J'ai essayé Python> autopep8

Environnement d'exploitation

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

Connexes http://qiita.com/7of9/items/872d80d2a1cc36b5a053

J'ai essayé autopep8 enseigné par [Comment] de @ shiracamus (http://qiita.com/7of9/items/872d80d2a1cc36b5a053/#comment-f30eaf90a9471da4455f).

Installation

$sudo apt-get install python-autopep8

Code de prétraitement

Code TensorFlow en cours

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

'''
v0.1 Feb. 06, 2017
    - read [test_in.csv],[test_out.csv]
'''

'''
codingrule:PEP8
'''

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])
outputs = tf.pack([output1])

batch_size = 1
inputs_batch, output_batch = tf.train.shuffle_batch([inputs, outputs], batch_size, capacity=1, min_after_dequeue=batch_size)

input_ph = tf.placeholder("float", [None, 1])
output_ph = tf.placeholder("float", [None, 1])

# network
hiddens = slim.stack(input_ph, slim.fully_connected, [7,7,7],
    activation_fn=tf.nn.sigmoid, scope="hidden")
prediction = slim.fully_connected(hiddens, 1, 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(10):
            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))
    finally:
        coord.request_stop()

En traitement

Comme le résultat du traitement est sorti en standard, il a été enregistré avec un nom de fichier approprié.

$autopep8 learn_in100out100.py > res.py

Comparaison

$ diff learn_in100out100.py res.py 
38c38,39
< inputs_batch, output_batch = tf.train.shuffle_batch([inputs, outputs], batch_size, capacity=1, min_after_dequeue=batch_size)
---
> inputs_batch, output_batch = tf.train.shuffle_batch(
>     [inputs, outputs], batch_size, capacity=1, min_after_dequeue=batch_size)
44,46c45,48
< hiddens = slim.stack(input_ph, slim.fully_connected, [7,7,7],
<     activation_fn=tf.nn.sigmoid, scope="hidden")
< prediction = slim.fully_connected(hiddens, 1, activation_fn=None, scope="output")
---
> hiddens = slim.stack(input_ph, slim.fully_connected, [7, 7, 7],
>                      activation_fn=tf.nn.sigmoid, scope="hidden")
> prediction = slim.fully_connected(
>     hiddens, 1, activation_fn=None, scope="output")
61c63,64
<             _, t_loss = sess.run([train_op, loss], feed_dict={input_ph:inpbt, output_ph:outbt})
---
>             _, t_loss = sess.run(
>                 [train_op, loss], feed_dict={input_ph: inpbt, output_ph: outbt})
63,64c66,67
<             if (idx+1) % 100 == 0:
<             	print("%d,%f" % (idx+1, t_loss))
---
>             if (idx + 1) % 100 == 0:
>                 print("%d,%f" % (idx + 1, t_loss))
67d69
< 

Si l'argument est une liste, la règle d'ajustement à la position ( est-elle exclue? (La partie de tf.train.shuffle_batch () et _, t_loss = sess.run ())

Pep8 avant et après le traitement

$ pep8 learn_in100out100.py 
learn_in100out100.py:38:80: E501 line too long (124 > 79 characters)
learn_in100out100.py:44:56: E231 missing whitespace after ','
learn_in100out100.py:44:58: E231 missing whitespace after ','
learn_in100out100.py:45:5: E128 continuation line under-indented for visual indent
learn_in100out100.py:46:80: E501 line too long (81 > 79 characters)
learn_in100out100.py:61:71: E231 missing whitespace after ':'
learn_in100out100.py:61:80: E501 line too long (95 > 79 characters)
learn_in100out100.py:61:88: E231 missing whitespace after ':'
learn_in100out100.py:64:13: E101 indentation contains mixed spaces and tabs
learn_in100out100.py:64:13: W191 indentation contains tabs
learn_in100out100.py:67:1: W391 blank line at end of file

$ pep8 res.py 
res.py:64:80: E501 line too long (80 > 79 characters)

Un seul reste en dessous.

            _, t_loss = sess.run(
                [train_op, loss], feed_dict={input_ph: inpbt, output_ph: outbt})

Vous pouvez améliorer votre codage en vous référant à la pièce traitée par autopep8.

Si vous le transmettez via autopep8, il ne sera pas bon de l'utiliser comme OK pour le moment.