continue_low_val.py

from tensorflow import keras
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, Dropout, SpatialDropout2D, AveragePooling2D
import numpy as np
import pickle
x_val=(pickle.load(open('x_val.bin','rb')))
y_val=pickle.load(open('y_val.bin','rb'))
x_val = x_val[::5]
y_val = y_val[::5]

print('\n \nTraining: \n \n')
	
checkpoint = keras.callbacks.ModelCheckpoint('ckpt.hdf5', monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=15)

model = keras.models.load_model("AWS")

from keras.preprocessing.image import ImageDataGenerator
# Generator Object with transformation settings
train_datagen = ImageDataGenerator(
   		shear_range=0.1,	height_shift_range=0.2,
        zoom_range=0.2,     horizontal_flip=True,
        vertical_flip=True, width_shift_range=0.2)
# Vectorize Images in Training Directory
batch=12

train_generator = train_datagen.flow_from_directory(
        'train',           target_size=(256, 256),
        batch_size=batch,      class_mode='categorical')

#Similarly generate validation set too called validation_generator

# Train Model
model.fit(
        train_generator, steps_per_epoch=1995//batch,
        epochs=100, validation_data=(x_val,y_val), callbacks=[checkpoint, stop])

model.save("AWS")

print('\n\nSaving Model Successful')