Merge pull request #15209 from harupy:specify-stacklevel

PiperOrigin-RevId: 393882385

keras/applications/efficientnet_weight_update_util.py

@@ -103,8 +103,10 @@ def write_ckpt_to_h5(path_h5, path_ckpt, keras_model, use_ema=True):
         changed_weights += 1
     except ValueError as e:
       if any([x in w.name for x in ['top', 'predictions', 'probs']]):
-        warnings.warn('Fail to load top layer variable {}'
-                      'from {} because of {}.'.format(w.name, tf_name, e))
+        warnings.warn(
+            'Fail to load top layer variable {}'
+            'from {} because of {}.'.format(w.name, tf_name, e),
+            stacklevel=2)
       else:
         raise ValueError('Fail to load {} from {}'.format(w.name, tf_name))
 
@@ -329,8 +331,10 @@ def check_match(keras_block, tf_block, keras_weight_names, tf_weight_names,
 
   names_unused = names_from_tf - names_from_keras
   if names_unused:
-    warnings.warn('{} variables from checkpoint file are not used: {}'.format(
-        len(names_unused), names_unused))
+    warnings.warn(
+        '{} variables from checkpoint file are not used: {}'.format(
+            len(names_unused), names_unused),
+        stacklevel=2)
 
 
 if __name__ == '__main__':

keras/applications/imagenet_utils.py

@@ -324,15 +324,19 @@ def obtain_input_shape(input_shape,
   if weights != 'imagenet' and input_shape and len(input_shape) == 3:
     if data_format == 'channels_first':
       if input_shape[0] not in {1, 3}:
-        warnings.warn('This model usually expects 1 or 3 input channels. '
-                      'However, it was passed an input_shape with ' +
-                      str(input_shape[0]) + ' input channels.')
+        warnings.warn(
+            'This model usually expects 1 or 3 input channels. '
+            'However, it was passed an input_shape with ' +
+            str(input_shape[0]) + ' input channels.',
+            stacklevel=2)
       default_shape = (input_shape[0], default_size, default_size)
     else:
       if input_shape[-1] not in {1, 3}:
-        warnings.warn('This model usually expects 1 or 3 input channels. '
-                      'However, it was passed an input_shape with ' +
-                      str(input_shape[-1]) + ' input channels.')
+        warnings.warn(
+            'This model usually expects 1 or 3 input channels. '
+            'However, it was passed an input_shape with ' +
+            str(input_shape[-1]) + ' input channels.',
+            stacklevel=2)
       default_shape = (default_size, default_size, input_shape[-1])
   else:
     if data_format == 'channels_first':

keras/backend.py

@@ -469,10 +469,12 @@ def learning_phase_scope(value):
   Raises:
      ValueError: if `value` is neither `0` nor `1`.
   """
-  warnings.warn('`tf.keras.backend.learning_phase_scope` is deprecated and '
-                'will be removed after 2020-10-11. To update it, simply '
-                'pass a True/False value to the `training` argument of the '
-                '`__call__` method of your layer or model.')
+  warnings.warn(
+      '`tf.keras.backend.learning_phase_scope` is deprecated and '
+      'will be removed after 2020-10-11. To update it, simply '
+      'pass a True/False value to the `training` argument of the '
+      '`__call__` method of your layer or model.',
+      stacklevel=2)
   with deprecated_internal_learning_phase_scope(value):
     try:
       yield
@@ -4999,7 +5001,8 @@ def categorical_crossentropy(target, output, from_logits=False, axis=-1):
       warnings.warn(
           '"`categorical_crossentropy` received `from_logits=True`, but '
           'the `output` argument was produced by a sigmoid or softmax '
-          'activation and thus does not represent logits. Was this intended?"')
+          'activation and thus does not represent logits. Was this intended?"',
+          stacklevel=2)
     from_logits = True
 
