tfm.vision.classification_model.ClassificationModel

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A classification class builder.

tfm.vision.classification_model.ClassificationModel(
    backbone: tf.keras.Model,
    num_classes: int,
    input_specs: tf.keras.layers.InputSpec = layers.InputSpec(shape=[None, None, None, 3]),
    dropout_rate: float = 0.0,
    kernel_initializer: str = 'random_uniform',
    kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    add_head_batch_norm: bool = False,
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    skip_logits_layer: bool = False,
    **kwargs
)

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.

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.