Question

当我尝试编译使用Keras

的python脚本时，我收到此错误

ValueError                                Traceback (most recent call last)
/home/cse/abdelrahmanML/project/cervix/cervixXception.py in <module>()
    160                         validation_steps=len(valid_list)//conf['batch_size'],
    161                         verbose=1,
--> 162             callbacks=myCallbacks)
    163 
    164 

/home/cse/venv/local/lib/python2.7/site-packages/keras/legacy/interfaces.pyc in wrapper(*args, **kwargs)
     85                 warnings.warn('Update your `' + object_name +
     86                               '` call to the Keras 2 API: ' + signature)
---> 87             return func(*args, **kwargs)
     88         return wrapper
     89     return legacy_support

/home/cse/venv/local/lib/python2.7/site-packages/keras/engine/training.pyc in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
   1781                    hasattr(validation_data, '__next__'))
   1782         if val_gen and not validation_steps:
-> 1783             raise ValueError('When using a generator for validation data, '
   1784                              'you must specify a value for '
   1785                              '`validation_steps`.')

ValueError: When using a generator for validation data, you must specify a value for `validation_steps`.

这是我生成错误的代码片段，您可以注意到它确实为validation_steps指定了一个值。我无法找出问题所在：

fit = model.fit_generator(generator=batch_generator_train(train_list, conf['batch_size']),
            steps_per_epoch=len(train_list)//conf['batch_size'],
            nb_epoch=conf['nb_epoch'],
            validation_data=batch_generator_train(valid_list, conf['batch_size']),
            validation_steps=len(valid_list)//conf['batch_size'],
            verbose=1,
            callbacks=myCallbacks)

请注意：

conf = dict()
conf['batch_size'] = 16

Answer 1

好的第二次尝试回答这个问题。

查看training.py的来源后，我只能看到一个情况，当len（valid_list）小于16（batch_size的值）时会发生这种情况。这将导致楼层划分返回0并导致如果传递并引发您看到的错误。

Model.fit：当您使用validation_generator时，您必须为validation_steps指定一个值

1 个答案: