- paddle.static. save_inference_model ( path_prefix, feed_vars, fetch_vars, executor, **kwargs )
Save current model and its parameters to given path. i.e. Given path_prefix = “/path/to/modelname”, after invoking save_inference_model(path_prefix, feed_vars, fetch_vars, executor), you will find two files named modelname.pdmodel and modelname.pdiparams under “/path/to”, which represent your model and parameters respectively.
path_prefix (str) – Directory path to save model + model name without suffix.
feed_vars (Variable | list[Variable]) – Variables needed by inference.
fetch_vars (Variable | list[Variable]) – Variables returned by inference.
executor (Executor) – The executor that saves the inference model. You can refer to Executor for more details.
kwargs – Supported keys including ‘program’ and “clip_extra”. Attention please, kwargs is used for backward compatibility mainly. - program(Program): specify a program if you don’t want to use default main program. - clip_extra(bool): set to True if you want to clip extra information for every operator.
ValueError – If feed_vars is not a Variable or a list of Variable, an exception is thrown.
ValueError – If fetch_vars is not a Variable or a list of Variable, an exception is thrown.
import paddle paddle.enable_static() path_prefix = "./infer_model" # User defined network, here a softmax regession example image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # Feed data and train process # Save inference model. Note we don't save label and loss in this example paddle.static.save_inference_model(path_prefix, [image], [predict], exe) # In this example, the save_inference_mode inference will prune the default # main program according to the network's input node (img) and output node(predict). # The pruned inference program is going to be saved in file "./infer_model.pdmodel" # and parameters are going to be saved in file "./infer_model.pdiparams".