2017-02-19 Python> Link> Redirect to output file immediately> sys.stdout.flush ()
Operating environment
GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 14.04 LTS desktop amd64
TensorFlow v0.11
cuDNN v5.1 for Linux
CUDA v8.0
Python 2.7.6
IPython 5.1.0 -- An enhanced Interactive Python.
gcc (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4
The error output of the learning process of TensorFlow is performed by print ().
I'm running TensorFlow code with redirection (> res), but the size of the destination file (res) didn't increase immediately. It was an action that greatly increased at some timing.
I wondered if I should flush and found the following.
http://7rpn.hatenablog.com/entry/2015/11/29/131601
sys.stdout.flush()
By adding sys.stdout.flush () to the 92nd line as shown below, the error output of the learning process can be output in a timely manner.
Thank you for the information.
learn_in100out100.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
'''
v0.6 Feb. 19, 2017
- add sys.stdout.flush() to immediately print
v0.5 Feb. 18, 2017
- add fc_drop()
v0.4 Feb. 15, 2017
- fix bug > ZeroDivisionError: float division by zero @ shuffle_batch()
v0.3 Feb. 15, 2017
- tweak [batch_size] for shuffle_batch()
v0.2 Feb. 15, 2017
- fix bug > different dimensions for placeholder and network
v0.1 Feb. 06, 2017
- read [test_in.csv],[test_out.csv]
'''
'''
codingrule:PEP8
'''
def fc_drop(inputs, *args, **kwargs):
# Thanks to: http://qiita.com/shngt/items/f532601b4f059ce8584f
net = slim.fully_connected(inputs, *args, **kwargs)
return slim.dropout(net, 0.5)
filename_inp = tf.train.string_input_producer(["test_in.csv"])
filename_out = tf.train.string_input_producer(["test_out.csv"])
NUM_INP_NODE = 100
NUM_OUT_NODE = 100
# parse csv
# a. input node
reader = tf.TextLineReader()
key, value = reader.read(filename_inp)
deflist = [[0.] for idx in range(NUM_INP_NODE)]
input1 = tf.decode_csv(value, record_defaults=deflist)
# b. output node
key, value = reader.read(filename_out)
deflist = [[0.] for idx in range(NUM_OUT_NODE)]
output1 = tf.decode_csv(value, record_defaults=deflist)
# c. pack
# inputs = tf.pack([input1])
inputs = input1
# outputs = tf.pack([output1])
outputs = output1
batch_size = 2
inputs_batch, output_batch = tf.train.shuffle_batch(
[inputs, outputs], batch_size, capacity=10, min_after_dequeue=batch_size)
input_ph = tf.placeholder("float", [None, 100])
output_ph = tf.placeholder("float", [None, 100])
# network
hiddens = slim.stack(input_ph, slim.fully_connected, [7, 7, 7],
activation_fn=tf.nn.sigmoid, scope="hidden")
# a. without dropout
# prediction = slim.fully_connected(
# hiddens, 100, activation_fn=None, scope="output")
# b. with dropout
drpout = slim.stack(hiddens, fc_drop, [100, 100], scope='fc')
prediction = slim.fully_connected(
drpout, 100, activation_fn=None, scope="output")
loss = tf.contrib.losses.mean_squared_error(prediction, output_ph)
train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.001))
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
sess.run(init_op)
# for idx in range(10000):
for idx in range(100000):
inpbt, outbt = sess.run([inputs_batch, output_batch])
_, t_loss = sess.run(
[train_op, loss],
feed_dict={input_ph: inpbt, output_ph: outbt})
if (idx+1) % 100 == 0:
print("%d,%f" % (idx+1, t_loss))
sys.stdout.flush()
finally:
coord.request_stop()
coord.join(threads)
$ python learn_in100out100.py > res.learn.N\=1000_dropout@output_longrun_170218
res.learn.N \ = 1000_dropout @ output_longrun_170218 has been updated accordingly, making it easier to check the error graph in Jupyter.
Below is the result during learning. It soon became clear that the result was completely useless. .. ..
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