experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Fix some metric doc strings.

Browse Source 
PiperOrigin-RevId: 227880840
This commit is contained in:
Pavithra Vijay 2019-01-04 10:57:17 -08:00 committed by TensorFlower Gardener
parent 59d5fbcc1a
commit d28fddf859
1 changed files with 21 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
tensorflow/python/keras/metrics.py
View File

@ -1382,8 +1382,8 @@ class MeanAbsoluteError(MeanMetricWrapper):
Usage: Usage:
```python ```python
mae = tf.metrics.MeanAbsoluteError() m = tf.metrics.MeanAbsoluteError()
mae.update_state([0., 0., 1., 1.], [1., 1., 1., 0.]) m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Final result: ', m.result().numpy()) # Final result: 0.75 print('Final result: ', m.result().numpy()) # Final result: 0.75
``` ```
@ -1391,7 +1391,7 @@ class MeanAbsoluteError(MeanMetricWrapper):
```python ```python
model = keras.models.Model(inputs, outputs) model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.MeanAbsoluteError()) model.compile('sgd', metrics=[tf.keras.metrics.MeanAbsoluteError()])
``` ```
""" """
@ -1416,8 +1416,8 @@ class MeanAbsolutePercentageError(MeanMetricWrapper):
Usage: Usage:
```python ```python
mape = tf.keras.losses.MeanAbsolutePercentageError() m = tf.keras.metrics.MeanAbsolutePercentageError()
mape.update_state([0., 0., 1., 1.], [1., 1., 1., 0.]) m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Final result: ', m.result().numpy()) # Final result: 5e+08 print('Final result: ', m.result().numpy()) # Final result: 5e+08
``` ```
@ -1425,7 +1425,7 @@ class MeanAbsolutePercentageError(MeanMetricWrapper):
```python ```python
model = keras.models.Model(inputs, outputs) model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.MeanAbsolutePercentageError()) model.compile('sgd', metrics=[tf.keras.metrics.MeanAbsolutePercentageError()])
``` ```
""" """
@ -1450,8 +1450,8 @@ class MeanSquaredError(MeanMetricWrapper):
Usage: Usage:
```python ```python
mape = tf.keras.losses.MeanSquaredError() m = tf.keras.metrics.MeanSquaredError()
mape.update_state([0., 0., 1., 1.], [1., 1., 1., 0.]) m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Final result: ', m.result().numpy()) # Final result: 0.75 print('Final result: ', m.result().numpy()) # Final result: 0.75
``` ```
@ -1459,7 +1459,7 @@ class MeanSquaredError(MeanMetricWrapper):
```python ```python
model = keras.models.Model(inputs, outputs) model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.MeanSquaredError()) model.compile('sgd', metrics=[tf.keras.metrics.MeanSquaredError()])
``` ```
""" """
@ -1484,8 +1484,8 @@ class MeanSquaredLogarithmicError(MeanMetricWrapper):
Usage: Usage:
```python ```python
msle = tf.keras.losses.MeanSquaredLogarithmicError() m = tf.keras.metrics.MeanSquaredLogarithmicError()
msle.update_state([0., 0., 1., 1.], [1., 1., 1., 0.]) m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Final result: ', m.result().numpy()) # Final result: 0.36034 print('Final result: ', m.result().numpy()) # Final result: 0.36034
``` ```
@ -1493,7 +1493,7 @@ class MeanSquaredLogarithmicError(MeanMetricWrapper):
```python ```python
model = keras.models.Model(inputs, outputs) model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.MeanSquaredLogarithmicError()) model.compile('sgd', metrics=[tf.keras.metrics.MeanSquaredLogarithmicError()])
``` ```
""" """
@ -1518,8 +1518,8 @@ class Hinge(MeanMetricWrapper):
Usage: Usage:
```python ```python
h = tf.keras.metrics.Hinge() m = tf.keras.metrics.Hinge()
h.update_state([0., 1., 1.], [1., 0., 1.]) m.update_state([0., 1., 1.], [1., 0., 1.])
print('Final result: ', m.result().numpy()) # Final result: 0.66 print('Final result: ', m.result().numpy()) # Final result: 0.66
``` ```
@ -1527,7 +1527,7 @@ class Hinge(MeanMetricWrapper):
```python ```python
model = keras.models.Model(inputs, outputs) model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.metrics.Hinge()) model.compile('sgd', metrics=[tf.keras.metrics.Hinge()])
``` ```
""" """
@ -1551,8 +1551,8 @@ class SquaredHinge(MeanMetricWrapper):
Usage: Usage:
```python ```python
h = tf.keras.metrics.SquaredHinge() m = tf.keras.metrics.SquaredHinge()
h.update_state([0., 1., 1.], [1., 0., 1.]) m.update_state([0., 1., 1.], [1., 0., 1.])
print('Final result: ', m.result().numpy()) # Final result: 0.66 print('Final result: ', m.result().numpy()) # Final result: 0.66
``` ```
@ -1560,7 +1560,7 @@ class SquaredHinge(MeanMetricWrapper):
```python ```python
model = keras.models.Model(inputs, outputs) model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.metrics.SquaredHinge()) model.compile('sgd', metrics=[tf.keras.metrics.SquaredHinge()])
``` ```
""" """
@ -1584,8 +1584,8 @@ class CategoricalHinge(MeanMetricWrapper):
Usage: Usage:
```python ```python
h = tf.keras.metrics.CategoricalHinge() m = tf.keras.metrics.CategoricalHinge()
h.update_state([0., 1., 1.], [1., 0., 1.]) m.update_state([0., 1., 1.], [1., 0., 1.])
print('Final result: ', m.result().numpy()) # Final result: 1.0 print('Final result: ', m.result().numpy()) # Final result: 1.0
``` ```
@ -1593,7 +1593,7 @@ class CategoricalHinge(MeanMetricWrapper):
```python ```python
model = keras.models.Model(inputs, outputs) model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.metrics.CategoricalHinge()) model.compile('sgd', metrics=[tf.keras.metrics.CategoricalHinge()])
``` ```
""" """