Cast a tensor to a different dtype. Only called on a full array as provided by the user. Args: x: the tensor to cast.

Configures the model for training. Example: ```python model.compile( optimizer=keras.optimizers.Adam(learning_ra

Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the *first time* the operation was called.

Trains the model for a fixed number of epochs (dataset iterations). Args: x: Input data. It can be: - A NumPy arr

Convert a tensor to a NumPy array. Only called after slicing using `__getitem__`. Args: x: the tensor to convert.

Run basic checks on a layer. Args: layer_cls: The class of the layer to test. init_kwargs: Dict of arguments to be us

Generates output predictions for the input samples. Computation is done in batches. This method is designed for batch processing of l

Add a weight variable to the layer. Args: shape: Shape tuple for the variable. Must be fully-defined (no `None` e

Used to instantiate a Keras tensor. A Keras tensor is a symbolic tensor-like object, which we augment with certain attributes that allow us t

Compute the current metric value. Returns: A scalar tensor, or a dictionary of scalar tensors.

Accumulate statistics for the metric.

Returns the loss value & metrics values for the model in test mode. Computation is done in batches (see the `batch_size` arg.) Args:

Load the weights from a single file or sharded files. Weights are loaded based on the network's topology. This means the architecture

Saves a model as a `.keras` file. Note that `model.save()` is an alias for `keras.saving.save_model()`. The saved `.keras` file cont

Broadcast input shapes to a unified shape. Convert to list for mutability. Args: shape1: A tuple or list of integers. shape2

Adds a layer instance on top of the layer stack. Args: layer: layer instance.

Standardize an axis to a tuple if it is a list in the numpy backend.

Test whether all array elements along a given axis evaluate to `True`. Args: x: Input tensor. axis: An integer or tuple of intege

Return the maximum of a tensor or maximum along an axis. Args: x: Input tensor. axis: Axis or axes along which to operate. By def

Return the default float type, as a string. E.g. `'bfloat16'`, `'float16'`, `'float32'`, `'float64'`. Returns: String, the current d

Saves all weights to a single file or sharded files. By default, the weights will be saved in a single `.weights.h5` file. If shardin

Return the minimum of a tensor or minimum along an axis. Args: x: Input tensor. axis: Axis or axes along which to operate. By def

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

DEPRECATED.

Context manager to make the `Distribution` current.

Assign a value to a variable. This should be used in optimizers instead of `variable.assign(value)` to support backend specific optim

Test whether any array element along a given axis evaluates to `True`. Args: x: Input tensor. axis: An integer or tuple of intege

Save the `FeatureSpace` instance to a `.keras` file. You can reload it via `keras.models.load_model()`: ```python feature_sp

Build the layer at `Layer.__init__`. We can only safely mark the layer as `built=True` in `Layer.__init__` if `build` is not overridd

Update traininable variables according to provided gradient values. `grads` should be a list of gradient tensors with 1:1 mapping to

Save the edited weights file. Args: filepath: Path to save the file to. Must be a `.weights.h5` file.