我在使用 Apple Turi Create 和图像分类器时遇到问题。我已经成功创建了一个包含 22 个类别的模型。我最近又添加了 5 个类别，控制台给了我错误警告
Please use dropna() to drop rows with missing target values.
完整的控制台日志如下所示：
[16:30:30] src/nnvm/legacy_json_util.cc:190: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
[16:30:30] src/nnvm/legacy_json_util.cc:198: Symbol successfully upgraded!
Resizing images...
Performing feature extraction on resized images...
Premature end of JPEG file
Completed 512/1883
Completed 1024/1883
Completed 1536/1883
Completed 1883/1883
PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
You can set ``validation_set=None`` to disable validation tracking.
[ERROR] turicreate.toolkits._main: Toolkit error: Target column has missing value.
Please use dropna() to drop rows with missing target values.
Traceback (most recent call last):
File "train.py", line 8, in <module>
model = tc.image_classifier.create(train_data, target='label', max_iterations=1000)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/turicreate/toolkits/image_classifier/image_classifier.py", line 132, in create
verbose=verbose)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/turicreate/toolkits/classifier/logistic_classifier.py", line 312, in create
seed=seed)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/turicreate/toolkits/_supervised_learning.py", line 397, in create
options, verbose)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/turicreate/toolkits/_main.py", line 75, in run
raise ToolkitError(str(message))
turicreate.toolkits._main.ToolkitError: Target column has missing value.
Please use dropna() to drop rows with missing target values.
我已将turi和coremltools升级到最新版本，但我不知道应该在代码中的何处实现dropna()。我只找到了这个参考并遵循了代码。
它看起来像这样：
数据.py
import turicreate as tc
image_data = tc.image_analysis.load_images('images', with_path=True)
labels = ['A', 'B', 'C', 'D']
def get_label(path, labels=labels):
for label in labels:
if label in path:
return label
image_data['label'] = image_data['path'].apply(get_label)
#import os
#image_data['label'] = image_data['path'].apply(lambda path: os.path.dirname(path).split('/')[-1])
image_data.save('boxes.sframe')
image_data.explore()
火车.py
import turicreate as tc
data = tc.SFrame('boxes.sframe')
data.dropna()
train_data, test_data = data.random_split(0.8)
model = tc.image_classifier.create(train_data, target='label', max_iterations=1000)
predictions = model.classify(test_data)
results = model.evaluate(test_data)
print "Accuracy : %s" % results['accuracy']
print "Confusion Matrix : \n%s" % results['confusion_matrix']
model.save('boxes.model')
请问如何删除所有空列和行？max_iterations =1000对错误也有影响吗？
谢谢你的建议