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from tensorflow.keras import Model

from tensorflow.keras.applications.vgg16 import VGG16

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization

import tensorflow as tf

from tensorflow.keras.applications.vgg16 import VGG16
vgg16 = VGG16(input_shape=(image_width, image_height, 3), include_top=False, weights='imagenet')
for layer in vgg16.layers:
    layer.trainable = False
vgg16.summary()

from tensorflow.keras import Model
first_layer = vgg16.get_layer(index=0)
last_layer = vgg16.get_layer(index=-1)
vgg16_partical = Model(inputs=first_layer.input, outputs=last_layer.output)

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
model_using_vgg16 = tf.keras.models.Sequential([vgg16_partical, Flatten(), Dense(512, activation='relu'), BatchNormalization(), Dropout(0.5), Dense(6, activation='softmax')])
model_using_vgg16.summary()