snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -56,7 +56,8 @@ label_param = 'cld_opd_dcomp'
 # label_param = 'cloud_probability'
 
 params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', label_param]
-data_params = ['temp_11_0um_nom']
+# data_params = ['temp_11_0um_nom']
+data_params = []
 
 label_idx = params.index(label_param)
 
@@ -462,7 +463,7 @@ class SRCNN:
         activation = tf.nn.relu
         momentum = 0.99
 
-        num_filters = 48
+        num_filters = 64
 
         input_2d = self.inputs[0]
         print('input: ', input_2d.shape)
@@ -485,6 +486,8 @@ class SRCNN:
 
         conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)
 
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_6', kernel_size=KERNEL_SIZE, scale=scale)
+
         conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, activation=activation, kernel_initializer='he_uniform', padding=padding)(conv_b)
 
         conv = conv + conv_b