diff --git a/tensorflow/python/keras/backend_config.py b/tensorflow/python/keras/backend_config.py index 407235c54bd..c1bf163c444 100644 --- a/tensorflow/python/keras/backend_config.py +++ b/tensorflow/python/keras/backend_config.py @@ -86,8 +86,8 @@ def set_floatx(value): likely cause numeric stability issues. Instead, mixed precision, which is using a mix of float16 and float32, can be used by calling `tf.keras.mixed_precision.experimental.set_policy('mixed_float16')`. See the - [mixed precision - guide](https://www.tensorflow.org/guide/keras/mixed_precision) for details. + [mixed precision guide]( + https://www.tensorflow.org/guide/keras/mixed_precision) for details. Arguments: value: String; `'float16'`, `'float32'`, or `'float64'`. diff --git a/tensorflow/python/keras/callbacks_v1.py b/tensorflow/python/keras/callbacks_v1.py index 09af890b76c..251fb3476dc 100644 --- a/tensorflow/python/keras/callbacks_v1.py +++ b/tensorflow/python/keras/callbacks_v1.py @@ -83,15 +83,16 @@ class TensorBoard(callbacks.TensorBoard): embeddings_layer_names: a list of names of layers to keep eye on. If None or empty list all the embedding layer will be watched. embeddings_metadata: a dictionary which maps layer name to a file name in - which metadata for this embedding layer is saved. See the - [details](https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional) + which metadata for this embedding layer is saved. + [Here are details]( + https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional) about metadata files format. In case if the same metadata file is used for all embedding layers, string can be passed. embeddings_data: data to be embedded at layers specified in `embeddings_layer_names`. Numpy array (if the model has a single input) - or list of Numpy arrays (if the model has multiple inputs). Learn [more - about - embeddings](https://www.tensorflow.org/programmers_guide/embedding) + or list of Numpy arrays (if the model has multiple inputs). Learn more + about embeddings [in this guide]( + https://www.tensorflow.org/programmers_guide/embedding). update_freq: `'batch'` or `'epoch'` or integer. When using `'batch'`, writes the losses and metrics to TensorBoard after each batch. The same applies for `'epoch'`. If using an integer, let's say `1000`, the diff --git a/tensorflow/python/keras/engine/input_layer.py b/tensorflow/python/keras/engine/input_layer.py index bcaaa5c46f6..efa3de959f1 100644 --- a/tensorflow/python/keras/engine/input_layer.py +++ b/tensorflow/python/keras/engine/input_layer.py @@ -80,7 +80,7 @@ class InputLayer(base_layer.Layer): ragged: Boolean, whether the placeholder created is meant to be ragged. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see - https://www.tensorflow.org/guide/ragged_tensors. + [this guide](https://www.tensorflow.org/guide/ragged_tensors). Default to False. name: Optional name of the layer (string). """ @@ -231,7 +231,7 @@ def Input( # pylint: disable=invalid-name ragged. Only one of 'ragged' and 'sparse' can be True. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see - https://www.tensorflow.org/guide/ragged_tensors. + [this guide](https://www.tensorflow.org/guide/ragged_tensors). **kwargs: deprecated arguments support. Supports `batch_shape` and `batch_input_shape`. diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 1793177398f..1e084fd1f00 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -163,9 +163,6 @@ class Model(network.Network, version_utils.ModelVersionSelector): Once the model is created, you can config the model with losses and metrics with `model.compile()`, train the model with `model.fit()`, or use the model to do prediction with `model.predict()`. - - Checkout [guide](https://www.tensorflow.org/guide/keras/overview) for - additional details. """ _TF_MODULE_IGNORED_PROPERTIES = frozenset( itertools.chain(('_train_counter', '_test_counter', '_predict_counter', diff --git a/tensorflow/python/keras/estimator/__init__.py b/tensorflow/python/keras/estimator/__init__.py index 4dde07e770b..35605206a65 100644 --- a/tensorflow/python/keras/estimator/__init__.py +++ b/tensorflow/python/keras/estimator/__init__.py @@ -46,8 +46,8 @@ def model_to_estimator( model to an Estimator for use with downstream systems. For usage example, please see: - [Creating estimators from Keras - Models](https://www.tensorflow.org/guide/estimators#creating_estimators_from_keras_models). + [Creating estimators from Keras Models]( + https://www.tensorflow.org/guide/estimators#creating_estimators_from_keras_models). Sample Weights: Estimators returned by `model_to_estimator` are configured so that they can @@ -144,8 +144,8 @@ def model_to_estimator_v2(keras_model=None, model to an Estimator for use with downstream systems. For usage example, please see: - [Creating estimators from Keras - Models](https://www.tensorflow.org/guide/estimators#creating_estimators_from_keras_models). + [Creating estimators from Keras Models]( + https://www.tensorflow.org/guide/estimators#creating_estimators_from_keras_models). Sample Weights: Estimators returned by `model_to_estimator` are configured so that they can diff --git a/tensorflow/python/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/layers/convolutional_recurrent.py