python - Keras:CNN 多类分类器

Question

从 Keras 的官方二元分类示例开始(参见 here )后，我正在实现一个以 Tensorflow 作为后端的多类分类器。在这个例子中，有两个类(狗/猫)，我现在有 50 个类，数据以相同的方式存储在文件夹中。

训练的时候，loss不会下降，accuracy也不会上升。我已经将使用 sigmoid 函数的最后一层更改为使用 softmax，将 binary_crossentropy 更改为 categorical_crossentropy，并将 class_mode 更改为 categorical。

这是我的代码:

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import keras.optimizers



optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

# dimensions of our images.
img_width, img_height = 224, 224

train_data_dir = 'images/train'
validation_data_dir = 'images/val'
nb_train_samples = 209222
nb_validation_samples = 40000
epochs = 50
batch_size = 16

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(50))
model.add(Activation('softmax'))



model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])


train_datagen = ImageDataGenerator()

train_generator = train_datagen.flow_from_directory(
    directory=train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = train_datagen.flow_from_directory(
    directory=validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

model.save_weights('weights.h5')

知道我哪里可能错了吗？任何输入将不胜感激!

编辑:正如@RobertValencia 所问，这是最新培训日志的开头:

Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.7.5 locally
Found 3517 images belonging to 50 classes.
<keras.preprocessing.image.DirectoryIterator object at 0x7fd1d4515c10>
Found 2451 images belonging to 50 classes.
Epoch 1/50
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GRID K520
major: 3 minor: 0 memoryClockRate (GHz) 0.797
pciBusID 0000:00:03.0
Total memory: 3.94GiB
Free memory: 3.91GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GRID K520, pci bus id: 0000:00:03.0)
 8098/13076 [=================>............] - ETA: 564s - loss: 15.6869 - categorical_accuracy: 0.0267   

Answer 1

考虑到您需要区分的类别数量，或许增加模型的复杂性以及使用不同的优化器可能会产生更好的结果。尝试使用这个模型，它部分基于 VGG-16 CNN 架构，但没有那么复杂:

model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(Dense(50, activation='softmax'))

optimizer = Nadam(lr=0.002,
                  beta_1=0.9,
                  beta_2=0.999,
                  epsilon=1e-08,
                  schedule_decay=0.004)

model.compile(loss='categorical_crossentropy',
              optimizer=optimizer,
              metrics=['categorical_accuracy'])

如果你得到更好的结果，我建议研究 VGG-16 模型:

1. https://github.com/fchollet/keras/blob/master/keras/applications/vgg16.py或
2. https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3 (包括零填充和丢弃层)