Python 基于tf.estimator.estimator框架的迁移学习
我正在尝试使用我自己的数据集和类,对imagenet上预训练的Inception resnet v2模型进行迁移学习。 我原来的代码库是对Python 基于tf.estimator.estimator框架的迁移学习,python,tensorflow,tensorflow-estimator,Python,Tensorflow,Tensorflow Estimator,我正在尝试使用我自己的数据集和类,对imagenet上预训练的Inception resnet v2模型进行迁移学习。 我原来的代码库是对tf.slim示例的修改,我再也找不到了,现在我正试图使用tf.estimator.*框架重写相同的代码 但是,我遇到的问题是,只从预先训练的检查点加载一些权重,用默认的初始值设定项初始化其余的层 通过研究这个问题,我发现了和,都提到需要在我的模型中使用tf.train.init\u from\u checkpoint。我试过了,但考虑到这两方面都缺乏实例,我
tf.slim
示例的修改,我再也找不到了,现在我正试图使用tf.estimator.*
框架重写相同的代码
但是,我遇到的问题是,只从预先训练的检查点加载一些权重,用默认的初始值设定项初始化其余的层
通过研究这个问题,我发现了和,都提到需要在我的模型中使用tf.train.init\u from\u checkpoint
。我试过了,但考虑到这两方面都缺乏实例,我想我弄错了
这是我最简单的例子:
import sys
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
import numpy as np
import inception_resnet_v2
NUM_CLASSES = 900
IMAGE_SIZE = 299
def input_fn(mode, num_classes, batch_size=1):
# some code that loads images, reshapes them to 299x299x3 and batches them
return tf.constant(np.zeros([batch_size, 299, 299, 3], np.float32)), tf.one_hot(tf.constant(np.zeros([batch_size], np.int32)), NUM_CLASSES)
def model_fn(images, labels, num_classes, mode):
with tf.contrib.slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits, end_points = inception_resnet_v2.inception_resnet_v2(images,
num_classes,
is_training=(mode==tf.estimator.ModeKeys.TRAIN))
predictions = {
'classes': tf.argmax(input=logits, axis=1),
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
exclude = ['InceptionResnetV2/Logits', 'InceptionResnetV2/AuxLogits']
variables_to_restore = tf.contrib.slim.get_variables_to_restore(exclude=exclude)
scopes = { os.path.dirname(v.name) for v in variables_to_restore }
tf.train.init_from_checkpoint('inception_resnet_v2_2016_08_30.ckpt',
{s+'/':s+'/' for s in scopes})
tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
total_loss = tf.losses.get_total_loss() #obtain the regularization losses as well
# Configure the training op
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer(learning_rate=0.00002)
train_op = optimizer.minimize(total_loss, global_step)
else:
train_op = None
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=total_loss,
train_op=train_op)
def main(unused_argv):
# Create the Estimator
classifier = tf.estimator.Estimator(
model_fn=lambda features, labels, mode: model_fn(features, labels, NUM_CLASSES, mode),
model_dir='model/MCVE')
# Train the model
classifier.train(
input_fn=lambda: input_fn(tf.estimator.ModeKeys.TRAIN, NUM_CLASSES, batch_size=1),
steps=1000)
# Evaluate the model and print results
eval_results = classifier.evaluate(
input_fn=lambda: input_fn(tf.estimator.ModeKeys.EVAL, NUM_CLASSES, batch_size=1))
print()
print('Evaluation results:\n %s' % eval_results)
if __name__ == '__main__':
tf.app.run(main=main, argv=[sys.argv[0]])
其中inception\u resnet\u v2
为
如果我运行这个脚本,我会从检查点获得一堆信息日志,但是在会话创建时,它似乎试图从检查点加载Logits
权重,并且由于形状不兼容而失败。这是完整的回溯:
Traceback (most recent call last):
File "<ipython-input-6-06fadd69ae8f>", line 1, in <module>
runfile('C:/Users/1/Desktop/transfer_learning_tutorial-master/MCVE.py', wdir='C:/Users/1/Desktop/transfer_learning_tutorial-master')
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile
execfile(filename, namespace)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/1/Desktop/transfer_learning_tutorial-master/MCVE.py", line 77, in <module>
tf.app.run(main=main, argv=[sys.argv[0]])
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "C:/Users/1/Desktop/transfer_learning_tutorial-master/MCVE.py", line 68, in main
