train.py

import json
import math
import os

from PIL import Image
import numpy as np
from numpy import load
from keras import layers
from keras.applications.inception_v3 import InceptionV3
from keras.callbacks import Callback, ModelCheckpoint, ReduceLROnPlateau, TensorBoard
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
import matplotlib.pyplot as plt
import pandas as pd
import scipy
from tqdm import tqdm
import tensorflow as tf
from keras import backend as K
import gc
from functools import partial
from sklearn import metrics
from collections import Counter
import json
import itertools

from model import build_model

if __name__ == "__main__":

	
	dict_data = load('data.npz')

	X_train, y_train, X_test, y_test = (dict_data['arr_0'],dict_data['arr_1'],dict_data['arr_2'],dict_data['arr_3'])

	K.clear_session()
	gc.collect()

	BATCH_SIZE = 16

	# Using original generator
	train_generator = ImageDataGenerator()

	inceptionNet = InceptionV3(
	    weights='imagenet',
	    include_top=False,
	    input_shape=(256, 256, 3)
	)

	model = build_model(inceptionNet,lr = 1e-4)

	print(model.summary())

	# Learning Rate Reducer
	learn_control = ReduceLROnPlateau(monitor='val_acc', patience=5,
		                          verbose=1,factor=0.2, min_lr=1e-7)

	# Checkpoint
	filepath="./weights_best.h5"
	checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')

	history = model.fit_generator(
	    train_generator.flow(X_train, y_train, batch_size=BATCH_SIZE),
	    steps_per_epoch=X_train.shape[0] / BATCH_SIZE,
	    epochs=15,
	    validation_data=(X_test, y_test),
	    callbacks=[learn_control, checkpoint]
	)

	with open('history.json', 'w') as f:
	  json.dump(str(history.history), f)

	model.save("./malware.h5")