--- a
+++ b/inceptionscore.py
@@ -0,0 +1,102 @@
+# calculate inception score for cifar-10 in Keras
+from math import floor
+from numpy import ones
+from numpy import expand_dims
+from numpy import log
+from numpy import mean
+from numpy import std
+from numpy import exp
+from numpy.random import shuffle
+from keras.applications.inception_v3 import InceptionV3
+from keras.applications.inception_v3 import preprocess_input
+from keras.datasets import cifar10
+from skimage.transform import resize
+from numpy import asarray
+from glob import glob
+from natsort import natsorted
+import cv2
+import numpy as np
+from tqdm import tqdm
+import tensorflow as tf
+import os
+
+os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
+os.environ["CUDA_DEVICE_ORDER"]= "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"]= '0'
+
+model = InceptionV3(weights='tmp/imagenet/inception-2015-12-05.tgz')
+
+# scale an array of images to a new size
+def scale_images(images, new_shape):
+	images_list = list()
+	for image in images:
+		# resize with nearest neighbor interpolation
+		new_image = resize(image, new_shape, 0)
+		# store
+		images_list.append(new_image)
+	return asarray(images_list)
+
+# assumes images have any shape and pixels in [0,255]
+def calculate_inception_score(images, n_split=10, eps=1E-16):
+	# load inception v3 model
+	global model
+	# enumerate splits of images/predictions
+	scores = list()
+	n_part = floor(images.shape[0] / n_split)
+	for idx, i in enumerate(range(n_split), start=0):
+		# retrieve images
+		ix_start, ix_end = i * n_part, (i+1) * n_part
+		subset = images[ix_start:ix_end]
+		# convert from uint8 to float32
+		subset = subset.astype('float32')
+		# scale images to the required size
+		subset = scale_images(subset, (299,299,3))
+		# pre-process images, scale to [-1,1]
+		subset = preprocess_input(subset)
+		# predict p(y|x)
+		batch = tf.data.Dataset.from_tensor_slices((subset,)).batch(256)
+		p_yx  = []
+		for subset in tqdm(batch):
+			subset = subset[0]
+			p_y = model.predict(subset)
+			p_yx.extend(p_y)
+		p_yx = np.array(p_yx)
+		print(p_yx.shape)
+		# calculate p(y)
+		p_y = expand_dims(p_yx.mean(axis=0), 0)
+		# calculate KL divergence using log probabilities
+		kl_d = p_yx * (log(p_yx + eps) - log(p_y + eps))
+		# sum over classes
+		sum_kl_d = kl_d.sum(axis=1)
+		# average over images
+		avg_kl_d = mean(sum_kl_d)
+		# undo the log
+		is_score = exp(avg_kl_d)
+		# store
+		scores.append(is_score)
+		print('Inception score of class {}: {}'.format(idx, is_score))
+		with open('experiments/thought_inceptionscore.txt', 'a') as file:
+			file.write('Inception score of class {}: {}\n'.format(idx, is_score))
+	# average across images
+	is_avg, is_std = mean(scores), std(scores)
+	return is_avg, is_std
+
+# # load cifar10 images
+# (images, _), (_, _) = cifar10.load_data()
+# # shuffle images
+# shuffle(images)
+# print('loaded', images.shape)
+# calculate inception score
+for path in natsorted(glob('experiments/inception/*')):
+	images = []
+	for im_path in tqdm(natsorted(glob(path+'/*'))):
+		images.append(cv2.cvtColor(cv2.imread(im_path), cv2.COLOR_BGR2RGB))
+	images = np.array(images)
+	is_mean, is_std = calculate_inception_score(images)
+
+	print('Inception score for epoch {}: ({}, {})'.format(os.path.split(path)[-1], is_mean, is_std))
+
+	with open('experiments/thought_inceptionscore.txt', 'a') as file:
+		file.write('-'*30+'\n')
+		file.write('Inception score for epoch {}: ({}, {})\n'.format(os.path.split(path)[-1], is_mean, is_std))
+		file.write('-'*30+'\n\n')
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