Keras 3 API documentation / Layers API / The base Layer class

The base Layer class


Layer class


This is the class from which all layers inherit.

A layer is a callable object that takes as input one or more tensors and that outputs one or more tensors. It involves computation, defined in the call() method, and a state (weight variables). State can be created:

  • in __init__(), for instance via self.add_weight();
  • in the optional build() method, which is invoked by the first __call__() to the layer, and supplies the shape(s) of the input(s), which may not have been known at initialization time.

Layers are recursively composable: If you assign a Layer instance as an attribute of another Layer, the outer layer will start tracking the weights created by the inner layer. Nested layers should be instantiated in the __init__() method or build() method.

Users will just instantiate a layer and then treat it as a callable.


  • trainable: Boolean, whether the layer's variables should be trainable.
  • name: String name of the layer.
  • dtype: The dtype of the layer's computations and weights. Can also be a keras.DTypePolicy, which allows the computation and weight dtype to differ. Defaults to None. None means to use keras.config.dtype_policy(), which is a float32 policy unless set to different value (via keras.config.set_dtype_policy()).


  • name: The name of the layer (string).
  • dtype: Dtype of the layer's weights. Alias of layer.variable_dtype.
  • variable_dtype: Dtype of the layer's weights.
  • compute_dtype: The dtype of the layer's computations. Layers automatically cast inputs to this dtype, which causes the computations and output to also be in this dtype. When mixed precision is used with a keras.DTypePolicy, this will be different than variable_dtype.
  • trainable_weights: List of variables to be included in backprop.
  • non_trainable_weights: List of variables that should not be included in backprop.
  • weights: The concatenation of the lists trainable_weights and non_trainable_weights (in this order).
  • trainable: Whether the layer should be trained (boolean), i.e. whether its potentially-trainable weights should be returned as part of layer.trainable_weights.
  • input_spec: Optional (list of) InputSpec object(s) specifying the constraints on inputs that can be accepted by the layer.

We recommend that descendants of Layer implement the following methods:

  • __init__(): Defines custom layer attributes, and creates layer weights that do not depend on input shapes, using add_weight(), or other state.
  • build(self, input_shape): This method can be used to create weights that depend on the shape(s) of the input(s), using add_weight(), or other state. __call__() will automatically build the layer (if it has not been built yet) by calling build().
  • call(self, *args, **kwargs): Called in __call__ after making sure build() has been called. call() performs the logic of applying the layer to the input arguments. Two reserved keyword arguments you can optionally use in call() are: 1. training (boolean, whether the call is in inference mode or training mode). 2. mask (boolean tensor encoding masked timesteps in the input, used e.g. in RNN layers). A typical signature for this method is call(self, inputs), and user could optionally add training and mask if the layer need them.
  • get_config(self): Returns a dictionary containing the configuration used to initialize this layer. If the keys differ from the arguments in __init__(), then override from_config(self) as well. This method is used when saving the layer or a model that contains this layer.


Here's a basic example: a layer with two variables, w and b, that returns y = w . x + b. It shows how to implement build() and call(). Variables set as attributes of a layer are tracked as weights of the layers (in layer.weights).

class SimpleDense(Layer):
    def __init__(self, units=32):
        self.units = units

    # Create the state of the layer (weights)
    def build(self, input_shape):
        self.kernel = self.add_weight(
            shape=(input_shape[-1], self.units),
        self.bias = self.add_weight(

    # Defines the computation
    def call(self, inputs):
        return ops.matmul(inputs, self.kernel) + self.bias

# Instantiates the layer.
linear_layer = SimpleDense(4)

# This will also call `build(input_shape)` and create the weights.
y = linear_layer(ops.ones((2, 2)))
assert len(linear_layer.weights) == 2

# These weights are trainable, so they're listed in `trainable_weights`:
assert len(linear_layer.trainable_weights) == 2

Besides trainable weights, updated via backpropagation during training, layers can also have non-trainable weights. These weights are meant to be updated manually during call(). Here's a example layer that computes the running sum of its inputs:

class ComputeSum(Layer):

  def __init__(self, input_dim):
      super(ComputeSum, self).__init__()
      # Create a non-trainable weight. = self.add_weight(

  def call(self, inputs): + ops.sum(inputs))

my_sum = ComputeSum(2)
x = ops.ones((2, 2))
y = my_sum(x)

assert my_sum.weights == []
assert my_sum.non_trainable_weights == []
assert my_sum.trainable_weights == []

weights property


List of all weight variables of the layer.

Unlike, layer.variables this excludes metric state and random seeds.

trainable_weights property


List of all trainable weight variables of the layer.

These are the weights that get updated by the optimizer during training.

non_trainable_weights property


List of all non-trainable weight variables of the layer.

These are the weights that should not be updated by the optimizer during training. Unlike, layer.non_trainable_variables this excludes metric state and random seeds.


add_weight method


Add a weight variable to the layer.


  • shape: Shape tuple for the variable. Must be fully-defined (no None entries). Defaults to () (scalar) if unspecified.
  • initializer: Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "glorot_uniform" for floating-point variables and to "zeros" for all other types (e.g. int, bool).
  • dtype: Dtype of the variable to create, e.g. "float32". If unspecified, defaults to the layer's variable dtype (which itself defaults to "float32" if unspecified).
  • trainable: Boolean, whether the variable should be trainable via backprop or whether its updates are managed manually. Defaults to True.
  • autocast: Boolean, whether to autocast layers variables when accessing them. Defaults to True.
  • regularizer: Regularizer object to call to apply penalty on the weight. These penalties are summed into the loss function during optimization. Defaults to None.
  • constraint: Contrainst object to call on the variable after any optimizer update, or string name of a built-in constraint. Defaults to None.
  • aggregation: String, one of 'mean', 'sum', 'only_first_replica'. Annotates the variable with the type of multi-replica aggregation to be used for this variable when writing custom data parallel training loops.
  • name: String name of the variable. Useful for debugging purposes.

trainable property


Settable boolean, whether this layer should be trainable or not.


get_weights method


Return the values of layer.weights as a list of NumPy arrays.


set_weights method


Sets the values of layer.weights from a list of NumPy arrays.


get_config method


Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.


add_loss method


Can be called inside of the call() method to add a scalar loss.


class MyLayer(Layer):
    def call(self, x):
        return x

losses property


List of scalar losses from add_loss, regularizers and sublayers.