MY_Model | Base CRUD pattern for Codeigniter | Web Framework library

my_model = models.Sequential() 
#first convolutional block 
my_model.add(Conv2D(16, (3, 3), input_shape = (178, 218, 3), activation="relu", padding="same")) 
#add dropout 
my_model.add(Dropout(0.5))
my_model.add(MaxPooling2D((2, 2), padding="same")) 

#second block 
my_model.add(Conv2D(32, (3, 3), activation="relu", padding="same"))
#add dropout 
my_model.add(Dropout(0.5))

my_model.add(MaxPooling2D((2, 2), padding="same"))

#third block 
my_model.add(Conv2D(64, (3, 3), activation="relu", padding="same"))
#add dropout 
my_model.add(Dropout(0.5))

my_model.add(MaxPooling2D((2, 2), padding="same"))

#fourth block 
my_model.add(Conv2D(128, (3, 3), activation="relu", padding="same"))
#add dropout 
my_model.add(Dropout(0.5))

my_model.add(MaxPooling2D((2, 2), padding="same")) 

#global average pooling 
my_model.add(GlobalAveragePooling2D())

#fully connected layer 
my_model.add(Dense(64, activation='relu'))
my_model.add(BatchNormalization())

#make predictions 
my_model.add(Dense(18, activation="softmax")) 

from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint 
es = EarlyStopping(monitor="val_loss", mode="min",verbose=1, patience=5) 

mc = ModelCheckpoint('/content/model.h5', monitor="val_loss", mode="min", verbose=1, save_best_only=True) 

cb_list=[es,mc]  

# compile model 
from keras.optimizers import Adam 
my_model.compile(optimizer=Adam(learning_rate=0.00005),loss="categorical_crossentropy", metrics=["accuracy"]) 

from tensorflow.python.keras.applications.vgg16 import preprocess_input
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator 

#set up data generator 
data_generator = ImageDataGenerator(preprocessing_function=preprocess_input) 

#get batches of training images from the directory 
train_generator = data_generator.flow_from_directory(
        '/content/output7/train',
        target_size=(224, 224),
        batch_size=32,
        class_mode='categorical')

validation_generator = data_generator.flow_from_directory(
        '/content/output7/val',
        target_size=(224, 224),
        batch_size=32,
        class_mode='categorical') 

history = my_model.fit(train_generator, epochs = 100, steps_per_epoch=350, validation_data = validation_generator, validation_steps = 100, callbacks = cb_list)