tf_summary_reader.py

import os
import sys
import pickle
import numpy as np
import tensorflow as tf
from tensorflow.python.summary.event_accumulator import EventAccumulator


event_file = "/home/trunia1/dev/python/LSTMCounting/output/screens/" \
             "summaries/004_rmsprop_0.0005_lstm_512x2_grad_10_batch_128_dropkp_0.75/" \
             "events.out.tfevents.1468336776.DTA16004"


size_guidance = {
    'compressedHistograms': 1,
    'images': 1,
    'audio': 1,
    'scalars': 10000,
    'histograms': 1,
}


event_acc = EventAccumulator(event_file, size_guidance=size_guidance)
event_acc.Reload()

tags = event_acc.Tags()
print(tags)

tags_to_save = [
    "loss/total",
    "loss/classification",
    "loss/attention_explore",
    "loss/attention_smooth",
    "loss/weight_decay",

    "train/abs_error",
    "train/examples_per_second",
    "train/learning_rate",
    "train/mse_error",
    "train/predictions_non_zero",

    "validation/loss",
    "validation/mse_error"
]


output_dir = os.path.join(os.path.dirname(event_file), "scalar_events")
if not os.path.exists(output_dir):
    os.makedirs(output_dir)


for tag in tags_to_save:

    if tag not in tags['scalars']:
        print("Skipping tag '%s' because no events founds in file." % tag)
        continue

    print("Exporting events for tag '%s'..." % tag)

    # ScalarEvent(wall_time=double, step=1L, value=double)
    events = event_acc.Scalars(tag)
    data = np.zeros([len(events), 2], dtype=np.float32)

    for step in range(len(events)):
        data[step, 0] = float(events[step].step)
        data[step, 1] = float(events[step].value)
        #print(data[step,])

    output_file = os.path.join(output_dir, tag.replace('/', '_') + ".pickle")
    pickle.dump(data, open(output_file, 'w'))

print("Done.")