# coding: UTF-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import tensorflow as tf
import model as model
import numpy as np
from reader import Cifar10Reader
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('epoch', 30, "訓練するEpoch数")
tf.app.flags.DEFINE_string('data_dir', './data/', "訓練データのディレクトリ")
tf.app.flags.DEFINE_string('checkpoint_dir', './checkpoints/',
"チェックポイントを保存するディレクトリ")
tf.app.flags.DEFINE_string('test_data', None, "テストデータのパス")
def _loss(logits, label):
labels = tf.cast(label, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels,name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
return cross_entropy_mean
def _train(total_loss, global_step):
opt = tf.train.GradientDescentOptimizer(learning_rate=0.001)
grads = opt.compute_gradients(total_loss)
train_op = opt.apply_gradients(grads, global_step=global_step)
return train_op
filenames = [
os.path.join(
FLAGS.data_dir, 'data_batch_%d.bin' % i) for i in range(1, 6)
]
def main(argv=None):
global_step = tf.Variable(0,trainable=False)
train_placeholder = tf.placeholder(tf.float32,
shape=[32, 32, 3],
name='input_image')
label_placeholder = tf.placeholder(tf.int32,shape=[1],name='label')
# (width, height, depth) -> (batch, width, height, depth)
image_node = tf.expand_dims(train_placeholder, 0)
logits = model.inference(image_node)
total_loss = _loss(logits,label_placeholder)
train_op = _train(total_loss,global_step)
top_k_op = tf.nn.in_top_k(logits,label_placeholder,1)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
total_duration = 0
for epoch in range(1, FLAGS.epoch + 1):
start_time = time.time()
for file_index in range(5):
print('Epoch %d: %s' % (epoch, filenames[file_index]))
reader = Cifar10Reader(filenames[file_index])
for index in range(10000):
image = reader.read(index)
logits_value = sess.run([logits],
feed_dict={
train_placeholder: image.byte_array
})
_,loss_value = sess.run([train_op,total_loss],
feed_dict={
train_placeholder: image.byte_array,
label_placeholder: image.label
}
)
if index % 1000 == 0:
print('[%d]: %r' % (image.label, logits_value))
assert not np.isnan(loss_value), \
'Model diverged with loss = NaN'
reader.close()
duration = time.time() - start_time
total_duration += duration
prediction = _eval(sess,top_k_op,train_placeholder,label_placeholder)
print('epoch %d duration = %d sec' % (epoch, duration))
tf.train.SummaryWriter(FLAGS.checkpoint_dir, sess.graph)
print('Total duration = %d sec' % total_duration)
def _eval(sess,top_k_op,train_placeholder,label_placeholder):
if not FLAGS.test_data:
return np.nan
image_reader = Cifar10Reader(FLAGS.test_data)
true_count = 0
for index in range(10000):
image = image_reader.read(index)
predictions = sess.run([top_k_op],
feed_dict={
input_image: image.image,
label_placeholder:image.label
}
)
true_count += np.sum(predictions)
image_reader.close()
if __name__ == '__main__':
tf.app.run()
python inference.py --file ./cifar-10-batches-bin 2
と実行すると（./cifar-10-batches-bin 2は学習させたいファイルを置いてある場所）
./data/data_batch_1.bin is not exist
Traceback (most recent call last):
File "inference.py", line 120, in <module>
tf.app.run()
File "/Users/XXX/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "inference.py", line 71, in main
image = reader.read(index)
File "/Users/XXX/Desktop/cifar/reader.py", line 42, in read
self.bytestream.seek(record_bytes * index,0)
AttributeError: 'Cifar10Reader' object has no attribute 'bytestream'
とエラーが出ました。
AttributeErrorで'Cifar10Reader'が'bytestream'という属性を持っていないことはわかるのですが、どこを直せば良いのでしょうか？
Q&A
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退会済みユーザー
2017/04/23 00:54
2017/04/23 03:41
退会済みユーザー
2017/06/18 16:40