TensorFlow > sine curveの学習 > v0.5 > placeholderを使っているがlossは0.4が中心となる

動作環境

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.

v0.1 http://qiita.com/7of9/items/b364d897b95476a30754

sine curveを学習するコードをplaceholder使用に変更しているがlossが0.4を中心としている。

input.csv生成

original (placeholder不使用)

linreg2.py

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

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

# ファイル名の Queue を作成
filename_queue = tf.train.string_input_producer(["input.csv"])

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

batch_size=4 # [4]
inputs_batch, output_batch = tf.train.shuffle_batch([inputs, output], batch_size, capacity=40, min_after_dequeue=batch_size)

## NN のグラフ生成
hiddens = slim.stack(inputs_batch, slim.fully_connected, [1,7,7,7], 
  activation_fn=tf.nn.sigmoid, scope="hidden")
prediction = slim.fully_connected(hiddens, 1, activation_fn=tf.nn.sigmoid, scope="output")

loss = tf.contrib.losses.mean_squared_error(prediction, output_batch)
#train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.01))
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]
      _, t_loss = sess.run([train_op, loss])
      if (i+1) % 100 == 0:
        print("%d,%f" % (i+1, t_loss))
#        print("%d,%f,#step, loss" % (i+1, t_loss))
  finally:
    coord.request_stop()

  coord.join(threads)

placeholder使用

linreg2_feeddict.py

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

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

# ファイル名の Queue を作成
filename_queue = tf.train.string_input_producer(["input.csv"])

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

## NN のグラフ生成
hiddens = slim.stack(input_ph, slim.fully_connected, [1,7,7,7], 
  activation_fn=tf.nn.sigmoid, scope="hidden")
prediction = slim.fully_connected(hiddens, 1, activation_fn=tf.nn.sigmoid, scope="output")
loss = tf.contrib.losses.mean_squared_error(prediction, output_ph)

#train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.01))
train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.001))

def feed_dict(inputs, output):
    return {input_ph: inputs.eval(), output_ph: output.eval()}

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]
      _, t_loss = sess.run([train_op, loss], feed_dict=feed_dict(inputs_batch, output_batch))
      if (i+1) % 100 == 0:
        print("%d,%f" % (i+1, t_loss))
#        print("%d,%f,#step, loss" % (i+1, t_loss))
  finally:
    coord.request_stop()

  coord.join(threads)

結果

python linreg2.py > log.learn_original
python linreg2_feeddict.py > log.learn_placeholder

matplotlibコード on Jupyter

参考 http://qiita.com/ynakayama/items/8d3b1f7356da5bcbe9bc

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

#data = np.loadtxt('log.learn_non_qmc', delimiter=',')
data1 = np.loadtxt('log.learn_original', delimiter=',')
#data = np.loadtxt('log.learn_original.batch1', delimiter=',')
#data = np.loadtxt('log.learn_no_reshape', delimiter=',')
data2 = np.loadtxt('log.learn_placeholder', delimiter=',')

input1 = data1[:,0]
output1 = data1[:,1]
input2 = data2[:,0]
output2 = data2[:,1]

fig = plt.figure()
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)

#ax.plot(input1, output1, color='black', linestyle='dotted', label='rate=0.001')
ax1.plot(input1, output1, color='black', linestyle='solid', label='original')
ax2.plot(input2, output2, color='red', linestyle='solid', label='placeholder')
#ax.scatter(input1, output1)

ax1.set_title('loss')
ax1.set_xlabel('step')
ax1.set_ylabel('loss')
ax1.grid(True)
ax1.legend()

#ax2.set_title('loss')
ax2.set_xlabel('step')
ax2.set_ylabel('loss')
ax2.grid(True)
ax2.legend()

fig.show()

2つのコードのdiff

$ diff linreg2.py linreg2_feeddict.py 
20a21,23
> input_ph = tf.placeholder("float", [None,1])
> output_ph = tf.placeholder("float",[None,1])
> 
22c25
< hiddens = slim.stack(inputs_batch, slim.fully_connected, [1,7,7,7], 
---
> hiddens = slim.stack(input_ph, slim.fully_connected, [1,7,7,7], 
24a28
> loss = tf.contrib.losses.mean_squared_error(prediction, output_ph)
26d29
< loss = tf.contrib.losses.mean_squared_error(prediction, output_batch)
29a33,35
> def feed_dict(inputs, output):
>     return {input_ph: inputs.eval(), output_ph: output.eval()}
> 
39c45
<       _, t_loss = sess.run([train_op, loss])
---
>       _, t_loss = sess.run([train_op, loss], feed_dict=feed_dict(inputs_batch, output_batch))