From 6332f7e42aaad45eb6fc29439dd0ac92c1bd4ef6 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 23 Jan 2024 14:34:50 -0600
Subject: [PATCH] snapshot...
---
.../cloud_fraction_fcn_abi_hkm_refl.py | 20 +++++++++++++++++++
1 file changed, 20 insertions(+)
diff --git a/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py b/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
index 704bfaf7..fd5a4f6d 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
+++ b/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
@@ -114,6 +114,26 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
return conv
+def build_conv2d_block(conv, num_filters, activation, block_name, padding='SAME'):
+ with tf.name_scope(block_name):
+ skip = conv
+
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
+ conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
+ conv = tf.keras.layers.BatchNormalization()(conv)
+ print(conv.shape)
+
+ skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
+ skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
+ skip = tf.keras.layers.BatchNormalization()(skip)
+
+ conv = conv + skip
+ conv = tf.keras.layers.LeakyReLU()(conv)
+ print(conv.shape)
+
+ return conv
+
+
def upsample_mean(grd):
bsize, ylen, xlen = grd.shape
up = np.zeros((bsize, ylen*2, xlen*2))
--
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