model.py

from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, BatchNormalization, Dropout,AveragePooling2D
import tensorflow as tf
from tensorflow.keras.applications import DenseNet201
from keras.models import Model
from keras.models import Sequential
from keras.regularizers import *
from tensorflow import keras
from tensorflow.keras import layers

def download_model():
  model = Sequential()

  conv_base = DenseNet201(input_shape=(224,224,3), include_top=False, pooling='max',weights='imagenet')
  model.add(conv_base)
  model.add(BatchNormalization())
  model.add(Dense(2048, activation='relu', kernel_regularizer=l1_l2(0.01)))
  model.add(BatchNormalization())
  model.add(Dense(8, activation='softmax'))

  train_layers = [layer for layer in conv_base.layers[::-1][:5]]

  for layer in conv_base.layers:
    if layer in train_layers:
      layer.trainable = True
  model.save("model/model.h5")