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Commit 1a725ccf authored by tomrink's avatar tomrink
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......@@ -150,8 +150,6 @@ class IcingIntensityNN:
self.test_recall = None
self.test_precision = None
self.test_confusion_matrix = None
self.test_f1score = None
self.test_mcc = None
self.test_true_pos = None
self.test_true_neg = None
self.test_false_pos = None
......@@ -485,8 +483,6 @@ class IcingIntensityNN:
self.test_auc = tf.keras.metrics.AUC(name='test_auc')
self.test_recall = tf.keras.metrics.Recall(name='test_recall')
self.test_precision = tf.keras.metrics.Precision(name='test_precision')
self.test_f1score = tfa.metrics.F1Score(2, threshold=0.5, name='test_f1score')
self.test_mcc = tfa.metrics.MatthewsCorrelationCoefficient(2, name='test_mcc')
self.test_true_neg = tf.keras.metrics.TrueNegatives(name='test_true_neg')
self.test_true_pos = tf.keras.metrics.TruePositives(name='test_true_pos')
self.test_false_neg = tf.keras.metrics.FalseNegatives(name='test_false_neg')
......@@ -546,8 +542,6 @@ class IcingIntensityNN:
self.test_true_pos(labels, pred)
self.test_false_neg(labels, pred)
self.test_false_pos(labels, pred)
self.test_f1score(labels, pred)
self.test_mcc(labels, pred)
def predict(self, mini_batch):
inputs = [mini_batch[0]]
......@@ -574,15 +568,23 @@ class IcingIntensityNN:
self.test_auc.reset_states()
self.test_recall.reset_states()
self.test_precision.reset_states()
self.test_f1score.reset_states()
self.test_mcc.reset_states()
self.test_true_neg.reset_states()
self.test_true_pos.reset_states()
self.test_false_neg.reset_states()
self.test_false_pos.reset_states()
def do_metrics(self):
pass
def get_metrics(self):
recall = self.test_recall.result()
precsn = self.test_precision.result()
f1 = 2 * (precsn * recall) / (precsn + recall)
tn = self.test_true_neg.result()
tp = self.test_true_pos.result()
fn = self.test_false_neg.result()
fp = self.test_false_pos.result()
mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
return f1, mcc
def do_training(self, ckpt_dir=None):
......@@ -631,16 +633,18 @@ class IcingIntensityNN:
for mini_batch_test in tst_ds:
self.test_step(mini_batch_test)
recall = self.test_recall.result()
precsn = self.test_precision.result()
f1 = 2 * (precsn * recall) / (precsn + recall)
tn = self.test_true_neg.result()
tp = self.test_true_pos.result()
fn = self.test_false_neg.result()
fp = self.test_false_pos.result()
# recall = self.test_recall.result()
# precsn = self.test_precision.result()
# f1 = 2 * (precsn * recall) / (precsn + recall)
#
# tn = self.test_true_neg.result()
# tp = self.test_true_pos.result()
# fn = self.test_false_neg.result()
# fp = self.test_false_pos.result()
#
# mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
f1, mcc = self.get_metrics()
with self.writer_valid.as_default():
tf.summary.scalar('loss_val', self.test_loss.result(), step=step)
......@@ -673,20 +677,21 @@ class IcingIntensityNN:
for mini_batch in ds:
self.test_step(mini_batch)
recall = self.test_recall.result().numpy()
precsn = self.test_precision.result().numpy()
f1 = 2 * (precsn * recall) / (precsn + recall)
tn = self.test_true_neg.result().numpy()
tp = self.test_true_pos.result().numpy()
fn = self.test_false_neg.result().numpy()
fp = self.test_false_pos.result().numpy()
mcc = ((tp*tn) - (fp*fn))/np.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))
# recall = self.test_recall.result().numpy()
# precsn = self.test_precision.result().numpy()
# f1 = 2 * (precsn * recall) / (precsn + recall)
#
# tn = self.test_true_neg.result().numpy()
# tp = self.test_true_pos.result().numpy()
# fn = self.test_false_neg.result().numpy()
# fp = self.test_false_pos.result().numpy()
#
# mcc = ((tp*tn)-(fp*fn))/np.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))
f1, mcc = self.get_metrics()
print('loss, acc, recall, precision, auc, f1, mcc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
recall, precsn, self.test_auc.result().numpy(), f1, self.test_f1score.result().numpy(), mcc, self.test_mcc.result().numpy())
print('--------------------------------------------------')
self.test_recall.result().numpy(), self.test_precision.result().numpy(), self.test_auc.result().numpy(), f1.numpy(), mcc.numpy())
print('------------------------------------------------------')
ckpt_manager.save()
if self.DISK_CACHE and epoch == 0:
......
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