Initial code release for PLDI '21

Contains the initial code release for PRDNN, including the experiments from PLDI '21.

35 files changed, 3651 insertions, 2 deletions

diff --git a/.github/workflows/test_prdnn.yml b/.github/workflows/test_prdnn.yml
new file mode 100644
index 0000000..014f692
--- /dev/null
+++ b/.github/workflows/test_prdnn.yml
@@ -0,0 +1,30 @@
+# Adapted from: https://github.com/pubref/rules_protobuf/blob/master/.travis.yml
+name: Test PRDNN
+
+on: [push]
+
+jobs:
+  test-prdnn:
+    runs-on: ubuntu-20.04
+    steps:
+    - uses: actions/checkout@v1
+    - name: Build and test PRDNN
+      run: |
+        V=4.0.0
+        OS=linux
+        ARCH=x86_64
+        GH_BASE="https://github.com/bazelbuild/bazel/releases/download/$V"
+        GH_ARTIFACT="bazel-$V-installer-$OS-$ARCH.sh"
+        CI_BASE="http://ci.bazel.io/job/Bazel/JAVA_VERSION=1.8,PLATFORM_NAME=$OS-$ARCH/lastSuccessfulBuild/artifact/output/ci"
+        CI_ARTIFACT="bazel--installer.sh"
+        URL="$GH_BASE/$GH_ARTIFACT"
+        echo $URL
+        wget -O install.sh $URL
+        chmod +x install.sh
+        ./install.sh --user
+        rm -f install.sh
+        git clone https://github.com/95616ARG/bazel_python.git
+        cd bazel_python
+        echo y | ./setup_python.sh 3.7.4 $HOME/.bazel_python
+        cd .. && rm -rf bazel_python
+        /home/runner/bin/bazel test prdnn/...
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..231dd53
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+__pycache__
+*.sw*
+experiments/results
diff --git a/BUILD b/BUILD
new file mode 100644
index 0000000..d27f4b5
--- /dev/null
+++ b/BUILD
@@ -0,0 +1,46 @@
+load("@bazel_python//:bazel_python.bzl", "bazel_python_coverage_report", "bazel_python_interpreter")
+
+bazel_python_interpreter(
+    name = "bazel_python_venv",
+    python_version = "3.7.4",
+    requirements_file = "requirements.txt",
+    run_after_pip = """
+        pip3 install -i https://pypi.gurobi.com gurobipy || exit 1
+    """,
+    visibility = ["//:__subpackages__"],
+)
+
+bazel_python_coverage_report(
+    name = "coverage_report",
+    code_paths = ["prdnn/*.py"],
+    test_paths = ["prdnn/tests/*"],
+)
+
+# For wheel-ifying the Python code.
+# Thanks!
+# https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/
+genrule(
+    name = "wheel",
+    srcs = [
+        "prdnn",
+        "requirements.txt",
+        "LICENSE",
+        "pip_info/__metadata__.py",
+        "pip_info/README.md",
+        "pip_info/setup.cfg",
+        "pip_info/setup.py",
+    ],
+    outs = ["prdnn.dist"],
+    cmd = """
+    PYTHON_VENV=$(location //:bazel_python_venv)
+    pushd $$PYTHON_VENV/..
+    source bazel_python_venv_installed/bin/activate
+    popd
+    cp pip_info/* .
+    python3 setup.py sdist bdist_wheel
+    cp -r dist $@
+    """,
+    tools = [
+        "//:bazel_python_venv",
+    ],
+)
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..6e2c4d2
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Matthew A. Sotoudeh and Aditya V. Thakur
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 4c87da4..9b3ac9a 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,120 @@
-# PRDNN: Provable Repair of Deep Neural Networks
-Code will be uploaded here before PLDI 2021.
+# PRDNN
+PRDNN (pronounced "pardon") is a library for provable repair of Deep Neural
+Networks. DNN behavior involving either finitely-many or entire polytopes of
+points can be repaired using PRDNN.
+
+The code in this repository is the latest artifact from our paper
+***Provable Repair of Deep Neural Networks***, to appear in PLDI 2021 and
+currently available [on arXiv](https://arxiv.org/abs/2104.04413).
