运行胶囊网络程序的报错
问题遇到的现象和发生背景
问题相关代码，请勿粘贴截图
运行结果及报错内容
我的解答思路和尝试过的方法
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Implementation of the flags interface."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse as _argparse
from tensorflow.python.platform import tf_logging as _logging
from tensorflow.python.util.all_util import remove_undocumented
_global_parser = _argparse.ArgumentParser()
# pylint: disable=invalid-name
class _FlagValues(object):
"""Global container and accessor for flags and their values."""
def __init__(self):
self.__dict__['__flags'] = {}
self.__dict__['__parsed'] = False
self.__dict__['__required_flags'] = set()
def _parse_flags(self, args=None):
result, unparsed = _global_parser.parse_known_args(args=args)
for flag_name, val in vars(result).items():
self.__dict__['__flags'][flag_name] = val
self.__dict__['__parsed'] = True
self._assert_all_required()
return unparsed
def __getattr__(self, name):
"""Retrieves the 'value' attribute of the flag --name."""
try:
parsed = self.__dict__['__parsed']
except KeyError:
# May happen during pickle.load or copy.copy
raise AttributeError(name)
if not parsed:
self._parse_flags()
if name not in self.__dict__['__flags']:
raise AttributeError(name)
return self.__dict__['__flags'][name]
def __setattr__(self, name, value):
"""Sets the 'value' attribute of the flag --name."""
if not self.__dict__['__parsed']:
self._parse_flags()
self.__dict__['__flags'][name] = value
self._assert_required(name)
def _add_required_flag(self, item):
self.__dict__['__required_flags'].add(item)
def _assert_required(self, flag_name):
if (flag_name not in self.__dict__['__flags'] or
self.__dict__['__flags'][flag_name] is None):
raise AttributeError('Flag --%s must be specified.' % flag_name)
def _assert_all_required(self):
for flag_name in self.__dict__['__required_flags']:
self._assert_required(flag_name)
def _define_helper(flag_name, default_value, docstring, flagtype):
"""Registers 'flag_name' with 'default_value' and 'docstring'."""
_global_parser.add_argument('--' + flag_name,
default=default_value,
help=docstring,
type=flagtype)
# Provides the global object that can be used to access flags.
FLAGS = _FlagValues()
def DEFINE_string(flag_name, default_value, docstring):
"""Defines a flag of type 'string'.
Args:
flag_name: The name of the flag as a string.
default_value: The default value the flag should take as a string.
docstring: A helpful message explaining the use of the flag.
"""
_define_helper(flag_name, default_value, docstring, str)
def DEFINE_integer(flag_name, default_value, docstring):
"""Defines a flag of type 'int'.
Args:
flag_name: The name of the flag as a string.
default_value: The default value the flag should take as an int.
docstring: A helpful message explaining the use of the flag.
"""
_define_helper(flag_name, default_value, docstring, int)
def DEFINE_boolean(flag_name, default_value, docstring):
"""Defines a flag of type 'boolean'.
Args:
flag_name: The name of the flag as a string.
default_value: The default value the flag should take as a boolean.
docstring: A helpful message explaining the use of the flag.
"""
# Register a custom function for 'bool' so --flag=True works.
def str2bool(v):
return v.lower() in ('true', 't', '1')
_global_parser.add_argument('--' + flag_name,
nargs='?',
const=True,
help=docstring,
default=default_value,
type=str2bool)
# Add negated version, stay consistent with argparse with regard to
# dashes in flag names.
_global_parser.add_argument('--no' + flag_name,
action='store_false',
dest=flag_name.replace('-', '_'))
# The internal google library defines the following alias, so we match
# the API for consistency.
DEFINE_bool = DEFINE_boolean # pylint: disable=invalid-name
def DEFINE_float(flag_name, default_value, docstring):
"""Defines a flag of type 'float'.
Args:
flag_name: The name of the flag as a string.
default_value: The default value the flag should take as a float.
docstring: A helpful message explaining the use of the flag.
"""
_define_helper(flag_name, default_value, docstring, float)
def mark_flag_as_required(flag_name):
"""Ensures that flag is not None during program execution.
It is recommended to call this method like this:
if __name__ == '__main__':
tf.flags.mark_flag_as_required('your_flag_name')
tf.app.run()
Args:
flag_name: string, name of the flag to mark as required.
Raises:
AttributeError: if flag_name is not registered as a valid flag name.
NOTE: The exception raised will change in the future.
"""
if _global_parser.get_default(flag_name) is not None:
_logging.warn(
'Flag %s has a non-None default value; therefore, '
'mark_flag_as_required will pass even if flag is not specified in the '
'command line!' % flag_name)
FLAGS._add_required_flag(flag_name)
def mark_flags_as_required(flag_names):
"""Ensures that flags are not None during program execution.
Recommended usage:
if __name__ == '__main__':
tf.flags.mark_flags_as_required(['flag1', 'flag2', 'flag3'])
tf.app.run()
Args:
flag_names: a list/tuple of flag names to mark as required.
Raises:
AttributeError: If any of flag name has not already been defined as a flag.
NOTE: The exception raised will change in the future.
