diff --git a/Implementation/Discriminator_Images.py b/Implementation/Discriminator_Images.py
new file mode 100644
index 0000000..7f7bd48
--- /dev/null
+++ b/Implementation/Discriminator_Images.py
@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+import os
+import glob
+
+INPUT_DIR = "Results/Discriminator"
+
+
+def main():
+	folders = ["Input", "PixelGAN", "PatchGAN1", "PatchGAN2", "PatchGAN3"]
+	
+	plt.figure(figsize=(20, 15))
+	type = 0
+	fsize = 35
+	for file in folders:
+		test_images = sorted(glob.glob(os.path.join(INPUT_DIR, file, '*.jpg')))
+		type += 1
+		counter = 0
+		for img_path in test_images[0:5]:
+			img = plt.imread(img_path)
+			idx = 5 * counter + type
+			plt.subplot(5, 5, idx)
+			plt.imshow(img)
+			if idx == 1:
+				plt.title("Input", fontsize=fsize)
+			elif idx == 2:
+				plt.title("1x1", fontsize=fsize)
+			elif idx == 3:
+				plt.title("16x16", fontsize=fsize)
+			elif idx == 4:
+				plt.title("70x70", fontsize=fsize)
+			elif idx == 5:
+				plt.title("256x256", fontsize=fsize)
+			plt.axis('off')
+			counter += 1
+	# plt.savefig(fname="Artistic_Style3.png", bbox_inches='tight')
+	plt.show()
+	print('Saved Images')
+
+
+if __name__ == '__main__':
+	main()
diff --git a/Implementation/Literature Comparison.py b/Implementation/Literature Comparison.py
new file mode 100644
index 0000000..65bd446
--- /dev/null
+++ b/Implementation/Literature Comparison.py	
@@ -0,0 +1,102 @@
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+INPUT_DIR = "Results/Literature Comparison/"
+
+
+def preprocess_image(image_path):
+	image = plt.imread(image_path)
+	
+	# resize to 286x286
+	Ht = tf.shape(image)[0]
+	Wt = tf.cast(tf.math.multiply(1.5, tf.cast(Ht, tf.float16)), tf.int32)
+	image = tf.image.resize(image, [Ht, Wt], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+	
+	return image
+
+
+def main():
+	
+	plt.figure(figsize=(45, 15))
+	fsize =45
+	img_path = INPUT_DIR
+	
+	# Chicago
+	img = preprocess_image(img_path + "chicago.jpg")
+	plt.subplot(2, 5, 1)
+	plt.imshow(img)
+	plt.title("Input", fontsize=fsize)
+	plt.axis('off')
+	
+	# Style Image
+	img = preprocess_image(img_path + "Starry_night.jpg")
+	plt.subplot(2, 5, 2)
+	plt.imshow(img)
+	plt.title("Starry Night", fontsize=fsize)
+	plt.axis('off')
+	
+	
+	# Gatys Starry
+	img = preprocess_image(img_path + "Gatys_Starry.jpg")
+	plt.subplot(2, 5, 3)
+	plt.imshow(img)
+	plt.title("Gatys et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	
+	# Justin Starry
+	img = preprocess_image(img_path + "Justin_Starry.jpg")
+	plt.subplot(2, 5, 4)
+	plt.imshow(img)
+	plt.title("Johnson et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# CycleGAN Starry
+	img = preprocess_image(img_path + "CycleGAN_Starry.jpg")
+	plt.subplot(2, 5, 5)
+	plt.imshow(img)
+	plt.title("CycleGAN", fontsize=fsize)
+	plt.axis('off')
+	
+	# Chicago
+	img = preprocess_image(img_path + "chicago.jpg")
+	plt.subplot(2, 5, 6)
+	plt.imshow(img)
+	plt.title("Input", fontsize=fsize)
+	plt.axis('off')
+	
+	# Style Image
+	img = preprocess_image(img_path + "Wave.jpg")
+	plt.subplot(2, 5, 7)
+	plt.imshow(img)
+	plt.title("Gatys et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# Gatys Starry
+	img = preprocess_image(img_path + "Gatys_Wave.jpg")
+	plt.subplot(2, 5, 8)
