# Make the predictions_and_gradients function
inception_predictions_and_gradients = make_predictions_and_gradients(sess, graph)
def load_image(img_path):
# "/Users/pin-jutien/Integrated-Gradients/Images/70bfca4555cca92e.jpg"
image = tf.image.decode_jpeg(tf.read_file(img_path), channels=3)
img = sess.run(image)
return img
img = load_image("/Users/pin-jutien/Integrated-Gradients/Images/70bfca4555cca92e.jpg")
# Determine top label and score.
top_label_id, score = top_label_id_and_score(img, inception_predictions_and_gradients)
print("Top label: %s, score: %f" % (labels[top_label_id], score))
# Compute attributions based on just the gradients.
_, gradients = inception_predictions_and_gradients([img], top_label_id)
attributions = random_baseline_integrated_gradients(
img,
top_label_id,
inception_predictions_and_gradients,
steps=50,
num_random_trials=10)
Visualize(
attributions, img,
clip_above_percentile=95,
softMaxLabelStore = np.zeros(1000)
softMaxConfStore = np.zeros(1000)
actualLabelStore = np.zeros(1000)
print(sameFlag.shape[0])
for i in range(5):
labels, images, path = next(image_iterator)
_, attrs, path = next(attr_iterator)
for j in range(0, batch_size):
count = i * batch_size + j
topLabel, px = top_label_id_and_layer(
images[j], inception_predictions_and_gradients)
topLabel, softMaxScore = top_label_id_and_score(
images[j], inception_predictions_and_gradients)
softMaxLabelStore[count] = topLabel
softMaxConfStore[count] = softMaxScore
actualLabelStore[count] = labels[j]
attributions = attrs[j]
img = (images[j] + 1.0) / 2.0 * 255.0
#img = np.uint8((images[j]+1.0)/2.0*255.0)
#targeted attack is successfull only when the new label is equal to the target label
if topLabel == labels[j]:
counterSame += 1
sameFlag[count] = 1
elif topLabel == targetLabel:
counterPert += 1
counterSame = 0
counterPert = 0
counterSamePos = np.zeros(len(percent));
counterPertPos = np.zeros(len(percent));
counterSameNeg = np.zeros(len(percent));
counterPertNeg = np.zeros(len(percent));
scoreStore = np.zeros((3,1000,len(percent),2))
for i in range(20):
labels, images , path= next(image_iterator)
_, attrs , path= next(attr_iterator)
for j in range(0,batch_size):
topLabel, score = top_label_id_and_score(images[j], inception_predictions_and_gradients)
attributions = attrs[j]
img = (images[j]+1.0)/2.0*255.0
#img = np.uint8((images[j]+1.0)/2.0*255.0)
if topLabel == labels[j]:
counterSame += 1
else:
counterPert += 1
count = i*batch_size+j;
for k in range(len(percent)):
#mask = np.array(Visualize(
#attributions, img,