Creates a MobileNet family model.

model_id A str of MobileNet version. The supported values are MobileNetV1, MobileNetV2, MobileNetV3Large, MobileNetV3Small, MobileNetV3EdgeTPU, MobileNetMultiMAX and MobileNetMultiAVG.
filter_size_scale A float of multiplier for the filters (number of channels) for all convolution ops. The value must be greater than zero. Typical usage will be to set this value in (0, 1) to reduce the number of parameters or computation cost of the model.
input_specs A tf.keras.layers.InputSpec of specs of the input tensor.
norm_momentum A float of normalization momentum for the moving average.
norm_epsilon A float added to variance to avoid dividing by zero.
kernel_initializer A str for kernel initializer of convolutional layers.
kernel_regularizer A tf.keras.regularizers.Regularizer object for Conv2D. Default to None.
bias_regularizer A tf.keras.regularizers.Regularizer object for Conv2D. Default to None.
output_stride An int that specifies the requested ratio of input to output spatial resolution. If not None, then we invoke atrous convolution if necessary to prevent the network from reducing the spatial resolution of activation maps. Allowed values are 8 (accurate fully convolutional mode), 16 (fast fully convolutional mode), 32 (classification mode).
min_depth An int of minimum depth (number of channels) for all convolution ops. Enforced when filter_size_scale < 1, and not an active constraint when filter_size_scale >= 1.
divisible_by An int that ensures all inner dimensions are divisible by this number.
stochastic_depth_drop_rate A float of drop rate for drop connect layer.
regularize_depthwise If Ture, apply regularization on depthwise.
use_sync_bn If True, use synchronized batch normalization.
finegrain_classification_mode If True, the model will keep the last layer large even for small multipliers, following
output_intermediate_endpoints A bool of whether or not output the intermediate endpoints.
**kwargs Additional keyword arguments to be passed.

output_specs A dict of {level: TensorShape} pairs for the model output.



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.