+	plt.imshow(img)
+	plt.title("Gatys et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# Justin Starry
+	img = preprocess_image(img_path + "Justin_Wave.jpg")
+	plt.subplot(2, 5, 9)
+	plt.imshow(img)
+	plt.title("Johnson et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# CycleGAN Starry
+	img = preprocess_image(img_path + "CycleGAN_Wave.jpg")
+	plt.subplot(2, 5, 10)
+	plt.imshow(img)
+	plt.title("CycleGAN", fontsize=fsize)
+	plt.axis('off')
+	
+	# plt.savefig(fname="Artistic_Style3.png", bbox_inches='tight')
+	plt.show()
+	print('Saved Images')
+
+
+if __name__ == '__main__':
+	main()
diff --git a/Implementation/generate_figures.py b/Implementation/generate_figures.py
new file mode 100644
index 0000000..156aba6
--- /dev/null
+++ b/Implementation/generate_figures.py
@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+import os
+import glob
+
+OUTPUT_DIR = "Results/"
+INPUT_DIR = "Results/"
+
+def main():
+	folders = ["Input", "MonetPaintings", "CezannePaintings", "Ukiyo_ePaintings", "VanGoghPaintings"]
+	
+	plt.figure(figsize=(20, 31))
+	type = 0
+	fsize = 40
+	for file in folders:
+		test_images = sorted(glob.glob(os.path.join(INPUT_DIR, file, '*.jpg')))
+		type += 1
+		counter = 0
+		for img_path in test_images[20:30]:
+			img = plt.imread(img_path)
+			idx = 5*counter + type
+			plt.subplot(10, 5, idx)
+			plt.imshow(img)
+			if idx == 1:
+				plt.title("Input", fontsize=fsize)
+			elif idx == 2:
+				plt.title("Monet", fontsize=fsize)
+			elif idx == 3:
+				plt.title("Cezanne", fontsize=fsize)
+			elif idx == 4:
+				plt.title("Ukiyo-e", fontsize=fsize)
+			elif idx == 5:
+				plt.title("Van Gogh", fontsize=fsize)
+			plt.axis('off')
+			counter += 1
+	plt.savefig(fname="Artistic_Style3.png", bbox_inches='tight')
+	plt.show()
+	print('Saved Images')
+
+
+if __name__ == '__main__':
+	main()
diff --git a/Implementation/generate_real_pictures.py b/Implementation/generate_real_pictures.py
new file mode 100644
index 0000000..3d3799c
--- /dev/null
+++ b/Implementation/generate_real_pictures.py
@@ -0,0 +1,71 @@
+import matplotlib.pyplot as plt
+import os
+import glob
+
+INPUT_DIR = "Results/"
+
+def main():
+	folders = ["MonetToLandscapeInput", "MonetToLandscape"]
+	
+	# plt.figure(figsize=(13, 25))
+	# type = 0
+	# fsize = 30
+	# for file in folders:
+	# 	test_images = sorted(glob.glob(os.path.join(INPUT_DIR, file, '*.jpg')))
+	# 	type += 1
+	# 	counter = 0
+	# 	for img_path in test_images[:6]:
+	# 		img = plt.imread(img_path)
+	# 		idx = 2 * counter + type
+	# 		plt.subplot(6, 2, idx)
+	# 		plt.imshow(img)
+	# 		if idx == 1:
+	# 			plt.title("Input", fontsize=fsize)
+	# 		elif idx == 2:
+	# 			plt.title("Output", fontsize=fsize)
+	# 		plt.axis('off')
+	# 		counter += 1
+	#
+	# plt.savefig(fname="MonetToLandscape1.png", bbox_inches='tight')
+	# plt.show()
+	# print('Saved Images 1')
+	#
+	# plt.figure(figsize=(13, 25))
+	# type = 0
+	# for file in folders:
+	# 	test_images = sorted(glob.glob(os.path.join(INPUT_DIR, file, '*.jpg')))
+	# 	type += 1
+	# 	counter = 0
+	# 	for img_path in test_images[6:12]:
+	# 		img = plt.imread(img_path)
+	# 		idx = 2 * counter + type
+	# 		plt.subplot(6, 2, idx)
+	# 		plt.imshow(img)
+	# 		if idx == 1:
+	# 			plt.title("Input", fontsize=fsize)