+```
+@inproceedings{PLDI2021,
+  author = {Sotoudeh, Matthew and Thakur, Aditya V.},
+  title = {Provable Repair of Deep Neural Networks},
+  booktitle = {42nd {ACM} {SIGPLAN} International Conference on Programming Language Design and Implementation ({PLDI})},
+  publisher = {ACM},
+  year = {2021},
+  note = {To appear}
+}
+```
+
+# Quickstart
+## Prerequisites
+#### Using as a package
+If you only wish to use PRDNN as a package in your own code, and not run any of
+the experiments in `experiments`, then the instructions are:
+1. Install [Gurobi](https://www.gurobi.com) (we tested 9.0.1 and 9.0.2)
+2. In your project, install the `prdnn` package from PyPI: `python3 -m pip
+   install prdnn`.
+3. _If you want to do polytope patching_, then in another session clone
+   [SyReNN](https://github.com/95616ARG/SyReNN) and run `make start_server`.
+   This step is only necessary for polytope patching, not pointwise patching.
+
+#### Reproducing our experiments
+On the other hand, if you wish to reproduce the experiments in our paper, you
+will need the following prerequisites:
+1. Follow the instructions in
+   [bazel_python](https://github.com/95616ARG/bazel_python/) to build a
+   reproducible version of Python 3.7.4, which will be used by this project. It
+   must be built with the relevant OpenSSL libraries installed.
+2. Install [Bazel](https://bazel.build) (we have tested on a variety of
+   versions, including 3.4.1).
+3. Install [Gurobi](https://www.gurobi.com) (we tested 9.0.1 and 9.0.2)
+5. If you want to patch the ImageNet model, see `ImageNet` below.
+6. In another session, clone [SyReNN](https://github.com/95616ARG/SyReNN) and
+   run `make start_server`.
+7. Run your desired tests or experiments (see below).
+
+The only supported way to run our experiments is through Bazel and
+Bazel-Python, as described above. This ensures a reasonably reproducable
+environment.
+
+However, PRDNN is written entirely in Python and it should be possible in many
+cases to run the experiments directly.  However, in this scenario you will have
+to manage dependencies and downloading data on your own.
+
+#### Hardware Requirements
+Most of the experiments can be run on consumer-grade laptop hardware with no
+problems. When prompted for the number of rows to produce, note that the 4-row
+experiments generally require significantly more memory.
+
+The paper experiments were run using 32 threads and maximum 300 GB of memory.
+
+## Running Tests
+To run the library unit tests, use:
+```bash
+bazel test //prdnn/...
+```
+NOTE: Bazel does not pass the `$HOME` environment variable into tests. This
+means that if your Gurobi license file is stored in `$HOME/gurobi.lic` it will
+not be picked up by default, causing a failed test. To resolve this, you should
+explicitly set `GRB_LICENSE_FILE` before running the tests, e.g.:
+```bash
+GRB_LICENSE_FILE=$HOME/gurobi.lic bazel test //...
+```
+To get the coverage report after running the tests, use
+```
+bazel run coverage_report
+```
+
+## Running Experiments
+To run an experiment, use:
+```bash
+bazel run experiments:{experiment_name}
+```
+Where `{experiment_name}` is one of:
+* `squeezenet_repair`
+* `mnist_repair`
+* `acas_repair`
+
+The baselines are experiments:
+* `squeezenet_ft`, `squeezenet_mft`
+* `mnist_ft`, `mnist_mft`
+* `acas_ft`, `acas_mft`
+
+Results from the experiment will be printed, with detailed results placed in
+`experiments/results/{experiment_name}.exp.tgz`.
+
+## Known Issues
+There are currently known issues on macOS. We have tested it successfully on
+Ubuntu 16.04, 18.04, and 20.04.
+
+## ImageNet
+Currently, Bazel does not support archives with spaces in path names. This
+prevents us from using Bazel to manage downloading/unarchiving of the
+ImageNet-A and ImageNet datasets.
+
+The below instructions are only necessary to run
+`experiments:squeezenet_*`.
