diff --git a/neuralmonkey/decoders/transformer.py b/neuralmonkey/decoders/transformer.py
index 67ea0a7ab..b91bb3dac 100644
--- a/neuralmonkey/decoders/transformer.py
+++ b/neuralmonkey/decoders/transformer.py
@@ -214,6 +214,8 @@ def __init__(self,
         self._variable_scope.set_initializer(tf.variance_scaling_initializer(
             mode="fan_avg", distribution="uniform"))
 
+        if reuse:
+            self._variable_scope.reuse_variables()
         log("Decoder cost op: {}".format(self.cost))
         self._variable_scope.reuse_variables()
         log("Runtime logits: {}".format(self.runtime_logits))
diff --git a/scripts/compute_fisher.py b/scripts/compute_fisher.py
new file mode 100755
index 000000000..2690bd924
--- /dev/null
+++ b/scripts/compute_fisher.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""Compute the Empirical Fisher matrix using a list of gradients.
+
+The gradient tensors can be spread over multiple npz files. The mean
+is computed over the first dimension (supposed to be a batch).
+
+"""
+
+import argparse
+import os
+import re
+import glob
+
+import numpy as np
+
+from neuralmonkey.logging import log as _log
+
+
+def log(message: str, color: str = "blue") -> None:
+    _log(message, color)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--file_prefix", type=str,
+                        help="prefix of the npz files containing the gradients")
+    parser.add_argument("--output_path", type=str,
+                        help="Path to output the Empirical Fisher to.")
+    args = parser.parse_args()
+
+    output_dict = {}
+    n = 0
+    for file in glob.glob("{}.*npz".format(args.file_prefix)):
+        log("Processing {}".format(file))
+        tensors = np.load(file)
+
+        # first dimension must be equal for all tensors (batch)
+        shapes = [tensors[f].shape for f in tensors.files]
+        assert all([x[0] == shapes[0][0] for x in shapes])
+
+        for varname in tensors.files:
+            res = np.sum(np.square(tensors[varname]), 0)
+            if varname in output_dict:
+                output_dict[varname] += res
+            else:
+                output_dict[varname] = res
+        n += shapes[0][0]
+
+    for name in output_dict:
+        output_dict[name] /= n
+
+    np.savez(args.output_path, **output_dict)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/vocab.ini b/tests/vocab.ini
index 0f4883d44..ce180216d 100644
--- a/tests/vocab.ini
+++ b/tests/vocab.ini
@@ -4,7 +4,7 @@ tf_manager=<tf_manager>
 output="tests/outputs/vocab"
 overwrite_output_dir=True
 batch_size=16
-epochs=0
+epochs=1
 train_dataset=<train_data>
 val_dataset=<val_data>
 trainer=<trainer>
@@ -66,11 +66,20 @@ dropout_keep_prob=0.5
 data_id="target"
 vocabulary=<decoder_vocabulary>
 
-[trainer]
+[trainer1]
 class=trainers.cross_entropy_trainer.CrossEntropyTrainer
 decoders=[<decoder>]
 regularizers=[<train_l2>]
 
+[trainer2]
+class=trainers.cross_entropy_trainer.CrossEntropyTrainer
+decoders=[<decoder>]
+regularizers=[<train_l2>]
+
+[trainer]
+class=trainers.multitask_trainer.MultitaskTrainer
+trainers=[<trainer1>, <trainer2>]
+
 [train_l2]
 class=trainers.regularizers.L2Regularizer
 weight=1.0e-8