+	# 		elif idx == 2:
+	# 			plt.title("Output", fontsize=fsize)
+	# 		plt.axis('off')
+	# 		counter += 1
+	#
+	# plt.savefig(fname="MonetToLandscape2.png", bbox_inches='tight')
+	# plt.show()
+	# print('Saved Images 2')
+	
+	plt.figure(figsize=(27, 25))
+	counter = 0
+	folders = ["MonetToLandscape1.png", "MonetToLandscape2.png"]
+	for img_path in folders:
+		img = plt.imread(img_path)
+		counter += 1
+		plt.subplot(1, 2, counter)
+		plt.imshow(img)
+		plt.axis('off')
+	
+	plt.savefig(fname="MonetToLandscape.png", bbox_inches='tight')
+	plt.show()
+	print('Saved Images')
+	
+	
+if __name__ == '__main__':
+	main()
diff --git a/Implementation/loss_function_images.py b/Implementation/loss_function_images.py
new file mode 100644
index 0000000..96e3f4e
--- /dev/null
+++ b/Implementation/loss_function_images.py
@@ -0,0 +1,99 @@
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+INPUT_DIR = "Results/Literature Comparison/"
+
+
+def preprocess_image(image_path):
+	image = plt.imread(image_path)
+	
+	# resize to 286x286
+	Ht = tf.shape(image)[0]
+	Wt = tf.cast(tf.math.multiply(1.5, tf.cast(Ht, tf.float16)), tf.int32)
+	image = tf.image.resize(image, [Ht, Wt], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+	
+	return image
+
+
+def main():
+	plt.figure(figsize=(45, 15))
+	fsize = 45
+	img_path = INPUT_DIR
+	
+	# Chicago
+	img = preprocess_image(img_path + "chicago.jpg")
+	plt.subplot(2, 5, 1)
+	plt.imshow(img)
+	plt.title("Input", fontsize=fsize)
+	plt.axis('off')
+	
+	# Style Image
+	img = preprocess_image(img_path + "Starry_night.jpg")
+	plt.subplot(2, 5, 2)
+	plt.imshow(img)
+	plt.title("Starry Night", fontsize=fsize)
+	plt.axis('off')
+	
+	# Gatys Starry
+	img = preprocess_image(img_path + "Gatys_Starry.jpg")
+	plt.subplot(2, 5, 3)
+	plt.imshow(img)
+	plt.title("Gatys et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# Justin Starry
+	img = preprocess_image(img_path + "Justin_Starry.jpg")
+	plt.subplot(2, 5, 4)
+	plt.imshow(img)
+	plt.title("Johnson et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# CycleGAN Starry
+	img = preprocess_image(img_path + "CycleGAN_Starry.jpg")
+	plt.subplot(2, 5, 5)
+	plt.imshow(img)
+	plt.title("CycleGAN", fontsize=fsize)
+	plt.axis('off')
+	
+	# Chicago
+	img = preprocess_image(img_path + "chicago.jpg")
+	plt.subplot(2, 5, 6)
+	plt.imshow(img)
+	plt.title("Input", fontsize=fsize)
+	plt.axis('off')
+	
+	# Style Image
+	img = preprocess_image(img_path + "Wave.jpg")
+	plt.subplot(2, 5, 7)
+	plt.imshow(img)
+	plt.title("Gatys et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# Gatys Starry
+	img = preprocess_image(img_path + "Gatys_Wave.jpg")
+	plt.subplot(2, 5, 8)
+	plt.imshow(img)
+	plt.title("Gatys et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# Justin Starry
+	img = preprocess_image(img_path + "Justin_Wave.jpg")
+	plt.subplot(2, 5, 9)
+	plt.imshow(img)
+	plt.title("Johnson et al.", fontsize=fsize)
+	plt.axis('off')
+	
+	# CycleGAN Starry
+	img = preprocess_image(img_path + "CycleGAN_Wave.jpg")
+	plt.subplot(2, 5, 10)
+	plt.imshow(img)
+	plt.title("CycleGAN", fontsize=fsize)
+	plt.axis('off')
+	
+	# plt.savefig(fname="Artistic_Style3.png", bbox_inches='tight')
+	plt.show()
+	print('Saved Images')
+
+
+if __name__ == '__main__':
+	main()