index 1929b145561..19831429b73 100644 --- a/tensorflow/python/keras/layers/convolutional_recurrent.py +++ b/tensorflow/python/keras/layers/convolutional_recurrent.py @@ -458,8 +458,8 @@ class ConvLSTM2DCell(DropoutRNNCellMixin, Layer): unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Use in combination with `bias_initializer="zeros"`. - This is recommended in [Jozefowicz et al.] - (http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) + This is recommended in [Jozefowicz et al., 2015]( + http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to @@ -739,8 +739,8 @@ class ConvLSTM2D(ConvRNN2D): unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Use in combination with `bias_initializer="zeros"`. - This is recommended in [Jozefowicz et al.] - (http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) + This is recommended in [Jozefowicz et al., 2015]( + http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to @@ -807,10 +807,9 @@ class ConvLSTM2D(ConvRNN2D): ValueError: in case of invalid constructor arguments. References: - - [Convolutional LSTM Network: A Machine Learning Approach for - Precipitation Nowcasting](http://arxiv.org/abs/1506.04214v1) - The current implementation does not include the feedback loop on the - cells output. + - [Shi et al., 2015](http://arxiv.org/abs/1506.04214v1) + (the current implementation does not include the feedback loop on the + cells output). """ def __init__(self, diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index 76e24f1ba64..b6dd3c3500b 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -92,8 +92,8 @@ class Masking(Layer): # The time step 3 and 5 will be skipped from LSTM calculation. ``` - See [the masking and padding - guide](https://www.tensorflow.org/guide/keras/masking_and_padding) + See [the masking and padding guide]( + https://www.tensorflow.org/guide/keras/masking_and_padding) for more details. """ @@ -734,15 +734,15 @@ class Lambda(Layer): The `Lambda` layer exists so that arbitrary TensorFlow functions can be used when constructing `Sequential` and Functional API models. `Lambda` layers are best suited for simple operations or - quick experimentation. For more advanced usecases, follow + quick experimentation. For more advanced usecases, follow [this guide](https://www.tensorflow.org/guide/keras/custom_layers_and_models) - for subclassing `tf.keras.layers.Layer`. - - The main reason to subclass `tf.keras.layers.Layer` instead of using a - `Lambda` layer is saving and inspecting a Model. `Lambda` layers - are saved by serializing the Python bytecode, whereas subclassed - Layers can be saved via overriding their `get_config` method. Overriding - `get_config` improves the portability of Models. Models that rely on + for subclassing `tf.keras.layers.Layer`. + + The main reason to subclass `tf.keras.layers.Layer` instead of using a + `Lambda` layer is saving and inspecting a Model. `Lambda` layers + are saved by serializing the Python bytecode, whereas subclassed + Layers can be saved via overriding their `get_config` method. Overriding + `get_config` improves the portability of Models. Models that rely on subclassed Layers are also often easier to visualize and reason about. Examples: diff --git a/tensorflow/python/keras/layers/normalization.py b/tensorflow/python/keras/layers/normalization.py index c5062163889..e94b591f572 100644 --- a/tensorflow/python/keras/layers/normalization.py +++ b/tensorflow/python/keras/layers/normalization.py @@ -88,8 +88,8 @@ class BatchNormalizationBase(Layer): gamma_regularizer: Optional regularizer for the gamma weight. beta_constraint: Optional constraint for the beta weight. gamma_constraint: Optional constraint for the gamma weight. - renorm: Whether to use Batch Renormalization - (https://arxiv.org/abs/1702.03275). This adds extra variables during + renorm: Whether to use [Batch Renormalization]( + https://arxiv.org/abs/1702.03275). This adds extra variables during training. The inference is the same for either value of this parameter. renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to scalar `Tensors` used to clip the renorm correction. The correction @@ -164,9 +164,9 @@ class BatchNormalizationBase(Layer): \\({y_i} = {\gamma * \hat{x_i} + \beta}\\) - References: - - [Batch Normalization: Accelerating Deep Network Training by Reducing - Internal Covariate Shift](https://arxiv.org/abs/1502.03167) + Reference: + + - [Ioffe and Szegedy, 2015](https://arxiv.org/abs/1502.03167). """ # By default, the base class uses V2 behavior. The BatchNormalization V1 @@ -998,8 +998,8 @@ class LayerNormalization(Layer): Output shape: Same shape as input. - References: - - [Layer Normalization](https://arxiv.org/abs/1607.06450) + Reference: + - [Lei Ba et al., 2016](https://arxiv.org/abs/1607.06450). """ def __init__(self, diff --git a/tensorflow/python/keras/layers/normalization_v2.py b/tensorflow/python/keras/layers/normalization_v2.py index 0e529ee35e9..48af6b97ce1 100644 --- a/tensorflow/python/keras/layers/normalization_v2.py +++ b/tensorflow/python/keras/layers/normalization_v2.py @@ -76,8 +76,8 @@ class SyncBatchNormalization(normalization.BatchNormalizationBase): gamma_regularizer: Optional