A classification class builder.

backbone a 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.
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.
add_head_batch_norm bool whether to add a batch normalization layer before pool.
use_sync_bn bool if True, use synchronized batch normalization.
norm_momentum float normalization momentum for the moving average.
norm_epsilon float small float added to variance to avoid dividing by zero.
skip_logits_layer bool, whether to skip the prediction layer.
**kwargs keyword arguments to be passed.


checkpoint_items Returns a dictionary of items to be additionally checkpointed.



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).

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.

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