PR #17967: [keras/layers/preprocessing] Standardise docstring usage of "Default to"

Imported from GitHub PR #17967

This is one of many PRs. Discussion + request to split into multiple PRs @ #17748
Copybara import of the project:

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2aec8c1 by Samuel Marks <[email protected]>:

[keras/layers/preprocessing/category_encoding.py,keras/layers/preprocessing/discretization.py,keras/layers/preprocessing/hashed_crossing.py,keras/layers/preprocessing/hashing.py,keras/layers/preprocessing/image_preprocessing.py,keras/layers/preprocessing/image_preprocessing_test.py,keras/layers/preprocessing/index_lookup.py,keras/layers/preprocessing/integer_lookup.py,keras/layers/preprocessing/normalization.py,keras/layers/preprocessing/string_lookup.py,keras/layers/preprocessing/text_vectorization.py] Standardise docstring usage of "Default to"

--
a1925ec by Samuel Marks <[email protected]>:

[keras/layers/preprocessing/hashing.py] Use backticks for defaults in docstrings

Merging this change closes #17967

FUTURE_COPYBARA_INTEGRATE_REVIEW=#17967 from SamuelMarks:keras.layers.preprocessing-defaults-to a1925ec
PiperOrigin-RevId: 527017153

Author: tensorflower-gardener

keras/layers/preprocessing/category_encoding.py

@@ -90,7 +90,7 @@ class CategoryEncoding(base_layer.Layer):
         inputs to the layer must integers in the range `0 <= value <
         num_tokens`, or an error will be thrown.
       output_mode: Specification for the output of the layer.
-        Defaults to `"multi_hot"`. Values can be `"one_hot"`, `"multi_hot"` or
+        Values can be `"one_hot"`, `"multi_hot"` or
         `"count"`, configuring the layer as follows:
           - `"one_hot"`: Encodes each individual element in the input into an
             array of `num_tokens` size, containing a 1 at the element index. If
@@ -105,6 +105,7 @@ class CategoryEncoding(base_layer.Layer):
           - `"count"`: Like `"multi_hot"`, but the int array contains a count of
             the number of times the token at that index appeared in the sample.
         For all output modes, currently only output up to rank 2 is supported.
+        Defaults to `"multi_hot"`.
       sparse: Boolean. If true, returns a `SparseTensor` instead of a dense
         `Tensor`. Defaults to `False`.
 

keras/layers/preprocessing/discretization.py

@@ -164,8 +164,8 @@ class Discretization(base_preprocessing_layer.PreprocessingLayer):
         0.01). Higher values of epsilon increase the quantile approximation, and
         hence result in more unequal buckets, but could improve performance
         and resource consumption.
-      output_mode: Specification for the output of the layer. Defaults to
-        `"int"`.  Values can be `"int"`, `"one_hot"`, `"multi_hot"`, or
+      output_mode: Specification for the output of the layer. Values can be
+       `"int"`, `"one_hot"`, `"multi_hot"`, or
         `"count"` configuring the layer as follows:
           - `"int"`: Return the discretized bin indices directly.
           - `"one_hot"`: Encodes each individual element in the input into an
@@ -180,9 +180,10 @@ class Discretization(base_preprocessing_layer.PreprocessingLayer):
             will be `(..., num_tokens)`.
           - `"count"`: As `"multi_hot"`, but the int array contains a count of
             the number of times the bin index appeared in the sample.
+        Defaults to `"int"`.
       sparse: Boolean. Only applicable to `"one_hot"`, `"multi_hot"`,
         and `"count"` output modes. If True, returns a `SparseTensor` instead of
-        a dense `Tensor`. Defaults to False.
+        a dense `Tensor`. Defaults to `False`.
 
     Examples:
 

keras/layers/preprocessing/hashed_crossing.py

@@ -51,15 +51,15 @@ class HashedCrossing(base_layer.Layer):
 
     Args:
       num_bins: Number of hash bins.
-      output_mode: Specification for the output of the layer. Defaults to
-        `"int"`.  Values can be `"int"`, or `"one_hot"` configuring the layer as
-        follows:
+      output_mode: Specification for the output of the layer. Values can be
+        `"int"`, or `"one_hot"` configuring the layer as follows:
           - `"int"`: Return the integer bin indices directly.
           - `"one_hot"`: Encodes each individual element in the input into an
             array the same size as `num_bins`, containing a 1 at the input's bin
             index.
+        Defaults to `"int"`.
       sparse: Boolean. Only applicable to `"one_hot"` mode. If True, returns a
-        `SparseTensor` instead of a dense `Tensor`. Defaults to False.
+        `SparseTensor` instead of a dense `Tensor`. Defaults to `False`.
       **kwargs: Keyword arguments to construct a layer.
 