   if from_logits:
@@ -5059,7 +5062,8 @@ def sparse_categorical_crossentropy(target, output, from_logits=False, axis=-1):
       warnings.warn(
           '"`sparse_categorical_crossentropy` received `from_logits=True`, but '
           'the `output` argument was produced by a sigmoid or softmax '
-          'activation and thus does not represent logits. Was this intended?"')
+          'activation and thus does not represent logits. Was this intended?"',
+          stacklevel=2)
     from_logits = True
   elif (not from_logits and
         not isinstance(output, (tf.__internal__.EagerTensor, tf.Variable)) and
@@ -5146,7 +5150,8 @@ def binary_crossentropy(target, output, from_logits=False):
       warnings.warn(
           '"`binary_crossentropy` received `from_logits=True`, but the `output`'
           ' argument was produced by a sigmoid or softmax activation and thus '
-          'does not represent logits. Was this intended?"')
+          'does not represent logits. Was this intended?"',
+          stacklevel=2)
     from_logits = True
 
   if from_logits:
@@ -6239,9 +6244,11 @@ def random_binomial(shape, p=0.0, dtype=None, seed=None):
   <tf.Tensor: shape=(2, 3), dtype=float32, numpy=...,
   dtype=float32)>
   """
-  warnings.warn('`tf.keras.backend.random_binomial` is deprecated, '
-                'and will be removed in a future version.'
-                'Please use `tf.keras.backend.random_bernoulli` instead.')
+  warnings.warn(
+      '`tf.keras.backend.random_binomial` is deprecated, '
+      'and will be removed in a future version.'
+      'Please use `tf.keras.backend.random_bernoulli` instead.',
+      stacklevel=2)
   return random_bernoulli(shape, p, dtype, seed)
 
 

keras/engine/base_layer.py

@@ -1392,9 +1392,11 @@ class Layer(tf.Module, version_utils.LayerVersionSelector):
   @property
   @doc_controls.do_not_generate_docs
   def updates(self):
-    warnings.warn('`layer.updates` will be removed in a future version. '
-                  'This property should not be used in TensorFlow 2.0, '
-                  'as `updates` are applied automatically.')
+    warnings.warn(
+        '`layer.updates` will be removed in a future version. '
+        'This property should not be used in TensorFlow 2.0, '
+        'as `updates` are applied automatically.',
+        stacklevel=2)
     return []
 
   @property
@@ -1925,9 +1927,11 @@ class Layer(tf.Module, version_utils.LayerVersionSelector):
     Returns:
       List of update ops of the layer that depend on `inputs`.
     """
-    warnings.warn('`layer.get_updates_for` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `layer.updates` method instead.')
+    warnings.warn(
+        '`layer.get_updates_for` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `layer.updates` method instead.',
+        stacklevel=2)
     return self.updates
 
   @doc_controls.do_not_generate_docs
@@ -1942,9 +1946,11 @@ class Layer(tf.Module, version_utils.LayerVersionSelector):
     Returns:
       List of loss tensors of the layer that depend on `inputs`.
     """
-    warnings.warn('`layer.get_losses_for` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `layer.losses` instead.')
+    warnings.warn(
+        '`layer.get_losses_for` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `layer.losses` instead.',
+        stacklevel=2)
     return self.losses
 
   @doc_controls.do_not_doc_inheritable
@@ -2264,17 +2270,21 @@ class Layer(tf.Module, version_utils.LayerVersionSelector):
     Returns:
       Output tensor(s).
     """
-    warnings.warn('`layer.apply` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `layer.__call__` method instead.')
+    warnings.warn(
+        '`layer.apply` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `layer.__call__` method instead.',
+        stacklevel=2)
     return self.__call__(inputs, *args, **kwargs)
 
   @doc_controls.do_not_doc_inheritable
   def add_variable(self, *args, **kwargs):
     """Deprecated, do NOT use! Alias for `add_weight`."""
-    warnings.warn('`layer.add_variable` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `layer.add_weight` method instead.')
+    warnings.warn(
+        '`layer.add_variable` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `layer.add_weight` method instead.',
+        stacklevel=2)
     return self.add_weight(*args, **kwargs)
 