+
+For the ImageNet-A dataset, it can be downloaded as below:
+```
+URL: https://people.eecs.berkeley.edu/~hendrycks/imagenet-a.tar
+SHA256: 3bb3632277e6ba6392ea64c02ddbf4dd2266c9caffd6bc09c9656d28f012589e
+```
+You should extract it to some place on disk and provide the path when requested
+by the ImageNet patching script.
+
+In order to evaluate the patched network, you will also need to download and
+extract the original ImageNet validation set somewhere.  AcademicTorrents
+should have it. We only need the validation set itself, not the surrounding
+devkit/etc.
diff --git a/WORKSPACE b/WORKSPACE
new file mode 100644
index 0000000..784cf9f
--- /dev/null
+++ b/WORKSPACE
@@ -0,0 +1,42 @@
+workspace(name = "dnn_patching")
+
+load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive", "http_file")
+load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")
+
+git_repository(
+    name = "bazel_python",
+    commit = "f99ab8738dced7257c97dc719457f50a601ed84c",
+    remote = "https://github.com/95616ARG/bazel_python.git",
+)
+
+load("@bazel_python//:bazel_python.bzl", "bazel_python")
+
+bazel_python()
+
+# See the README in: https://github.com/bazelbuild/rules_foreign_cc
+all_content = """filegroup(name = "all", srcs = glob(["**"]), visibility = ["//visibility:public"])"""
+
+# MODELS https://github.com/eth-sri/eran
+http_file(
+    name = "mnist_relu_3_100_model",
+    downloaded_file_path = "model.eran",
+    sha256 = "e4151dfced1783360ab8353c8fdedbfd76f712c2c56e4b14799b2f989217229f",
+    urls = ["https://files.sri.inf.ethz.ch/eran/nets/tensorflow/mnist/mnist_relu_3_100.tf"],
+)
+
+http_archive(
+    name = "onnx_squeezenet",
+    build_file_content = all_content,
+    sha256 = "aff6280d73c0b826f088f7289e4495f01f6e84ce75507279e1b2a01590427723",
+    strip_prefix = "squeezenet1.1",
+    urls = ["https://s3.amazonaws.com/onnx-model-zoo/squeezenet/squeezenet1.1/squeezenet1.1.tar.gz"],
+)
+
+# DATASETS
+http_archive(
+    name = "mnist_c",
+    build_file_content = all_content,
+    sha256 = "af9ee8c6a815870c7fdde5af84c7bf8db0bcfa1f41056db83871037fba70e493",
+    strip_prefix = "mnist_c",
+    urls = ["https://zenodo.org/record/3239543/files/mnist_c.zip"],
+)
diff --git a/experiments/BUILD b/experiments/BUILD
new file mode 100644
index 0000000..ef89d3c
--- /dev/null
+++ b/experiments/BUILD
@@ -0,0 +1,109 @@
+py_library(
+    name = "experiment",
+    srcs = ["experiment.py"],
+)
+
+py_binary(
+    name = "mnist_repair",
+    srcs = ["mnist_repair.py"],
+    data = [
+        "@mnist_c//:all",
+        "@mnist_relu_3_100_model//file",
+    ],
+    deps = [
+        ":experiment",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "mnist_ft",
+    srcs = ["mnist_ft.py"],
+    data = [
+        "@mnist_c//:all",
+        "@mnist_relu_3_100_model//file",
+    ],
+    deps = [
+        ":mnist_repair",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "mnist_mft",
+    srcs = ["mnist_mft.py"],
+    data = [
+        "@mnist_c//:all",
+        "@mnist_relu_3_100_model//file",
+    ],
+    deps = [
+        ":mnist_repair",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "acas_repair",
+    srcs = ["acas_repair.py"],
+    data = glob(["models/acas_2_9.eran"]),
+    deps = [
+        ":experiment",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "acas_ft",
+    srcs = ["acas_ft.py"],
+    data = glob(["models/acas_2_9.eran"]),
+    deps = [
+        ":acas_repair",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "acas_mft",
+    srcs = ["acas_mft.py"],
+    data = glob(["models/acas_2_9.eran"]),
+    deps = [
+        ":acas_repair",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "squeezenet_repair",
+    srcs = ["squeezenet_repair.py"],
+    data = ["@onnx_squeezenet//:all"],
+    deps = [
+        ":experiment",
+        ":imagenet_helpers",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "squeezenet_ft",
+    srcs = ["squeezenet_ft.py"],
+    data = ["@onnx_squeezenet//:all"],
+    deps = [
+        ":squeezenet_repair",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "squeezenet_mft",
+    srcs = ["squeezenet_mft.py"],
+    data = ["@onnx_squeezenet//:all"],
+    deps = [
+        ":squeezenet_repair",
+        "//prdnn",
+    ],
+)
+
+py_binary(
+    name = "imagenet_helpers",
+    srcs = ["imagenet_helpers.py"],
+)
diff --git a/experiments/README.md b/experiments/README.md
new file mode 100644
index 0000000..0e7a035
--- /dev/null
+++ b/experiments/README.md
@@ -0,0 +1,9 @@
+# Experiments
+These are Python scripts to reproduce experiments from our paper. Experiments
+can be run by calling `bazel run experiments:{experiment_name}`. Results are
+placed in a ``results`` folder in this directory.
