tensorflow - tf.estimator.Estimator write tensor value to tensorboard every step in EVAL mode
I want to write a tensor value after every evaluation step to tensorboard. I call estimator.evaluate(..., steps=3000) once after finishing training, with a step count covering the whole test set.
I tried:
tf.summary.scalar("mean", mean)
tf.summary.scalar("standard_deviation", standard_deviation)
summary_hook = tf.train.SummarySaverHook(
save_steps=1,
output_dir=self.output_dir + "/eval",
summary_op=tf.summary.merge_all()
)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, evaluation_hooks=[summary_hook])
&
mean, mean_op = tf.metrics.mean(mean)
standard_deviation, standard_deviation_op = tf.metrics.mean(standard_deviation)
metrics = {
'mean': (mean, mean_op),
'standard_deviation': (standard_deviation, standard_deviation_op),
}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=metrics)
both ways write summaries declared in my model_fn once per evaluate() call for the final global training_step.
However, i want to have points written for every step. Is this possible within the estimator API? Neither eval_metric_ops nor tf.train.SummarySaverHook seem to be able to achieve this result.
python tensorflow tensorboard tensorflow-estimator
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
add a comment |
I want to write a tensor value after every evaluation step to tensorboard. I call estimator.evaluate(..., steps=3000) once after finishing training, with a step count covering the whole test set.
I tried:
tf.summary.scalar("mean", mean)
tf.summary.scalar("standard_deviation", standard_deviation)
summary_hook = tf.train.SummarySaverHook(
save_steps=1,
output_dir=self.output_dir + "/eval",
summary_op=tf.summary.merge_all()
)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, evaluation_hooks=[summary_hook])
&
mean, mean_op = tf.metrics.mean(mean)
standard_deviation, standard_deviation_op = tf.metrics.mean(standard_deviation)
metrics = {
'mean': (mean, mean_op),
'standard_deviation': (standard_deviation, standard_deviation_op),
}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=metrics)
both ways write summaries declared in my model_fn once per evaluate() call for the final global training_step.
However, i want to have points written for every step. Is this possible within the estimator API? Neither eval_metric_ops nor tf.train.SummarySaverHook seem to be able to achieve this result.
python tensorflow tensorboard tensorflow-estimator
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
add a comment |
I want to write a tensor value after every evaluation step to tensorboard. I call estimator.evaluate(..., steps=3000) once after finishing training, with a step count covering the whole test set.
I tried:
tf.summary.scalar("mean", mean)
tf.summary.scalar("standard_deviation", standard_deviation)
summary_hook = tf.train.SummarySaverHook(
save_steps=1,
output_dir=self.output_dir + "/eval",
summary_op=tf.summary.merge_all()
)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, evaluation_hooks=[summary_hook])
&
mean, mean_op = tf.metrics.mean(mean)
standard_deviation, standard_deviation_op = tf.metrics.mean(standard_deviation)
metrics = {
'mean': (mean, mean_op),
'standard_deviation': (standard_deviation, standard_deviation_op),
}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=metrics)
both ways write summaries declared in my model_fn once per evaluate() call for the final global training_step.
However, i want to have points written for every step. Is this possible within the estimator API? Neither eval_metric_ops nor tf.train.SummarySaverHook seem to be able to achieve this result.
python tensorflow tensorboard tensorflow-estimator
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
I want to write a tensor value after every evaluation step to tensorboard. I call estimator.evaluate(..., steps=3000) once after finishing training, with a step count covering the whole test set.
I tried:
tf.summary.scalar("mean", mean)
tf.summary.scalar("standard_deviation", standard_deviation)
summary_hook = tf.train.SummarySaverHook(
save_steps=1,
output_dir=self.output_dir + "/eval",
summary_op=tf.summary.merge_all()
)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, evaluation_hooks=[summary_hook])
&
mean, mean_op = tf.metrics.mean(mean)
standard_deviation, standard_deviation_op = tf.metrics.mean(standard_deviation)
metrics = {
'mean': (mean, mean_op),
'standard_deviation': (standard_deviation, standard_deviation_op),
}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=metrics)
both ways write summaries declared in my model_fn once per evaluate() call for the final global training_step.
However, i want to have points written for every step. Is this possible within the estimator API? Neither eval_metric_ops nor tf.train.SummarySaverHook seem to be able to achieve this result.
python tensorflow tensorboard tensorflow-estimator
python tensorflow tensorboard tensorflow-estimator
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
asked Nov 23 '18 at 17:30
ChocolateChocolate
1391211
1391211
add a comment |
add a comment |
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