snapshot...

modules/deeplearning/unet.py

@@ -610,22 +610,26 @@ class UNET:
         conv = tf.keras.layers.concatenate([conv, conv_4])
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
+        print(conv.shape)
 
         num_filters /= 2
         conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
         conv = tf.keras.layers.concatenate([conv, conv_3])
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
+        print(conv.shape)
 
         num_filters /= 2
         conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
         conv = tf.keras.layers.concatenate([conv, conv_2])
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
+        print(conv.shape)
 
         num_filters /= 2
         conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
         conv = tf.keras.layers.concatenate([conv, conv_1])
+        print(conv.shape)
 
         if NumClasses == 2:
             activation = tf.nn.sigmoid  # For binary