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Keras API reference /
Losses

The purpose of loss functions is to compute the quantity that a model should seek to minimize during training.

Note that all losses are available both via a class handle and via a function handle.
The class handles enable you to pass configuration arguments to the constructor
(e.g.
`loss_fn = CategoricalCrossentropy(from_logits=True)`

),
and they perform reduction by default when used in a standalone way (see details below).

- BinaryCrossentropy class
- CategoricalCrossentropy class
- SparseCategoricalCrossentropy class
- Poisson class
- binary_crossentropy function
- categorical_crossentropy function
- sparse_categorical_crossentropy function
- poisson function
- KLDivergence class
- kl_divergence function

- MeanSquaredError class
- MeanAbsoluteError class
- MeanAbsolutePercentageError class
- MeanSquaredLogarithmicError class
- CosineSimilarity class
- mean_squared_error function
- mean_absolute_error function
- mean_absolute_percentage_error function
- mean_squared_logarithmic_error function
- cosine_similarity function
- Huber class
- huber function
- LogCosh class
- log_cosh function

- Hinge class
- SquaredHinge class
- CategoricalHinge class
- hinge function
- squared_hinge function
- categorical_hinge function

`compile()`

& `fit()`

A loss function is one of the two arguments required for compiling a Keras model:

```
from tensorflow import keras
from tensorflow.keras import layers
model = keras.Sequential()
model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(layers.Activation('softmax'))
loss_fn = keras.losses.SparseCategoricalCrossentropy()
model.compile(loss=loss_fn, optimizer='adam')
```

All built-in loss functions may also be passed via their string identifier:

```
# pass optimizer by name: default parameters will be used
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
```

Loss functions are typically created by instantiating a loss class (e.g. `keras.losses.SparseCategoricalCrossentropy`

).
All losses are also provided as function handles (e.g. `keras.losses.sparse_categorical_crossentropy`

).

Using classes enables you to pass configuration arguments at instantiation time, e.g.:

```
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
```

A loss is a callable with arguments `loss_fn(y_true, y_pred, sample_weight=None)`

:

**y_true**: Ground truth values, of shape`(batch_size, d0, ... dN)`

. For sparse loss functions, such as sparse categorical crossentropy, the shape should be`(batch_size, d0, ... dN-1)`

**y_pred**: The predicted values, of shape`(batch_size, d0, .. dN)`

.**sample_weight**: Optional`sample_weight`

acts as reduction weighting coefficient for the per-sample losses. If a scalar is provided, then the loss is simply scaled by the given value. If`sample_weight`

is a tensor of size`[batch_size]`

, then the total loss for each sample of the batch is rescaled by the corresponding element in the`sample_weight`

vector. If the shape of`sample_weight`

is`(batch_size, d0, ... dN-1)`

(or can be broadcasted to this shape), then each loss element of`y_pred`

is scaled by the corresponding value of`sample_weight`

. (Note on`dN-1`

: all loss functions reduce by 1 dimension, usually`axis=-1`

.)

By default, loss functions return one scalar loss value per input sample, e.g.

```
>>> tf.keras.losses.mean_squared_error(tf.ones((2, 2,)), tf.zeros((2, 2)))
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>
```

However, loss class instances feature a `reduction`

constructor argument,
which defaults to `"sum_over_batch_size"`

(i.e. average). Allowable values are
"sum_over_batch_size", "sum", and "none":

- "sum_over_batch_size" means the loss instance will return the average of the per-sample losses in the batch.
- "sum" means the loss instance will return the sum of the per-sample losses in the batch.
- "none" means the loss instance will return the full array of per-sample losses.

```
>>> loss_fn = tf.keras.losses.MeanSquaredError(reduction='sum_over_batch_size')
>>> loss_fn(tf.ones((2, 2,)), tf.zeros((2, 2)))
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
```

```
>>> loss_fn = tf.keras.losses.MeanSquaredError(reduction='sum')
>>> loss_fn(tf.ones((2, 2,)), tf.zeros((2, 2)))
<tf.Tensor: shape=(), dtype=float32, numpy=2.0>
```

```
>>> loss_fn = tf.keras.losses.MeanSquaredError(reduction='none')
>>> loss_fn(tf.ones((2, 2,)), tf.zeros((2, 2)))
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>
```

Note that this is an important difference between loss functions like `tf.keras.losses.mean_squared_error`

and default loss class instances like `tf.keras.losses.MeanSquaredError`

: the function version
does not perform reduction, but by default the class instance does.

```
>>> loss_fn = tf.keras.losses.mean_squared_error
>>> loss_fn(tf.ones((2, 2,)), tf.zeros((2, 2)))
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>
```

```
>>> loss_fn = tf.keras.losses.MeanSquaredError()
>>> loss_fn(tf.ones((2, 2,)), tf.zeros((2, 2)))
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
```

When using `fit()`

, this difference is irrelevant since reduction is handled by the framework.