+
+Note that in order to run `experiments:squeezenet_*`, you must have both the
+ImageNet-A and ImageNet-Validation datasets extracted on the local machine and
+provide their paths when requested. See [../README](../README) for more
+information.
diff --git a/experiments/acas_ft.py b/experiments/acas_ft.py
new file mode 100644
index 0000000..bab975b
--- /dev/null
+++ b/experiments/acas_ft.py
@@ -0,0 +1,71 @@
+"""Methods for fine-tuning the ACAS Xu network."""
+from timeit import default_timer as timer
+import numpy as np
+from prdnn import DDNN, FTRepair
+from experiments.acas_repair import ACASRepair
+
+class ACASFT(ACASRepair):
+    """Experiment testing fine-tuning repair on an ACAS Xu model."""
+    def do_repair(self, train_regions, train_syrenn, syrenn_time):
+        n_unique = len(set({tuple(point)
+                            for upolytope in train_syrenn
+                            for pre_poly in upolytope
+                            for point in pre_poly}))
+        samples_per_plane = [n_unique // len(train_regions) for _ in range(len(train_regions))]
+
+        patcher = FTRepair.from_spec_function(
+            self.network, train_regions, self.property,
+            samples_per_plane=samples_per_plane)
+        patcher.epochs = 1000
+        patcher.lr = 0.001
+        patcher.momentum = 0.9
+        patcher.batch_size = 16
+        patched = patcher.compute()
+        assert patched is not None
+        self.record_artifact(patched, f"patched", "network")
+        self.record_artifact(patcher.inputs, "train_inputs", "pickle")
+        self.record_artifact(patcher.labels, "train_labels", "pickle")
+        timing = patcher.timing.copy()
+        timing["syrenn"] = syrenn_time
+        self.record_artifact(timing, f"timing", "pickle")
+
+    def analyze(self):
+        """Analyze the results."""
+        unpatched = self.read_artifact("unpatched")
+        self.network = unpatched
+        patched = self.read_artifact("patched")
+        timing = self.read_artifact("timing")
+        train_syrenn = self.read_artifact("train_syrenn")
+        test_syrenn = self.read_artifact("test_syrenn")
+        train_inputs = self.read_artifact("train_inputs")
+        train_labels = self.read_artifact("train_labels")
+
+        total_points = sum(len(pre_poly) for upolytope in train_syrenn
+                           for pre_poly in upolytope)
+        print("Size of repair set:", total_points)
+
+        og_train_outputs = np.argmax(unpatched.compute(train_inputs), axis=1)
+        print("Number of repair set points originally buggy:",
+              np.sum(og_train_outputs != train_labels))
+
+        gen_set, drawdown_set = self.find_counterexamples(unpatched, test_syrenn)
+        print("Size of generalization, drawdown sets:", len(gen_set), len(drawdown_set))
+
+        print("Time (seconds):", timing["total"])
+
+        train_outputs = np.argmax(patched.compute(train_inputs), axis=1)
+        print("Number of repair set points buggy after repair:",
+              np.sum(train_outputs != train_labels))
+
+        dd_desired = self.property(drawdown_set)
+        dd_outputs = np.argmax(patched.compute(drawdown_set), axis=1)
+        print("Drawdown-set counterexamples after repair:", np.sum(dd_desired != dd_outputs))
+
+        gen_desired = self.property(gen_set)
+        gen_outputs = np.argmax(patched.compute(gen_set), axis=1)
+        print("Generalization-set counterexamples after repair:", np.sum(gen_desired != gen_outputs))
+
+        return True
+
+if __name__ == "__main__":
+    ACASFT("acas_ft").main()
diff --git a/experiments/acas_mft.py b/experiments/acas_mft.py
new file mode 100644
index 0000000..b16a070
--- /dev/null
+++ b/experiments/acas_mft.py
@@ -0,0 +1,86 @@
+"""Methods for patching the ACAS Xu network with SyReNN."""
