor = ragged_tensor.convert_to_tensor_or_ragged_tensor(input)
@ -269,14 +269,14 @@ def unicode_decode_with_offsets(input,
`tf.RaggedTensor`s otherwise.
#### Example:
```python
>>> input = [s.encode('utf8') for s in (u'G\xf6\xf6dnight', u'\U0001f60a')]
>>> result = tf.strings.unicode_decode_with_offsets(input, 'UTF-8')
>>> result[0].tolist() # codepoints
[[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]]
>>> result[1].tolist() # offsets
[[0, 1, 3, 5, 6, 7, 8, 9, 10], [0]]
```
>>> input = [s.encode('utf8') for s in (u'G\xf6\xf6dnight', u'\U0001f60a')]
>>> result = tf.strings.unicode_decode_with_offsets(input, 'UTF-8')
>>> result[0].tolist() # codepoints
[[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]]
>>> result[1].tolist() # offsets
[[0, 1, 3, 5, 6, 7, 8, 9, 10], [0]]
"""
with ops.name_scope(name, "UnicodeDecodeWithOffsets", [input]):
return _unicode_decode(input, input_encoding, errors, replacement_char,
@ -374,15 +374,15 @@ def unicode_split_with_offsets(input,
`tf.RaggedTensor`s otherwise.
#### Example:
```python
>>> input = [s.encode('utf8') for s in (u'G\xf6\xf6dnight', u'\U0001f60a')]
>>> result = tf.strings.unicode_split_with_offsets(input, 'UTF-8')
>>> result[0].tolist() # character substrings
[['G', '\xc3\xb6', '\xc3\xb6', 'd', 'n', 'i', 'g', 'h', 't'],
['\xf0\x9f\x98\x8a']]
>>> result[1].tolist() # offsets
[[0, 1, 3, 5, 6, 7, 8, 9, 10], [0]]
```
>>> input = [s.encode('utf8') for s in (u'G\xf6\xf6dnight', u'\U0001f60a')]
>>> result = tf.strings.unicode_split_with_offsets(input, 'UTF-8')
>>> result[0].tolist() # character substrings
[['G', '\xc3\xb6', '\xc3\xb6', 'd', 'n', 'i', 'g', 'h', 't'],
['\xf0\x9f\x98\x8a']]
>>> result[1].tolist() # offsets
[[0, 1, 3, 5, 6, 7, 8, 9, 10], [0]]
"""
with ops.name_scope(name, "UnicodeSplitWithOffsets", [input]):
codepoints, offsets = _unicode_decode(input, input_encoding, errors,

19
tensorflow/python/ops/ragged/ragged_tensor.py
View File

@ -104,12 +104,10 @@ class RaggedTensor(composite_tensor.CompositeTensor):
Example:
```python
>>> print(tf.RaggedTensor.from_row_splits(
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... row_splits=[0, 4, 4, 7, 8, 8]))
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... row_splits=[0, 4, 4, 7, 8, 8]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
```
### Alternative Row-Partitioning Schemes
@ -139,7 +137,6 @@ class RaggedTensor(composite_tensor.CompositeTensor):
Example: The following ragged tensors are equivalent, and all represent the
nested list `[[3, 1, 4, 1], [], [5, 9, 2], [6], []]`.
```python
>>> values = [3, 1, 4, 1, 5, 9, 2, 6]
>>> rt1 = RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8])
>>> rt2 = RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0])
@ -147,7 +144,6 @@ class RaggedTensor(composite_tensor.CompositeTensor):
... values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5)
>>> rt4 = RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8])
>>> rt5 = RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8])
```
### Multiple Ragged Dimensions
@ -155,7 +151,6 @@ class RaggedTensor(composite_tensor.CompositeTensor):
a nested `RaggedTensor` for the `values` tensor. Each nested `RaggedTensor`
adds a single ragged dimension.
```python
>>> inner_rt = RaggedTensor.from_row_splits( # =rt1 from above
... values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8])
>>> outer_rt = RaggedTensor.from_row_splits(
@ -164,25 +159,21 @@ class RaggedTensor(composite_tensor.CompositeTensor):
[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]
>>> print outer_rt.ragged_rank
2
```
The factory function `RaggedTensor.from_nested_row_splits` may be used to
construct a `RaggedTensor` with multiple ragged dimensions directly, by
providing a list of `row_splits` tensors:
```python
>>> RaggedTensor.from_nested_row_splits(
... flat_values=[3, 1, 4, 1, 5, 9, 2, 6],
... nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])).to_list()
[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]
```
### Uniform Inner Dimensions
`RaggedTensor`s with uniform inner dimensions can be defined
by using a multidimensional `Tensor` for `values`.
```python
>>> rt = RaggedTensor.from_row_splits(values=tf.ones([5, 3]),
.. row_splits=[0, 2, 5])
>>> print rt.to_list()
@ -190,7 +181,6 @@ class RaggedTensor(composite_tensor.CompositeTensor):
[[1, 1, 1], [1, 1, 1], [1, 1, 1]]]
>>> print rt.shape
(2, ?, 3)
```
### Uniform Outer Dimensions
@ -200,7 +190,6 @@ class RaggedTensor(composite_tensor.CompositeTensor):
constructed with this method from a `RaggedTensor` values with shape
`[4, None]`:
```python
>>> values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]])
>>> print values.shape
(4, None)
@ -209,17 +198,14 @@ class RaggedTensor(composite_tensor.CompositeTensor):
<tf.RaggedTensor [[[1, 2, 3], [4]], [[5, 6], [7, 8, 9, 10]]])>
>>> print rt1.shape
(2, 2, None)
```
Note that `rt1` only contains one ragged dimension (the innermost
dimension). In contrast, if `from_row_splits` is used to construct a similar
`RaggedTensor`, then that `RaggedTensor` will have two ragged dimensions:
```python
>>> rt2 = tf.RaggedTensor.from_row_splits(values, [0, 2, 4])
>>> print rt2.shape
(2, None, None)
```
Uniform and ragged outer dimensions may be interleaved, meaning that a
tensor with any combination of ragged and uniform dimensions may be created.
@ -232,6 +218,7 @@ class RaggedTensor(composite_tensor.CompositeTensor):
t2 = RaggedTensor.from_uniform_row_length(t1, 8) # [20, 8, None, 2]
t3 = RaggedTensor.from_uniform_row_length(t2, 4) # [5, 4, 8, None, 2]
t4 = RaggedTensor.from_row_lengths(t3, [...]) # [3, None, 4, 8, None, 2]
```
"""
#=============================================================================