Autoencoder_training.py

import numpy as np  
import pandas as pd  
from processing_functions_yale_faces import *
import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir) 
from support_functions import *
from Autoencoder_Functions import *

# Import Dataset
# Define the images to be read and the corresponding labels
data_path = "CroppedYale/"
# Read image matrix (n*m), labels (vector of m), and image size
imgs, labels, height, width = get_yale_faces_data(data_path)
img_size = height*width

# Specify the model config
encoder_layers_size, decoder_layers_size = get_deep_model_config(img_size)

# Since we only has very few images, we replicates the data 
imgs_rep = np.tile(imgs, (300,1))
labels_rep = np.tile(labels, 300)
autoencoder,encoder = train_autoencoder(imgs_rep, labels_rep,encoder_layers_size,decoder_layers_size,save_model = True)