diff --git a/lsd/tutorial/example_nets/fib25/acrlsd/mknet.py b/lsd/tutorial/example_nets/fib25/acrlsd/mknet.py
index 2215fcd..191f087 100644
--- a/lsd/tutorial/example_nets/fib25/acrlsd/mknet.py
+++ b/lsd/tutorial/example_nets/fib25/acrlsd/mknet.py
@@ -1,7 +1,7 @@
 import json
-import mala
+from funlib.learn.tensorflow import models
+from funlib.learn.tensorflow.models.unet import crop
 import tensorflow as tf
-from mala.networks.unet import crop_zyx
 
 def create_auto(input_shape, output_shape, name):
 
@@ -12,20 +12,20 @@ def create_auto(input_shape, output_shape, name):
         raw = tf.placeholder(tf.float32, shape=input_shape)
         raw_batched = tf.reshape(raw, (1, 1) + input_shape)
 
-        unet, _, _ = mala.networks.unet(
+        unet, _, _ = models.unet(
                 raw_batched,
                 12,
                 6,
                 [[2,2,2],[2,2,2],[3,3,3]])
 
-        embedding_batched, _ = mala.networks.conv_pass(
+        embedding_batched, _ = models.conv_pass(
             unet,
             kernel_sizes=[1],
             num_fmaps=10,
             activation='sigmoid',
             name='embedding')
-
-        embedding_batched = crop_zyx(embedding_batched, (1, 10) + output_shape)
+        
+        embedding_batched = crop(embedding_batched, (1, 10) + output_shape)
         embedding = tf.reshape(embedding_batched, (10,) + output_shape)
 
         print("input shape : %s"%(input_shape,))
@@ -50,7 +50,7 @@ def create_affs(input_shape, intermediate_shape, expected_output_shape, name):
         raw = tf.placeholder(tf.float32, shape=input_shape)
         raw_batched = tf.reshape(raw, (1, 1) + input_shape)
         raw_in = tf.reshape(raw_batched, input_shape)
-        raw_batched = crop_zyx(raw_batched, (1, 1) + intermediate_shape)
+        raw_batched = crop(raw_batched, (1, 1) + intermediate_shape)
         raw_cropped = tf.reshape(raw_batched, intermediate_shape)
 
         pretrained_lsd = tf.placeholder(tf.float32, shape=(10,) + intermediate_shape)
@@ -58,13 +58,13 @@ def create_affs(input_shape, intermediate_shape, expected_output_shape, name):
 
         concat_input = tf.concat([raw_batched, pretrained_lsd_batched], axis=1)
 
-        unet, _, _ = mala.networks.unet(
+        unet, _, _ = models.unet(
                     concat_input,
                     12,
                     6,
                     [[2,2,2],[2,2,2],[3,3,3]])
 
-        affs_batched, _ = mala.networks.conv_pass(
+        affs_batched, _ = models.conv_pass(
             unet,
             kernel_sizes=[1],
             num_fmaps=3,
@@ -129,15 +129,15 @@ def create_config(input_shape, output_shape, name):
 
 if __name__ == "__main__":
 
-    train_input_shape = (304, 304, 304)
+    train_input_shape = (328, 328, 328)
     train_intermediate_shape = (196, 196, 196)
-    train_output_shape = (92, 92, 92)
+    train_output_shape = (72, 72, 72)
 
     create_auto(train_input_shape, train_intermediate_shape, 'train_auto_net')
     create_affs(train_input_shape, train_intermediate_shape, train_output_shape, 'train_net')
 
     test_input_shape = (364, 364, 364)
-    test_output_shape = (260, 260, 260)
+    test_output_shape = (240, 240, 240)
 
     create_affs(test_input_shape, test_input_shape, test_output_shape, 'test_net')