snapshot...

modules/deeplearning/espcn.py

@@ -437,9 +437,11 @@ class ESPCN:
         print(conv.shape)
 
         conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+        conv.trainable = False
         print(conv.shape)
 
-        conv = tf.keras.layers.Conv2DTranspose(num_filters // 8, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
+        conv.trainable = False
         print(conv.shape)
 
         #self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
@@ -449,11 +451,6 @@ class ESPCN:
         # conv = tf.keras.layers.Activation(activation=activation)(conv)
         # print(conv.shape)
         #
-        # if NumClasses == 2:
-        #     activation = tf.nn.sigmoid  # For binary
-        # else:
-        #     activation = tf.nn.softmax  # For multi-class
-        #
         # # Called logits, but these are actually probabilities, see activation
         # self.logits = tf.keras.layers.Activation(activation=activation)(conv)