From 0e7f4cda15f160ba8e8668844bffe50cc5bca5df Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 8 Aug 2022 17:20:40 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/espcn.py | 9 +++------
1 file changed, 3 insertions(+), 6 deletions(-)
diff --git a/modules/deeplearning/espcn.py b/modules/deeplearning/espcn.py
index 990036ec..f8d30d5b 100644
--- a/modules/deeplearning/espcn.py
+++ b/modules/deeplearning/espcn.py
@@ -437,9 +437,11 @@ class ESPCN:
print(conv.shape)
conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+ conv.trainable = False
print(conv.shape)
- conv = tf.keras.layers.Conv2DTranspose(num_filters // 8, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+ conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+ conv.trainable = False
print(conv.shape)
#self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
@@ -449,11 +451,6 @@ class ESPCN:
# conv = tf.keras.layers.Activation(activation=activation)(conv)
# print(conv.shape)
#
- # if NumClasses == 2:
- # activation = tf.nn.sigmoid # For binary
- # else:
- # activation = tf.nn.softmax # For multi-class
- #
# # Called logits, but these are actually probabilities, see activation
# self.logits = tf.keras.layers.Activation(activation=activation)(conv)
--
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