From 98f951766aebc15c481b54c1b5d9888ba0fadcf3 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 24 Feb 2022 12:19:19 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 111 +++++++++++++++++++-----------
1 file changed, 71 insertions(+), 40 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index c99fe687..7aec99f8 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -75,43 +75,75 @@ zero_out_params = ['cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
DO_ZERO_OUT = False
-def build_residual_block(input, drop_rate, num_neurons, activation, block_name, doDropout=True, doBatchNorm=True):
+# def build_residual_block(input, drop_rate, num_neurons, activation, block_name, doDropout=True, doBatchNorm=True):
+# with tf.name_scope(block_name):
+# if doDropout:
+# fc = tf.keras.layers.Dropout(drop_rate)(input)
+# fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+# else:
+# fc = tf.keras.layers.Dense(num_neurons, activation=activation)(input)
+# if doBatchNorm:
+# fc = tf.keras.layers.BatchNormalization()(fc)
+# print(fc.shape)
+# fc_skip = fc
+#
+# if doDropout:
+# fc = tf.keras.layers.Dropout(drop_rate)(fc)
+# fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+# if doBatchNorm:
+# fc = tf.keras.layers.BatchNormalization()(fc)
+# print(fc.shape)
+#
+# if doDropout:
+# fc = tf.keras.layers.Dropout(drop_rate)(fc)
+# fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
+# if doBatchNorm:
+# fc = tf.keras.layers.BatchNormalization()(fc)
+# print(fc.shape)
+#
+# if doDropout:
+# fc = tf.keras.layers.Dropout(drop_rate)(fc)
+# fc = tf.keras.layers.Dense(num_neurons, activation=None)(fc)
+# if doBatchNorm:
+# fc = tf.keras.layers.BatchNormalization()(fc)
+#
+# fc = fc + fc_skip
+# fc = tf.keras.layers.LeakyReLU()(fc)
+# print(fc.shape)
+#
+# return fc
+
+
+def build_residual_block_1x1(input_layer, num_filters, activation, block_name, padding='SAME', drop_rate=0.5,
+ do_drop_out=True, do_batch_norm=True):
+
with tf.name_scope(block_name):
- if doDropout:
- fc = tf.keras.layers.Dropout(drop_rate)(input)
- fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
- else:
- fc = tf.keras.layers.Dense(num_neurons, activation=activation)(input)
- if doBatchNorm:
- fc = tf.keras.layers.BatchNormalization()(fc)
- print(fc.shape)
- fc_skip = fc
-
- if doDropout:
- fc = tf.keras.layers.Dropout(drop_rate)(fc)
- fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
- if doBatchNorm:
- fc = tf.keras.layers.BatchNormalization()(fc)
- print(fc.shape)
-
- if doDropout:
- fc = tf.keras.layers.Dropout(drop_rate)(fc)
- fc = tf.keras.layers.Dense(num_neurons, activation=activation)(fc)
- if doBatchNorm:
- fc = tf.keras.layers.BatchNormalization()(fc)
- print(fc.shape)
-
- if doDropout:
- fc = tf.keras.layers.Dropout(drop_rate)(fc)
- fc = tf.keras.layers.Dense(num_neurons, activation=None)(fc)
- if doBatchNorm:
- fc = tf.keras.layers.BatchNormalization()(fc)
-
- fc = fc + fc_skip
- fc = tf.keras.layers.LeakyReLU()(fc)
- print(fc.shape)
-
- return fc
+ skip = input_layer
+ if do_drop_out:
+ input_layer = tf.keras.layers.Dropout(drop_rate)(input_layer)
+ if do_batch_norm:
+ input_layer = tf.keras.layers.BatchNormalization()(input_layer)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(input_layer)
+ print(conv.shape)
+
+ if do_drop_out:
+ conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ if do_batch_norm:
+ conv = tf.keras.layers.BatchNormalization()(conv)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+ print(conv.shape)
+
+ if do_drop_out:
+ conv = tf.keras.layers.Dropout(drop_rate)(conv)
+ if do_batch_norm:
+ conv = tf.keras.layers.BatchNormalization()(conv)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=None)(conv)
+
+ conv = conv + skip
+ conv = tf.keras.layers.LeakyReLU()(conv)
+ print(conv.shape)
+
+ return conv
class IcingIntensityFCN:
@@ -609,21 +641,20 @@ class IcingIntensityFCN:
# activation = tf.nn.relu
# activation = tf.nn.elu
activation = tf.nn.leaky_relu
- momentum = 0.99
# padding = "VALID"
padding = "SAME"
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(input_layer)
+ conv = build_residual_block_1x1(input_layer, num_filters, activation, 'Residual_Block_1', padding=padding)
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+ conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_2', padding=padding)
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
print(conv.shape)
if NumClasses == 2:
activation = tf.nn.sigmoid # For binary
else:
activation = tf.nn.softmax # For multi-class
+
# Called logits, but these are actually probabilities, see activation
logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
--
GitLab