Add V2 APIs for losses: MeanAbsoluteError, MeanAbsolutePercentageError, MeanSquaredLogarithmicError

PiperOrigin-RevId: 223422208

tensorflow/python/keras/losses.py

@@ -116,7 +116,7 @@ class Loss(object):
     NotImplementedError('Must be implemented in subclasses.')
 
 
-@tf_export('losses.MeanSquaredError', 'keras.losses.MeanSquaredError')
+@tf_export('keras.losses.MeanSquaredError')
 class MeanSquaredError(Loss):
   """Computes the mean of squares of errors between labels and predictions.
 
@@ -126,7 +126,7 @@ class MeanSquaredError(Loss):
   Usage:
 
   ```python
-  mse = tf.losses.MeanSquaredError()
+  mse = tf.keras.losses.MeanSquaredError()
   loss = mse([0., 0., 1., 1.], [1., 1., 1., 0.])
   print('Loss: ', loss.numpy())  # Loss: 0.75
   ```
@@ -135,7 +135,7 @@ class MeanSquaredError(Loss):
 
   ```python
   model = keras.models.Model(inputs, outputs)
-  model.compile('sgd', loss=tf.losses.MeanSquaredError())
+  model.compile('sgd', loss=tf.keras.losses.MeanSquaredError())
   ```
   """
 
@@ -154,6 +154,7 @@ class MeanSquaredError(Loss):
     return mean_squared_error(y_true, y_pred)
 
 
+@tf_export('keras.losses.MeanAbsoluteError')
 class MeanAbsoluteError(Loss):
   """Computes the mean of absolute difference between labels and predictions.
 
@@ -163,7 +164,7 @@ class MeanAbsoluteError(Loss):
   Usage:
 
   ```python
-  mae = tf.losses.MeanAbsoluteError()
+  mae = tf.keras.losses.MeanAbsoluteError()
   loss = mae([0., 0., 1., 1.], [1., 1., 1., 0.])
   print('Loss: ', loss.numpy())  # Loss: 0.75
   ```
@@ -172,7 +173,7 @@ class MeanAbsoluteError(Loss):
 
   ```python
   model = keras.models.Model(inputs, outputs)
-  model.compile('sgd', loss=tf.losses.MeanAbsoluteError())
+  model.compile('sgd', loss=tf.keras.losses.MeanAbsoluteError())
   ```
   """
 
@@ -191,6 +192,7 @@ class MeanAbsoluteError(Loss):
     return mean_absolute_error(y_true, y_pred)
 
 
+@tf_export('keras.losses.MeanAbsolutePercentageError')
 class MeanAbsolutePercentageError(Loss):
   """Computes the mean absolute percentage error between `y_true` and `y_pred`.
 
@@ -200,7 +202,7 @@ class MeanAbsolutePercentageError(Loss):
   Usage:
 
   ```python
-  mape = tf.losses.MeanAbsolutePercentageError()
+  mape = tf.keras.losses.MeanAbsolutePercentageError()
   loss = mape([0., 0., 1., 1.], [1., 1., 1., 0.])
   print('Loss: ', loss.numpy())  # Loss: 5e+08
   ```
@@ -209,7 +211,7 @@ class MeanAbsolutePercentageError(Loss):
 
   ```python
   model = keras.models.Model(inputs, outputs)
-  model.compile('sgd', loss=tf.losses.MeanAbsolutePercentageError())
+  model.compile('sgd', loss=tf.keras.losses.MeanAbsolutePercentageError())
   ```
   """
 
@@ -228,6 +230,7 @@ class MeanAbsolutePercentageError(Loss):
     return mean_absolute_percentage_error(y_true, y_pred)
 
 
+@tf_export('keras.losses.MeanSquaredLogarithmicError')
 class MeanSquaredLogarithmicError(Loss):
   """Computes the mean squared logarithmic error between `y_true` and `y_pred`.
 
