There is no specific weight initialization for the keras model. The source code says conv and dense layer kernels are initialized with glorot_uniform. So I will try to implement glorot_uniform now, thanks! (Is it the same as Xavier?)
Here is a scratch of keras model, I am not allowed to share it as it is.
Conv2D(filters = '64',
        kernel_size = 3,
        activation = None,
        padding='same',
        kernel_regularizer = regularizers.l2(weight_decay))
BatchNormalization()
Activation('relu')
MaxPooling2D( pool_size = 2)

Conv2D(filters = '128',
        kernel_size = 3,
        activation = None,
        padding='same',
        kernel_regularizer = regularizers.l2(weight_decay))
BatchNormalization()
Activation('relu')
MaxPooling2D( pool_size = 2)

Conv2D(filters = '256',
        kernel_size = 3,
        activation = None,
        padding='same',
        kernel_regularizer = regularizers.l2(weight_decay))
BatchNormalization()
Activation('relu')

GlobalAveragePooling2D()
Dense( units = 10,
        activation = 'softmax',
        kernel_regularizer = regularizers.l2(weight_decay) 
        )

opt_algo = optimizers.SGD(lr = 0.01, momentum = 0.9)
keras_model.compile(optimizer = opt_algo,
                    loss = 'categorical_crossentropy',
                    metrics = ['accuracy'])


train_datagen_pre = ImageDataGenerator(
                featurewise_center=False,  
                samplewise_center=False, 
                featurewise_std_normalization=False, 
                samplewise_std_normalization=False,  
                zca_whitening=False, 
                rotation_range=0, 
                width_shift_range=0.125,  
                height_shift_range=0.125,  
                horizontal_flip=True,
        vertical_flip=False,
        preprocessing_function = utils.cutout) 
train_datagen_pre.fit(X_train)

    
train_datagen = MixupGenerator(X_train, Y_train, batch_size=batch_size, alpha=1.0, datagen=train_datagen_pre)()

batch_size = 128

keras_model.fit_generator(generator = train_datagen,
                        steps_per_epoch=X_train.shape[0] // batch_size,                    
                        epochs=20, 
                        validation_data=test_datagen.flow(X_test,Y_test, batch_size =batch_size),
                        validation_steps = X_test.shape[0] //batch_size)