+from timeit import default_timer as timer
+import numpy as np
+from pysyrenn import FullyConnectedLayer
+from prdnn import DDNN, FTRepair
+from experiments.acas_repair import ACASRepair
+
+class ACASMFT(ACASRepair):
+    """Experiment testing patching performance on an ACAS Xu model."""
+    def do_repair(self, train_regions, train_syrenn, syrenn_time):
+        n_unique = len(set({tuple(point)
+                            for upolytope in train_syrenn
+                            for pre_poly in upolytope
+                            for point in pre_poly}))
+        samples_per_plane = [n_unique // len(train_regions) for _ in range(len(train_regions))]
+
+        _patcher = FTRepair.from_spec_function(
+            self.network, train_regions, self.property,
+            samples_per_plane=samples_per_plane)
+        self.record_artifact(_patcher.inputs, "train_inputs", "pickle")
+        self.record_artifact(_patcher.labels, "train_labels", "pickle")
+
+        patchable = [i for i, layer in enumerate(self.network.layers)
+                     if isinstance(layer, FullyConnectedLayer)]
+        for layer in patchable:
+            patcher = FTRepair(self.network, _patcher.inputs.copy(), _patcher.labels.copy())
+            patcher.layer = layer
+            patcher.norm_objective = True
+            patcher.auto_stop = False
+            patcher.make_holdout_set()
+            patcher.epochs = 1000
+            patcher.lr = 0.001
+            patcher.momentum = 0.9
+            patcher.batch_size = 16
+            patched = patcher.compute()
+            assert patched is not None
+            self.record_artifact(patched, f"{layer}_patched", "network")
+            timing = patcher.timing.copy()
+            timing["syrenn"] = syrenn_time
+            self.record_artifact(timing, f"{layer}_timing", "pickle")
+
+    def analyze(self):
+        unpatched = self.read_artifact("unpatched")
+        self.network = unpatched
+
+        train_syrenn = self.read_artifact("train_syrenn")
+        test_syrenn = self.read_artifact("test_syrenn")
+        train_inputs = self.read_artifact("train_inputs")
+        train_labels = self.read_artifact("train_labels")
+
+        patchable = [i for i, layer in enumerate(self.network.layers)
+                     if isinstance(layer, FullyConnectedLayer)]
+        for layer in patchable:
+            print("~~~~~ Layer:", layer, "~~~~~")
+            patched = self.read_artifact(f"{layer}_patched")
+            timing = self.read_artifact(f"{layer}_timing")
+
+            total_points = sum(len(pre_poly) for upolytope in train_syrenn
+                               for pre_poly in upolytope)
+            print("Size of repair set:", total_points)
+
+            og_train_outputs = np.argmax(unpatched.compute(train_inputs), axis=1)
+            print("Number of repair set points originally buggy:",
+                  np.sum(og_train_outputs != train_labels))
+
+            gen_set, drawdown_set = self.find_counterexamples(unpatched, test_syrenn)
+            print("Size of generalization, drawdown sets:", len(gen_set), len(drawdown_set))
+
+            print("Time (seconds):", timing["total"])
+
+            train_outputs = np.argmax(patched.compute(train_inputs), axis=1)
+            print("Number of repair set points buggy after repair:",
+                  np.sum(train_outputs != train_labels))
+
+            dd_desired = self.property(drawdown_set)
+            dd_outputs = np.argmax(patched.compute(drawdown_set), axis=1)
+            print("Drawdown-set counterexamples after repair:", np.sum(dd_desired != dd_outputs))
+
+            gen_desired = self.property(gen_set)
+            gen_outputs = np.argmax(patched.compute(gen_set), axis=1)
+            print("Generalization-set counterexamples after repair:", np.sum(gen_desired != gen_outputs))
+
+        return True
+
+if __name__ == "__main__":
+    ACASMFT("acas_mft").main()
diff --git a/experiments/acas_repair.py b/experiments/acas_repair.py
new file mode 100644
index 0000000..dcf3ca8
--- /dev/null
+++ b/experiments/acas_repair.py
@@ -0,0 +1,222 @@
+"""Experiment for repairing the ACAS Xu network with SyReNN."""
