snapshot...

modules/deeplearning/srcnn_cld_frac.py

@@ -148,6 +148,12 @@ def build_residual_block_conv2d_down2x(x_in, num_filters, activation, padding='S
     if do_batch_norm:
         conv = tf.keras.layers.BatchNormalization()(conv)
 
+    conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+    if do_drop_out:
+        conv = tf.keras.layers.Dropout(drop_rate)(conv)
+    if do_batch_norm:
+        conv = tf.keras.layers.BatchNormalization()(conv)
+
     skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
     skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
     if do_drop_out:
@@ -501,7 +507,7 @@ class SRCNN:
 
         conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=KERNEL_SIZE, scale=scale)
 
-        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
 
         # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)