def evaluation(session, model, mode='validation', percent_limit=None, save_path=None):
def log(msg, add_trailing=True):
LOG(msg, add_trailing)
dataset = DataGenerator(mode)
target_formulas = []
predicted_formulas = []
pp_hist = []
last_log_percentage = 0
log_percentage_every = 10
for epoch, percentage, images, formulas, _ in dataset.generator(1, percent_limit):
target = dataset.decode_formulas(formulas)
prediction, pp = model.predict(sess=session, images=images)
prediction = dataset.decode_formulas(prediction)
target_formulas += target
predicted_formulas += prediction
pp_hist += [pp]
max_per = 1 if percent_limit is None else percent_limit
percentage = int(100 * (percentage/max_per))
if percentage >= last_log_percentage + log_percentage_every:
idx = random.randint(0, len(prediction) - 1)
last_log_percentage += log_percentage_every
log('Evaluation prediction progress completion: {}%\ntrue -> {}\npred -> {}'.
format(percentage, '' if len(target) <= idx else target[idx], prediction[idx]))
if save_path is not None:
with open(save_path, 'w+') as f:
f.write('\n'.join(predicted_formulas))
if len(target_formulas) != len(predicted_formulas):
log("number of formulas doesn't match", False)
return None
bleu_score = bleu_eval(target_formulas, predicted_formulas)
edit_distance_score = edit_distance_eval(target_formulas, predicted_formulas)
pp_mean = np.mean(np.array(pp_hist))
log('Bleu score: {0:2.3f} %'.format(100 * bleu_score))
log('Edit distance score: {0:2.3f} %'.format(100 * edit_distance_score))
log('Perplexity: {0:2.3f}'.format(pp_mean))
return bleu_score, edit_distance_score
import random
import matplotlib.pyplot as plt
import numpy as np
from data.data_generator import DataGenerator
gen = DataGenerator('train')
for ep, per, images, formulas, l in gen.generator(1):
for i in range(1):
idx = random.randint(0, images.shape[0] - 1)
img = images[idx]
formula = np.expand_dims(formulas[idx], axis=0)
formula = gen.decode_formulas(formula)[0]
print(formula)
plt.imshow(img, cmap='gray')
plt.title(formula)
plt.show()