def test_model(net, name):
print(' ------ testing ------')
os.makedirs(V.experiments_folder + "/keras/" + name + '/Test/')
data = Data(V.images_test_dir, V.labels_test_txt)
images, labels = data.generator(4450).__next__()
decoded_label = data.decode_labels(labels, depad=True)
prediction = net.predict(images)
argmax_prediction = data.pred2OneHot(prediction)
decoded_prediction = data.decode_labels(argmax_prediction, depad=True)
with open(V.experiments_folder + "/keras/" + name + '/Test/predictions.csv', 'w') as f:
fieldnames = ['label', 'prediction', 'error']
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
for l, p in zip(decoded_label, decoded_prediction):
writer.writerow({'label': l, 'prediction': p, 'error': CER(l, p)})
cross_val = net.evaluate(images, labels, verbose=False)
label_error = [CER(l, p) for l, p in zip(decoded_label, decoded_prediction)]
label_error_mean = np.sum(label_error) / len(label_error)
word_error = [0 if cer == 0 else 1 for cer in label_error]
word_error_mean = np.mean(word_error)
with open(V.experiments_folder + "/keras/" + name + '/Test/loss.csv', 'w') as f:
fieldnames = ['name', 'value']
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer.writerow({'name': 'cross-entropie', 'value': cross_val})
writer.writerow({'name': 'label error', 'value': label_error_mean})
writer.writerow({'name': 'word error', 'value': word_error_mean})
def see_animation(name):
data = Data(V.images_train_dir, V.labels_train_txt)
net = load_xp_model(name)
net_attention = create_net_attention_maps(net, name)
images, labels = data.generator(1).__next__()
preds = data.decode_labels(data.pred2OneHot(net.predict(images)))
atts = net_attention.predict(images)
im_index = 0
image = images[im_index, :, :, 0]
pred = preds[im_index]
att = atts[im_index, :, :, 0]
ratio = image.shape[0] / att.shape[0]
list_images_att = []
for a in att:
image_att = np.copy(image)
for x in range(image.shape[0]):
image_att[x, :] = image_att[x, :] * a[int(x / ratio)]
list_images_att.append(
[imshow(np.rot90(image_att, k=1), animated=True)])
list_images_att.append(
[imshow(np.rot90(np.zeros((384, 28)), k=1), animated=True)])
fig = figure()
ani = animation.ArtistAnimation(fig,
list_images_att,
interval=800,
blit=True,
repeat_delay=6e5)
show()
def maps(name):
data = Data(V.images_train_dir, V.labels_train_txt)
net = load_xp_model(name)
net_attention = create_net_attention_maps(net, name)
images, labels = data.generator(10).__next__()
preds = data.decode_labels(data.pred2OneHot(net.predict(images)))
atts = net_attention.predict(images)
panel = np.zeros((35, 35, 3), dtype=np.int)
att = atts[0, :, :, 0]
x = 4
y = 2
panel[x:x + att.shape[0], y:y + att.shape[1], 1] = att * 255 / np.max(att)
panel = cv2.resize(panel, (500, 500))
cv2.imwrite('experiments/' + name + '/panel.jpg', panel)
print('fin')