From 7f8db191fb15e637b87210ef8ee4fc0397b67ad2 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 18 Aug 2022 15:44:58 -0500
Subject: [PATCH] minor
---
modules/deeplearning/srcnn.py | 54 -----------------------------------
1 file changed, 54 deletions(-)
diff --git a/modules/deeplearning/srcnn.py b/modules/deeplearning/srcnn.py
index 6b56b7af..f4985fb9 100644
--- a/modules/deeplearning/srcnn.py
+++ b/modules/deeplearning/srcnn.py
@@ -408,27 +408,11 @@ class SRCNN:
self.initial_learning_rate = initial_learning_rate
def build_evaluation(self):
- #self.train_loss = tf.keras.metrics.Mean(name='train_loss')
- #self.test_loss = tf.keras.metrics.Mean(name='test_loss')
self.train_accuracy = tf.keras.metrics.MeanAbsoluteError(name='train_accuracy')
self.test_accuracy = tf.keras.metrics.MeanAbsoluteError(name='test_accuracy')
self.train_loss = tf.keras.metrics.Mean(name='train_loss')
self.test_loss = tf.keras.metrics.Mean(name='test_loss')
- # if NumClasses == 2:
- # self.train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')
- # self.test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy')
- # 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_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')
- # self.test_false_pos = tf.keras.metrics.FalsePositives(name='test_false_pos')
- # else:
- # self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
- # self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
-
@tf.function
def train_step(self, mini_batch):
inputs = [mini_batch[0]]
@@ -459,14 +443,6 @@ class SRCNN:
self.test_loss(t_loss)
self.test_accuracy(labels, pred)
- # if NumClasses == 2:
- # self.test_auc(labels, pred)
- # self.test_recall(labels, pred)
- # self.test_precision(labels, pred)
- # self.test_true_neg(labels, pred)
- # self.test_true_pos(labels, pred)
- # self.test_false_neg(labels, pred)
- # self.test_false_pos(labels, pred)
def predict(self, mini_batch):
inputs = [mini_batch[0]]
@@ -483,14 +459,6 @@ class SRCNN:
def reset_test_metrics(self):
self.test_loss.reset_states()
self.test_accuracy.reset_states()
- # if NumClasses == 2:
- # self.test_auc.reset_states()
- # self.test_recall.reset_states()
- # self.test_precision.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 get_metrics(self):
recall = self.test_recall.result()
@@ -570,14 +538,6 @@ class SRCNN:
with self.writer_valid.as_default():
tf.summary.scalar('loss_val', self.test_loss.result(), step=step)
tf.summary.scalar('acc_val', self.test_accuracy.result(), step=step)
- # if NumClasses == 2:
- # tf.summary.scalar('auc_val', self.test_auc.result(), step=step)
- # tf.summary.scalar('recall_val', self.test_recall.result(), step=step)
- # tf.summary.scalar('prec_val', self.test_precision.result(), step=step)
- # tf.summary.scalar('f1_val', f1, step=step)
- # tf.summary.scalar('mcc_val', mcc, step=step)
- # tf.summary.scalar('num_train_steps', step, step=step)
- # tf.summary.scalar('num_epochs', epoch, step=step)
with self.writer_train_valid_loss.as_default():
tf.summary.scalar('loss_trn', loss.numpy(), step=step)
@@ -605,25 +565,11 @@ class SRCNN:
self.test_step(mini_batch)
print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
- # if NumClasses == 2:
- # f1, mcc = self.get_metrics()
- # print('loss, acc, recall, precision, auc, f1, mcc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
- # self.test_recall.result().numpy(), self.test_precision.result().numpy(), self.test_auc.result().numpy(), f1.numpy(), mcc.numpy())
- # else:
- # print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
print('------------------------------------------------------')
tst_loss = self.test_loss.result().numpy()
if tst_loss < best_test_loss:
best_test_loss = tst_loss
- # if NumClasses == 2:
- # best_test_acc = self.test_accuracy.result().numpy()
- # best_test_recall = self.test_recall.result().numpy()
- # best_test_precision = self.test_precision.result().numpy()
- # best_test_auc = self.test_auc.result().numpy()
- # best_test_f1 = f1.numpy()
- # best_test_mcc = mcc.numpy()
-
ckpt_manager.save()
if EARLY_STOP and es.check_stop(tst_loss):
--
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