   @property

keras/engine/base_layer_v1.py

@@ -1673,17 +1673,21 @@ class Layer(base_layer.Layer):
     Returns:
       Output tensor(s).
     """
-    warnings.warn('`layer.apply` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `layer.__call__` method instead.')
+    warnings.warn(
+        '`layer.apply` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `layer.__call__` method instead.',
+        stacklevel=2)
     return self.__call__(inputs, *args, **kwargs)
 
   @doc_controls.do_not_doc_inheritable
   def add_variable(self, *args, **kwargs):
     """Deprecated, do NOT use! Alias for `add_weight`."""
-    warnings.warn('`layer.add_variable` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `layer.add_weight` method instead.')
+    warnings.warn(
+        '`layer.add_variable` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `layer.add_weight` method instead.',
+        stacklevel=2)
     return self.add_weight(*args, **kwargs)
 
   @property

keras/engine/functional.py

@@ -593,8 +593,8 @@ class Functional(training_lib.Model):
         warnings.warn(
             'Input dict contained keys {} which did not match any model input. '
             'They will be ignored by the model.'.format(
-                [n for n in tensors.keys() if n not in ref_input_names])
-            )
+                [n for n in tensors.keys() if n not in ref_input_names]),
+            stacklevel=2)
 
       try:
         # Flatten in the order `Input`s were passed during Model construction.

keras/engine/training.py

@@ -1717,10 +1717,12 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
           options.experimental_distribute.auto_shard_policy = data_option
           x = x.with_options(options)
         except ValueError:
-          warnings.warn('Using Model.predict with '
-                        'MultiWorkerDistributionStrategy or TPUStrategy and '
-                        'AutoShardPolicy.FILE might lead to out-of-order result'
-                        '. Consider setting it to AutoShardPolicy.DATA.')
+          warnings.warn(
+              'Using Model.predict with '
+              'MultiWorkerDistributionStrategy or TPUStrategy and '
+              'AutoShardPolicy.FILE might lead to out-of-order result'
+              '. Consider setting it to AutoShardPolicy.DATA.',
+              stacklevel=2)
 
       data_handler = data_adapter.get_data_handler(
           x=x,
@@ -1975,9 +1977,11 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
       `Model.fit` now supports generators, so there is no longer any need to use
       this endpoint.
     """
-    warnings.warn('`Model.fit_generator` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `Model.fit`, which supports generators.')
+    warnings.warn(
+        '`Model.fit_generator` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `Model.fit`, which supports generators.',
+        stacklevel=2)
     return self.fit(
         generator,
         steps_per_epoch=steps_per_epoch,
@@ -2009,9 +2013,11 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
       `Model.evaluate` now supports generators, so there is no longer any need
       to use this endpoint.
     """
-    warnings.warn('`Model.evaluate_generator` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `Model.evaluate`, which supports generators.')
+    warnings.warn(
+        '`Model.evaluate_generator` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `Model.evaluate`, which supports generators.',
+        stacklevel=2)
     self._check_call_args('evaluate_generator')
 
     return self.evaluate(
@@ -2038,9 +2044,11 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
       `Model.predict` now supports generators, so there is no longer any need
       to use this endpoint.
     """
-    warnings.warn('`Model.predict_generator` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `Model.predict`, which supports generators.')
+    warnings.warn(
+        '`Model.predict_generator` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `Model.predict`, which supports generators.',
+        stacklevel=2)
     return self.predict(
         generator,
         steps=steps,
@@ -2478,9 +2486,11 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
     Returns:
         A list of update ops.
     """
-    warnings.warn('`Model.state_updates` will be removed in a future version. '
-                  'This property should not be used in TensorFlow 2.0, '
-                  'as `updates` are applied automatically.')
+    warnings.warn(
+        '`Model.state_updates` will be removed in a future version. '
+        'This property should not be used in TensorFlow 2.0, '
+        'as `updates` are applied automatically.',
+        stacklevel=2)
     state_updates = []
     for layer in self.layers:
       if getattr(layer, 'stateful', False):