+from timeit import default_timer as timer
+import numpy as np
+from prdnn import DDNN, ProvableRepair
+from experiments.experiment import Experiment
+
+class ACASRepair(Experiment):
+    """Experiment testing Provable Repair on an ACAS Xu model."""
+    def run(self):
+        """Repair the ACAS Xu network and record results."""
+        np.random.seed(24)
+        self.network = self.load_network("acas_2_9")
+        input_helpers = self.load_input_data("acas")
+        process = input_helpers["process"]
+
+        regions = self.find_regions(
+            # These are sampled:
+            (-0.1, 0.1), (600, 1200), (600, 1200),
+            # These are SyReNN'd:
+            (0, 60760), (-np.pi, -0.75 * np.pi),
+            process, n_samples=20)
+
+        network = self.network
+        train_regions = regions[:10]
+        test_regions = regions[10:]
+        assert len(test_regions) == 12
+
+        self.record_artifact(network, "unpatched", "network")
+
+        # Now that we have the regions, compute the SyReNN.
+        syrenn_start = timer()
+        train_syrenn = network.transform_planes(
+            train_regions, compute_preimages=True, include_post=False)
+        syrenn_time = timer() - syrenn_start
+        # We don't time this because it's just for evaluation.
+        test_syrenn = network.transform_planes(
+            test_regions, compute_preimages=True, include_post=False)
+
+        self.record_artifact(train_syrenn, "train_syrenn", "pickle")
+        self.record_artifact(test_syrenn, "test_syrenn", "pickle")
+
+        self.do_repair(train_regions, train_syrenn, syrenn_time)
+
+    def do_repair(self, train_regions, train_syrenn, syrenn_time):
+        # Then we start the repair.
+        patcher = ProvableRepair.from_spec_function(
+            self.network, 12, train_regions, self.property,
+            use_representatives=True)
+        patcher.constraint_type = "linf"
+        patcher.soft_constraint_slack_lb = -2.0
+        patcher.soft_constraint_slack_ub = 0.
+        patcher.soft_constraint_weight = 100.
+        patcher.constraint_buffer = 0.
+        patcher.gurobi_crossover = 0
+        # Batching any more than this doesn't really seem to help.
+        patcher.batch_size = 2048
+        patched = patcher.compute()
+        assert patched is not None
+        self.record_artifact(patched, f"patched", "ddnn")
+        timing = patcher.timing.copy()
+        timing["syrenn"] = syrenn_time
+        timing["total"] += syrenn_time
+        self.record_artifact(timing, f"timing", "pickle")
+
+    def analyze(self):
+        """Analyze the results."""
