From 2010eabc6b76c755688794e5243bb1d3561d4a60 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 14 Mar 2022 20:52:42 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 42 ++++++++++++++++++++++++++-----
1 file changed, 36 insertions(+), 6 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 5f0ba197..a6d9f862 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -87,12 +87,12 @@ def build_residual_block_1x1(input_layer, num_filters, activation, block_name, p
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(input_layer)
print(conv.shape)
- if do_drop_out:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- if do_batch_norm:
- conv = tf.keras.layers.BatchNormalization()(conv)
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
- print(conv.shape)
+ # if do_drop_out:
+ # conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ # if do_batch_norm:
+ # conv = tf.keras.layers.BatchNormalization()(conv)
+ # conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+ # print(conv.shape)
if do_drop_out:
conv = tf.keras.layers.Dropout(drop_rate)(conv)
@@ -582,25 +582,53 @@ class IcingIntensityFCN:
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
print(conv.shape)
+ # -----------------------------------------------------------------------------------------------------------
+ skip = conv
num_filters *= 2
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, 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)
+ # ----------------------------------------------------------------------------------------------------------
+
+ skip = conv
num_filters *= 2
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, 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)
+ # -----------------------------------------------------------------------------------------------------------
+
+ skip = conv
num_filters *= 2
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, 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)
+
return conv
def build_fcl(self, input_layer):
@@ -619,6 +647,8 @@ class IcingIntensityFCN:
conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_2', padding=padding)
+ conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_3', padding=padding)
+
print(conv.shape)
if NumClasses == 2:
--
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