I am building a neural network with my own custom loss function (pretty long and complicated). My network is unsupervised so my input and expected output are identical and also at the moment I am using one single input (just trying to optimize the loss for a single input).
I am trying to use tensorboard.plugins.hparams api for hyperparameter tuning and don't know how to incorporate my custom loss function there. I'm trying to follow the code suggested on the Tensorflow 2.0 website.
This is what the website suggests:
HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32])) HP_DROPOUT = hp.HParam('dropout', hp.RealInterval(0.1, 0.2)) HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd'])) METRIC_ACCURACY = 'accuracy' with tf.summary.create_file_writer('logs/hparam_tuning').as_default(): hp.hparams_config( hparams=[HP_NUM_UNITS, HP_DROPOUT, HP_OPTIMIZER], metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')], ) I need to change that as I don't want to use the dropout layer, so I can just delete that. In terms of the METRIC_ACCURACY, I don't want to use accuracy as that has no use in my model but rather use my custom loss function. If I were to do the regular fit model it would look like this:
model.compile(optimizer=adam,loss=dl_tf_loss, metrics=[dl_tf_loss]) So I tried to change the suggested code into the following code but I get an error and am wondering how I should change it so that it suits my needs. Here is what I tried:
HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32])) HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd'])) #METRIC_LOSS = dl_tf_loss with tf.summary.create_file_writer('logs/hparam_tuning').as_default(): hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics= [hp.Metric(dl_tf_loss, display_name='Loss')]) It gives me the following error:
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-26-27d079c6be49> in <module>() 5 6 with tf.summary.create_file_writer('logs/hparam_tuning').as_default(): ----> 7 hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=[hp.Metric(dl_tf_loss, display_name='Loss')]) 8 3 frames /usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in hparams_config(hparams, metrics, time_created_secs) 127 hparams=hparams, 128 metrics=metrics, --> 129 time_created_secs=time_created_secs, 130 ) 131 return _write_summary("hparams_config", pb) /usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in hparams_config_pb(hparams, metrics, time_created_secs) 161 domain.update_hparam_info(info) 162 hparam_infos.append(info) --> 163 metric_infos = [metric.as_proto() for metric in metrics] 164 experiment = api_pb2.Experiment( 165 hparam_infos=hparam_infos, /usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in <listcomp>(.0) 161 domain.update_hparam_info(info) 162 hparam_infos.append(info) --> 163 metric_infos = [metric.as_proto() for metric in metrics] 164 experiment = api_pb2.Experiment( 165 hparam_infos=hparam_infos, /usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in as_proto(self) 532 name=api_pb2.MetricName( 533 group=self._group, --> 534 tag=self._tag, 535 ), 536 display_name=self._display_name, TypeError: <tensorflow.python.eager.def_function.Function object at 0x7f9f3a78e5c0> has type Function, but expected one of: bytes, unicode I also tried running the following code:
with tf.summary.create_file_writer('logs/hparam_tuning').as_default(): hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics= [dl_tf_loss]) but got the following error:
AttributeError Traceback (most recent call last) <ipython-input-28-6778bdf7f1b1> in <module>() 8 9 with tf.summary.create_file_writer('logs/hparam_tuning').as_default(): ---> 10 hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=[dl_tf_loss]) 2 frames /usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in <listcomp>(.0) 161 domain.update_hparam_info(info) 162 hparam_infos.append(info) --> 163 metric_infos = [metric.as_proto() for metric in metrics] 164 experiment = api_pb2.Experiment( 165 hparam_infos=hparam_infos, AttributeError: 'Function' object has no attribute 'as_proto' Would greatly appreciate any help. Thanks in advance!
