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TensorFlow v1.1 / 移行 > tf.pack()はtf.stack()になった

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動作環境

GeForce GTX 1070 (8GB)

ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 16.04 LTS desktop amd64
TensorFlow v1.1.0 (以下TF)
cuDNN v5.1 for Linux
CUDA v8.0
Python 3.5.2

TensorFlow / ADDA > 線形方程式の初期値用データの学習 > 学習コード:v0.3 / 学習結果

Ubuntu 14.04 + TensorFlow v0.8の環境からUbuntu 16.04 + TensorFlow v1.1.0に移行した。

TF v0.8用のコードを実行しようとすると以下のようになる。

$ python3 learnExr_170422.py 

Traceback (most recent call last):
File "learnExr_170422.py", line 53, in <module>
inputs = tf.pack([xpos, ypos, zpos])
AttributeError: module 'tensorflow' has no attribute 'pack'

https://github.com/tensorflow/tensorflow/issues/7550


As far as I know, tf.pack has been renamed as tf.stack.


tf.pack()からtf.stack()に変わったとのこと。

tf.stack()に変更すると動作した。


learnExr_170504.py

#!/usr/bin/env python

# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np

'''
v0.5 MAr. 04, 2017
- use [tf.stack] instead of [tf.pack]
=== on Ubuntu 16.04 / CUDA8 / cuDNN5.1 / Python 3 ===
v0.4 Mar. 03, 2017
- learn [Exr, Exi, Eyr, Eyi, Ezr, Ezi]
v0.3 Mar. 03, 2017
- learn [Exr] and [Exi]
- add [Eyr, Eri, Ezr, Ezi] for decode_csv()
v0.2 Apr. 29, 2017
- save to [model_variables_170429.npy]
- learn [Exr] only, instead of [Exr, Exi]
v0.1 Apr. 23, 2017
- change [NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN] from [100] to [9328]
- change input layer's node from [2] to [3]
- [input.csv] has 9 columns
=== branched from [learn_xxyyfunc_170321.py] to [learnExr_170422.py] ===
v0.5 Apr. 01, 2017
- change network from [7,7,7] to [100, 100, 100]
v0.4 Mar. 31, 2017
- calculate [capacity] from [min_queue_examples] and [batch_size]
v0.3 Mar. 24, 2017
- change [capacity] from 100 to 40
v0.2 Mar. 24, 2017
- change [capacity] from 40 to 100
- output [model_variables] after training
v0.1 Mar. 22, 2017
- learn mapping of R^2 input to R^2 output
+ using data prepared by [prep_data_170321.py]
- branched from sine curve learning at
http://qiita.com/7of9/items/ce58e66b040a0795b2ae
'''

# codingrule:PEP8

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

# prase CSV
reader = tf.TextLineReader()
key, value = reader.read(filename_queue)
def_rec = [[0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.]]
wrk = tf.decode_csv(value, record_defaults=def_rec)
xpos, ypos, zpos, Exr, Exi, Eyr, Eyi, Ezr, Ezi = wrk
inputs = tf.stack([xpos, ypos, zpos])
output = tf.stack([Exr, Exi, Eyr, Eyi, Ezr, Ezi])

batch_size = 4 # [4]
# Ref: cifar10_input.py
min_fraction_of_examples_in_queue = 0.2 # 0.4
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 9328
min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
min_fraction_of_examples_in_queue)
#
inputs_batch, output_batch = tf.train.shuffle_batch(
[inputs, output], batch_size, capacity=min_queue_examples + 3 * batch_size,
min_after_dequeue=batch_size)

input_ph = tf.placeholder("float", [None, 3])
output_ph = tf.placeholder("float", [None, 6])

## network
hiddens = slim.stack(input_ph, slim.fully_connected, [100, 100, 100],
activation_fn=tf.nn.sigmoid, scope="hidden")
prediction = slim.fully_connected(
hiddens, 6, 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(90000): # 30000
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))
sys.stdout.flush()

finally:
coord.request_stop()

# output the model
model_variables = slim.get_model_variables()
res = sess.run(model_variables)
np.save('model_variables_170429.npy', res)

coord.join(threads)