python - Tensorflow - Extract metric keys and/or dict from tf.contrib.learn.DNNLinearCombinedRegressor -


i having problem getting metrics pre-built estimator models.this code extract metrics model:

v_metrics = {     "accuracy":         tf.contrib.learn.metricspec(             metric_fn=tf.contrib.metrics.streaming_accuracy,             prediction_key = tf.contrib.learn.predictionkey.scores),     "precision":         tf.contrib.learn.metricspec(             metric_fn=tf.contrib.metrics.streaming_precision,             prediction_key = tf.contrib.learn.predictionkey.scores),     "recall":         tf.contrib.learn.metricspec(             metric_fn=tf.contrib.metrics.streaming_recall,             prediction_key = tf.contrib.learn.predictionkey.scores) }  v_monitor = tf.contrib.learn.monitors.validationmonitor(                 input_fn=lambda: input_fn(df_test),                 eval_steps = 1,                 every_n_steps=10,                 metrics=v_metrics )   m = build_estimator(model_dir, model_type) m.fit(input_fn=lambda: input_fn(df_train), steps=train_steps, monitors=[v_monitor]) results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)

however, when run gives me: 

info:tensorflow:saving dict global step 28: accuracy = 0.0, global_step = 28, loss = 24.7031, precision = 1.0, recall = 1.0

i looked api , estimator.py , metric_spec.py scripts, did find way extract tensors responsible accuracy, precision, , recall. looked @ other answers, them solutions custom estimators. checked results on tensorboard , still 0s , 1s.

note when used exact same method tf.contrib.learn.dnnlinearcombinedregressor prediction_key = tf.contrib.learn.predictionkey.classes works properly.

i not sure doing wrong combined regressor. know proper prediction keys (or doing wrong), when run model, gives me proper metric results.


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