def _eval_on_full_set(self, sess, epoch_i, step, silent=False):
"""
Evaluate on the full data set and print information.
"""
eval_start_time = time.time()
if not silent:
utils.thick_line()
print('Calculating losses using full data set...')
# Calculate losses and accuracies of full train set
if self.cfg.EVAL_WITH_FULL_TRAIN_SET:
loss_train, clf_loss_train, rec_loss_train, acc_train = \
self._eval_on_batches('train', sess, self.x_train, self.y_train,
self.n_batch_train, silent=silent)
else:
loss_train, clf_loss_train, rec_loss_train, acc_train = \
None, None, None, None
# Calculate losses and accuracies of full valid set
loss_valid, clf_loss_valid, rec_loss_valid, acc_valid = \
self._eval_on_batches('valid', sess, self.x_valid, self.y_valid,
self.n_batch_valid, silent=silent)
if not silent:
utils.print_full_set_eval(epoch_i, self.cfg.EPOCHS, step,
self.start_time, loss_train,
clf_loss_train, rec_loss_train,
acc_train, loss_valid, clf_loss_valid,
rec_loss_valid, acc_valid,
self.cfg.EVAL_WITH_FULL_TRAIN_SET,
self.cfg.WITH_RECONSTRUCTION)
file_path = join(self.train_log_path, 'full_set_eval_log.csv')
if not silent:
utils.thin_line()
print('Saving {}...'.format(file_path))
utils.save_log(file_path, epoch_i + 1, step,
time.time() - self.start_time, loss_train,
clf_loss_train, rec_loss_train, acc_train, loss_valid,
clf_loss_valid, rec_loss_valid, acc_valid,
self.cfg.WITH_RECONSTRUCTION)
if not silent:
utils.thin_line()
print(
'Evaluation done! Using time: {:.2f}'.format(time.time() -
eval_start_time))
def _save_logs(self, sess, train_writer, valid_writer, x_batch, y_batch,
imgs_batch, epoch_i, step):
"""Save logs and ddd summaries to TensorBoard while training."""
valid_batch_idx = np.random.choice(range(len(self.x_valid)),
self.cfg.BATCH_SIZE).tolist()
x_valid_batch = self.x_valid[valid_batch_idx]
y_valid_batch = self.y_valid[valid_batch_idx]
imgs_valid_batch = self.imgs_valid[valid_batch_idx]
if self.cfg.WITH_REC:
summary_train, loss_train, clf_loss_train, rec_loss_train, acc_train = \
sess.run([self.summary, self.loss, self.clf_loss,
self.rec_loss, self.accuracy],
feed_dict={self.inputs: x_batch,
self.labels: y_batch,
self.input_imgs: imgs_batch,
self.is_training: False})
summary_valid, loss_valid, clf_loss_valid, rec_loss_valid, acc_valid = \
sess.run([self.summary, self.loss, self.clf_loss,
self.rec_loss, self.accuracy],
feed_dict={self.inputs: x_valid_batch,
self.labels: y_valid_batch,
self.input_imgs: imgs_valid_batch,
self.is_training: False})
else:
summary_train, loss_train, acc_train = \
sess.run([self.summary, self.loss, self.accuracy],
feed_dict={self.inputs: x_batch,
self.labels: y_batch,
self.input_imgs: imgs_batch,
self.is_training: False})
summary_valid, loss_valid, acc_valid = \
sess.run([self.summary, self.loss, self.accuracy],
feed_dict={self.inputs: x_valid_batch,
self.labels: y_valid_batch,
self.input_imgs: imgs_valid_batch,
self.is_training: False})
clf_loss_train, rec_loss_train, clf_loss_valid, rec_loss_valid = \
None, None, None, None
train_writer.add_summary(summary_train, step)
valid_writer.add_summary(summary_valid, step)
utils.save_log(join(self.train_log_path,
'train_log.csv'), epoch_i + 1, step,
time.time() - self.start_time, loss_train,
clf_loss_train, rec_loss_train, acc_train, loss_valid,
clf_loss_valid, rec_loss_valid, acc_valid,
self.cfg.WITH_REC)