Question

我正在尝试使用一堆图像对VGG19模型进行微调以进行分类。共有18个类别，每个位置精心策划了6000张图片。使用Keras 2.2.4

型号：

INIT_LR = 0.00001
BATCH_SIZE = 128
IMG_SIZE = (256, 256)
epochs = 150

model_base = keras.applications.vgg19.VGG19(include_top=False, input_shape=(*IMG_SIZE, 3), weights='imagenet')
 output = Flatten()(model_base.output)

output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(64, activation='relu')(output)
output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(len(all_character_names), activation='softmax')(output)
model = Model(model_base.input, output)
for layer in model_base.layers[:-10]:
    layer.trainable = False


opt = optimizers.Adam(lr=INIT_LR, decay=INIT_LR / epochs)
model.compile(optimizer=opt,
              loss='categorical_crossentropy',
               metrics=['accuracy', 'top_k_categorical_accuracy'])

数据扩充：

image_datagen = ImageDataGenerator(
    rotation_range=15,
    width_shift_range=.15,
    height_shift_range=.15,
    #rescale=1./255,
    shear_range=0.15,
    zoom_range=0.15,
    channel_shift_range=1,
    vertical_flip=True,
    horizontal_flip=True)

火车模型：

validation_steps = data_generator.validation_samples/BATCH_SIZE
steps_per_epoch = data_generator.train_samples/BATCH_SIZE 

model.fit_generator(
        generator,
        steps_per_epoch=steps_per_epoch,
        epochs=epochs,
        validation_data=validation_data,
        validation_steps=validation_steps
    )

模型摘要：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 256, 256, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    
_________________________________________________________________
block3_conv4 (Conv2D)        (None, 64, 64, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   
_________________________________________________________________
block4_conv4 (Conv2D)        (None, 32, 32, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   
_________________________________________________________________
block5_conv4 (Conv2D)        (None, 16, 16, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 32768)             0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 32768)             131072    
_________________________________________________________________
dropout_1 (Dropout)          (None, 32768)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                2097216   
_________________________________________________________________
batch_normalization_2 (Batch (None, 64)                256       
_________________________________________________________________
dropout_2 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 19)                1235      
=================================================================
Total params: 22,254,163
Trainable params: 19,862,931
Non-trainable params: 2,391,232
_________________________________________________________________
<keras.engine.input_layer.InputLayer object at 0x00000224568D0D68> False
<keras.layers.convolutional.Conv2D object at 0x00000224568D0F60> False
<keras.layers.convolutional.Conv2D object at 0x00000224568F0438> False
<keras.layers.pooling.MaxPooling2D object at 0x00000224570A5860> False
<keras.layers.convolutional.Conv2D object at 0x00000224570A58D0> False
<keras.layers.convolutional.Conv2D object at 0x00000224574196D8> False
<keras.layers.pooling.MaxPooling2D object at 0x0000022457524048> False
<keras.layers.convolutional.Conv2D object at 0x0000022457524D30> False
<keras.layers.convolutional.Conv2D object at 0x0000022457053160> False
<keras.layers.convolutional.Conv2D object at 0x00000224572E15C0> False
<keras.layers.convolutional.Conv2D object at 0x000002245707B080> False
<keras.layers.pooling.MaxPooling2D object at 0x0000022457088400> False
<keras.layers.convolutional.Conv2D object at 0x0000022457088E10> True
<keras.layers.convolutional.Conv2D object at 0x00000224575DB240> True
<keras.layers.convolutional.Conv2D object at 0x000002245747A320> True
<keras.layers.convolutional.Conv2D object at 0x0000022457486160> True
<keras.layers.pooling.MaxPooling2D object at 0x00000224574924E0> True
<keras.layers.convolutional.Conv2D object at 0x0000022457492D68> True
<keras.layers.convolutional.Conv2D object at 0x00000224574AD320> True
<keras.layers.convolutional.Conv2D object at 0x00000224574C6400> True
<keras.layers.convolutional.Conv2D object at 0x00000224574D2240> True
<keras.layers.pooling.MaxPooling2D object at 0x00000224574DAF98> True
<keras.layers.core.Flatten object at 0x00000224574EA080> True
<keras.layers.normalization.BatchNormalization object at 0x00000224574F82B0> True
<keras.layers.core.Dropout object at 0x000002247134BA58> True
<keras.layers.core.Dense object at 0x000002247136A7B8> True
<keras.layers.normalization.BatchNormalization object at 0x0000022471324438> True
<keras.layers.core.Dropout object at 0x00000224713249B0> True
<keras.layers.core.Dense object at 0x00000224713BF7F0> True
batchsize:128
LR:1e-05

注定的图：

尝试：

尝试了几个LR
在没有进行最后10、5层训练的情况下尝试，这很糟糕，根本无法收敛
尝试了几个批次大小，128个给出最佳结果
还尝试了resnet50，但根本没有收敛（即使使用最后三层可训练对象）
尝试VGG16运气不佳。

我每天添加约2000张新图像，以尝试达到每类约20000张图像，因为我认为这是我的问题。

Answer 1

在较低层中，网络学习了诸如边缘，轮廓等低层特征。在较高层中，这些特征组合在一起。因此，在您的情况下，您需要更精细的功能，例如头发的颜色，人的身材等。您可以尝试从最后几层（从块4-5开始）进行微调的一件事。另外，您可以使用不同的学习率，对于VGG块来说，学习率非常低，对于全新的dense层，学习率却稍高。对于实施，this-thread会有所帮助。

在Keras图像分类中不会减少的损失验证

1 个答案: