From 4f329b22477cd0309be0cc78cb2196be0ad70c84 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 30 Apr 2021 12:42:11 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_cnn.py | 15 ++++++++++++++-
1 file changed, 14 insertions(+), 1 deletion(-)
diff --git a/modules/deeplearning/icing_cnn.py b/modules/deeplearning/icing_cnn.py
index 98f27613..57bc98b0 100644
--- a/modules/deeplearning/icing_cnn.py
+++ b/modules/deeplearning/icing_cnn.py
@@ -145,6 +145,10 @@ class IcingIntensityNN:
self.test_auc = None
self.test_recall = None
self.test_precision = None
+ self.test_confusion_matrix = None
+
+ self.test_labels = []
+ self.test_preds = []
self.learningRateSchedule = None
self.num_data_samples = None
@@ -370,7 +374,7 @@ class IcingIntensityNN:
fc = tf.keras.layers.BatchNormalization()(fc)
print(fc.shape)
- # activation = tf.nn.softmax
+ # activation = tf.nn.softmax # For multi-class
activation = tf.nn.sigmoid # For binary
logits = tf.keras.layers.Dense(NumLabels, activation=activation)(fc)
@@ -459,6 +463,9 @@ class IcingIntensityNN:
pred = self.model(inputs, training=False)
t_loss = self.loss(labels, pred)
+ self.test_labels.append(labels)
+ self.test_preds.append(pred.result().numpy())
+
self.test_loss(t_loss)
self.test_accuracy(labels, pred)
self.test_auc(labels, pred)
@@ -517,6 +524,9 @@ class IcingIntensityNN:
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)
+ 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('num_train_steps', step, step=step)
tf.summary.scalar('num_epochs', epoch, step=step)
@@ -584,6 +594,9 @@ class IcingIntensityNN:
for mini_batch_test in ds:
self.predict(mini_batch_test)
print('loss, acc: ', self.test_loss.result(), self.test_accuracy.result())
+ cm = tf.math.confusion_matrix(np.concatenate(self.test_labels), np.concatenate(self.test_preds), num_classes=2)
+ cm = cm.result().numpy()
+ print(cm)
def run(self, filename, filename_l1b=None):
with tf.device('/device:GPU:'+str(self.gpu_device)):
--
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