Here's how you would use a loss class instance as part of a simple training loop:

```
loss_fn = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()
# Iterate over the batches of a dataset.
for x, y in dataset:
with tf.GradientTape() as tape:
logits = model(x)
# Compute the loss value for this batch.
loss_value = loss_fn(y, logits)
# Update the weights of the model to minimize the loss value.
gradients = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(gradients, model.trainable_weights))
```

Any callable with the signature `loss_fn(y_true, y_pred)`

that returns an array of losses (one of sample in the input batch) can be passed to `compile()`

as a loss.
Note that sample weighting is automatically supported for any such loss.

Here's a simple example:

```
def my_loss_fn(y_true, y_pred):
squared_difference = tf.square(y_true - y_pred)
return tf.reduce_mean(squared_difference, axis=-1) # Note the `axis=-1`
model.compile(optimizer='adam', loss=my_loss_fn)
```

`add_loss()`

APILoss functions applied to the output of a model aren't the only way to create losses.

When writing the `call`

method of a custom layer or a subclassed model,
you may want to compute scalar quantities that you want to minimize during
training (e.g. regularization losses). You can use the `add_loss()`

layer method
to keep track of such loss terms.

Here's an example of a layer that adds a sparsity regularization loss based on the L2 norm of the inputs:

```
from tensorflow.keras.layers import Layer
class MyActivityRegularizer(Layer):
"""Layer that creates an activity sparsity regularization loss."""
def __init__(self, rate=1e-2):
super(MyActivityRegularizer, self).__init__()
self.rate = rate
def call(self, inputs):
# We use `add_loss` to create a regularization loss
# that depends on the inputs.
self.add_loss(self.rate * tf.reduce_sum(tf.square(inputs)))
return inputs
```

Loss values added via `add_loss`

can be retrieved in the `.losses`

list property of any `Layer`

or `Model`

(they are recursively retrieved from every underlying layer):

```
from tensorflow.keras import layers
class SparseMLP(Layer):
"""Stack of Linear layers with a sparsity regularization loss."""
def __init__(self, output_dim):
super(SparseMLP, self).__init__()
self.dense_1 = layers.Dense(32, activation=tf.nn.relu)
self.regularization = MyActivityRegularizer(1e-2)
self.dense_2 = layers.Dense(output_dim)
def call(self, inputs):
x = self.dense_1(inputs)
x = self.regularization(x)
return self.dense_2(x)
mlp = SparseMLP(1)
y = mlp(tf.ones((10, 10)))
print(mlp.losses) # List containing one float32 scalar
```

These losses are cleared by the top-level layer at the start of each forward pass -- they don't accumulate.
So `layer.losses`

always contain only the losses created during the last forward pass.
You would typically use these losses by summing them before computing your gradients when writing a training loop.

```
# Losses correspond to the *last* forward pass.
mlp = SparseMLP(1)
mlp(tf.ones((10, 10)))
assert len(mlp.losses) == 1
mlp(tf.ones((10, 10)))
assert len(mlp.losses) == 1 # No accumulation.
```

When using `model.fit()`

, such loss terms are handled automatically.

When writing a custom training loop, you should retrieve these terms
by hand from `model.losses`

, like this:

```
loss_fn = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()
# Iterate over the batches of a dataset.
for x, y in dataset:
with tf.GradientTape() as tape:
# Forward pass.
logits = model(x)
# Loss value for this batch.
loss_value = loss_fn(y, logits)
# Add extra loss terms to the loss value.
loss_value += sum(model.losses)
# Update the weights of the model to minimize the loss value.
gradients = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(gradients, model.trainable_weights))
```

See the `add_loss()`

documentation for more details.