Predictions on a saved TensorFlow model from the Experiment framework -


i've trained , validated tensorflow model via experiment framework. i'm little lost on how can make predictions on new samples saved model. experiment code looks feature columns defined in previous cell:

def experiment_fn(output_dir):     return tflearn.experiment(         tflearn.dnnclassifier(feature_columns=feature_cols,                               hidden_units=[30, 2],                               n_classes=2,                               model_dir=output_dir),         train_input_fn=get_train(),         eval_input_fn=get_valid(),         eval_metrics = {     "accuracy":         tf.contrib.learn.metricspec(             metric_fn=tf.contrib.metrics.streaming_accuracy,             prediction_key=tf.contrib.learn.predictionkey.classes),     "precision":         tf.contrib.learn.metricspec(             metric_fn=tf.contrib.metrics.streaming_precision,             prediction_key=tf.contrib.learn.predictionkey.classes),     "recall":         tf.contrib.learn.metricspec(             metric_fn=tf.contrib.metrics.streaming_recall,             prediction_key=tf.contrib.learn.predictionkey.classes) }     )  learn_runner.run(experiment_fn, 'model_trained')

looking @ this post on saving , restoring model, thought may able retrieve predict method on dnnclassifier this:

op_to_restore = graph.get_tensor_by_name("predict:0")

and build feed_dict new samples , run:

print sess.run(op_to_restore,feed_dict)

however, after restoring model, tensorflow complains cannot find 'predict' operation.

any here appreciated. thanks!


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