Загрузите предварительно обученную сверточную нейронную сеть VGG-19 и исследуйте слои и классы.
Используйте vgg19, чтобы загрузить предварительно обученную сеть VGG-19. Вывод net является объектом SeriesNetwork.
net =
SeriesNetwork with properties:
Layers: [47×1 nnet.cnn.layer.Layer]
Просмотрите сетевую архитектуру с помощью свойства Layers. Сеть имеет 47 слоев. Существует 19 слоев с learnable весами: 16 сверточных слоев и 3 полносвязных слоя.
ans =
47x1 Layer array with layers:
1 'input' Image Input 224x224x3 images with 'zerocenter' normalization
2 'conv1_1' Convolution 64 3x3x3 convolutions with stride [1 1] and padding [1 1 1 1]
3 'relu1_1' ReLU ReLU
4 'conv1_2' Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1]
5 'relu1_2' ReLU ReLU
6 'pool1' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
7 'conv2_1' Convolution 128 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1]
8 'relu2_1' ReLU ReLU
9 'conv2_2' Convolution 128 3x3x128 convolutions with stride [1 1] and padding [1 1 1 1]
10 'relu2_2' ReLU ReLU
11 'pool2' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
12 'conv3_1' Convolution 256 3x3x128 convolutions with stride [1 1] and padding [1 1 1 1]
13 'relu3_1' ReLU ReLU
14 'conv3_2' Convolution 256 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
15 'relu3_2' ReLU ReLU
16 'conv3_3' Convolution 256 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
17 'relu3_3' ReLU ReLU
18 'conv3_4' Convolution 256 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
19 'relu3_4' ReLU ReLU
20 'pool3' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
21 'conv4_1' Convolution 512 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
22 'relu4_1' ReLU ReLU
23 'conv4_2' Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
24 'relu4_2' ReLU ReLU
25 'conv4_3' Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
26 'relu4_3' ReLU ReLU
27 'conv4_4' Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
28 'relu4_4' ReLU ReLU
29 'pool4' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
30 'conv5_1' Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
31 'relu5_1' ReLU ReLU
32 'conv5_2' Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
33 'relu5_2' ReLU ReLU
34 'conv5_3' Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
35 'relu5_3' ReLU ReLU
36 'conv5_4' Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
37 'relu5_4' ReLU ReLU
38 'pool5' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
39 'fc6' Fully Connected 4096 fully connected layer
40 'relu6' ReLU ReLU
41 'drop6' Dropout 50% dropout
42 'fc7' Fully Connected 4096 fully connected layer
43 'relu7' ReLU ReLU
44 'drop7' Dropout 50% dropout
45 'fc8' Fully Connected 1000 fully connected layer
46 'prob' Softmax softmax
47 'output' Classification Output crossentropyex with 'tench' and 999 other classes
Чтобы просмотреть имена классов, изученных сетью, можно просмотреть свойство Classes классификации выходной слой (последний слой). Просмотрите первые 10 классов путем указывания первых 10 элементов.
ans = 10×1 categorical array
tench
goldfish
great white shark
tiger shark
hammerhead
electric ray
stingray
cock
hen
ostrich