regularizer for the gamma weight. beta_constraint: Optional constraint for the beta weight. gamma_constraint: Optional constraint for the gamma weight. - renorm: Whether to use Batch Renormalization - (https://arxiv.org/abs/1702.03275). This adds extra variables during + renorm: Whether to use [Batch Renormalization]( + https://arxiv.org/abs/1702.03275). This adds extra variables during training. The inference is the same for either value of this parameter. renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to scalar `Tensors` used to clip the renorm correction. The correction diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index 4c5892b3595..dc41c8b067f 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -2233,8 +2233,8 @@ class LSTMCell(DropoutRNNCellMixin, Layer): unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. - This is recommended in [Jozefowicz et - al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) + This is recommended in [Jozefowicz et al., 2015]( + http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to @@ -2503,7 +2503,8 @@ class PeepholeLSTMCell(LSTMCell): well as the previous hidden state (which is what LSTMCell is limited to). This allows PeepholeLSTMCell to better learn precise timings over LSTMCell. - From [Gers et al.](http://www.jmlr.org/papers/volume3/gers02a/gers02a.pdf): + From [Gers et al., 2002]( + http://www.jmlr.org/papers/volume3/gers02a/gers02a.pdf): "We find that LSTM augmented by 'peephole connections' from its internal cells to its multiplicative gates can learn the fine distinction between @@ -2512,9 +2513,7 @@ class PeepholeLSTMCell(LSTMCell): The peephole implementation is based on: - [Long short-term memory recurrent neural network architectures for - large scale acoustic modeling. - ](https://research.google.com/pubs/archive/43905.pdf) + [Sak et al., 2014](https://research.google.com/pubs/archive/43905.pdf) Example: @@ -2601,8 +2600,8 @@ class LSTM(RNN): unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. - This is recommended in [Jozefowicz et - al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf). + This is recommended in [Jozefowicz et al., 2015]( + http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf). kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to diff --git a/tensorflow/python/keras/losses.py b/tensorflow/python/keras/losses.py index d97f8d94d50..5629829351d 100644 --- a/tensorflow/python/keras/losses.py +++ b/tensorflow/python/keras/losses.py @@ -63,8 +63,8 @@ class Loss(object): types, and reduce losses explicitly in your training loop. Using 'AUTO' or 'SUM_OVER_BATCH_SIZE' will raise an error. - Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) for more + Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details on this. You can implement 'SUM_OVER_BATCH_SIZE' using global batch size like: @@ -88,8 +88,8 @@ class Loss(object): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. """ @@ -222,8 +222,8 @@ class LossFunctionWrapper(Loss): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: (Optional) name for the loss. **kwargs: The keyword arguments that are passed on to `fn`. @@ -305,8 +305,8 @@ class MeanSquaredError(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'mean_squared_error'. """ @@ -364,8 +364,8 @@ class MeanAbsoluteError(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'mean_absolute_error'. """ @@ -424,8 +424,8 @@ class MeanAbsolutePercentageError(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'mean_absolute_percentage_error'. @@ -485,8 +485,8 @@ class MeanSquaredLogarithmicError(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'mean_squared_logarithmic_error'. @@ -561,8 +561,8 @@ class BinaryCrossentropy(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: (Optional) Name for the op. Defaults to 'binary_crossentropy'. """ @@ -641,8 +641,8 @@ class CategoricalCrossentropy(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'categorical_crossentropy'. """ @@ -718,8 +718,8 @@ class SparseCategoricalCrossentropy(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'sparse_categorical_crossentropy'. @@ -782,8 +782,8 @@ class Hinge(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'hinge'. """ @@ -843,8 +843,8 @@ class SquaredHinge(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'squared_hinge'. """ @@ -903,8 +903,8 @@ class CategoricalHinge(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'categorical_hinge'. """ @@ -960,8 +960,8 @@ class Poisson(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'poisson'. """ @@ -1017,8 +1017,8 @@ class LogCosh(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'log_cosh'. """ @@ -1077,8 +1077,8 @@ class KLDivergence(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'kl_divergence'. """ @@ -1145,8 +1145,8 @@ class Huber(LossFunctionWrapper): this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` - will raise an error. Please see this custom training [tutorial] - (https://www.tensorflow.org/tutorials/distribute/custom_training) + will raise an error. Please see this custom training [tutorial]( + https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the op. Defaults to 'huber_loss'. """ diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index 