How to implement TensorBoard v2 (tf.contrib.summary) with while_loop? - python-3.x

Question

I want to log the loss and accuracy value evaluated inside a nested body function of a while_loop during training. This is the structure: I have a class, a method of this class builds the graph using a while_loop (build_graph()). Another method calls build_graph() and then runs the session. It works. Or it seems to work. However, I would like to use TensorBoard to check if loss and accuracy are actually improving, but I'm not able to summary those tensors. I've tried to define a tf.contrib.summary.create_file_writer('summary') and a graph and pass them to build_graph() as parameters, so that the body function can see them. I have checked the list during graph execution coming from tf.contrib.summary.all_summary_ops() and it isn't empty. However, when I open TensorBoard I get "No dashboards are active for the current data set.". Neither the graph. I am aware that tf.summary does not work in while_loop but it seems that tf.contrib.summary works.
Here is a working example
import tensorflow as tf
import sys
import datamanagement
class myNet:
def __init__(self):
self.varlist = ["x", "y"]
self.data = []
self.hsize = [10, 10]
self.batch_size = 10
self.tr_mainsteps = 1000
self.learnrate = 0.001
self.sourcedatafile = "XYfit.csv" # source file
# Dataset parameters
self.seq_params = {'dim': len(self.varlist),
'batch_size': self.batch_size,
'shuffle': True,
'filepath': self.sourcedatafile}
# Dataset from CSV file
self.dataset = datamanagement.CSVDataSet(**self.seq_params).finaldataset
# Iterator on the CSV file
self.dataiterator = self.dataset.make_initializable_iterator()
# Optimizer
self.optim = tf.train.RMSPropOptimizer(learning_rate=self.learnrate)
# Official creation of the graph
self.graph = tf.get_default_graph()
with self.graph.as_default():
# Writer creation
self.writer = tf.contrib.summary.create_file_writer('./summary')
with self.writer.as_default():
tf.contrib.summary.always_record_summaries()
def mymodel(self, Zinp, reuse=False):
# This function builds the graph of the network
with tf.variable_scope("mymod/net", reuse=reuse):
h1 = tf.layers.dense(Zinp, self.hsize[0], activation=tf.nn.leaky_relu, name='h1')
h2 = tf.layers.dense(h1, self.hsize[1], activation=tf.nn.leaky_relu, name='h2')
out = tf.layers.dense(h2, len(self.varlist), activation=None, name='final') # None means linear activation
return out
def _trainepoch(self, ind):
with self.writer.as_default():
# Real data tensor from CSV file
self.realdata = self.dataiterator.get_next()
# random input vector
self.Znoise = tf.random_uniform([self.batch_size, len(self.varlist)], minval=-1., maxval=1.)
# Model and output tensor
self.output = self.mymodel(self.Znoise, reuse=tf.AUTO_REUSE)
# Loss
self.loss = tf.losses.mean_squared_error(self.realdata, self.output)
tf.contrib.summary.scalar("loss", self.loss)
#Trainable variables
t_vars = tf.trainable_variables()
# Evaluation of the weight gradients
grad = self.optim.compute_gradients(self.loss, var_list=t_vars)
# Update weights based on gradients
return self.optim.apply_gradients(grad), tf.contrib.summary.all_summary_ops()
def _train_buildgraph(self):
def body(ind, ops):
train_up, ops = self._trainepoch(ind)
# Ensure that the update is applied before continuing.
with tf.control_dependencies([train_up]):
ind = ind + 1
return ind, ops
def cond(ind, ops):
return ind < self.tr_mainsteps
return tf.while_loop(cond, body, [tf.constant(0), [tf.Variable(False)]])
def config_run(self, trepoch=50, testNet=False):
self.tr_mainsteps = trepoch # Number of adversarial training epoch
with self.graph.as_default():
with self.writer.as_default():
tr_loop, summary_ops = self._train_buildgraph()
# Graph execution
with self.graph.as_default():
with self.writer.as_default():
with tf.Session() as sess:
sess.run(tf.initializers.global_variables())
sess.run(self.dataiterator.initializer)
tf.contrib.summary.initialize(
graph=tf.get_default_graph()
)
sess.run([summary_ops, tr_loop, summary_ops])
def main(argv):
hmodel = myNet()
hmodel.config_run()
if __name__ == "__main__":
main(sys.argv[1:])
Can someone help me?