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An easy way to graph your tensorflow metric ops in FloydHub
import tensorflow as tf
import numpy as np
class FloydHubMetricHook(tf.train.SessionRunHook):
"""An easy way to output your metric_ops to FloydHub's training metric graphs
This is designed to fit into TensorFlow's EstimatorSpec. Assuming you've
already defined some metric_ops for monitoring your training/evaluation,
this helper class will compute those operations then print them out in
the format that FloydHub is expecting. For example:
```
def model_fn(features, labels, mode, params):
# Set up your model
loss = ...
my_predictions = ...
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(labels=labels, predictions=my_predictions)
"loss": tf.metrics.mean(loss)
}
return EstimatorSpec(mode,
eval_metric_ops = eval_metric_ops,
# **Here it is! The magic!! **
eval_hooks = [FloydHubMetricHook(eval_metric_ops)]
)
```
FloydHubMetricHook has one optional parameter, *prefix* for using it multiple times
(e.g. prefix="train_" for training metrics, prefix="eval_" for evaluation metrics).
"""
def __init__(self, metric_ops, prefix=""):
self.metric_ops = metric_ops
self.prefix = prefix
self.readings = {}
def before_run(self, run_context):
return tf.train.SessionRunArgs(self.metric_ops)
def after_run(self, run_context, run_values):
if run_values.results is not None:
for k,v in run_values.results.items():
try:
self.readings[k].append(v[1])
except KeyError:
self.readings[k] = [v[1]]
def end(self, session):
for k, v in self.readings.items():
a = np.average(v)
print(f'{{"metric": "{self.prefix}{k}", "value": {a}}}')
self.readings = {}
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