Inceptionv3_model.py

import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.utils import plot_model

#tf.get_logger().setLevel(40) # suppress deprecation messages

#tf.compat.v1.disable_v2_behavior() # disable TF2 behaviour as alibi code still relies on TF1 constructs

import os
from distutils.dir_util import copy_tree, remove_tree

from PIL import Image
from random import randint
import matplotlib.pyplot as plt

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
from sklearn.metrics import matthews_corrcoef as MCC
from sklearn.metrics import balanced_accuracy_score as BAS
from sklearn.metrics import classification_report, confusion_matrix

from keras.utils.vis_utils import plot_model
from tensorflow.keras import Sequential, Input
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.layers import Conv2D, Flatten
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing.image import ImageDataGenerator as IDG
from tensorflow.keras.layers import SeparableConv2D, BatchNormalization, GlobalAveragePooling2D

print("working dir contents:", os.listdir("./dataset(kaggle)/"))

WORK_DIR = './dataset(kaggle)/'

CLASSES = [ 'NonDemented',
            'VeryMildDemented',
            'MildDemented',
            'ModerateDemented']

IMG_SIZE = 152
IMAGE_SIZE = [152, 152]
DIM = (IMG_SIZE, IMG_SIZE)

#Performing Image Augmentation to have more data samples

ZOOM = [.99, 1.01]
BRIGHT_RANGE = [0.8, 1.2]
HORZ_FLIP = True
FILL_MODE = "constant"
DATA_FORMAT = "channels_last"

work_dr = IDG(rescale = 1./255, brightness_range=BRIGHT_RANGE, zoom_range=ZOOM, data_format=DATA_FORMAT, fill_mode=FILL_MODE, horizontal_flip=HORZ_FLIP)

train_data_gen = work_dr.flow_from_directory(directory=WORK_DIR, target_size=DIM, batch_size=6500, shuffle=False)

#Retrieving the data from the ImageDataGenerator iterator

train_data, train_labels = train_data_gen.next()

#Getting to know the dimensions of our dataset

print(train_data.shape, train_labels.shape)

#Getting to know the dimensions of our

sm = SMOTE(random_state=42)

train_data, train_labels = sm.fit_resample(train_data.reshape(-1, IMG_SIZE * IMG_SIZE * 3), train_labels)

train_data = train_data.reshape(-1, IMG_SIZE, IMG_SIZE, 3)

print(train_data.shape, train_labels.shape)

#Splitting the data into train, test, and validation sets

train_data, test_data, train_labels, test_labels = train_test_split(train_data, train_labels, test_size = 0.2, random_state=42)
train_data, val_data, train_labels, val_labels = train_test_split(train_data, train_labels, test_size = 0.2, random_state=42)

# saving the test data to use in my model
print(type(train_data))
np.save("/projectnb/ec523kb/students/gdmac/project/alibi/data/kaggle/test_data_kaggle.npy", test_data)
np.save("/projectnb/ec523kb/students/gdmac/project/alibi/data/kaggle/train_data_kaggle.npy", train_data)
np.save("/projectnb/ec523kb/students/gdmac/project/alibi/data/kaggle/test_labels_kaggle.npy", test_labels)
np.save("/projectnb/ec523kb/students/gdmac/project/alibi/data/kaggle/train_labels_kaggle.npy", train_labels)

inception_model = InceptionV3(input_shape=(152, 152, 3), include_top=False, weights="imagenet")

for layer in inception_model.layers:
    layer.trainable=False
    
# The actual model
custom_inception_model = Sequential([
        inception_model,
        Dropout(0.5),
        GlobalAveragePooling2D(),
        Flatten(),
        BatchNormalization(),
        Dense(512, activation='relu'),
        BatchNormalization(),
        Dropout(0.5),
        Dense(256, activation='relu'),
        BatchNormalization(),
        Dropout(0.5),
        Dense(128, activation='relu'),
        BatchNormalization(),
        Dropout(0.5),
        Dense(64, activation='relu'),
        Dropout(0.5),
        BatchNormalization(),
        Dense(4, activation='softmax')        
    ], name = "inception_cnn_model")
    
METRICS = [
      tf.keras.metrics.BinaryAccuracy(name='accuracy'),
      tf.keras.metrics.Precision(name='precision'),
      tf.keras.metrics.Recall(name='recall'),  
      tf.keras.metrics.AUC(name='auc')
]

# CALLBACKS = rop_callback
    
custom_inception_model.compile(optimizer='adam',
                              loss=tf.losses.CategoricalCrossentropy(),
                              metrics=METRICS)

custom_inception_model.summary()

#Fit the training data to the model and validate it using the validation data
EPOCHS = 20

history = custom_inception_model.fit(train_data, train_labels, validation_data=(val_data, val_labels), epochs=EPOCHS)

custom_inception_model.save("./models/kaggle_model.h5", save_format='h5')

plot_model(custom_inception_model, to_file="model.png", show_shapes=True, show_layer_names=True)