From eaee2230fc34258babe4b7024902148709d97cc1 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 4 May 2021 09:22:20 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing.py | 51 -----------------------------------
1 file changed, 51 deletions(-)
diff --git a/modules/deeplearning/icing.py b/modules/deeplearning/icing.py
index a80d58b2..f04c4321 100644
--- a/modules/deeplearning/icing.py
+++ b/modules/deeplearning/icing.py
@@ -207,12 +207,6 @@ class IcingIntensityNN:
label = np.where(label == -1, 0, label)
# binary, two class
- # label = np.where(label != 0, 1, label)
- # label = label.reshape((label.shape[0], 1))
-
- keep = (label == 0) | (label == 3) | (label == 4) | (label == 5) | (label == 6)
- data = data[keep,]
- label = label[keep]
label = np.where(label != 0, 1, label)
label = label.reshape((label.shape[0], 1))
@@ -295,51 +289,6 @@ class IcingIntensityNN:
return flat
- def build_cnn(self):
- print('build_cnn')
- # padding = "VALID"
- padding = "SAME"
-
- # activation = tf.nn.relu
- # activation = tf.nn.elu
- activation = tf.nn.leaky_relu
- momentum = 0.99
-
- num_filters = 8
-
- conv = tf.keras.layers.Conv2D(num_filters, 5, strides=[1, 1], padding=padding, activation=activation)(self.inputs[0])
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
- conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
-
- num_filters *= 2
- conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
- conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
-
- num_filters *= 2
- conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
- conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
-
- num_filters *= 2
- conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
- conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
-
- num_filters *= 2
- conv = tf.keras.layers.Conv2D(num_filters, 3, strides=[1, 1], padding=padding, activation=activation)(conv)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
- conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
-
- flat = tf.keras.layers.Flatten()(conv)
-
- return flat
-
def build_dnn(self, input_layer=None):
print('build fully connected layer')
drop_rate = 0.5
--
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