Use tensorboard to log any variables over time.

tensorboard_logging.py This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters Show hidden characters import tensorflow as tf import os import time import logging from util.config import LOG_DIR TF_LOG_DIR = os.path.join(LOG_DIR, 'tensorboard') loggers = {} class TensorboardLogger: """ Logs variables visibly to the tensorboard interface. """ _is_in_setup_phase = True _writer = None _sess = None _iteration = 0 _tracked_vars = {} _graph = None _logs_path = None def __init__(self, logs_path): self._graph = tf.Graph() self._logs_path = logs_path + str(int(time.time())) self._sess = tf.Session(graph=self._graph) print("Start this tensorboard with \n\ttensorboard --logdir={}".format(self._logs_path)) # Functions for setting up which variables should be tracked def is_in_setup_phase(self): if not self._is_in_setup_phase: raise RuntimeError("Logger has been already written something. Can't use this action.") def name_not_used(self, name): if name in self._tracked_vars.keys(): raise RuntimeError("You tried to create the variable {}, which has already been assigned.".format(name)) def track_scalar(self, name): self.is_in_setup_phase() self.name_not_used(name) with self._graph.as_default(): self._tracked_vars[name] = {} self._tracked_vars[name]['node'] = tf.placeholder(tf.float32, shape=(), name=name) tf.summary.scalar(name, self._tracked_vars[name]['node']) self._tracked_vars[name]['value'] = None def track_histogram(self, name): self.is_in_setup_phase() self.name_not_used(name) with self._graph.as_default(): self._tracked_vars[name] = {} self._tracked_vars[name]['node'] = tf.placeholder(tf.float32, shape=None, name=name) tf.summary.histogram(name, self._tracked_vars[name]['node']) self._tracked_vars[name]['value'] = None # TODO mabe track metadata as well (run parameters) # Functions for updating and writing the summaries def start_writer(self): self._writer = tf.summary.FileWriter(self._logs_path, graph=self._graph,flush_secs=1) with self._graph.as_default(): self._sess.run(tf.global_variables_initializer()) self._is_in_setup_phase = False def update(self, name, value): self._tracked_vars[name]['value'] = value def write_summary(self): with self._graph.as_default(): summary_op = tf.summary.merge_all() summary = self._sess.run(summary_op, feed_dict=self._feed_dict()) print(summary) self._writer.add_summary(summary, self._iteration) self._iteration = 1 + self._iteration def close(self): self._sess.close() # Getters and Setters def _feed_dict(self): return {var['node']: var['value'] for var in self._tracked_vars.values()} ### Functions for retrieving loggers of a specific context def new_logger(name, logs_path=None): if logs_path is None: logs_path = os.path.join(TF_LOG_DIR, name) loggers[name] = TensorboardLogger(logs_path) return loggers[name] def get_logger(name): if name in loggers.keys(): return loggers[name] else: logger = logging.getLogger('MetaExp.TL') logger.debug("Creted new Logger Instance") return new_logger(name)