forked from TensorLayer/tensorlayer3
142 lines
4.3 KiB
Python
142 lines
4.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import unittest
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import time
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorflow as tf
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import tensorlayer as tl
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from tensorlayer.logging.contrib import hyperdash as hd
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from tests.utils import CustomTestCase
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class TL_Logger_Test(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.apikey = os.getenv('HYPERDASH_APIKEY', "test_api_key")
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def test_apikey_unset(self):
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with self.assertRaises(ValueError):
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hd.HyperDashHandler.reset_apikey()
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hd.HyperDashHandler.get_apikey()
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def test_apikey_set(self):
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with self.assertNotRaises(ValueError):
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hd.HyperDashHandler.set_apikey(self.apikey)
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hd.HyperDashHandler.get_apikey()
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def test_monitor(self):
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with self.assertNotRaises(Exception):
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hd.HyperDashHandler.set_apikey(self.apikey)
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@hd.monitor("TRAVIS 1 - dogs vs. cats")
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def train_dogs_vs_cats(exp=None):
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# Record the value of hyperparameter gamma for this experiment
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lr = exp.param("learning rate", 0.005)
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tl.logging.debug("Learning Rate: %f" % lr)
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for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
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tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))
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# Record a numerical performance metric
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exp.metric(name="accuracy", value=accuracy)
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time.sleep(0.1)
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train_dogs_vs_cats()
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def test_monitor_variant(self):
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with self.assertNotRaises(Exception):
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@hd.monitor("TRAVIS 2 - dogs vs. cats", api_key=self.apikey)
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def train_dogs_vs_cats(exp=None):
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# Record the value of hyperparameter gamma for this experiment
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lr = exp.param("learning rate", 0.005)
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tl.logging.debug("Learning Rate: %f" % lr)
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for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
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tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))
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# Record a numerical performance metric
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exp.metric(name="accuracy", value=accuracy)
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time.sleep(0.1)
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train_dogs_vs_cats()
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def test_Experiment(self):
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hd.HyperDashHandler.set_apikey(self.apikey)
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with self.assertNotRaises(Exception):
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def train_dogs_vs_cats():
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# Create an experiment with a model name, then autostart
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exp = hd.Experiment("TRAVIS 3 - dogs vs. cats")
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# Record the value of hyperparameter gamma for this experiment
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lr = exp.param("learning rate", 0.005)
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tl.logging.debug("Learning Rate: %f" % lr)
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for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
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tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))
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# Record a numerical performance metric
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exp.metric(name="accuracy", value=accuracy)
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time.sleep(0.1)
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# Cleanup and mark that the experiment successfully completed
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exp.end()
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train_dogs_vs_cats()
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def test_Experiment_variant(self):
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with self.assertNotRaises(Exception):
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def train_dogs_vs_cats():
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# Create an experiment with a model name, then autostart
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exp = hd.Experiment("TRAVIS 4 - dogs vs. cats", api_key=self.apikey)
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# Record the value of hyperparameter gamma for this experiment
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lr = exp.param("learning rate", 0.005)
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tl.logging.debug("Learning Rate: %f" % lr)
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for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
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tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))
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# Record a numerical performance metric
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exp.metric(name="accuracy", value=accuracy)
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time.sleep(0.1)
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# Cleanup and mark that the experiment successfully completed
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exp.end()
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train_dogs_vs_cats()
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if __name__ == '__main__':
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tf.logging.set_verbosity(tf.logging.DEBUG)
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tl.logging.set_verbosity(tl.logging.DEBUG)
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unittest.main()
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