Question

我在基本的Kaggle Cat vs Dog数据（https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data）上使用一些Keras示例拼凑了一个简单的神经网络。我能够使用

训练和保存模型

model.fit_generator(
         #train_generator,
         #samples_per_epoch=2000,
         #nb_epoch=50,
         #validation_data=validation_generator,
         #nb_val_samples=800)
model.save('first_model.h5')

但是当我尝试加载模型进行预测时，我得到了

Traceback (most recent call last):
  File "/Users/me/PycharmProjects/CatVsDog/SampleML.py", line 48, in <module>
    print(saved_model.predict_generator(test_generator, 12500))
  File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/keras/models.py", line 1012, in predict_generator
    pickle_safe=pickle_safe)
  File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/keras/engine/training.py", line 1763, in predict_generator
    outs = self.predict_on_batch(x)
  File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/keras/engine/training.py", line 1371, in predict_on_batch
    self.internal_input_shapes)
  File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/keras/engine/training.py", line 85, in standardize_input_data
    'Found: ' + str(data)[:200] + '...')
TypeError: Error when checking : data should be a Numpy array, or list/dict of Numpy arrays. Found: None...
Exception ignored in: <bound method Session.__del__ of <tensorflow.python.client.session.Session object at 0x10c7586d8>>
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 581, in __del__
UnboundLocalError: local variable 'status' referenced before assignment

这里是我的PyCharm解决方案中保存图像的地方。 Test目录中有1-12500个.jpg文件，每个Training set cat和dog目录中有11500个标记为.jpgs，每个验证目录中有1000个标记为.jpgs的文件。

这是我的代码

from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        'train',
        target_size=(64, 64),
        batch_size=32,
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        'validate',
        target_size=(64, 64),
        batch_size=32,
        class_mode='binary')

test_generator = train_datagen.flow_from_directory(
         'test',
         target_size=(64, 64),
         batch_size=32,
         class_mode='binary')


nb_filters = 32
kernel_size = (3,3)
pool_size = (2, 2)
nb_classes = 2
input_shape = (64, 64, 3)


saved_model = load_model('first_model.h5')
score = saved_model.evaluate_generator(validation_generator, 2000)


print('Test score:', score[0])
print('Test accuracy:', score[1])
print(saved_model.predict_generator(test_generator, 12500))

Answer 1

我相信你提供给predict_generator的内容并不是正确的格式。

当您预测价值观时，与培训和评估相反，您不想提供标签。因此，我会尝试将您的test_generator更改为此：

test_generator = train_datagen.flow_from_directory(
     'test',
     target_size=(64, 64),
     batch_size=32,
     #This will not output the targets. 
     class_mode=None)

您可以找到documentation about the ImageDataGenerator here。

加载预先训练的Keras模型并进行预测

1 个答案: