If you are not satisfied with training results when using MoXing to build a model, you can perform incremental training after modifying some data and label information.
After modifying labeling data or datasets, you can modify the log_dir parameter in and add the checkpoint_path parameter to mox.run. Set log_dir to a new directory and checkpoint_path to the output path of the previous training results. If the output path is an OBS directory, set the path to a value starting with obs://.
If labels are changed for label data, perform operations in If Labels Are Changed before running mox.run.
mox.run(input_fn=input_fn,
model_fn=model_fn,
optimizer_fn=optimizer_fn,
run_mode=flags.run_mode,
inter_mode=mox.ModeKeys.EVAL if use_eval_data else None,
log_dir=log_dir,
batch_size=batch_size_per_device,
auto_batch=False,
max_number_of_steps=max_number_of_steps,
log_every_n_steps=flags.log_every_n_steps,
save_summary_steps=save_summary_steps,
save_model_secs=save_model_secs,
checkpoint_path=flags.checkpoint_url,
export_model=mox.ExportKeys.TF_SERVING)
If the labels in a dataset have changed, execute the following statement. The statement must be executed before running mox.run.
In the statement, the logits variable indicates classification layer weights in different networks, and different parameters are configured. Set this parameter to the corresponding keyword.
mox.set_flag('checkpoint_exclude_patterns', 'logits')
If the built-in network of MoXing is used, the corresponding keyword needs to be obtained by calling the following API. In this example, the Resnet_v1_50 keyword is the value of logits.
import moxing.tensorflow as mox model_meta = mox.get_model_meta(mox.NetworkKeys.RESNET_V1_50) logits_pattern = model_meta.default_logits_pattern print(logits_pattern)
You can also obtain a list of networks supported by MoXing by calling the following API:
import moxing.tensorflow as mox print(help(mox.NetworkKeys))
The following information is displayed:
Help on class NetworkKeys in module moxing.tensorflow.nets.nets_factory: class NetworkKeys(builtins.object) | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | ALEXNET_V2 = 'alexnet_v2' | | CIFARNET = 'cifarnet' | | INCEPTION_RESNET_V2 = 'inception_resnet_v2' | | INCEPTION_V1 = 'inception_v1' | | INCEPTION_V2 = 'inception_v2' | | INCEPTION_V3 = 'inception_v3' | | INCEPTION_V4 = 'inception_v4' | | LENET = 'lenet' | | MOBILENET_V1 = 'mobilenet_v1' | | MOBILENET_V1_025 = 'mobilenet_v1_025' | | MOBILENET_V1_050 = 'mobilenet_v1_050' | | MOBILENET_V1_075 = 'mobilenet_v1_075' | | MOBILENET_V2 = 'mobilenet_v2' | | MOBILENET_V2_035 = 'mobilenet_v2_035' | | MOBILENET_V2_140 = 'mobilenet_v2_140' | | NASNET_CIFAR = 'nasnet_cifar' | | NASNET_LARGE = 'nasnet_large' | | NASNET_MOBILE = 'nasnet_mobile' | | OVERFEAT = 'overfeat' | | PNASNET_LARGE = 'pnasnet_large' | | PNASNET_MOBILE = 'pnasnet_mobile' | | PVANET = 'pvanet' | | RESNET_V1_101 = 'resnet_v1_101' | | RESNET_V1_110 = 'resnet_v1_110' | | RESNET_V1_152 = 'resnet_v1_152' | | RESNET_V1_18 = 'resnet_v1_18' | | RESNET_V1_20 = 'resnet_v1_20' | | RESNET_V1_200 = 'resnet_v1_200' | | RESNET_V1_50 = 'resnet_v1_50' | | RESNET_V1_50_8K = 'resnet_v1_50_8k' | | RESNET_V1_50_MOX = 'resnet_v1_50_mox' | | RESNET_V1_50_OCT = 'resnet_v1_50_oct' | | RESNET_V2_101 = 'resnet_v2_101' | | RESNET_V2_152 = 'resnet_v2_152' | | RESNET_V2_200 = 'resnet_v2_200' | | RESNET_V2_50 = 'resnet_v2_50' | | RESNEXT_B_101 = 'resnext_b_101' | | RESNEXT_B_50 = 'resnext_b_50' | | RESNEXT_C_101 = 'resnext_c_101' | | RESNEXT_C_50 = 'resnext_c_50' | | VGG_16 = 'vgg_16' | | VGG_16_BN = 'vgg_16_bn' | | VGG_19 = 'vgg_19' | | VGG_19_BN = 'vgg_19_bn' | | VGG_A = 'vgg_a' | | VGG_A_BN = 'vgg_a_bn' | | XCEPTION_41 = 'xception_41' | | XCEPTION_65 = 'xception_65' | | XCEPTION_71 = 'xception_71'