"""
for flag_name in flag_names:
mark_flag_as_required(flag_name)
_allowed_symbols = [
# We rely on gflags documentation.
'DEFINE_bool',
'DEFINE_boolean',
'DEFINE_float',
'DEFINE_integer',
'DEFINE_string',
'FLAGS',
'mark_flag_as_required',
'mark_flags_as_required',
]
remove_undocumented(__name__, _allowed_symbols)
我想要达到的结果
import os
import sys
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from config import cfg
from utils import load_data
from capsNet import CapsNet
def save_to():
if not os.path.exists(cfg.results):
os.mkdir(cfg.results)
if cfg.is_training:
loss = cfg.results + '/loss.csv'
train_acc = cfg.results + '/train_acc.csv'
val_acc = cfg.results + '/val_acc.csv'
if os.path.exists(val_acc):
os.remove(val_acc)
if os.path.exists(loss):
os.remove(loss)
if os.path.exists(train_acc):
os.remove(train_acc)
fd_train_acc = open(train_acc, 'w')
fd_train_acc.write('step,train_acc\n')
fd_loss = open(loss, 'w')
fd_loss.write('step,loss\n')
fd_val_acc = open(val_acc, 'w')
fd_val_acc.write('step,val_acc\n')
return(fd_train_acc, fd_loss, fd_val_acc)
else:
test_acc = cfg.results + '/test_acc.csv'
if os.path.exists(test_acc):
os.remove(test_acc)
fd_test_acc = open(test_acc, 'w')
fd_test_acc.write('test_acc\n')
return(fd_test_acc)
def train(model, supervisor, num_label):
trX, trY, num_tr_batch, valX, valY, num_val_batch = load_data(cfg.dataset, cfg.batch_size, is_training=True)
Y = valY[:num_val_batch * cfg.batch_size].reshape((-1, 1))
fd_train_acc, fd_loss, fd_val_acc = save_to()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with supervisor.managed_session(config=config) as sess:
print("\nNote: all of results will be saved to directory: " + cfg.results)
for epoch in range(cfg.epoch):
print("Training for epoch %d/%d:" % (epoch, cfg.epoch))
if supervisor.should_stop():
print('supervisor stoped!')
break
for step in tqdm(range(num_tr_batch), total=num_tr_batch, ncols=70, leave=False, unit='b'):
start = step * cfg.batch_size
end = start + cfg.batch_size
global_step = epoch * num_tr_batch + step
if global_step % cfg.train_sum_freq == 0:
_, loss, train_acc, summary_str = sess.run([model.train_op, model.total_loss, model.accuracy, model.train_summary])
assert not np.isnan(loss), 'Something wrong! loss is nan...'
supervisor.summary_writer.add_summary(summary_str, global_step)
fd_loss.write(str(global_step) + ',' + str(loss) + "\n")
fd_loss.flush()
fd_train_acc.write(str(global_step) + ',' + str(train_acc / cfg.batch_size) + "\n")
fd_train_acc.flush()
else:
sess.run(model.train_op)
if cfg.val_sum_freq != 0 and (global_step) % cfg.val_sum_freq == 0:
val_acc = 0
for i in range(num_val_batch):
start = i * cfg.batch_size
end = start + cfg.batch_size
acc = sess.run(model.accuracy, {model.X: valX[start:end], model.labels: valY[start:end]})
val_acc += acc
val_acc = val_acc / (cfg.batch_size * num_val_batch)
fd_val_acc.write(str(global_step) + ',' + str(val_acc) + '\n')
fd_val_acc.flush()
if (epoch + 1) % cfg.save_freq == 0:
supervisor.saver.save(sess, cfg.logdir + '/model_epoch_%04d_step_%02d' % (epoch, global_step))
fd_val_acc.close()
fd_train_acc.close()
fd_loss.close()
def evaluation(model, supervisor, num_label):
teX, teY, num_te_batch = load_data(cfg.dataset, cfg.batch_size, is_training=False)
fd_test_acc = save_to()
with supervisor.managed_session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
supervisor.saver.restore(sess, tf.train.latest_checkpoint(cfg.logdir))
tf.logging.info('Model restored!')
test_acc = 0
for i in tqdm(range(num_te_batch), total=num_te_batch, ncols=70, leave=False, unit='b'):
start = i * cfg.batch_size
end = start + cfg.batch_size
acc = sess.run(model.accuracy, {model.X: teX[start:end], model.labels: teY[start:end]})
test_acc += acc
test_acc = test_acc / (cfg.batch_size * num_te_batch)
fd_test_acc.write(str(test_acc))
fd_test_acc.close()
print('Test accuracy has been saved to ' + cfg.results + '/test_acc.csv')
def main(_):
tf.logging.info(' Loading Graph...')
num_label = 10
model = CapsNet()
tf.logging.info(' Graph loaded')
sv = tf.train.Supervisor(graph=model.graph, logdir=cfg.logdir, save_model_secs=0)
if cfg.is_training:
tf.logging.info(' Start training...')
train(model, sv, num_label)
tf.logging.info('Training done')
else:
evaluation(model, sv, num_label)
if __name__ == "__main__":
tf.app.run()
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