@@ -237,7 +240,7 @@ class MeanSquaredLogarithmicError(Loss):
   Usage:
 
   ```python
-  msle = tf.losses.MeanSquaredLogarithmicError()
+  msle = tf.keras.losses.MeanSquaredLogarithmicError()
   loss = msle([0., 0., 1., 1.], [1., 1., 1., 0.])
   print('Loss: ', loss.numpy())  # Loss: 0.36034
   ```
@@ -246,7 +249,7 @@ class MeanSquaredLogarithmicError(Loss):
 
   ```python
   model = keras.models.Model(inputs, outputs)
-  model.compile('sgd', loss=tf.losses.MeanSquaredLogarithmicError())
+  model.compile('sgd', loss=tf.keras.losses.MeanSquaredLogarithmicError())
   ```
   """
 

tensorflow/tools/api/golden/v1/tensorflow.keras.losses.-mean-absolute-error.pbtxt

@@ -0,0 +1,22 @@
+path: "tensorflow.keras.losses.MeanAbsoluteError"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.losses.MeanAbsoluteError\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'sum_over_batch_size\', \'None\'], "
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.losses.-mean-absolute-percentage-error.pbtxt

@@ -0,0 +1,22 @@
+path: "tensorflow.keras.losses.MeanAbsolutePercentageError"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.losses.MeanAbsolutePercentageError\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'sum_over_batch_size\', \'None\'], "
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.losses.-mean-squared-logarithmic-error.pbtxt

@@ -1,6 +1,6 @@
-path: "tensorflow.losses.MeanSquaredError"
+path: "tensorflow.keras.losses.MeanSquaredLogarithmicError"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.MeanSquaredError\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.MeanSquaredLogarithmicError\'>"
   is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
   is_instance: "<type \'object\'>"
   member_method {

tensorflow/tools/api/golden/v1/tensorflow.keras.losses.pbtxt

@@ -1,9 +1,21 @@
 path: "tensorflow.keras.losses"
 tf_module {
+  member {
+    name: "MeanAbsoluteError"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "MeanAbsolutePercentageError"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "MeanSquaredError"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "MeanSquaredLogarithmicError"
+    mtype: "<type \'type\'>"
+  }
   member_method {
     name: "KLD"
     argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.losses.pbtxt

@@ -1,9 +1,5 @@
 path: "tensorflow.losses"
 tf_module {
-  member {
-    name: "MeanSquaredError"
-    mtype: "<type \'type\'>"
-  }
   member {
     name: "Reduction"
     mtype: "<type \'type\'>"

tensorflow/tools/api/golden/v2/tensorflow.keras.losses.-mean-absolute-error.pbtxt

@@ -0,0 +1,22 @@
+path: "tensorflow.keras.losses.MeanAbsoluteError"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.losses.MeanAbsoluteError\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'sum_over_batch_size\', \'None\'], "
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.losses.-mean-absolute-percentage-error.pbtxt

@@ -0,0 +1,22 @@
+path: "tensorflow.keras.losses.MeanAbsolutePercentageError"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.losses.MeanAbsolutePercentageError\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'sum_over_batch_size\', \'None\'], "
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.losses.-mean-squared-logarithmic-error.pbtxt

@@ -1,6 +1,6 @@
-path: "tensorflow.losses.MeanSquaredError"
+path: "tensorflow.keras.losses.MeanSquaredLogarithmicError"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.losses.MeanSquaredError\'>"
+  is_instance: "<class \'tensorflow.python.keras.losses.MeanSquaredLogarithmicError\'>"
   is_instance: "<class \'tensorflow.python.keras.losses.Loss\'>"
   is_instance: "<type \'object\'>"
   member_method {

tensorflow/tools/api/golden/v2/tensorflow.keras.losses.pbtxt

@@ -1,9 +1,21 @@
 path: "tensorflow.keras.losses"
 tf_module {
+  member {
+    name: "MeanAbsoluteError"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "MeanAbsolutePercentageError"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "MeanSquaredError"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "MeanSquaredLogarithmicError"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Reduction"
     mtype: "<type \'type\'>"

tensorflow/tools/api/golden/v2/tensorflow.losses.pbtxt

@@ -1,9 +1,5 @@
 path: "tensorflow.losses"
 tf_module {
-  member {
-    name: "MeanSquaredError"
-    mtype: "<type \'type\'>"
-  }
   member {
     name: "Reduction"
     mtype: "<type \'type\'>"