From 3c2f1b3302d92f754d45dc776fd4001bffc2bbbc Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 24 Oct 2022 13:25:01 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 46 +++++++++++++++++++++++--------
1 file changed, 35 insertions(+), 11 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 8cec3080..c8f4d4b4 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -557,7 +557,7 @@ class IcingIntensityFCN:
self.get_evaluate_dataset(idxs)
- def build_cnn(self):
+ def build_cnn(self, do_drop_out=False, do_batch_norm=True, drop_rate=0.5):
print('build_cnn')
# padding = "VALID"
padding = "SAME"
@@ -578,12 +578,18 @@ class IcingIntensityFCN:
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)
+ if do_drop_out:
+ conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ if do_batch_norm:
+ 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)
+ 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)
@@ -594,12 +600,18 @@ class IcingIntensityFCN:
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)
+ if do_drop_out:
+ conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ if do_batch_norm:
+ 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)
+ 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)
@@ -610,12 +622,18 @@ class IcingIntensityFCN:
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)
+ if do_drop_out:
+ conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ if do_batch_norm:
+ 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)
+ 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)
@@ -625,12 +643,18 @@ class IcingIntensityFCN:
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)
+ if do_drop_out:
+ conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ if do_batch_norm:
+ 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)
+ 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)
@@ -651,9 +675,9 @@ 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)
+ conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_3', padding=padding)
- # conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_4', padding=padding)
+ conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_4', padding=padding)
# conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_5', padding=padding)
--
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