tfm.vision.models.VideoClassificationModel

A video classification class builder.

tfm.vision.models.VideoClassificationModel(
    backbone: tf_keras.Model,
    num_classes: int,
    input_specs: Optional[Mapping[str, tf_keras.layers.InputSpec]] = None,
    dropout_rate: float = 0.0,
    aggregate_endpoints: bool = False,
    kernel_initializer: str = 'random_uniform',
    kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
    require_endpoints: Optional[List[Text]] = None,
    **kwargs
)

backbone a 3d backbone network.
num_classes int number of classes in classification task.
input_specs tf_keras.layers.InputSpec specs of the input tensor.
dropout_rate float rate for dropout regularization.
aggregate_endpoints bool aggregate all end ponits or only use the final end point.
kernel_initializer kernel initializer for the dense layer.
kernel_regularizer tf_keras.regularizers.Regularizer object. Default to None.
bias_regularizer tf_keras.regularizers.Regularizer object. Default to None.
require_endpoints the required endpoints for prediction. If None or empty, then only uses the final endpoint.
**kwargs keyword arguments to be passed.

backbone

checkpoint_items Returns a dictionary of items to be additionally checkpointed.

Methods

call

call(
    inputs, training=None, mask=None
)

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args
inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.