     Examples:

keras/layers/preprocessing/hashing.py

@@ -109,17 +109,16 @@ class Hashing(base_layer.Layer):
         bin, so the effective number of bins is `(num_bins - 1)` if `mask_value`
         is set.
       mask_value: A value that represents masked inputs, which are mapped to
-        index 0. Defaults to None, meaning no mask term will be added and the
-        hashing will start at index 0.
+        index 0. `None` means no mask term will be added and the
+        hashing will start at index 0. Defaults to `None`.
       salt: A single unsigned integer or None.
         If passed, the hash function used will be SipHash64, with these values
         used as an additional input (known as a "salt" in cryptography).
-        These should be non-zero. Defaults to `None` (in that
-        case, the FarmHash64 hash function is used). It also supports
-        tuple/list of 2 unsigned integer numbers, see reference paper for
-        details.
-      output_mode: Specification for the output of the layer. Defaults to
-        `"int"`.  Values can be `"int"`, `"one_hot"`, `"multi_hot"`, or
+        These should be non-zero. If `None`, uses the FarmHash64 hash function.
+        It also supports tuple/list of 2 unsigned integer numbers, see
+        reference paper for details. Defaults to `None`.
+      output_mode: Specification for the output of the layer. Values can bes
+        `"int"`, `"one_hot"`, `"multi_hot"`, or
         `"count"` configuring the layer as follows:
           - `"int"`: Return the integer bin indices directly.
           - `"one_hot"`: Encodes each individual element in the input into an
@@ -134,9 +133,10 @@ class Hashing(base_layer.Layer):
             will be `(..., num_tokens)`.
           - `"count"`: As `"multi_hot"`, but the int array contains a count of
             the number of times the bin index appeared in the sample.
+        Defaults to `"int"`.
       sparse: Boolean. Only applicable to `"one_hot"`, `"multi_hot"`,
         and `"count"` output modes. If True, returns a `SparseTensor` instead of
-        a dense `Tensor`. Defaults to False.
+        a dense `Tensor`. Defaults to `False`.
       **kwargs: Keyword arguments to construct a layer.
 
     Input shape:

keras/layers/preprocessing/image_preprocessing.py

@@ -65,9 +65,9 @@ class Resizing(base_layer.Layer):
         height: Integer, the height of the output shape.
         width: Integer, the width of the output shape.
         interpolation: String, the interpolation method.
-            Defaults to `"bilinear"`.
             Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`,
             `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
+            Defaults to `"bilinear"`.
         crop_to_aspect_ratio: If True, resize the images without aspect
             ratio distortion. When the original aspect ratio differs
             from the target aspect ratio, the output image will be
@@ -420,9 +420,9 @@ class RandomFlip(base_layer.BaseRandomLayer):
 
     Args:
         mode: String indicating which flip mode to use. Can be `"horizontal"`,
-            `"vertical"`, or `"horizontal_and_vertical"`. Defaults to
-            `"horizontal_and_vertical"`. `"horizontal"` is a left-right flip and
-            `"vertical"` is a top-bottom flip.
+            `"vertical"`, or `"horizontal_and_vertical"`. `"horizontal"` is a
+            left-right flip and `"vertical"` is a top-bottom flip. Defaults to
+            `"horizontal_and_vertical"`
         seed: Integer. Used to create a random seed.
     """
 
@@ -1055,9 +1055,9 @@ class RandomZoom(base_layer.BaseRandomLayer):
             result in an output
             zooming out between 20% to 30%.
             `width_factor=(-0.3, -0.2)` result in an
-            output zooming in between 20% to 30%. Defaults to `None`,
+            output zooming in between 20% to 30%. `None` means
             i.e., zooming vertical and horizontal directions
-            by preserving the aspect ratio.
+            by preserving the aspect ratio. Defaults to `None`.
         fill_mode: Points outside the boundaries of the input are
             filled according to the given mode
             (one of `{"constant", "reflect", "wrap", "nearest"}`).
@@ -1377,9 +1377,9 @@ class RandomBrightness(base_layer.BaseRandomLayer):
             will be used for upper bound.
         value_range: Optional list/tuple of 2 floats
             for the lower and upper limit
-            of the values of the input data. Defaults to [0.0, 255.0].
-            Can be changed to e.g. [0.0, 1.0] if the image input
-            has been scaled before this layer.
+            of the values of the input data.
+            To make no change, use [0.0, 1.0], e.g., if the image input
+            has been scaled before this layer. Defaults to [0.0, 255.0].
             The brightness adjustment will be scaled to this range, and the
             output values will be clipped to this range.
         seed: optional integer, for fixed RNG behavior.
@@ -1539,9 +1539,9 @@ class RandomHeight(base_layer.BaseRandomLayer):
             `factor=0.2` results in an output with
             height changed by a random amount in the range `[-20%, +20%]`.
         interpolation: String, the interpolation method.
-            Defaults to `"bilinear"`.
             Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`,
             `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
+            Defaults to `"bilinear"`.
         seed: Integer. Used to create a random seed.
 
