snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -411,10 +411,6 @@ class SRCNN:
 
         input_2d = self.inputs[0]
         print('input: ', input_2d.shape)
-        ##input_2d = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding='VALID', activation=None)(input_2d)
-        # conv = input_2d
-        # print('input: ', conv.shape)
-        print('input: ', input_2d.shape)
 
         conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, kernel_initializer='he_uniform', activation=activation, padding='VALID')(input_2d)
         print(conv.shape)
@@ -439,7 +435,8 @@ class SRCNN:
         conv = conv + conv_b
         print(conv.shape)
 
-        self.logits = tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding=padding, name='regression')(conv)
+        # This is effectively a Dense layer
+        self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='regression')(conv)
 
         print(self.logits.shape)