From 267e930b53b936137f9e0baef163dc0fe3572218 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sun, 6 Nov 2022 10:18:17 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/cnn_cld_frac.py | 10 +++++++---
1 file changed, 7 insertions(+), 3 deletions(-)
diff --git a/modules/deeplearning/cnn_cld_frac.py b/modules/deeplearning/cnn_cld_frac.py
index 312f42e3..bcfb8505 100644
--- a/modules/deeplearning/cnn_cld_frac.py
+++ b/modules/deeplearning/cnn_cld_frac.py
@@ -235,7 +235,7 @@ class CNN:
self.test_data_nda = None
self.test_label_nda = None
- self.n_chans = len(data_params) + 1
+ self.n_chans = len(data_params) + 2
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
# self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
@@ -282,7 +282,7 @@ class CNN:
# --------
tmp = input_data[:, label_idx, y_128, x_128]
tmp = np.where(np.isnan(tmp), 0, tmp) # shouldn't need this
- # data_norm.append(tmp)
+ data_norm.append(tmp)
# ---------
data = np.stack(data_norm, axis=3)
data = data.astype(np.float32)
@@ -448,7 +448,11 @@ class CNN:
conv = input_2d
print('input: ', conv.shape)
- conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=2, strides=2, kernel_initializer='he_uniform', activation=activation, padding='SAME')(input_2d)
+ 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 = tf.keras.layers.Conv2D(num_filters, kernel_size=2, strides=1, kernel_initializer='he_uniform', activation=activation, padding='SAME')(conv)
+
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=2, strides=2, kernel_initializer='he_uniform', activation=activation, padding='SAME')(conv)
print(conv.shape)
if NOISE_TRAINING:
--
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