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

More descriptive docstrings for losses

Browse Source 
PiperOrigin-RevId: 255266042
This commit is contained in:
Francois Chollet 2019-06-26 14:34:41 -07:00 committed by TensorFlower Gardener
parent ff9bb17674
commit 7d02afd419
7 changed files with 41 additions and 37 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

66
tensorflow/python/keras/losses.py
View File

@ -226,8 +226,7 @@ class LossFunctionWrapper(Loss):
class MeanSquaredError(LossFunctionWrapper):
"""Computes the mean of squares of errors between labels and predictions.
For example, if `y_true` is [0., 0., 1., 1.] and `y_pred` is [1., 1., 1., 0.]
then the mean squared error value is 3/4 (0.75).
`loss = square(y_true - y_pred)`
Usage:
@ -237,7 +236,7 @@ class MeanSquaredError(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.75
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -256,8 +255,7 @@ class MeanSquaredError(LossFunctionWrapper):
class MeanAbsoluteError(LossFunctionWrapper):
"""Computes the mean of absolute difference between labels and predictions.
For example, if `y_true` is [0., 0., 1., 1.] and `y_pred` is [1., 1., 1., 0.]
then the mean absolute error value is 3/4 (0.75).
`loss = abs(y_true - y_pred)`
Usage:
@ -267,7 +265,7 @@ class MeanAbsoluteError(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.75
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -286,8 +284,7 @@ class MeanAbsoluteError(LossFunctionWrapper):
class MeanAbsolutePercentageError(LossFunctionWrapper):
"""Computes the mean absolute percentage error between `y_true` and `y_pred`.
For example, if `y_true` is [0., 0., 1., 1.] and `y_pred` is [1., 1., 1., 0.]
then the mean absolute percentage error value is 5e+08.
`loss = 100 * abs(y_true - y_pred) / y_true`
Usage:
@ -297,7 +294,7 @@ class MeanAbsolutePercentageError(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 5e+08
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -316,8 +313,7 @@ class MeanAbsolutePercentageError(LossFunctionWrapper):
class MeanSquaredLogarithmicError(LossFunctionWrapper):
"""Computes the mean squared logarithmic error between `y_true` and `y_pred`.
For example, if `y_true` is [0., 0., 1., 1.] and `y_pred` is [1., 1., 1., 0.]
then the mean squared logarithmic error value is 0.36034.
`loss = square(log(y_true) - log(y_pred))`
Usage:
@ -327,7 +323,7 @@ class MeanSquaredLogarithmicError(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.36034
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -426,7 +422,7 @@ class CategoricalCrossentropy(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.3239
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -490,7 +486,7 @@ class SparseCategoricalCrossentropy(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.3239
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -515,7 +511,7 @@ class SparseCategoricalCrossentropy(LossFunctionWrapper):
def __init__(self,
from_logits=False,
reduction=losses_utils.ReductionV2.AUTO,
name=None):
name='sparse_categorical_crossentropy'):
super(SparseCategoricalCrossentropy, self).__init__(
sparse_categorical_crossentropy,
name=name,
@ -527,6 +523,8 @@ class SparseCategoricalCrossentropy(LossFunctionWrapper):
class Hinge(LossFunctionWrapper):
"""Computes the hinge loss between `y_true` and `y_pred`.
`loss = maximum(1 - y_true * y_pred, 0)`
`y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
provided we will convert them to -1 or 1.
@ -541,7 +539,7 @@ class Hinge(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 1.6
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -549,7 +547,7 @@ class Hinge(LossFunctionWrapper):
```
"""
def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name=None):
def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='hinge'):
super(Hinge, self).__init__(hinge, name=name, reduction=reduction)
@ -557,6 +555,8 @@ class Hinge(LossFunctionWrapper):
class SquaredHinge(LossFunctionWrapper):
"""Computes the squared hinge loss between `y_true` and `y_pred`.
`loss = square(maximum(1 - y_true * y_pred, 0))`
`y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
provided we will convert them to -1 or 1.
@ -571,7 +571,7 @@ class SquaredHinge(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 2.566666
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -590,6 +590,9 @@ class SquaredHinge(LossFunctionWrapper):
class CategoricalHinge(LossFunctionWrapper):
"""Computes the categorical hinge loss between `y_true` and `y_pred`.
`loss = maximum(neg - pos + 1, 0)`
where `neg = sum(y_true * y_pred)` and `pos = maximum(1 - y_true)`
Usage:
```python
@ -598,7 +601,7 @@ class CategoricalHinge(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 1.0
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -627,7 +630,7 @@ class Poisson(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: -0.35702705
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -643,7 +646,8 @@ class Poisson(LossFunctionWrapper):
class LogCosh(LossFunctionWrapper):
"""Computes the logarithm of the hyperbolic cosine of the prediction error.
`logcosh = log((exp(x) + exp(-x))/2)`, where x is the error (y_pred - y_true)
`logcosh = log((exp(x) + exp(-x))/2)`,
where x is the error `y_pred - y_true`.
Usage:
@ -653,7 +657,7 @@ class LogCosh(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.289
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -681,7 +685,7 @@ class KLDivergence(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.11891246
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -700,11 +704,11 @@ class KLDivergence(LossFunctionWrapper):
class Huber(LossFunctionWrapper):
"""Computes the Huber loss between `y_true` and `y_pred`.
For each value x in `error=y_true-y_pred`, the following is calculated:
For each value x in `error = y_true - y_pred`:
```
0.5 * x^2 if |x| <= d
0.5 * d^2 + d * (|x| - d) if |x| > d
loss = 0.5 * x^2 if |x| <= d
loss = 0.5 * d^2 + d * (|x| - d) if |x| > d
```
where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss
@ -716,7 +720,7 @@ class Huber(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.333
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)
@ -872,11 +876,11 @@ def categorical_hinge(y_true, y_pred):
def huber_loss(y_true, y_pred, delta=1.0):
"""Computes Huber loss value.
For each value x in `error=y_true-y_pred`, the following is calculated:
For each value x in `error = y_true - y_pred`:
```
0.5 * x^2 if |x| <= d
0.5 * d^2 + d * (|x| - d) if |x| > d
loss = 0.5 * x^2 if |x| <= d
loss = 0.5 * d^2 + d * (|x| - d) if |x| > d
```
where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss
@ -1085,7 +1089,7 @@ class CosineSimilarity(LossFunctionWrapper):
print('Loss: ', loss.numpy()) # Loss: 0.5
```
Usage with tf.keras API:
Usage with the `compile` API:
```python
model = tf.keras.Model(inputs, outputs)