+        unpatched = self.read_artifact("unpatched")
+        self.network = unpatched
+        patched = self.read_artifact("patched")
+        timing = self.read_artifact("timing")
+        train_syrenn = self.read_artifact("train_syrenn")
+        test_syrenn = self.read_artifact("test_syrenn")
+
+        total_points = sum(len(pre_poly) for upolytope in train_syrenn
+                           for pre_poly in upolytope)
+        print("Size of repair set:", total_points)
+
+        gen_set, drawdown_set = self.find_counterexamples(unpatched, test_syrenn)
+        print("Size of generalization, drawdown sets:", len(gen_set), len(drawdown_set))
+
+        print("Time (seconds):", timing)
+
+        print("Polytope accuracy in the repair set after repair (should be 1.0 = 100%):",
+              self.spec_accuracy(patched, train_syrenn)[2])
+
+        dd_desired = self.property(drawdown_set)
+        dd_outputs = np.argmax(patched.compute(drawdown_set), axis=1)
+        print("Drawdown-set counterexamples after repair:", np.sum(dd_desired != dd_outputs))
+
+        gen_desired = self.property(gen_set)
+        gen_outputs = np.argmax(patched.compute(gen_set), axis=1)
+        print("Generalization-set counterexamples after repair:", np.sum(gen_desired != gen_outputs))
+
+        return True
+
+    def find_regions(self, *args, **kwargs):
+        """Returns 20 input slices which have counterexamples.
+
+        Overall, this is accomplished by randomly sampling regions, applying
+        SyReNN, and seeing if they have a counterexample.
+
+        To speed this process up, I have manually run it for enough iterations
+        to see which of the first batch of randomly-sampled regions have
+        counterexamples. Then I specified their indices in @with_cexs, which
+        allows us to skip computing SyReNN for regions without counterexamples
+        (there are lots of them!). To verify this, you may set with_cexs=[] to
+        force the system to check for counterexamples on all randomly-sampled
+        regions explicitly.
+        """
+        np.random.seed(24)
+        n_samples = kwargs["n_samples"]
+        with_cexs = [[3, 5, 7, 38, 55],
+                     [5, 16, 23, 48, 70, 99],
+                     [1, 10, 16, 38, 59, 74, 99],
+                     [64, 71, 89, 92]]
+        found = []
+        iters = -1
+        while len(found) < n_samples:
+            iters += 1
+            kwargs["n_samples"] = 100
+            regions = self.compute_regions(*args, **kwargs)
+            if iters < len(with_cexs):
+                regions = [regions[i] for i in with_cexs[iters]]
+            syrenn = self.network.transform_planes(
+                regions, compute_preimages=True, include_post=False)
+            for i, upolytope in enumerate(syrenn):
+                all_points = np.concatenate(upolytope)
+                outputs = np.argmax(self.network.compute(all_points), axis=1)
+                if np.any(outputs >= 2):
+                    found.append(regions[i])
+        return found
+
+    def find_counterexamples(self, network, syrenn):
+        """Given SyReNN for a region, returns cexs and non-cexs."""
+        np.random.seed(24)
+        counterexamples = []
+        drawdown_pts = []
+        for upolytope in syrenn:
+            all_polys = np.concatenate(upolytope)
+            min_ = np.min(all_polys, axis=0)
+            max_ = np.max(all_polys, axis=0)
+
+            all_polys = []
+            for pre_poly in upolytope:
+                center = np.mean(pre_poly, axis=0)
+                for alpha in [0.25, 0.5, 0.75]:
+                    poly = pre_poly + (alpha * (pre_poly - center))
+                    all_polys.extend(poly)
+            desired = self.property(all_polys)
+            outputs = np.argmax(network.compute(all_polys), axis=1)
+            cex_points = np.asarray(all_polys)[outputs != desired]
+            counterexamples.extend(cex_points)
+
+            sample_points = np.random.uniform(min_, max_, size=(5*len(cex_points), 5))
+            desired = self.property(sample_points)
+            outputs = np.argmax(network.compute(sample_points), axis=1)
+            sample_points = sample_points[desired == outputs][:len(cex_points)]
+            drawdown_pts.extend(sample_points)
+        return counterexamples, drawdown_pts
+
+    def spec_accuracy(self, network, syrenn):
+        """Computes statistics about how well the given network meets the spec.
+
+        Note that these are based on SyReNN, by checking the vertices of the
+        linear regions. If it states that there are no bad points, then the
+        entire region is guaranteed to meet the spec.
+        """
+        n_good, n_bad, area_bad = 0, 0, 0.