keras/engine/training_v1.py

@@ -1225,9 +1225,11 @@ class Model(training_lib.Model):
       `Model.fit` now supports generators, so there is no longer any need to use
       this endpoint.
     """
-    warnings.warn('`model.fit_generator` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `Model.fit`, which supports generators.')
+    warnings.warn(
+        '`model.fit_generator` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `Model.fit`, which supports generators.',
+        stacklevel=2)
     return self.fit(
         generator,
         steps_per_epoch=steps_per_epoch,
@@ -1258,9 +1260,11 @@ class Model(training_lib.Model):
       `Model.evaluate` now supports generators, so there is no longer any need
       to use this endpoint.
     """
-    warnings.warn('`Model.evaluate_generator` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `Model.evaluate`, which supports generators.')
+    warnings.warn(
+        '`Model.evaluate_generator` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `Model.evaluate`, which supports generators.',
+        stacklevel=2)
     self._check_call_args('evaluate_generator')
 
     return self.evaluate(
@@ -1286,9 +1290,11 @@ class Model(training_lib.Model):
       `Model.predict` now supports generators, so there is no longer any need
       to use this endpoint.
     """
-    warnings.warn('`Model.predict_generator` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please use `Model.predict`, which supports generators.')
+    warnings.warn(
+        '`Model.predict_generator` is deprecated and '
+        'will be removed in a future version. '
+        'Please use `Model.predict`, which supports generators.',
+        stacklevel=2)
     return self.predict(
         generator,
         steps=steps,

keras/layers/core/lambda_layer.py

@@ -335,7 +335,9 @@ class Lambda(Layer):
       # Note: we don't know the name of the function if it's a lambda.
       warnings.warn(
           '{} is not loaded, but a Lambda layer uses it. '
-          'It may cause errors.'.format(module), UserWarning)
+          'It may cause errors.'.format(module),
+          UserWarning,
+          stacklevel=2)
     if custom_objects:
       globs.update(custom_objects)
     function_type = config.pop(func_type_attr_name)

keras/layers/legacy_rnn/rnn_cell_impl.py

@@ -413,10 +413,12 @@ class BasicRNNCell(LayerRNNCell):
                name=None,
                dtype=None,
                **kwargs):
-    warnings.warn("`tf.nn.rnn_cell.BasicRNNCell` is deprecated and will be "
-                  "removed in a future version. This class "
-                  "is equivalent as `tf.keras.layers.SimpleRNNCell`, "
-                  "and will be replaced by that in Tensorflow 2.0.")
+    warnings.warn(
+        "`tf.nn.rnn_cell.BasicRNNCell` is deprecated and will be "
+        "removed in a future version. This class "
+        "is equivalent as `tf.keras.layers.SimpleRNNCell`, "
+        "and will be replaced by that in Tensorflow 2.0.",
+        stacklevel=2)
     super(BasicRNNCell, self).__init__(
         _reuse=reuse, name=name, dtype=dtype, **kwargs)
     _check_supported_dtypes(self.dtype)
@@ -523,10 +525,12 @@ class GRUCell(LayerRNNCell):
                name=None,
                dtype=None,
                **kwargs):
-    warnings.warn("`tf.nn.rnn_cell.GRUCell` is deprecated and will be removed "
-                  "in a future version. This class "
-                  "is equivalent as `tf.keras.layers.GRUCell`, "
-                  "and will be replaced by that in Tensorflow 2.0.")
+    warnings.warn(
+        "`tf.nn.rnn_cell.GRUCell` is deprecated and will be removed "
+        "in a future version. This class "
+        "is equivalent as `tf.keras.layers.GRUCell`, "
+        "and will be replaced by that in Tensorflow 2.0.",
+        stacklevel=2)
     super(GRUCell, self).__init__(
         _reuse=reuse, name=name, dtype=dtype, **kwargs)
     _check_supported_dtypes(self.dtype)
@@ -699,10 +703,12 @@ class BasicLSTMCell(LayerRNNCell):
         When restoring from CudnnLSTM-trained checkpoints, must use
         `CudnnCompatibleLSTMCell` instead.
     """
-    warnings.warn("`tf.nn.rnn_cell.BasicLSTMCell` is deprecated and will be "
-                  "removed in a future version. This class "
-                  "is equivalent as `tf.keras.layers.LSTMCell`, "
-                  "and will be replaced by that in Tensorflow 2.0.")
+    warnings.warn(
+        "`tf.nn.rnn_cell.BasicLSTMCell` is deprecated and will be "
+        "removed in a future version. This class "
+        "is equivalent as `tf.keras.layers.LSTMCell`, "
+        "and will be replaced by that in Tensorflow 2.0.",
+        stacklevel=2)
     super(BasicLSTMCell, self).__init__(
         _reuse=reuse, name=name, dtype=dtype, **kwargs)
     _check_supported_dtypes(self.dtype)
@@ -902,10 +908,12 @@ class LSTMCell(LayerRNNCell):
         When restoring from CudnnLSTM-trained checkpoints, use
         `CudnnCompatibleLSTMCell` instead.
     """
-    warnings.warn("`tf.nn.rnn_cell.LSTMCell` is deprecated and will be "
-                  "removed in a future version. This class "
-                  "is equivalent as `tf.keras.layers.LSTMCell`, "
-                  "and will be replaced by that in Tensorflow 2.0.")
+    warnings.warn(
+        "`tf.nn.rnn_cell.LSTMCell` is deprecated and will be "
+        "removed in a future version. This class "
+        "is equivalent as `tf.keras.layers.LSTMCell`, "
+        "and will be replaced by that in Tensorflow 2.0.",
+        stacklevel=2)
     super(LSTMCell, self).__init__(
         _reuse=reuse, name=name, dtype=dtype, **kwargs)
     _check_supported_dtypes(self.dtype)

