snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -56,7 +56,7 @@ label_params = ['cloud_fraction']
 
 DO_ZERO_OUT = False
 
-label_idx = 3
+label_idx = 4
 label_param = params[label_idx]
 print('data_params: ', data_params)
 print('label_params: ', label_params)
@@ -396,11 +396,11 @@ class SRCNN:
 
         conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
 
         conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, kernel_initializer='he_uniform', padding=padding)(conv_b)
 
@@ -416,6 +416,7 @@ class SRCNN:
         #     self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)  # for two-class only
         # else:
         #     self.loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)  # For multi-class
+        # self.loss = tf.keras.losses.MeanAbsoluteError()  # Regression
         self.loss = tf.keras.losses.MeanSquaredError()  # Regression
 
         # decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)