2017-02-19 Python > Link > 出力ファイルへのリダイレクションをすぐに行う > sys.stdout.flush()

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

#!/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