     Input shape:
@@ -1661,9 +1661,9 @@ class RandomWidth(base_layer.BaseRandomLayer):
             `factor=0.2` results in an output with width changed
             by a random amount in the range `[-20%, +20%]`.
         interpolation: String, the interpolation method.
-            Defaults to `bilinear`.
             Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`,
             `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
+            Defaults to `bilinear`.
         seed: Integer. Used to create a random seed.
 
     Input shape:

keras/layers/preprocessing/index_lookup.py

@@ -134,10 +134,10 @@ class IndexLookup(base_preprocessing_layer.PreprocessingLayer):
         `"tf_idf"`, this argument must be supplied.
       invert: Only valid when `output_mode` is `"int"`. If True, this layer will
         map indices to vocabulary items instead of mapping vocabulary items to
-        indices. Default to False.
-      output_mode: Specification for the output of the layer. Defaults to
-        `"int"`.  Values can be `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`,
-        or `"tf_idf"` configuring the layer as follows:
+        indices. Defaults to `False`.
+      output_mode: Specification for the output of the layer. Values can be
+        `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`, or `"tf_idf"`
+        configuring the layer as follows:
           - `"int"`: Return the raw integer indices of the input tokens.
           - `"one_hot"`: Encodes each individual element in the input into an
             array the same size as the vocabulary, containing a 1 at the element
@@ -153,6 +153,7 @@ class IndexLookup(base_preprocessing_layer.PreprocessingLayer):
             the number of times the token at that index appeared in the sample.
           - `"tf_idf"`: As `"multi_hot"`, but the TF-IDF algorithm is applied to
             find the value in each token slot.
+        Defaults to `"int"`.
       pad_to_max_tokens: Only valid when `output_mode` is `"multi_hot"`,
         `"count"`, or `"tf_idf"`. If True, the output will have its feature axis
         padded to `max_tokens` even if the number of unique tokens in the
@@ -161,7 +162,7 @@ class IndexLookup(base_preprocessing_layer.PreprocessingLayer):
         False.
       sparse: Boolean. Only applicable to `"one_hot"`, `"multi_hot"`, `"count"`
         and `"tf-idf"` output modes. If True, returns a `SparseTensor` instead
-        of a dense `Tensor`. Defaults to False.
+        of a dense `Tensor`. Defaults to `False`.
     """
 
     def __init__(

keras/layers/preprocessing/integer_lookup.py

@@ -71,18 +71,18 @@ class IntegerLookup(index_lookup.IndexLookup):
         only be specified when adapting the vocabulary or when setting
         `pad_to_max_tokens=True`. If None, there is no cap on the size of the
         vocabulary. Note that this size includes the OOV and mask tokens.
-        Defaults to None.
+        Defaults to `None`.
       num_oov_indices: The number of out-of-vocabulary tokens to use. If this
         value is more than 1, OOV inputs are modulated to determine their OOV
         value. If this value is 0, OOV inputs will cause an error when calling
-        the layer. Defaults to 1.
+        the layer. Defaults to `1`.
       mask_token: An integer token that represents masked inputs. When
         `output_mode` is `"int"`, the token is included in vocabulary and mapped
         to index 0. In other output modes, the token will not appear in the
         vocabulary and instances of the mask token in the input will be dropped.
-        If set to None, no mask term will be added. Defaults to None.
+        If set to None, no mask term will be added. Defaults to `None`.
       oov_token: Only used when `invert` is True. The token to return for OOV
-        indices. Defaults to -1.
+        indices. Defaults to `-1`.
       vocabulary: Optional. Either an array of integers or a string path to a
         text file. If passing an array, can pass a tuple, list, 1D numpy array,
         or 1D tensor containing the integer vocbulary terms. If passing a file
@@ -98,10 +98,10 @@ class IntegerLookup(index_lookup.IndexLookup):
         `"tf_idf"`, this argument must be supplied.
       invert: Only valid when `output_mode` is `"int"`. If True, this layer will
         map indices to vocabulary items instead of mapping vocabulary items to
-        indices. Default to False.
-      output_mode: Specification for the output of the layer. Defaults to
-        `"int"`.  Values can be `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`,
-        or `"tf_idf"` configuring the layer as follows:
+        indices. Defaults to `False`.
+      output_mode: Specification for the output of the layer. Values can be
+        `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`, or `"tf_idf"`
+        configuring the layer as follows:
           - `"int"`: Return the vocabulary indices of the input tokens.
           - `"one_hot"`: Encodes each individual element in the input into an
             array the same size as the vocabulary, containing a 1 at the element
@@ -119,6 +119,7 @@ class IntegerLookup(index_lookup.IndexLookup):
             find the value in each token slot.
         For `"int"` output, any shape of input and output is supported. For all
         other output modes, currently only output up to rank 2 is supported.
+        Defaults to `"int"`.
       pad_to_max_tokens: Only applicable when `output_mode` is `"multi_hot"`,
         `"count"`, or `"tf_idf"`. If True, the output will have its feature axis
         padded to `max_tokens` even if the number of unique tokens in the
@@ -127,7 +128,7 @@ class IntegerLookup(index_lookup.IndexLookup):
         False.
       sparse: Boolean. Only applicable when `output_mode` is `"multi_hot"`,
         `"count"`, or `"tf_idf"`. If True, returns a `SparseTensor` instead of a
-        dense `Tensor`. Defaults to False.
+        dense `Tensor`. Defaults to `False`.
 
