From 078d3c01174f377cf9aa3a81582e93675bf3376c Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 2 Dec 2022 11:47:52 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 16 ++++++++--------
1 file changed, 8 insertions(+), 8 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index f00eac31..be333c98 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -20,8 +20,8 @@ if NumClasses == 2:
else:
NumLogits = NumClasses
-BATCH_SIZE = 256
-NUM_EPOCHS = 60
+BATCH_SIZE = 128
+NUM_EPOCHS = 50
TRACK_MOVING_AVERAGE = False
EARLY_STOP = True
@@ -584,18 +584,18 @@ class IcingIntensityFCN:
# activation = tf.nn.elu
activation = tf.nn.leaky_relu
- num_filters = len(self.train_params) * 16
+ num_filters = len(self.train_params) * 4
input_2d = self.inputs[0]
if NOISE_TRAINING:
conv = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(input_2d)
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
print(conv.shape)
skip = conv
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
+ 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)
if do_drop_out:
conv = tf.keras.layers.Dropout(drop_rate)(conv)
@@ -694,11 +694,11 @@ 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)
+ # conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_5', padding=padding)
print(conv.shape)
--
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