CODEWITHSUNDEEP
pythontensorflowmachine-learningdeep-learningtensorflow-estimator
secsilm
secsilm

Reputation: 420

How to do prediction using a saved model which is trained with Estimator and Dataset API?

I have trained a cnn model using tf.estimator and tf.data.TFRecordDataset, which define a model in model_fn funcition and input in input_fn function. Also using an one-shot iterator to get one batch examples at a time.

Now I have trained model files(ckpt, meta, index) in a directory. What I want to do is predicting a image's label based on the trained model without training and evaluation again. The image can be numpy array but not possible a TFRecords file(which used when traing).

I can't find an effictive solution after trying all day. I only can get the value of weights and biases and don't know how to make my predicting image and model compatible.

FYI, my training code is here.

The similar question is Prediction from model saved with tf.estimator.Estimator in Tensorflow , but no accepted answer and my model input is using the dataset api.

So reaaally need help. Thanks.

Upvotes: 1

Views: 1071

Answers (1)

Olivier Moindrot
Olivier Moindrot

Reputation: 28198

I have answered a similar question here.

To make predictions using a custom input, you need to use the built-in predict method of Estimators:

estimator = tf.estimator.Estimator(model_fn, ...)

predict_input_fn = ...  # define this using tf.data

predict_results = estimator.predict(predict_input_fn)
for idx, prediction in enumerate(predict_results):
    print(idx)
    for key in prediction:
        print("...{}: {}".format(key, prediction[key]))

Upvotes: 1

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