python - How to save a trained model (Estimator) and Load it back to test it with data in Tensorflow? -


i have snippet, model 

import pandas pd import tensorflow tf tensorflow.contrib import learn tensorflow.contrib.learn.python import skcompat #assume dataset using x['train'] input , y['train'] output  regressor = skcompat(learn.estimator(model_fn=lstm_model(timesteps, rnn_layers, dense_layers),model_dir=log_dir)) validation_monitor = learn.monitors.validationmonitor(x['val'], y['val'], every_n_steps=print_steps, early_stopping_rounds=1000) regressor.fit(x['train'], y['train'],               monitors=[validation_monitor],               batch_size=batch_size,               steps=training_steps)  #after training model want save in folder, can use trained model implementing in algorithm predict output #what correct format use here save model in folder called 'saved_model' regressor.export_savedmodel('/saved_model/')  #i want import later in other code, how can import it? #is there function import model file?

how can save estimator? tried finding examples tf.contrib.learn.estimator.export_savedmodel, did not have success? appreciated.


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