keras/layers/legacy_rnn/rnn_cell_wrapper_impl.py

@@ -468,8 +468,11 @@ def _parse_config_to_function(config, custom_objects, func_attr_name,
     globs.update(sys.modules[module].__dict__)
   elif module is not None:
     # Note: we don't know the name of the function if it's a lambda.
-    warnings.warn("{} is not loaded, but a layer uses it. "
-                  "It may cause errors.".format(module), UserWarning)
+    warnings.warn(
+        "{} is not loaded, but a layer uses it. "
+        "It may cause errors.".format(module),
+        UserWarning,
+        stacklevel=2)
   if custom_objects:
     globs.update(custom_objects)
   function_type = config.pop(func_type_attr_name)

keras/layers/recurrent.py

@@ -2596,10 +2596,12 @@ class PeepholeLSTMCell(LSTMCell):
                dropout=0.,
                recurrent_dropout=0.,
                **kwargs):
-    warnings.warn('`tf.keras.experimental.PeepholeLSTMCell` is deprecated '
-                  'and will be removed in a future version. '
-                  'Please use tensorflow_addons.rnn.PeepholeLSTMCell '
-                  'instead.')
+    warnings.warn(
+        '`tf.keras.experimental.PeepholeLSTMCell` is deprecated '
+        'and will be removed in a future version. '
+        'Please use tensorflow_addons.rnn.PeepholeLSTMCell '
+        'instead.',
+        stacklevel=2)
     super(PeepholeLSTMCell, self).__init__(
         units=units,
         activation=activation,

keras/legacy_tf_layers/base.py

@@ -243,10 +243,12 @@ class Layer(base_layer.Layer):
   # maintain API backward compatibility.
   @property
   def graph(self):
-    warnings.warn('`Layer.graph` is deprecated and '
-                  'will be removed in a future version. '
-                  'Please stop using this property because tf.layers layers no '
-                  'longer track their graph.')
+    warnings.warn(
+        '`Layer.graph` is deprecated and '
+        'will be removed in a future version. '
+        'Please stop using this property because tf.layers layers no '
+        'longer track their graph.',
+        stacklevel=2)
     if tf.executing_eagerly():
       raise RuntimeError('Layer.graph not supported when executing eagerly.')
     return None