     Examples:
 

keras/layers/preprocessing/normalization.py

@@ -52,11 +52,12 @@ class Normalization(base_preprocessing_layer.PreprocessingLayer):
           example, if shape is `(None, 5)` and `axis=1`, the layer will track 5
           separate mean and variance values for the last axis. If `axis` is set
           to `None`, the layer will normalize all elements in the input by a
-          scalar mean and variance. Defaults to -1, where the last axis of the
+          scalar mean and variance. When `-1` the last axis of the
           input is assumed to be a feature dimension and is normalized per
           index. Note that in the specific case of batched scalar inputs where
           the only axis is the batch axis, the default will normalize each index
           in the batch separately. In this case, consider passing `axis=None`.
+          Defaults to `-1`.
         mean: The mean value(s) to use during normalization. The passed value(s)
           will be broadcast to the shape of the kept axes above; if the value(s)
           cannot be broadcast, an error will be raised when this layer's

keras/layers/preprocessing/string_lookup.py

@@ -68,11 +68,11 @@ class StringLookup(index_lookup.IndexLookup):
         only be specified when adapting the vocabulary or when setting
         `pad_to_max_tokens=True`. If None, there is no cap on the size of the
         vocabulary. Note that this size includes the OOV and mask tokens.
-        Defaults to None.
+        Defaults to `None`.
       num_oov_indices: The number of out-of-vocabulary tokens to use. If this
         value is more than 1, OOV inputs are hashed to determine their OOV
         value. If this value is 0, OOV inputs will cause an error when calling
-        the layer.  Defaults to 1.
+        the layer.  Defaults to `1`.
       mask_token: A token that represents masked inputs. When `output_mode` is
         `"int"`, the token is included in vocabulary and mapped to index 0. In
         other output modes, the token will not appear in the vocabulary and
@@ -93,10 +93,10 @@ class StringLookup(index_lookup.IndexLookup):
         `"tf_idf"`, this argument must be supplied.
       invert: Only valid when `output_mode` is `"int"`. If True, this layer will
         map indices to vocabulary items instead of mapping vocabulary items to
-        indices. Default to False.
-      output_mode: Specification for the output of the layer. Defaults to
-        `"int"`.  Values can be `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`,
-        or `"tf_idf"` configuring the layer as follows:
+        indices. Defaults to `False`.
+      output_mode: Specification for the output of the layer. Values can be
+        `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`, or `"tf_idf"`
+        configuring the layer as follows:
           - `"int"`: Return the raw integer indices of the input tokens.
           - `"one_hot"`: Encodes each individual element in the input into an
             array the same size as the vocabulary, containing a 1 at the element
@@ -114,6 +114,7 @@ class StringLookup(index_lookup.IndexLookup):
             find the value in each token slot.
         For `"int"` output, any shape of input and output is supported. For all
         other output modes, currently only output up to rank 2 is supported.
+        Defaults to `"int"`
       pad_to_max_tokens: Only applicable when `output_mode` is `"multi_hot"`,
         `"count"`, or `"tf_idf"`. If True, the output will have its feature axis
         padded to `max_tokens` even if the number of unique tokens in the
@@ -122,7 +123,7 @@ class StringLookup(index_lookup.IndexLookup):
         False.
       sparse: Boolean. Only applicable when `output_mode` is `"multi_hot"`,
         `"count"`, or `"tf_idf"`. If True, returns a `SparseTensor` instead of a
-        dense `Tensor`. Defaults to False.
+        dense `Tensor`. Defaults to `False`.
       encoding: Optional. The text encoding to use to interpret the input
         strings. Defaults to `"utf-8"`.