2
tensorflow/tools/api/golden/v1/tensorflow.keras.losses.-hinge.pbtxt
View File

@ -6,7 +6,7 @@ tf_class {
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'auto\', \'None\'], "
argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'auto\', \'hinge\'], "
}
member_method {
name: "call"

2
tensorflow/tools/api/golden/v1/tensorflow.keras.losses.-sparse-categorical-crossentropy.pbtxt
View File

@ -6,7 +6,7 @@ tf_class {
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'from_logits\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'auto\', \'None\'], "
argspec: "args=[\'self\', \'from_logits\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'auto\', \'sparse_categorical_crossentropy\'], "
}
member_method {
name: "call"

2
tensorflow/tools/api/golden/v2/tensorflow.keras.losses.-hinge.pbtxt
View File

@ -6,7 +6,7 @@ tf_class {
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'auto\', \'None\'], "
argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'auto\', \'hinge\'], "
}
member_method {
name: "call"

2
tensorflow/tools/api/golden/v2/tensorflow.keras.losses.-sparse-categorical-crossentropy.pbtxt
View File

@ -6,7 +6,7 @@ tf_class {
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'from_logits\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'auto\', \'None\'], "
argspec: "args=[\'self\', \'from_logits\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'auto\', \'sparse_categorical_crossentropy\'], "
}
member_method {
name: "call"

2
tensorflow/tools/api/golden/v2/tensorflow.losses.-hinge.pbtxt
View File

@ -6,7 +6,7 @@ tf_class {
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'auto\', \'None\'], "
argspec: "args=[\'self\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'auto\', \'hinge\'], "
}
member_method {
name: "call"

2
tensorflow/tools/api/golden/v2/tensorflow.losses.-sparse-categorical-crossentropy.pbtxt
View File

@ -6,7 +6,7 @@ tf_class {
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'from_logits\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'auto\', \'None\'], "
argspec: "args=[\'self\', \'from_logits\', \'reduction\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'auto\', \'sparse_categorical_crossentropy\'], "
}
member_method {
name: "call"