keras/legacy_tf_layers/convolutional.py

@@ -260,9 +260,11 @@ def conv1d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.conv1d` is deprecated and '
-                'will be removed in a future version. '
-                'Please Use `tf.keras.layers.Conv1D` instead.')
+  warnings.warn(
+      '`tf.layers.conv1d` is deprecated and '
+      'will be removed in a future version. '
+      'Please Use `tf.keras.layers.Conv1D` instead.',
+      stacklevel=2)
   layer = Conv1D(
       filters=filters,
       kernel_size=kernel_size,
@@ -533,9 +535,11 @@ def conv2d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.conv2d` is deprecated and '
-                'will be removed in a future version. '
-                'Please Use `tf.keras.layers.Conv2D` instead.')
+  warnings.warn(
+      '`tf.layers.conv2d` is deprecated and '
+      'will be removed in a future version. '
+      'Please Use `tf.keras.layers.Conv2D` instead.',
+      stacklevel=2)
   layer = Conv2D(
       filters=filters,
       kernel_size=kernel_size,
@@ -808,9 +812,11 @@ def conv3d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.conv3d` is deprecated and '
-                'will be removed in a future version. '
-                'Please Use `tf.keras.layers.Conv3D` instead.')
+  warnings.warn(
+      '`tf.layers.conv3d` is deprecated and '
+      'will be removed in a future version. '
+      'Please Use `tf.keras.layers.Conv3D` instead.',
+      stacklevel=2)
   layer = Conv3D(
       filters=filters,
       kernel_size=kernel_size,
@@ -1241,9 +1247,11 @@ def separable_conv1d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.separable_conv1d` is deprecated and '
-                'will be removed in a future version. '
-                'Please Use `tf.keras.layers.SeparableConv1D` instead.')
+  warnings.warn(
+      '`tf.layers.separable_conv1d` is deprecated and '
+      'will be removed in a future version. '
+      'Please Use `tf.keras.layers.SeparableConv1D` instead.',
+      stacklevel=2)
   layer = SeparableConv1D(
       filters=filters,
       kernel_size=kernel_size,
@@ -1402,9 +1410,11 @@ def separable_conv2d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.separable_conv2d` is deprecated and '
-                'will be removed in a future version. '
-                'Please Use `tf.keras.layers.SeparableConv2D` instead.')
+  warnings.warn(
+      '`tf.layers.separable_conv2d` is deprecated and '
+      'will be removed in a future version. '
+      'Please Use `tf.keras.layers.SeparableConv2D` instead.',
+      stacklevel=2)
   layer = SeparableConv2D(
       filters=filters,
       kernel_size=kernel_size,
@@ -1657,9 +1667,11 @@ def conv2d_transpose(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.conv2d_transpose` is deprecated and '
-                'will be removed in a future version. '
-                'Please Use `tf.keras.layers.Conv2DTranspose` instead.')
+  warnings.warn(
+      '`tf.layers.conv2d_transpose` is deprecated and '
+      'will be removed in a future version. '
+      'Please Use `tf.keras.layers.Conv2DTranspose` instead.',
+      stacklevel=2)
   layer = Conv2DTranspose(
       filters=filters,
       kernel_size=kernel_size,
@@ -1898,9 +1910,11 @@ def conv3d_transpose(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.conv3d_transpose` is deprecated and '
-                'will be removed in a future version. '
-                'Please Use `tf.keras.layers.Conv3DTranspose` instead.')
+  warnings.warn(
+      '`tf.layers.conv3d_transpose` is deprecated and '
+      'will be removed in a future version. '
+      'Please Use `tf.keras.layers.Conv3DTranspose` instead.',
+      stacklevel=2)
   layer = Conv3DTranspose(
       filters=filters,
       kernel_size=kernel_size,

keras/legacy_tf_layers/core.py

@@ -233,9 +233,11 @@ def dense(
   @end_compatibility
 
   """
-  warnings.warn('`tf.layers.dense` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.Dense` instead.')
+  warnings.warn(
+      '`tf.layers.dense` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.Dense` instead.',
+      stacklevel=2)
   layer = Dense(units,
                 activation=activation,
                 use_bias=use_bias,
@@ -390,9 +392,11 @@ def dropout(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.dropout` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.Dropout` instead.')
+  warnings.warn(
+      '`tf.layers.dropout` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.Dropout` instead.',
+      stacklevel=2)
   layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
   return layer.apply(inputs, training=training)
 
