From 0ed39172561f6b774e40579834acb27f985245c3 Mon Sep 17 00:00:00 2001
From: nbwzlyd <420907013@qq.com>
Date: Sun, 11 Sep 2022 09:58:25 +0000
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20'tenSorboard1.py'?=
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---
 tenSorboard1.py | 37 -------------------------------------
 1 file changed, 37 deletions(-)
 delete mode 100644 tenSorboard1.py

diff --git a/tenSorboard1.py b/tenSorboard1.py
deleted file mode 100644
index adc5158..0000000
--- a/tenSorboard1.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# The following switch allows the program runs locally and in the Agit environment without modifications.
-import os
-
-path = os.path.dirname(__file__)
-print(path)
-
-if 'CLOUD_PROVIDER' in os.environ and os.environ['CLOUD_PROVIDER'] == 'Agit':
-    logdir = '/root/.agit'   
-else:
-    logdir = './runs'	
-# setup tensorboard path
-import tensorflow as tf
-writer = tf.summary.create_file_writer(logdir)
-
-''' alternative tensorboards
-
-# pytorch tensorboard :
- from torch.utils.tensorboard import SummaryWriter
- writer = SummaryWriter(log_dir=logdir)
-
-# tensorboardX :
-from tensorboardX import SummaryWriter
-writer = SummaryWriter(logdir=logdir)
-
-'''
-
-import numpy as np
-import time
-
-# a 5 minutes running example, the realtime tensorboard can be viewed in the training page
-with writer.as_default():
-    for n_iter in range(360):
-        tf.summary.scalar('Loss/train', np.random.random(), n_iter)
-        tf.summary.scalar('Loss/test', np.random.random(), n_iter)
-        tf.summary.scalar('Accuracy/train', np.random.random(), n_iter)
-        tf.summary.scalar('Accuracy/test', np.random.random(), n_iter)
-        time.sleep(1)
\ No newline at end of file