From 8009120f07f78afe249f803da367f518028ec4b0 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 3 Nov 2022 14:27:25 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cnn_cld_frac.py | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/modules/deeplearning/cnn_cld_frac.py b/modules/deeplearning/cnn_cld_frac.py
index f408659d..fc1b5627 100644
--- a/modules/deeplearning/cnn_cld_frac.py
+++ b/modules/deeplearning/cnn_cld_frac.py
@@ -313,10 +313,12 @@ class CNN:
def get_label_data(self, grd_k):
num, leny, lenx = grd_k.shape
- grd_down_2x = np.zeros((num, leny, lenx))
+ leny_d2x = int(leny / 2)
+ lenx_d2x = int(lenx / 2)
+ grd_down_2x = np.zeros((num, leny_d2x, lenx_d2x))
for t in range(num):
- for j in range(int(leny / 2)):
- for i in range(int(lenx / 2)):
+ for j in range(leny_d2x):
+ for i in range(lenx_d2x):
cell = grd_k[t, j:j + 2, i:i + 2]
if np.sum(np.isnan(cell)) == 0:
cnt = np.sum(cell)
@@ -450,14 +452,12 @@ class CNN:
conv = input_2d
print('input: ', conv.shape)
- conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=2, strides=1, kernel_initializer='he_uniform', activation=activation, padding='SAME')(input_2d)
+ conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=2, strides=2, kernel_initializer='he_uniform', activation=activation, padding='SAME')(input_2d)
print(conv.shape)
if NOISE_TRAINING:
conv = conv_b = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(conv)
- scale = 0.2
-
conv_b = build_residual_block_1x1(conv_b, num_filters, activation, 'Residual_Block_1')
conv_b = build_residual_block_1x1(conv_b, num_filters, activation, 'Residual_Block_2')
--
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