@@ -510,9 +514,11 @@ def flatten(inputs, name=None, data_format='channels_last'):
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.flatten` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.Flatten` instead.')
+  warnings.warn(
+      '`tf.layers.flatten` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.Flatten` instead.',
+      stacklevel=2)
   layer = Flatten(name=name, data_format=data_format)
   return layer.apply(inputs)
 

keras/legacy_tf_layers/normalization.py

@@ -426,7 +426,8 @@ def batch_normalization(inputs,
       'Please use `tf.keras.layers.BatchNormalization` instead. '
       'In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` '
       'should not be used (consult the `tf.keras.layers.BatchNormalization` '
-      'documentation).')
+      'documentation).',
+      stacklevel=2)
   layer = BatchNormalization(
       axis=axis,
       momentum=momentum,

keras/legacy_tf_layers/pooling.py

@@ -147,9 +147,11 @@ def average_pooling1d(inputs, pool_size, strides,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.average_pooling1d` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.AveragePooling1D` instead.')
+  warnings.warn(
+      '`tf.layers.average_pooling1d` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.AveragePooling1D` instead.',
+      stacklevel=2)
   layer = AveragePooling1D(pool_size=pool_size,
                            strides=strides,
                            padding=padding,
@@ -279,9 +281,11 @@ def max_pooling1d(inputs, pool_size, strides,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.max_pooling1d` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.MaxPooling1D` instead.')
+  warnings.warn(
+      '`tf.layers.max_pooling1d` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.MaxPooling1D` instead.',
+      stacklevel=2)
   layer = MaxPooling1D(pool_size=pool_size,
                        strides=strides,
                        padding=padding,
@@ -416,9 +420,11 @@ def average_pooling2d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.average_pooling2d` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.AveragePooling2D` instead.')
+  warnings.warn(
+      '`tf.layers.average_pooling2d` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.AveragePooling2D` instead.',
+      stacklevel=2)
   layer = AveragePooling2D(pool_size=pool_size, strides=strides,
                            padding=padding, data_format=data_format,
                            name=name)
@@ -551,9 +557,11 @@ def max_pooling2d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.max_pooling2d` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.MaxPooling2D` instead.')
+  warnings.warn(
+      '`tf.layers.max_pooling2d` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.MaxPooling2D` instead.',
+      stacklevel=2)
   layer = MaxPooling2D(pool_size=pool_size, strides=strides,
                        padding=padding, data_format=data_format,
                        name=name)
@@ -690,9 +698,11 @@ def average_pooling3d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.average_pooling3d` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.AveragePooling3D` instead.')
+  warnings.warn(
+      '`tf.layers.average_pooling3d` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.AveragePooling3D` instead.',
+      stacklevel=2)
   layer = AveragePooling3D(pool_size=pool_size, strides=strides,
                            padding=padding, data_format=data_format,
                            name=name)
@@ -827,9 +837,11 @@ def max_pooling3d(inputs,
   ```
   @end_compatibility
   """
-  warnings.warn('`tf.layers.max_pooling3d` is deprecated and '
-                'will be removed in a future version. '
-                'Please use `tf.keras.layers.MaxPooling3D` instead.')
+  warnings.warn(
+      '`tf.layers.max_pooling3d` is deprecated and '
+      'will be removed in a future version. '
+      'Please use `tf.keras.layers.MaxPooling3D` instead.',
+      stacklevel=2)
   layer = MaxPooling3D(pool_size=pool_size, strides=strides,
                        padding=padding, data_format=data_format,
                        name=name)

keras/metrics.py

@@ -255,10 +255,12 @@ class Metric(base_layer.Layer, metaclass=abc.ABCMeta):
     when a metric is evaluated during training.
     """
     if not generic_utils.is_default(self.reset_states):
