keras预测在多分类中总是输出相同的值 [英] keras predict always output same value in multi-classification

问题描述

这是我的代码。我想使用Keras进行多分类。训练时vcl_acc更好，但是预测值始终是相同的值。我很困惑，请帮助我

here is my code. I want to made a multi-classification using Keras. the vcl_acc is better when training, But the predict value always is a same value. I confused,please help me

train.py

# coding: UTF-8
# author: Sun Yongke ([e-mail])
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD


# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.

# dimensions of our images.
img_width, img_height = 300, 300
nb_epoch=20

train_data_dir = '../picture/samples_300_2/train'
validation_data_dir = '../picture/samples_300_2/validation'
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=40000,
    color_mode='grayscale',
    save_format="jpg",
    save_to_dir="after/train",
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=500,
    color_mode='grayscale',
    save_format="jpg",
    save_to_dir="after/test",
    class_mode='categorical')

model = Sequential()
# input: 100x100 images with 3 channels -> (3, 100, 100) tensors.
# this applies 32 convolution filters of size 3x3 each.
model.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(img_width, img_height,1)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64, 3, 3, border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
# Note: Keras does automatic shape inference.
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(14))
model.add(Activation('softmax'))

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)

nb_train_samples=len(train_generator.filenames)
nb_validation_samples=len(validation_generator.filenames)

early_stopping = EarlyStopping(monitor='val_loss', patience=2)

model.fit_generator(
    train_generator,
    samples_per_epoch=nb_train_samples,
    nb_epoch=nb_epoch,
    validation_data=validation_generator,
    nb_val_samples=nb_validation_samples, callbacks=[early_stopping])
#save model
model.save("sykm.2.h5")

培训结果如下

Epoch 2/50
3005/3005 [==============================] - 270s - loss: 0.2227 - acc: 0.9294 - val_loss: 0.1985 - val_acc: 0.9316
Epoch 3/50
3005/3005 [==============================] - 269s - loss: 0.2105 - acc: 0.9310 - val_loss: 0.1858 - val_acc: 0.9338
Epoch 4/50
3005/3005 [==============================] - 271s - loss: 0.1964 - acc: 0.9333 - val_loss: 0.3572 - val_acc: 0.9160
Epoch 5/50
3005/3005 [==============================] - 268s - loss: 0.1881 - acc: 0.9349 - val_loss: 0.1513 - val_acc: 0.9413
Epoch 6/50
3005/3005 [==============================] - 268s - loss: 0.1935 - acc: 0.9342 - val_loss: 0.1581 - val_acc: 0.936

predict.py

predict.py

# coding: UTF-8
# author: Sun Yongke ([e-mail])
from keras.models import load_model
model = load_model('sykm.2.h5')
img_width, img_height = 300, 300


from keras.preprocessing.image import ImageDataGenerator
test_datagen = ImageDataGenerator(rescale=1./255)
validation_data_dir = 'samples_300/validation'
validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=32,
    class_mode='categorical')
nb_validation_samples=len(validation_generator.filenames)



out=model.predict_generator(validation_generator,nb_validation_samples)

print "out"
print out

即使我使用不同的图片进行测试，输出也始终相同

the output always is same even I use different picture to test as follow

Using TensorFlow backend.
Found 60 images belonging to 2 classes.
out
[[ 0.06170857  0.06522226  0.06400252  0.08250671  0.07548683      0.07643672
0.07131153  0.07487586  0.07607967  0.04719007  0.07641899  0.08824327
0.05421595  0.08630092]
[ 0.06170857  0.06522226  0.06400252  0.08250671  0.07548683 0.07643672
0.07131153  0.07487586  0.07607967  0.04719007  0.07641899  0.08824327
0.05421595  0.08630092]
....]

推荐答案

好吧，在寻找解决方案的一个月后，我尝试了一切：
降低学习率，更改优化器，使用更大的数据集，增加和减少模型复杂度，改变将形状输入到越来越大的图像中，将从keras import 的到tensorflow.keras import 的的输入更改从tensorflow.python.keras导入 ，更改每个层的激活功能，组合它们，尝试其他数据集，等等。即使我使用了像VGG16 / 19这样的网，我的结果也一样。
但是，昨天，我正在读一本书（《与Keras一起深度学习》，Antonio Gulli，Sujit Pal），我意识到汽车制造商使用这样的进口商品：

Well, after 1 month looking for a solution, i tried everything: lowering the learning rate, changing the optimizer, using a bigger dataset, increasing and decreasing model complexity, changing the input shape to smaller and larger images, changin the imports from from keras import to from tensorflow.keras import and further to from tensorflow.python.keras import, changing the activation function of every layer, combining them, trying others datasets, etc... Nothing helped. Even if i used a net like VGG16/19 my results would be the same. But, yesterday, i was reading a book (Deep Learning with Keras - Antonio Gulli, Sujit Pal) and i realized that the autors use imports like this:

from keras.layers.core import Dense, Flatten, Dropout

而不是这样：

from keras.layers import Dense, Flatten, Dropout

对于Conv，我在使用：

the same for Conv, i was using:

from keras.layers import Conv2D, MaxPooling2D, SeparableConv2D

，而autors使用：

and the autors use:

from keras.layers.convolutional import Conv2D, MaxPooling2D, SeparableConv2D

当我更改导入内容时，一切终于开始工作了！
我不知道这是bug还是类似的东西，因为现在即使在预测相同类的数据集中，我的模型也始终可以工作。
所以现在我使用这样的导入，例如：

And when i changed the imports everything started to work finally! I don't know if this is a bug or something like this, cause now my models always works even in datasets that it was predicting the same class. So now i'm using the imports like this, for example:

from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D, SeparableConv2D

尝试如果这不起作用，请查看您的数据集是否平衡，例如，如果您的问题是对猫和狗的图像进行分类，并且您有200张猫图像和800张狗图像，请尝试使用许多没有太大差异的图像它可能会引起问题：如果我说10/10的图像是狗，那么您的模型就可以很好地思考，这样我可以获得80％的准确性，但这不是我们想要的。如果没有更多图像可以平衡数据集，则可以使用 class_weight ，并且一切都会好起来的，也可以使用数据增强。您也可以使用回调，例如 ReduceLROnPlateau ，当您的损失没有降低时，它们会降低学习率。您可以增加 batch_size ，别忘了将 ImageDataGenerator 上的数据混洗并标准化图像，就像这样：

Try this, if doesn't work, look if your dataset is balanced, for example if you problem is to classify images of cats and dogs and you have 200 cat images and 800 dog images, try to use a number of images not so different cause it can cause problem: your model can 'think' well if i say that 10/10 images are dogs so i get 80% accuracy, but this is not what we want. You can use class_weight if you don't have more images to balance you dataset and everything will be ok, you can as well use data-augmentation. You can use callbacks too, like ReduceLROnPlateau which reduce your learning rate when your loss is not getting lower. You can increase your batch_size, don't forget to shuffle the data on your ImageDataGenerator and normalize your images, like this:

g2 = ImageDataGenerator(rescale=1./255)

所有这些都很重要，但没有什么真正帮助我。唯一有效的方法是导入 keras.layers.core 和 keras.layers.convolutional ，这可能对您也有帮助！

All these things are very important, but nothing really helped me. The only thing that worked was importing keras.layers.core and keras.layers.convolutional, may this help you too!

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