VGG16 model for clustering

model.py

from keras.models import load_model, Model
from keras.applications.vgg16 import VGG16, preprocess_input

def setup_model(layer='fc2'):

    base_model = VGG16(weights='imagenet', include_top=True)
    # base_model.summary():
    #     ....
    #     block5_conv4 (Conv2D)        (None, 15, 15, 512)       2359808
    #     _________________________________________________________________
    #     block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0
    #     _________________________________________________________________
    #     flatten (Flatten)            (None, 25088)             0
    #     _________________________________________________________________
    #     fc1 (Dense)                  (None, 4096)              102764544
    #     _________________________________________________________________
    #     fc2 (Dense)                  (None, 4096)              16781312
    #     _________________________________________________________________
    #     predictions (Dense)          (None, 1000)              4097000
    #
    model = Model(inputs=base_model.input,
                outputs=base_model.get_layer(layer).output)
    return model