-      warnings.warn('Metric %s implements a `reset_states()` method; rename it '
-                    'to `reset_state()` (without the final "s"). The name '
-                    '`reset_states()` has been deprecated to improve API '
-                    'consistency.' % (self.__class__.__name__,))
+      warnings.warn(
+          'Metric %s implements a `reset_states()` method; rename it '
+          'to `reset_state()` (without the final "s"). The name '
+          '`reset_states()` has been deprecated to improve API '
+          'consistency.' % (self.__class__.__name__,),
+          stacklevel=2)
       return self.reset_states()
     else:
       backend.batch_set_value([(v, 0) for v in self.variables])

keras/optimizer_v2/optimizer_v2.py

@@ -354,7 +354,8 @@ class OptimizerV2(tf.__internal__.tracking.Trackable):
         raise ValueError("Expected {} >= 0, received: {}".format(k, kwargs[k]))
       if k == "lr":
         warnings.warn(
-            "The `lr` argument is deprecated, use `learning_rate` instead.")
+            "The `lr` argument is deprecated, use `learning_rate` instead.",
+            stacklevel=2)
 
     self._use_locking = True
     self._init_set_name(name)

keras/saving/saved_model_experimental.py

@@ -114,10 +114,12 @@ def export_saved_model(model,
     ValueError: If the input signature cannot be inferred from the model.
     AssertionError: If the SavedModel directory already exists and isn't empty.
   """
-  warnings.warn('`tf.keras.experimental.export_saved_model` is deprecated'
-                'and will be removed in a future version. '
-                'Please use `model.save(..., save_format="tf")` or '
-                '`tf.keras.models.save_model(..., save_format="tf")`.')
+  warnings.warn(
+      '`tf.keras.experimental.export_saved_model` is deprecated'
+      'and will be removed in a future version. '
+      'Please use `model.save(..., save_format="tf")` or '
+      '`tf.keras.models.save_model(..., save_format="tf")`.',
+      stacklevel=2)
   if serving_only:
     tf.saved_model.save(
         model,
@@ -398,9 +400,11 @@ def load_from_saved_model(saved_model_path, custom_objects=None):
   Returns:
     a keras.Model instance.
   """
-  warnings.warn('`tf.keras.experimental.load_from_saved_model` is deprecated'
-                'and will be removed in a future version. '
-                'Please switch to `tf.keras.models.load_model`.')
+  warnings.warn(
+      '`tf.keras.experimental.load_from_saved_model` is deprecated'
+      'and will be removed in a future version. '
+      'Please switch to `tf.keras.models.load_model`.',
+      stacklevel=2)
   # restore model topology from json string
   model_json_filepath = os.path.join(
       tf.compat.as_bytes(saved_model_path),

keras/utils/generic_utils.py

@@ -499,10 +499,12 @@ def serialize_keras_object(instance):
                       or (hasattr(instance, 'compute_mask')
                           and not is_default(instance.compute_mask)))
   if supports_masking and is_default(instance.get_config):
-    warnings.warn('Custom mask layers require a config and must override '
-                  'get_config. When loading, the custom mask layer must be '
-                  'passed to the custom_objects argument.',
-                  category=CustomMaskWarning)
+    warnings.warn(
+        'Custom mask layers require a config and must override '
+        'get_config. When loading, the custom mask layer must be '
+        'passed to the custom_objects argument.',
+        category=CustomMaskWarning,
+        stacklevel=2)
   # pylint: enable=protected-access
 
   if hasattr(instance, 'get_config'):

keras/wrappers/scikit_learn.py

@@ -197,8 +197,8 @@ class KerasClassifier(BaseWrapper):
     warnings.warn(
         'KerasClassifier is deprecated, '
         'use Sci-Keras (https://github.com/adriangb/scikeras) instead.',
-        DeprecationWarning
-    )
+        DeprecationWarning,
+        stacklevel=2)
     super().__init__(build_fn, **sk_params)
 
   def fit(self, x, y, **kwargs):
@@ -334,8 +334,8 @@ class KerasRegressor(BaseWrapper):
     warnings.warn(
         'KerasRegressor is deprecated, '
         'use Sci-Keras (https://github.com/adriangb/scikeras) instead.',
-        DeprecationWarning
-    )
+        DeprecationWarning,
+        stacklevel=2)
     super().__init__(build_fn, **sk_params)
 
   def predict(self, x, **kwargs):