From d89b3892c460c5dac78428bb26cc2708ef3aaf3e Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 3 May 2021 15:15:03 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_cnn.py | 38 ++++++++++++++++++++++---------
1 file changed, 27 insertions(+), 11 deletions(-)
diff --git a/modules/deeplearning/icing_cnn.py b/modules/deeplearning/icing_cnn.py
index 4fc9fc52..58dd9c05 100644
--- a/modules/deeplearning/icing_cnn.py
+++ b/modules/deeplearning/icing_cnn.py
@@ -17,7 +17,8 @@ CACHE_DATA_IN_MEM = True
PROC_BATCH_SIZE = 2046
PROC_BATCH_BUFFER_SIZE = 50000
-NumLabels = 1
+NumClasses = 3
+NumLogits = 1
BATCH_SIZE = 256
NUM_EPOCHS = 50
@@ -210,8 +211,12 @@ class IcingIntensityNN:
label = np.where(label == -1, 0, label)
# binary, two class
- label = np.where(label != 0, 1, label)
- label = label.reshape((label.shape[0], 1))
+ if NumClasses == 2:
+ label = np.where(label != 0, 1, label)
+ label = label.reshape((label.shape[0], 1))
+ elif NumClasses == 3:
+ label = np.where((label == 1 | label == 2), 1, label)
+ label = np.where((label == 3 | label == 4 | label == 5 | label == 6), 2, label)
if CACHE_DATA_IN_MEM:
self.in_mem_data_cache[key] = (data, label)
@@ -379,14 +384,17 @@ class IcingIntensityNN:
# activation = tf.nn.softmax # For multi-class
activation = tf.nn.sigmoid # For binary
- logits = tf.keras.layers.Dense(NumLabels, activation=activation)(fc)
+ # Called logits, but these are actually probabilities see activation
+ logits = tf.keras.layers.Dense(NumLogits, activation=activation)(fc)
print(logits.shape)
self.logits = logits
def build_training(self):
- self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=False) # for two-class only
- #self.loss = tf.keras.losses.SparseCategoricalCrossentropy() # For multi-class
+ if NumClasses == 2:
+ self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=False) # for two-class only
+ else:
+ self.loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) # For multi-class
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
initial_learning_rate = 0.002
@@ -411,14 +419,22 @@ class IcingIntensityNN:
self.initial_learning_rate = initial_learning_rate
def build_evaluation(self):
- 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.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')
+ else:
+ self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
+ self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(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')
+
def build_predict(self):
_, pred = tf.nn.top_k(self.logits)
self.pred_class = pred
--
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