what is output dimension of the inception and vgg16

Question

I have used two image net trained models i.e. VGG16 and inception using following lines in python using Keras API; where x is the input image and batch size is for simplicity =1.

VGGbase_model = InceptionV3(weights='imagenet', include_top=False, 
input_shape=(299,299,3))
Inceptionbase_model = VGG16(weights='imagenet', include_top=False, 
input_shape=(224,224,3))
predictVgg16= VGGbase_model.predict_on_batch(x)
predictinception= Inceptionbase_model.predict_on_batch(x)

I have observed that VGG16 model predict with an output dimension of (1,512) , i understand 512 is the Features as predicted by the VGG16. however the inception model outputs a dimension of 1,8,8,2048. I understand 2048 is the feature vector as predicted by inception , but what is 8,8 and why VGG16 only have two dimensions while inception have 3. Any comments please.

Algae

Answer 1

You can view all layers size by just typing:

print(Inceptionbase_model.summary())
print(VGGbase_model.summary())

of you can see it here: InceptionV3, vgg16

InceptionV3 has shape (None,8,8,2048) at the last convolutional layer and vgg16 (None, 7, 7, 512). If you want to get features from each model you can do that by calling the model with include_top=False and pooling='avg' or pooling='max' (this will add a pooling layer at the end and will output 2048 features for the InceptionV3 model and 512 for vgg16.

ex.

img_shape=(299,299,3)
Inceptionbase_model = InceptionV3(input_shape=img_shape, weights='imagenet', include_top=False, pooling='avg')

Toein

Answer 2

you can use

output_layer = VGG16_model.layers[i].output

Piliferous

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