Python3 + TensorFlow v1.1対応 > sine curveの学習

動作環境

GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 16.04 LTS desktop amd64
TensorFlow v1.1.0
cuDNN v5.1 for Linux
CUDA v8.0
Python 3.5.2
IPython 6.0.0 -- An enhanced Interactive Python.
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
GNU bash, version 4.3.48(1)-release (x86_64-pc-linux-gnu)

TensorFlow > sine curveの学習 > TensorFlowコードでpredictionをグラフ化してみた > sine curveになっていなかった > sine curveになった ( 誤差:0.01以下)
はUbuntu 14.04 LTS + TensorFlow v0.11 + Python2で実行していた。

Ubuntu 16.04 LTS + TensorFlow v1.1 + Python 3.5.2用にコードを変更した。

prep_data.py

import numpy as np
import random

"""
v0.2, Jul. 08, 2017
   - modify for Python3
"""

# codingrule: PEP8

numdata = 100
x_data = np.random.rand(numdata)
y_data = np.sin(2*np.pi*x_data) + 0.3 * np.random.rand()

for xs, ys in zip(x_data, y_data):
    print('%.5f, %.5f' % (xs, ys))

以下はPEP8対応していない。TFReocrd版は対応予定。

sigmoid_onlyHidden.py

# !/usr/bin/env python
# -*- coding: utf-8 -*-

import sys
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np

"""
v1.1 Jul. 8, 2017
   - modify for TensorFlow v1.1 (pre. v0.11)
   - modify for Python 3
"""

filename_queue = tf.train.string_input_producer(["input.csv"])

# parse CSV
reader = tf.TextLineReader()
key, value = reader.read(filename_queue)
input1, output = tf.decode_csv(value, record_defaults=[[0.], [0.]])
inputs = tf.stack([input1])
output = tf.stack([output])

batch_size=4 # [4]
inputs_batch, output_batch = tf.train.shuffle_batch([inputs, output], batch_size, capacity=40, 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=tf.nn.sigmoid, scope="output")
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 i in range(30000): #[10000]
      inpbt, outbt = sess.run([inputs_batch, output_batch])
      _, t_loss = sess.run([train_op, loss], feed_dict={input_ph:inpbt, output_ph: outbt})

      if (i+1) % 100 == 0:
        print("%d,%f" % (i+1, t_loss))

    # output to npy 
    model_variables = slim.get_model_variables()
    res = sess.run(model_variables)
    np.save('model_variables.npy', res)

  finally:
    coord.request_stop()


# output trained curve
  print('output') # used to separate from above lines (grep -A 200 output [outfile])
  for loop in range(10):
    inpbt, outbt = sess.run([inputs_batch, output_batch])
    pred = sess.run([prediction], feed_dict={input_ph:inpbt, output_ph: outbt})
    for din,dout in zip(inpbt, pred[0]):
      print('%.5f,%.5f' % (din,dout))

  coord.join(threads)

正常動作しているようだ。

これを元にTFRecords対応版を作り、TFRecordsの使い方を学ぶ。