VGG_16_TF.ipynb

from keras.layers import Dense,Flatten
from keras.models import Model
from keras.models import Sequential

# Image Size
IMAGE_SIZE = [224, 224]

# Load Pretrained Model
from keras.applications.vgg16 import VGG16
vgg = VGG16(input_shape= IMAGE_SIZE + [3], weights ='imagenet', 
                include_top= False)
# Freeze layers
for layer in vgg.layers:  
  layer.trainable = False

# Add custom output layer
x = Flatten()(vgg.output)
prediction = Dense(3, activation= 'softmax')(x)
model_1 = Model(inputs= vgg.input, outputs=prediction)


# Generate Model Summary
model_1.summary()