From a8547977fec04551141b9aadb09bcbfa0221927e Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 2 Mar 2023 11:10:08 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 12 ++++--------
1 file changed, 4 insertions(+), 8 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index cff7396a..3820f8be 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -19,7 +19,7 @@ from scipy.ndimage import gaussian_filter
LOG_DEVICE_PLACEMENT = False
PROC_BATCH_SIZE = 4
-PROC_BATCH_BUFFER_SIZE = 50000
+PROC_BATCH_BUFFER_SIZE = 5000
NumClasses = 2
if NumClasses == 2:
@@ -35,7 +35,7 @@ EARLY_STOP = True
NOISE_TRAINING = False
NOISE_STDDEV = 0.01
-DO_AUGMENT = True
+DO_AUGMENT = False
DO_SMOOTH = False
SIGMA = 1.0
@@ -267,7 +267,6 @@ class SRCNN:
for param in data_params_half:
idx = params.index(param)
tmp = input_data[:, idx, :, :]
- tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0, tmp)
if DO_ESPCN:
tmp = tmp[:, slc_y_2, slc_x_2]
@@ -281,7 +280,6 @@ class SRCNN:
for param in data_params_full:
idx = params.index(param)
tmp = input_data[:, idx, :, :]
- tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0, tmp)
# Full res:
tmp = tmp[:, slc_y, slc_x]
@@ -291,7 +289,6 @@ class SRCNN:
data_norm.append(tmp)
# ---------------------------------------------------
tmp = input_data[:, label_idx, :, :]
- tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0, tmp)
if DO_SMOOTH:
tmp = smooth_2d(tmp, sigma=SIGMA)
@@ -316,7 +313,6 @@ class SRCNN:
# -----------------------------------------------------
# -----------------------------------------------------
label = input_data[:, label_idx, :, :]
- label = label.copy()
if DO_SMOOTH:
label = np.where(np.isnan(label), 0, label)
label = smooth_2d(label, sigma=SIGMA)
@@ -468,10 +464,10 @@ class SRCNN:
self.loss = tf.keras.losses.MeanSquaredError() # Regression
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
- initial_learning_rate = 0.005
+ initial_learning_rate = 0.002
decay_rate = 0.95
steps_per_epoch = int(self.num_data_samples/BATCH_SIZE) # one epoch
- decay_steps = int(steps_per_epoch)
+ decay_steps = int(steps_per_epoch) * 2
print('initial rate, decay rate, steps/epoch, decay steps: ', initial_learning_rate, decay_rate, steps_per_epoch, decay_steps)
self.learningRateSchedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate, decay_steps, decay_rate)
--
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