从keras看VGG16结构图

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vgg16训练

上面放了一个keras用vgg16训练测试的例子，我也试过用vgg16训练然后测试自己的例子，效果一般，这里我们来分析一下vgg16的网络结果

keras代码如下

def VGG_16(weights_path=None):    model = Sequential()    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))#卷积输入层，指定了输入图像的大小    model.add(Convolution2D(64, 3, 3, activation='relu'))#64个3x3的卷积核，生成64*224*224的图像，激活函数为relu    model.add(ZeroPadding2D((1,1)))#补0，保证图像卷积后图像大小不变，其实用padding = 'valid'参数就可以了    model.add(Convolution2D(64, 3, 3, activation='relu'))#再来一次卷积 生成64*224*224    model.add(MaxPooling2D((2,2), strides=(2,2)))#pooling操作，相当于变成64*112*112    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(128, 3, 3, activation='relu'))    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(128, 3, 3, activation='relu'))    model.add(MaxPooling2D((2,2), strides=(2,2)))#128*56*56    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(256, 3, 3, activation='relu'))    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(256, 3, 3, activation='relu'))    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(256, 3, 3, activation='relu'))    model.add(MaxPooling2D((2,2), strides=(2,2)))#256*28*28    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(512, 3, 3, activation='relu'))    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(512, 3, 3, activation='relu'))    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(512, 3, 3, activation='relu'))    model.add(MaxPooling2D((2,2), strides=(2,2)))#512*14*14    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(512, 3, 3, activation='relu'))    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(512, 3, 3, activation='relu'))    model.add(ZeroPadding2D((1,1)))    model.add(Convolution2D(512, 3, 3, activation='relu'))    model.add(MaxPooling2D((2,2), strides=(2,2)))  #到这里已经变成了512*7*7    model.add(Flatten())#压平上述向量，变成一维25088    model.add(Dense(4096, activation='relu'))#全连接层有4096个神经核，参数个数就是4096*25088    model.add(Dropout(0.5))#0.5的概率抛弃一些连接    model.add(Dense(4096, activation='relu'))#再来一个全连接    model.add(Dropout(0.5))    model.add(Dense(1000, activation='softmax'))    if weights_path:        model.load_weights(weights_path)    return model

vgg16详细信息

下面是详细的参数个数

INPUT: [224x224x3]        memory:  224*224*3=150K   weights: 0CONV3-64: [224x224x64]  memory:  224*224*64=3.2M   weights: (3*3*3)*64 = 1,728CONV3-64: [224x224x64]  memory:  224*224*64=3.2M   weights: (3*3*64)*64 = 36,864POOL2: [112x112x64]  memory:  112*112*64=800K   weights: 0CONV3-128: [112x112x128]  memory:  112*112*128=1.6M   weights: (3*3*64)*128 = 73,728CONV3-128: [112x112x128]  memory:  112*112*128=1.6M   weights: (3*3*128)*128 = 147,456POOL2: [56x56x128]  memory:  56*56*128=400K   weights: 0CONV3-256: [56x56x256]  memory:  56*56*256=800K   weights: (3*3*128)*256 = 294,912CONV3-256: [56x56x256]  memory:  56*56*256=800K   weights: (3*3*256)*256 = 589,824CONV3-256: [56x56x256]  memory:  56*56*256=800K   weights: (3*3*256)*256 = 589,824POOL2: [28x28x256]  memory:  28*28*256=200K   weights: 0CONV3-512: [28x28x512]  memory:  28*28*512=400K   weights: (3*3*256)*512 = 1,179,648CONV3-512: [28x28x512]  memory:  28*28*512=400K   weights: (3*3*512)*512 = 2,359,296CONV3-512: [28x28x512]  memory:  28*28*512=400K   weights: (3*3*512)*512 = 2,359,296POOL2: [14x14x512]  memory:  14*14*512=100K   weights: 0CONV3-512: [14x14x512]  memory:  14*14*512=100K   weights: (3*3*512)*512 = 2,359,296CONV3-512: [14x14x512]  memory:  14*14*512=100K   weights: (3*3*512)*512 = 2,359,296CONV3-512: [14x14x512]  memory:  14*14*512=100K   weights: (3*3*512)*512 = 2,359,296POOL2: [7x7x512]  memory:  7*7*512=25K  weights: 0FC: [1x1x4096]  memory:  4096  weights: 7*7*512*4096 = 102,760,448FC: [1x1x4096]  memory:  4096  weights: 4096*4096 = 16,777,216FC: [1x1x1000]  memory:  1000 weights: 4096*1000 = 4,096,000TOTAL memory: 24M * 4 bytes ~= 93MB / image (only forward! ~*2 for bwd)TOTAL params: 138M parameters

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