vgg模型-卷积核2D转化3D
vgg模型源码（之前网上搜vgg模型，下了之后一直当源码模型用，本源码是参考PyCharm的源码）：
from keras.models import Sequential
from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten
def vgg():
model = Sequential()
model.add(Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1',
dim_ordering='tf',
input_shape=(255,255,3)))
model.add(Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'))
model.add(Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1'))
model.add(Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2'))
print(model.output.shape)
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'))
model.add(Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1'))
model.add(Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2'))
model.add(Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'))
model.add(Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1'))
model.add(Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2'))
model.add(Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'))
model.add(Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1'))
model.add(Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2'))
model.add(Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'))
model.add(Flatten(name='flatten'))
model.add(Dense(4096, activation='relu',name='fc1'))
model.add(Dense(4096, activation='relu', name='fc2'))
model.add(Dense(2, activation='sigmoid', name='predictions'))
print(model.summary())
return model
if __name__ == '__main__':
vgg()
因为需要训练四维的图片，所以这里面卷积核，池化层都需要改成3D的，先附上修改好的代码，再说坑：
from keras.models import Sequential
from keras.layers import Dense,Conv3D,MaxPooling3D,Flatten
def vgg():
model = Sequential()
model.add(Conv3D(64, (3, 3,3),
activation='relu',
padding='same',
name='block1_conv1',
dim_ordering='tf',
input_shape=(255,255,3,4)))
model.add(Conv3D(64, (3, 3,3),
activation='relu',
padding='same',
name='block1_conv2'))
model.add(MaxPooling3D(
pool_size=(2, 2,2),
strides=(2, 2,2),
name='block1_pool',
padding='same'))
model.add(Conv3D(128, (3, 3,3),
activation='relu',
padding='same',
name='block2_conv1'))
model.add(Conv3D(128, (3, 3,3),
activation='relu',
padding='same',
name='block2_conv2'))
model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block2_pool'))
model.add(Conv3D(256, (3, 3,3),
activation='relu',
padding='same',
name='block3_conv1'))
model.add(Conv3D(256, (3,3, 3),
activation='relu',
padding='same',
name='block3_conv2'))
model.add(Conv3D(256, (3,3, 3),
activation='relu',
padding='same',
name='block3_conv3'))
model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block3_pool'))
model.add(Conv3D(512, (3,3, 3),
activation='relu',
padding='same',
name='block4_conv1'))
model.add(Conv3D(512, (3,3, 3),
activation='relu',
padding='same',
name='block4_conv2'))
model.add(Conv3D(512, (3,3, 3),
activation='relu',
padding='same',
name='block4_conv3'))
model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block4_pool'))
model.add(Conv3D(512, (3,3, 3),
activation='relu',
padding='same',
name='block5_conv1'))
model.add(Conv3D(512, (3,3, 3),
activation='relu',
padding='same',
name='block5_conv2'))
model.add(Conv3D(512, (3,3, 3),
activation='relu',
padding='same',
name='block5_conv3'))
model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block5_pool'))
model.add(Flatten(name='flatten'))
model.add(Dense(4096, activation='relu',name='fc1'))
model.add(Dense(4096, activation='relu',name='fc2'))
model.add(Dense(2, activation='sigmoid', name='predictions'))
print(model.summary())
return model
if __name__ == '__main__':
vgg()
如果按照2D的源码修改，无非就是把2D改3D，然后，卷积核和池化层都改成3维的，验证了那句话，理想很骨感，现实很残忍。
现来看看报什么错：
ValueError: Negative dimension size caused by subtracting 2 from 1 for 'block2_pool/MaxPool3D'
报错的位置在这一句：model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),#padding='same',name='block2_pool'))
看语法也没错啊，网上搜了一下都是说tf和th的问题，发现我代码不存在这个问题，无奈只能一层一层的打印（对比之前随便写的一个3D模型），果然发现了问题：padding='same'，
先来看一下padding这个参数的解释：参考https://blog.csdn.net/wuzqchom/article/details/74785643
我在第一个MaxPooling3D层，没有设置padding这个参数，默认的padding='VALID',看下对应的网络结构（只看前四层）：
Layer (type) Output Shape Param #
=================================================================
block1_conv1 (Conv3D) (None, 255, 255, 3, 64) 6976
_________________________________________________________________
block1_conv2 (Conv3D) (None, 255, 255, 3, 64) 110656
_________________________________________________________________
block1_pool (MaxPooling3D) (None, 127, 127, 1, 64) 0
_________________________________________________________________
block2_conv1 (Conv3D) (None, 127, 127, 1, 128) 221312
_________________________________________________________________
block2_conv2 (Conv3D) (None, 127, 127, 1, 128) 442496
=================================================================
加了padding='same',再看网络结构（只看前四层）：
Layer (type) Output Shape Param #
=================================================================
block1_conv1 (Conv3D) (None, 255, 255, 3, 64) 6976
_________________________________________________________________
block1_conv2 (Conv3D) (None, 255, 255, 3, 64) 110656
_________________________________________________________________
block1_pool (MaxPooling3D) (None, 128, 128, 2, 64) 0
_________________________________________________________________
block2_conv1 (Conv3D) (None, 128, 128, 2, 128) 221312
_________________________________________________________________
block2_conv2 (Conv3D) (None, 128, 128, 2, 128) 442496
就是这里的问题，能力有限暂时解释不了这个原因。
所有2D改3D的时候，在所有的池化层加上参数padding='same',整个3D模型就ok了。