# -*- coding: utf-8 -*-

"""train.ipynb

**

 * This file is part of Hybrid CNN-LSTM for COVID-19 Severity Score Prediction paper.

 *

 * Written by Ankan Ghosh Dastider and Farhan Sadik.

 *

 * Copyright (c) by the authors under Apache-2.0 License. Some rights reserved, see LICENSE.

 */

"""

#EPOCHS=70

annealer = ReduceLROnPlateau(monitor='val_accuracy', factor=0.5, patience=5, verbose=1, min_lr=1e-3)

checkpoint = ModelCheckpoint('Model.h5', verbose=1, save_best_only=True)

# Generates batches of image data with data augmentationV

datagen = ImageDataGenerator(rotation_range=360, # Degree range for random rotations

                        width_shift_range=0.2, # Range for random horizontal shifts

                        height_shift_range=0.2, # Range for random vertical shifts

                        zoom_range=0.2, # Range for random zoom

                        horizontal_flip=True, # Randomly flip inputs horizontally

                        vertical_flip=True) # Randomly flip inputs vertically

datagen.fit(X_train)

# Fits the model on batches with real-time data augmentation

hist = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=BATCH_SIZE),

               steps_per_epoch=X_train.shape[0] //BATCH_SIZE,

               epochs=EPOCHS,

               verbose=2,

               callbacks=[annealer, checkpoint],

               validation_data=(X_val, Y_val))

#model.save("check.h5")

#model.fit(X_train,Y_train,batch_size=BATCH_SIZE,steps_per_epoch=X_train.shape[0] // BATCH_SIZE, epochs=EPOCHS)