4323c52209f..7530673344c 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -1416,8 +1416,8 @@ class Recall(Metric): class SensitivitySpecificityBase(Metric): """Abstract base class for computing sensitivity and specificity. - For additional information about specificity and sensitivity, see the - following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity + For additional information about specificity and sensitivity, see + [the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity). """ def __init__(self, value, num_thresholds=200, name=None, dtype=None): @@ -1523,8 +1523,8 @@ class SensitivityAtSpecificity(SensitivitySpecificityBase): If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values. - For additional information about specificity and sensitivity, see the - following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity + For additional information about specificity and sensitivity, see + [the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity). Args: specificity: A scalar value in range `[0, 1]`. @@ -1598,8 +1598,8 @@ class SpecificityAtSensitivity(SensitivitySpecificityBase): If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values. - For additional information about specificity and sensitivity, see the - following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity + For additional information about specificity and sensitivity, see + [the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity). Args: sensitivity: A scalar value in range `[0, 1]`. @@ -1828,13 +1828,14 @@ class AUC(Metric): use when discretizing the roc curve. Values must be > 1. curve: (Optional) Specifies the name of the curve to be computed, 'ROC' [default] or 'PR' for the Precision-Recall-curve. - summation_method: (Optional) Specifies the Riemann summation method used - (https://en.wikipedia.org/wiki/Riemann_sum): 'interpolation' [default], - applies mid-point summation scheme for `ROC`. For PR-AUC, interpolates - (true/false) positives but not the ratio that is precision (see Davis - & Goadrich 2006 for details); 'minoring' that applies left summation + summation_method: (Optional) Specifies the [Riemann summation method]( + https://en.wikipedia.org/wiki/Riemann_sum) used. + 'interpolation' (default) applies mid-point summation scheme for `ROC`. + For PR-AUC, interpolates (true/false) positives but not the ratio that + is precision (see Davis & Goadrich 2006 for details); + 'minoring' applies left summation for increasing intervals and right summation for decreasing intervals; - 'majoring' that does the opposite. + 'majoring' does the opposite. name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. thresholds: (Optional) A list of floating point values to use as the @@ -2226,8 +2227,9 @@ class AUC(Metric): class CosineSimilarity(MeanMetricWrapper): """Computes the cosine similarity between the labels and predictions. - cosine similarity = (a . b) / ||a|| ||b|| - [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) + `cosine similarity = (a . b) / ||a|| ||b||` + + See: [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity). This metric keeps the average cosine similarity between `predictions` and `labels` over a stream of data. diff --git a/tensorflow/python/keras/mixed_precision/__init__.py b/tensorflow/python/keras/mixed_precision/__init__.py index 0939d62a3d0..98f7b7b1a55 100644 --- a/tensorflow/python/keras/mixed_precision/__init__.py +++ b/tensorflow/python/keras/mixed_precision/__init__.py @@ -15,8 +15,8 @@ """Keras mixed precision API. -See [the mixed precision -guide](https://www.tensorflow.org/guide/keras/mixed_precision) to learn how to +See [the mixed precision guide]( + https://www.tensorflow.org/guide/keras/mixed_precision) to learn how to use the API. """ from __future__ import absolute_import diff --git a/tensorflow/python/keras/mixed_precision/experimental/policy.py b/tensorflow/python/keras/mixed_precision/experimental/policy.py index f9899679a86..77f9563e827 100644 --- a/tensorflow/python/keras/mixed_precision/experimental/policy.py +++ b/tensorflow/python/keras/mixed_precision/experimental/policy.py @@ -57,8 +57,8 @@ class Policy(object): not have a single dtype. When the variable dtype does not match the compute dtype, variables will be automatically casted to the compute dtype to avoid type errors. In this case, `tf.keras.layers.Layer.dtype` refers to the - variable dtype, not the compute dtype. See [the mixed precision - guide](https://www.tensorflow.org/guide/keras/mixed_precision) for more + variable dtype, not the compute dtype. See [the mixed precision guide]( + https://www.tensorflow.org/guide/keras/mixed_precision) for more information on how to use mixed precision. Certain policies also have a `tf.mixed_precision.experimental.LossScale` @@ -119,8 +119,8 @@ class Policy(object): `'mixed_bfloat16'`, no loss scaling is done and loss scaling never needs to be manually applied. - See [the mixed precision - guide](https://www.tensorflow.org/guide/keras/mixed_precision) for more + See [the mixed precision guide]( + https://www.tensorflow.org/guide/keras/mixed_precision) for more information on using mixed precision ### How to use float64 in a Keras model