deepof.models.VQVAE
- class deepof.models.VQVAE(*args, **kwargs)
VQ-VAE model adapted to the DeepOF setting.
- __init__(input_shape: tuple, edge_feature_shape: tuple, adjacency_matrix: ndarray | None = None, latent_dim: int = 8, n_components: int = 15, beta: float = 1.0, kmeans_loss: float = 0.0, use_gnn: bool = True, encoder_type: str = 'recurrent', interaction_regularization: float = 0.0, **kwargs)
Initialize a VQ-VAE model.
- Parameters:
input_shape (tuple) – Shape of the input to the full model.
edge_feature_shape (tuple) – shape of the edge feature matrix used for graph representations.
adjacency_matrix (np.ndarray) – adjacency matrix of the connectivity graph to use.
latent_dim (int) – Dimensionality of the latent space.
n_components (int) – Number of embeddings (clusters) in the embedding layer.
beta (float) – Beta parameter of the VQ loss, as described in the original VQVAE paper.
kmeans_loss (float) – Regularization parameter for the Gram matrix.
encoder_type (str) – Type of encoder to use. Can be set to “recurrent” (default), “TCN”, or “transformer”.
interaction_regularization (float) – Regularization parameter for the interaction features.
**kwargs – Additional keyword arguments.
Methods
__init__
(input_shape, edge_feature_shape[, ...])Initialize a VQ-VAE model.
add_loss
(losses, **kwargs)Add loss tensor(s), potentially dependent on layer inputs.
add_metric
(value[, name])Adds metric tensor to the layer.
add_update
(updates)Add update op(s), potentially dependent on layer inputs.
add_variable
(*args, **kwargs)Deprecated, do NOT use! Alias for add_weight.
add_weight
([name, shape, dtype, ...])Adds a new variable to the layer.
build
(input_shape)Builds the model based on input shapes received.
build_from_config
(config)call
(inputs, **kwargs)Call the VQVAE model.
compile
([optimizer, loss, metrics, ...])Configures the model for training.
compile_from_config
(config)compute_loss
([x, y, y_pred, sample_weight])Compute the total loss, validate it, and return it.
compute_mask
(inputs[, mask])Computes an output mask tensor.
compute_metrics
(x, y, y_pred, sample_weight)Update metric states and collect all metrics to be returned.
compute_output_shape
(input_shape)Computes the output shape of the layer.
compute_output_signature
(input_signature)Compute the output tensor signature of the layer based on the inputs.
count_params
()Count the total number of scalars composing the weights.
evaluate
([x, y, batch_size, verbose, ...])Returns the loss value & metrics values for the model in test mode.
evaluate_generator
(generator[, steps, ...])Evaluates the model on a data generator.
export
(filepath)Create a SavedModel artifact for inference (e.g. via TF-Serving).
finalize_state
()Finalizes the layers state after updating layer weights.
fit
([x, y, batch_size, epochs, verbose, ...])Trains the model for a fixed number of epochs (dataset iterations).
fit_generator
(generator[, steps_per_epoch, ...])Fits the model on data yielded batch-by-batch by a Python generator.
from_config
(config[, custom_objects])Creates a layer from its config.
get_build_config
()get_compile_config
()get_config
()Returns the config of the Model.
get_input_at
(node_index)Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at
(node_index)Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at
(node_index)Retrieves the input shape(s) of a layer at a given node.
get_layer
([name, index])Retrieves a layer based on either its name (unique) or index.
get_metrics_result
()Returns the model's metrics values as a dict.
get_output_at
(node_index)Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at
(node_index)Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at
(node_index)Retrieves the output shape(s) of a layer at a given node.
get_weight_paths
()Retrieve all the variables and their paths for the model.
get_weights
()Retrieves the weights of the model.
load_weights
(filepath[, skip_mismatch, ...])Loads all layer weights from a saved files.
make_predict_function
([force])Creates a function that executes one step of inference.
make_test_function
([force])Creates a function that executes one step of evaluation.
make_train_function
([force])Creates a function that executes one step of training.
predict
(x[, batch_size, verbose, steps, ...])Generates output predictions for the input samples.
predict_generator
(generator[, steps, ...])Generates predictions for the input samples from a data generator.
