Keras多类图像的分类与预测

Question

我正在用ImageDataGenerator进行图像分类。我的数据有这样的结构：

• 列车
  • 101
  • 102
  • 103
  • 104
• 测试
  • 101
  • 102
  • 103
  • 104

因此，如果我理解得很好，ImageGenerator会自动执行标记所需的操作。我训练了模型，并获得了一定的准确性。现在我想做预测。

- model.predict
- model.predict_proba
- model.predict_classes

所有这些都给了我同样的价值。你能快速解释或参考(我找不到任何关于我的问题的信息(我应该如何处理吗，或者我在代码中做了一些糟糕的事情。最大的问题是，我不明白4个不同类的输出会有什么不同。由于predict_classes给了我一个输出[[1]]，它不应该给我预测的类吗？

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.regularizers import l1, l2, l1_l2
model = Sequential()
model.add(Conv2D(60, (3, 3), input_shape=(480, 640,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(60, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(100, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(100, activation='relu', activity_regularizer=l1(0.001)))
#model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
batch_size = 32
# augmentation configuration for train
train_datagen = ImageDataGenerator(
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=False,
vertical_flip=True,
fill_mode = 'nearest')
# augmentation configuration for testing, only rescale
test_datagen = ImageDataGenerator(rescale=1./255)
# reading pictures and  generating batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'/media/data/working_dir/categories/readytotest/train',
target_size=(480, 640),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
'/media/data/working_dir/categories/readytotest/test',
target_size=(480, 640),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=800 // batch_size,
epochs=15,
validation_data=validation_generator,
validation_steps=800 // batch_size)

Answer 1

您的模型和生成器不用于多类，而是用于二进制分类。首先，您需要修复模型的最后一层，以获得类大小的输出。其次，您需要修复要在多类中使用的生成器。

(...)
model.add(Dense(CLS_SZ))
model.add(Activation('softmax'))
(...)
# I am not sure about this read some docs about generator you used.
train_generator = train_datagen.flow_from_directory(
'/media/data/working_dir/categories/readytotest/train',
target_size=(480, 640),
batch_size=batch_size,
class_mode=None)
validation_generator = test_datagen.flow_from_directory(
'/media/data/working_dir/categories/readytotest/test',
target_size=(480, 640),
batch_size=batch_size,
class_mode=None)