From 2dfbd8de8e1cc5c883bcb403d120a5d75aa9c913 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 19 Apr 2021 16:15:28 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing.py | 12 +++++++-----
1 file changed, 7 insertions(+), 5 deletions(-)
diff --git a/modules/deeplearning/icing.py b/modules/deeplearning/icing.py
index d97e2fbc..2d9924db 100644
--- a/modules/deeplearning/icing.py
+++ b/modules/deeplearning/icing.py
@@ -201,6 +201,7 @@ class IcingIntensityNN:
# binary, two class
label = np.where(label != 0, 1, label)
+ label = label.reshape((label.shape[0], 1))
# TODO: Implement in memory cache
# for key in keys:
@@ -351,6 +352,7 @@ class IcingIntensityNN:
else:
flat = self.X_img
n_hidden = self.X_img.shape[1]
+ n_hidden = 100
fac = 1
@@ -364,20 +366,20 @@ class IcingIntensityNN:
fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5')
- # activation = tf.nn.softmax
- activation = tf.nn.sigmoid # For binary
-
fc = tf.keras.layers.Dense(n_hidden, activation=activation)(fc)
fc = tf.keras.layers.BatchNormalization()(fc)
print(fc.shape)
- logits = tf.keras.layers.Dense(NumLabels)(fc)
+ # activation = tf.nn.softmax
+ activation = tf.nn.sigmoid # For binary
+
+ logits = tf.keras.layers.Dense(NumLabels, activation=activation)(fc)
print(logits.shape)
self.logits = logits
def build_training(self):
- self.loss = tf.keras.losses.BinaryCrossentropy() # for two-class only
+ self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=True) # for two-class only
#self.loss = tf.keras.losses.SparseCategoricalCrossentropy() # For multi-class
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
--
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