diff --git a/src/twomartens/masterthesis/weights.py b/src/twomartens/masterthesis/weights.py
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+++ b/src/twomartens/masterthesis/weights.py
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+#  -*- coding: utf-8 -*-
+#
+#  Copyright 2019 Jim Martens
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#         http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+"""
+Functionality to modify weights.
+
+Functions:
+    subsample_ssd(ssd_weights_file): sub-samples the weights of the SSD network
+"""
+import shutil
+
+import h5py
+import math
+import numpy as np
+
+from twomartens.masterthesis.ssd_keras.misc_utils import tensor_sampling_utils
+
+
+def subsample_ssd(ssd_weights_file: str, subsampled_weights_file: str) -> None:
+    """
+    Sub-samples the weights of the SSD 300 network.
+    
+    Args:
+        ssd_weights_file: file path for the SSD weights file
+        subsampled_weights_file: file path for file with sub-sampled weights
+    """
+    shutil.copy(ssd_weights_file, subsampled_weights_file)
+    with h5py.File(subsampled_weights_file) as weights_destination_file:
+    
+        classifier_names = ["conv4_3_norm_mbox_conf",
+                            "fc7_mbox_conf",
+                            "conv6_2_mbox_conf",
+                            "conv7_2_mbox_conf",
+                            "conv8_2_mbox_conf",
+                            "conv9_2_mbox_conf"]
+        n_classes_source = 81
+        classes_of_interest = [i for i in range(61)]  # first 60 classes are kept plus background
+        for name in classifier_names:
+            # Get the trained weights for this layer from the source HDF5 weights file.
+            kernel = weights_destination_file[name][name]['kernel:0'].value
+            bias = weights_destination_file[name][name]['bias:0'].value
+            
+            # Get the shape of the kernel. We're interested in sub-sampling
+            # the last dimension, 'o'.
+            height, width, in_channels, out_channels = kernel.shape
+            
+            subsampling_indices = []
+            for i in range(int(math.floor(out_channels / n_classes_source))):
+                indices = np.array(classes_of_interest) + i * n_classes_source
+                subsampling_indices.append(indices)
+            subsampling_indices = list(np.concatenate(subsampling_indices))
+            
+            # Sub-sample the kernel and bias.
+            # The `sample_tensors()` function used below provides extensive
+            # documentation, so don't hesitate to read it if you want to know
+            # what exactly is going on here.
+            new_kernel, new_bias = tensor_sampling_utils.sample_tensors(
+                weights_list=[kernel, bias],
+                sampling_instructions=[height, width, in_channels, subsampling_indices],
+                axes=[[3]],
+                # The one bias dimension corresponds to the last kernel dimension.
+                init=['gaussian', 'zeros'],
+                mean=0.0,
+                stddev=0.005
+            )
+            
+            # Delete the old weights from the destination file.
+            
+            del weights_destination_file[name][name]['kernel:0']
+            del weights_destination_file[name][name]['bias:0']
+            # Create new data sets for the sub-sampled weights.
+            weights_destination_file[name][name].create_dataset(name='kernel:0', data=new_kernel)
+            weights_destination_file[name][name].create_dataset(name='bias:0', data=new_bias)
+
+
+if __name__ == "__main__":
+    weights_file = "data/weights/ssd/VGG_coco_SSD_300x300_iter_400000.h5"
+    weights_destination_file = "data/weights/ssd/VGG_coco_SSD_300x300_iter_400000_subsampled.h5"
+    subsample_ssd(weights_file, weights_destination_file)