predict_on_batch
(x)Returns predictions for a single batch of samples.
predict_step
(data)The logic for one inference step.
reset_metrics
()Resets the state of all the metrics in the model.
reset_states
()save
(filepath[, overwrite, save_format])Saves a model as a TensorFlow SavedModel or HDF5 file.
save_spec
([dynamic_batch])Returns the tf.TensorSpec of call args as a tuple (args, kwargs).
save_weights
(filepath[, overwrite, ...])Saves all layer weights.
set_weights
(weights)Sets the weights of the layer, from NumPy arrays.
summary
([line_length, positions, print_fn, ...])Prints a string summary of the network.
test_on_batch
(x[, y, sample_weight, ...])Test the model on a single batch of samples.
test_step
(data)Performs a test step.
to_json
(**kwargs)Returns a JSON string containing the network configuration.
to_yaml
(**kwargs)Returns a yaml string containing the network configuration.
train_on_batch
(x[, y, sample_weight, ...])Runs a single gradient update on a single batch of data.
train_step
(data)Perform a training step.
with_name_scope
(method)Decorator to automatically enter the module name scope.
Attributes
activity_regularizer
Optional regularizer function for the output of this layer.
compute_dtype
The dtype of the layer's computations.
distribute_reduction_method
The method employed to reduce per-replica values during training.
distribute_strategy
The tf.distribute.Strategy this model was created under.
dtype
The dtype of the layer weights.
dtype_policy
The dtype policy associated with this layer.
dynamic
Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes
Return Functional API nodes upstream of this layer.
input
Retrieves the input tensor(s) of a layer.
input_mask
Retrieves the input mask tensor(s) of a layer.
input_shape
Retrieves the input shape(s) of a layer.
input_spec
InputSpec instance(s) describing the input format for this layer.
jit_compile
Specify whether to compile the model with XLA.
layers
losses
List of losses added using the add_loss() API.
metrics
Initialize VQVAE tracked metrics.
metrics_names
Returns the model's display labels for all outputs.
name
Name of the layer (string), set in the constructor.
name_scope
Returns a tf.name_scope instance for this class.
non_trainable_variables
Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights
List of all non-trainable weights tracked by this layer.
outbound_nodes
Return Functional API nodes downstream of this layer.
output
Retrieves the output tensor(s) of a layer.
output_mask
Retrieves the output mask tensor(s) of a layer.
output_shape
Retrieves the output shape(s) of a layer.
run_eagerly
Settable attribute indicating whether the model should run eagerly.
state_updates
Deprecated, do NOT use!
stateful
submodules
Sequence of all sub-modules.
supports_masking
Whether this layer supports computing a mask using compute_mask.
trainable
trainable_variables
Sequence of trainable variables owned by this module and its submodules.
trainable_weights
List of all trainable weights tracked by this layer.
updates
variable_dtype
Alias of Layer.dtype, the dtype of the weights.
variables
Returns the list of all layer variables/weights.
weights
Returns the list of all layer variables/weights.
- __init__(input_shape: tuple, edge_feature_shape: tuple, adjacency_matrix: ndarray | None = None, latent_dim: int = 8, n_components: int = 15, beta: float = 1.0, kmeans_loss: float = 0.0, use_gnn: bool = True, encoder_type: str = 'recurrent', interaction_regularization: float = 0.0, **kwargs)
Initialize a VQ-VAE model.
- Parameters:
input_shape (tuple) – Shape of the input to the full model.
edge_feature_shape (tuple) – shape of the edge feature matrix used for graph representations.
adjacency_matrix (np.ndarray) – adjacency matrix of the connectivity graph to use.
latent_dim (int) – Dimensionality of the latent space.
n_components (int) – Number of embeddings (clusters) in the embedding layer.
beta (float) – Beta parameter of the VQ loss, as described in the original VQVAE paper.
kmeans_loss (float) – Regularization parameter for the Gram matrix.
encoder_type (str) – Type of encoder to use. Can be set to “recurrent” (default), “TCN”, or “transformer”.
interaction_regularization (float) – Regularization parameter for the interaction features.
**kwargs – Additional keyword arguments.