def test(self):
print('STARTED TESTING EVALUATION!')
losses = []
preds = np.ndarray((0, ), dtype=np.int64)
labels = np.ndarray((0, ), dtype=np.int64)
for step in range(self.dataset.num_batches_test):
eval_tensors = [
self.Yoh, self.preds, self.loss, self.accuracy, self.precision,
self.recall
]
if (step + 1) * BATCH_SIZE % LOG_EVERY == 0:
print("Evaluating {}, done: {}/{}".format(
'test', (step + 1) * BATCH_SIZE,
self.dataset.num_test_examples))
eval_tensors += [self.merged_summary_op]
batch_x, batch_y = self.sess.run(
[self.dataset.test_images, self.dataset.test_labels])
feed_dict = {
self.is_training: False,
self.X: batch_x,
self.Y: batch_y
}
eval_ret = self.sess.run(eval_tensors, feed_dict=feed_dict)
eval_ret = dict(zip(eval_tensors, eval_ret))
if self.merged_summary_op in eval_tensors:
self.summary_test_writer.add_summary(
eval_ret[self.merged_summary_op],
self.global_step.eval(session=self.sess))
losses.append(eval_ret[self.loss])
if preds.size == 0:
labels = np.argmax(eval_ret[self.Yoh], axis=1)
preds = eval_ret[self.preds]
else:
labels = np.concatenate(
(labels, np.argmax(eval_ret[self.Yoh], axis=1)), axis=0)
preds = np.concatenate((preds, eval_ret[self.preds]), axis=0)
total_loss = np.mean(losses)
acc, pr, rec = util.acc_prec_rec_score(labels, np.argmax(preds,
axis=1))
prAtTop10, top5CorrectWords, top5IncorrectWords = util.testStatistics(
labels, preds)
util.write_test_results(total_loss, acc, pr, rec, prAtTop10,
top5CorrectWords, top5IncorrectWords,
self.dataset.name, self.name)
print(
"Validation results -> {} error: epoch {} loss={} accuracy={} precision={} recall={} prAtTop10={}"
.format('test', '1', total_loss, acc, pr, rec, prAtTop10))
def testLowMemory(self):
losses = []
preds = np.ndarray((0,), dtype=np.int64)
labels = np.ndarray((0,), dtype=np.int64)
for step in range(self.dataset.num_batches_test):
# get data
batch_x, batch_y = self.sess.run([self.dataset.test_images, self.dataset.test_labels])
# train towers logits
logits = []
for sequence_image in range(self.dataset.frames):
feed_dict = {self.towerImage: batch_x[:, sequence_image], self.is_training: False}
eval_tensors = self.towerNet
logits.append(self.sess.run(eval_tensors, feed_dict))
logits = np.transpose(np.array(logits), [1, 0, 2, 3, 4])
# logits
feed_dict = {self.towerLogits: logits, self.is_training: False}
eval_tensors = self.logits
logits = self.sess.run(eval_tensors, feed_dict)
# get predictions
feed_dict = {self.optLogits: logits, self.Y: batch_y, self.is_training: False}
eval_tensors = [self.loss, self.preds]
if (step + 1) * BATCH_SIZE % 5000 == 0:
eval_tensors += [self.merged_summary_op]
loss_val, predsEval, merged_ops = self.sess.run(eval_tensors, feed_dict=feed_dict)
self.summary_test_writer.add_summary(merged_ops, self.global_step.eval(session=self.sess))
else:
loss_val, predsEval = self.sess.run(eval_tensors, feed_dict)
if (step + 1) * BATCH_SIZE % LOG_EVERY == 0:
print("Evaluating {}, done: {}/{}".format('test', (step + 1) * BATCH_SIZE, self.dataset.num_test_examples))
losses.append(loss_val)
if preds.size == 0:
labels = batch_y
preds = predsEval
else:
labels = np.concatenate((labels, batch_y), axis=0)
preds = np.concatenate((preds, predsEval), axis=0)
total_loss = np.mean(losses)
acc, pr, rec = util.acc_prec_rec_score(labels, np.argmax(preds, axis=1))
prAtTop10, top5CorrectWords, top5IncorrectWords = util.testStatistics(labels, preds)
util.write_test_results(total_loss, acc, pr, rec, prAtTop10, top5CorrectWords, top5IncorrectWords,
self.dataset.name, self.name)
print("Validation results -> {} error: epoch {} loss={} accuracy={} precision={} recall={} prAtTop10={}".format(
'test', '1', total_loss, acc, pr, rec, prAtTop10))
def test(self):
print('STARTED TESTING EVALUATION!')
losses = []
preds = np.ndarray((0,), dtype=np.int64)
labels = np.ndarray((0,), dtype=np.int64)
for step in range(self.dataset.num_batches_test):
eval_tensors = [self.Yoh, self.preds, self.loss]
if (step + 1) * BATCH_SIZE % LOG_EVERY == 0:
print("Evaluating {}, done: {}/{}".format('test', (step + 1) * BATCH_SIZE, self.dataset.num_test_examples))
eval_tensors += [self.merged_summary_op]
batch_x, batch_y = self.sess.run([self.dataset.test_images, self.dataset.test_labels])
feed_dict = {self.is_training: False, self.X: batch_x, self.Y: batch_y}
eval_ret = self.sess.run(eval_tensors, feed_dict=feed_dict)
eval_ret = dict(zip(eval_tensors, eval_ret))
if self.merged_summary_op in eval_tensors:
self.summary_test_writer.add_summary(eval_ret[self.merged_summary_op], self.global_step.eval(session=self.sess))
losses.append(eval_ret[self.loss])
if preds.size == 0:
labels = np.argmax(eval_ret[self.Yoh], axis=1)
preds = eval_ret[self.preds]
else:
labels = np.concatenate((labels, np.argmax(eval_ret[self.Yoh], axis=1)), axis=0)
preds = np.concatenate((preds, eval_ret[self.preds]), axis=0)
total_loss = np.mean(losses)
acc, pr, rec = util.acc_prec_rec_score(labels, np.argmax(preds, axis=1))
prAtTop10, top5CorrectWords, top5IncorrectWords = util.testStatistics(labels, preds)
util.write_test_results(total_loss, acc, pr, rec, prAtTop10, top5CorrectWords, top5IncorrectWords, self.dataset.name, self.name)
print("Validation results -> {} error: epoch {} loss={} accuracy={} precision={} recall={} prAtTop10={}".format('test', '1', total_loss, acc, pr, rec, prAtTop10))