sample_indexes = random.sample(range(len(test_x)), 10)
sample_images = [test_x[i] for i in sample_indexes]
sample_labels = [test_y[i] for i in sample_indexes]
predicted = elm.test(sample_images, sample_labels)
sample_labels_get = sess.run(tf.argmax(sample_labels, 1))
endtime = datetime.datetime.now()
print(
'-----------------------------------endtime time---------------------------------------',
endtime)
print('time--->', endtime - starttime)
print('sample_labels', sample_labels_get)
print('pre', predicted)
classimgs = load.getClassImg()
# Display the predictions and the ground truth visually.
fig = plt.figure(figsize=(10, 10))
for i in range(len(sample_labels_get)):
truth = sample_labels_get[i]
prediction = predicted[i]
plt.subplot(10, 2, 1 + 2 * i)
plt.axis('off')
color = 'green' if truth == prediction else 'red'
plt.text(
40,
25,
"<------Truth: {0}\n Prediction: {1}------>".format(
truth, prediction),
fontsize=12,