steps=1000)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 302, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 780, in _train_model
log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 368, in MonitoredTrainingSession
stop_grace_period_secs=stop_grace_period_secs)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 673, in __init__
stop_grace_period_secs=stop_grace_period_secs)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 493, in __init__
self._sess = _RecoverableSession(self._coordinated_creator)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 851, in __init__
_WrappedSession.__init__(self, self._create_session())
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 856, in _create_session
return self._sess_creator.create_session()
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 554, in create_session
self.tf_sess = self._session_creator.create_session()
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 428, in create_session
init_fn=self._scaffold.init_fn)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\session_manager.py", line 279, in prepare_session
sess.run(init_op, feed_dict=init_feed_dict)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 889, in run
run_metadata_ptr)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1317, in _do_run
options, run_metadata)
File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [900] rhs shape= [1001] [[Node: Assign_1145 = Assign[T=DT_FLOAT,
_class=["loc:@InceptionResnetV2/Logits/Logits/biases"], use_locking=true, validate_shape=true,
_device="/job:localhost/replica:0/task:0/device:CPU:0"](InceptionResnetV2/Logits/Logits/biases, checkpoint_initializer_1145)]]
使用{v.name:v}
相反,如果我尝试使用name:variable
映射,则会出现以下错误:
ValueError: Tensor InceptionResnetV2/Conv2d_2a_3x3/weights:0 is not found in
inception_resnet_v2_2016_08_30.ckpt checkpoint
{'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean': [256],
'InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/beta': [32], ...
错误继续列出我认为检查点中的所有变量名(或者可能是作用域?)
更新(2)
在检查了上面的最新错误后,我看到检查点变量列表中有InceptionResnetV2/Conv2d\u 2a\u 3x3/weights
问题是,:0
最后强>
现在,我将验证这是否确实解决了问题,如果是这样,我将发布一个答案。多亏@KathyWu的评论,我找到了正确的方向并找到了问题
事实上,我计算作用域的方法将包括InceptionResnetV2/
作用域,这将在“作用域”(即网络中的所有变量)下触发所有变量的加载。然而,用正确的字典来替换它并不是件小事
在可能的作用域模式中,我必须使用的是'scope\u variable\u name':variable
one,但不使用实际的variable.name
属性
variable.name
看起来像:'some\u scope/variable\u name:0'
检查点变量的名称中没有:0
,因此使用作用域={v.name:v.name for v in variables\u to\u restore}
将引发“variable not found”错误
使其工作的技巧是从名称中去掉张量索引:
tf.train.init_from_checkpoint('inception_resnet_v2_2016_08_30.ckpt',
{v.name.split(':')[0]: v for v in variables_to_restore})
我发现{s+'/':s+'/'for s in scopes}
不起作用,只是因为要还原的变量包含类似的“全局步骤”
,所以作用域包括可以包含所有内容的全局作用域。您需要打印要还原的变量
,找到“全局步骤”
东西,并将其放入“排除”
估计器目录模型/MCVE
中是否有任何检查点?否,目录为空将行范围映射为变量
正在将InceptionResnetV2
添加到作用域列表中,因此将加载InceptionResnetV2/
下的所有变量。不必构建作用域列表,您可以尝试直接列出变量:tf.train.init\u from\u checkpoint('inception\u resnet\u v2\u 2016\u 08\u 30.ckpt',{v.name:v.name for v in variables})
这是可能的,是的。但是,如果我尝试使用您建议的代码,则会出现以下错误:ValueError:仅作用域名称为InceptionResnetV2/Conv2d\u 2a\u 3x3的赋值映射应映射到仅作用域的InceptionResnetV2/Conv2d\u 2a\u 3x3/权重:0。应为“scope/”:“other_scope/”。
。变量名称必须以不同的方式使用,如果您完全从“代码> SLIM
TF.Tr.Irr.Frask.GETYVIABABESLYtotoReals< /Cord>。这是相似的,但只是记账的问题(令人讨厌的一个)。
tf.train.init_from_checkpoint('inception_resnet_v2_2016_08_30.ckpt',
{v.name.split(':')[0]: v for v in variables_to_restore})