From 6ecd67406654cda1aa5ccaa7b19fb9f4afa6b530 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 25 Jan 2023 12:55:40 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_cld_frac.py | 8 +++++++-
1 file changed, 7 insertions(+), 1 deletion(-)
diff --git a/modules/deeplearning/srcnn_cld_frac.py b/modules/deeplearning/srcnn_cld_frac.py
index 22df4414..88313c32 100644
--- a/modules/deeplearning/srcnn_cld_frac.py
+++ b/modules/deeplearning/srcnn_cld_frac.py
@@ -148,6 +148,12 @@ def build_residual_block_conv2d_down2x(x_in, num_filters, activation, padding='S
if do_batch_norm:
conv = tf.keras.layers.BatchNormalization()(conv)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+ if do_drop_out:
+ conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ if do_batch_norm:
+ conv = tf.keras.layers.BatchNormalization()(conv)
+
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)
if do_drop_out:
@@ -501,7 +507,7 @@ class SRCNN:
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=KERNEL_SIZE, scale=scale)
- # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
+ conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
# conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)
--
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