From 044c890d58c5d2a9e2b1f9074ccf02ff7440881d Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 18 Aug 2022 11:26:26 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 36 ++++++++++++++++++++-----------
1 file changed, 23 insertions(+), 13 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 6ad32f0b..1364b5c7 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -76,19 +76,29 @@ DO_ZERO_OUT = False
lunar_map = {'cld_reff_dcomp': 'cld_reff_nlcomp', 'cld_opd_dcomp': 'cld_opd_nlcomp', 'iwc_dcomp': None, 'lwc_dcomp': None}
-# def build_residual_block_conv2d(x_in, num_filters, activation, block_name, padding='SAME', drop_rate=0.5,
-# do_drop_out=True, do_batch_norm=True):
-# conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(x_in)
-# conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
-# 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)
-# skip = tf.keras.layers.BatchNormalization()(skip)
-#
-# conv = conv + skip
-# conv = tf.keras.layers.LeakyReLU()(conv)
-# print(conv.shape)
+def build_residual_block_conv2d(x_in, num_filters, activation, padding='SAME', drop_rate=0.5,
+ do_drop_out=True, do_batch_norm=True):
+ skip = x_in
+
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(x_in)
+ conv = tf.keras.layers.MaxPool2D(padding=padding)(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:
+ skip = tf.keras.layers.Dropout(drop_rate)(skip)
+ if do_batch_norm:
+ skip = tf.keras.layers.BatchNormalization()(skip)
+
+ conv = conv + skip
+ conv = tf.keras.layers.LeakyReLU()(conv)
+ print(conv.shape)
+
+ return conv
def build_residual_block_1x1(input_layer, num_filters, activation, block_name, padding='SAME', drop_rate=0.5,
--
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