+        polytopes = (pre_poly for upolytope in syrenn for pre_poly in upolytope)
+        for pre_poly in polytopes:
+            desired = self.property(pre_poly)
+            if isinstance(network, DDNN):
+                representative = np.mean(pre_poly, axis=0)
+                representatives = np.array([representative for _ in pre_poly])
+                patched_output = network.compute(
+                    pre_poly, representatives=representatives)
+            else:
+                patched_output = network.compute(pre_poly)
+            patched_output = np.argmax(patched_output, axis=1)
+            is_correct = np.all(patched_output == desired)
+            if is_correct:
+                n_good += 1
+            else:
+                n_bad += 1
+        return (n_good, n_bad, n_good / (n_good + n_bad), area_bad)
+
+    def property(self, inputs):
+        """Property 8 from the Reluplex paper (Katz et al.).
+
+        Note this is a slightly strengthened version of that property, in order
+        to make it convex/conjunctive.
+        """
+        output = self.network.compute(inputs)
+        # labels is 0 when it should be COC, 1 when it should be WL.
+        label_wl = (output[:, 1] > output[:, 0]).astype(int)
+        return label_wl.astype(int)
+
+    def compute_regions(self,
+                        intruder_heading, own_velocity, intruder_velocity,
+                        rho, phi, process, n_samples):
+        """Samples @n_samples 2D slices from the space."""
+        regions = []
+        for _ in range(n_samples):
+            self.intruder_heading = np.random.uniform(*intruder_heading)
+            self.own_velocity = np.random.uniform(*own_velocity)
+            self.intruder_velocity = np.random.uniform(*intruder_velocity)
+            regions.append(process(np.array([
+                self.build_input(rho[0], phi[0]),
+                self.build_input(rho[1], phi[0]),
+                self.build_input(rho[1], phi[1]),
+                self.build_input(rho[0], phi[1]),
+            ])))
+        return regions
+
+    def build_input(self, distance, psi):
+        """Returns an (un-processed) input point corresponding to the scenario.
+        """
+        return np.array([distance, psi, self.intruder_heading,
+                         self.own_velocity, self.intruder_velocity])
+
+if __name__ == "__main__":
+    ACASRepair("acas_repair").main()
diff --git a/experiments/experiment.py b/experiments/experiment.py
new file mode 100644
index 0000000..2719075
--- /dev/null
+++ b/experiments/experiment.py
@@ -0,0 +1,357 @@
+"""Base class for executing, recording data for, and analyzing experiments.
+"""
+import base64
+import csv
+import io
+import os
+import pickle
+import shutil
+import tarfile
+import numpy as np
+from PIL import Image
+import imageio
+from pysyrenn import Network
+from prdnn import DDNN
+from syrenn_proto import syrenn_pb2 as syrenn_pb
+
+class Experiment:
+    """Abstract class describing a network experiment.
+    """
+    def __init__(self, directory_name):
+        """Initializes a new experiment.
+
+        Creates an output directory, removing any existing files in that
+        location. Also initializes a new artifacts_csv file which holds a list
+        of all the artifacts written to using the "record_artifact" interface.
+        """
+        # Create a directory outside of bazel-bin for storing the results.
+        global_dir = os.environ["BUILD_WORKING_DIRECTORY"]
+        self.directory = "{}/experiments/results/{}".format(
+            global_dir, directory_name)
+        shutil.rmtree(self.directory, ignore_errors=True)
+        os.makedirs(self.directory, exist_ok=True)
+
+        self.tar_name = "%s.exp.tgz" % self.directory
+        self.open_files = []
+        self.artifacts = None
+        self.artifacts_csv = self.begin_csv("artifacts", ["key", "type", "path"])
+
+    def close(self, tar=True, nicely=True):
+        """Ends the experiment, freeing open file pointers.
+
+        @tar determines whether the experiment directory should be tarred into
+        an archive. In general, this is done after the initial experiments and
+        then once more if the analysis produces any new files.
+
+        @nicely should indicate whether the closing is expected or not. For
+        example, if the program errors in the middle of an experiment, it is
+        not "nice." nicely=False will leave the experiment directory alone
+        (i.e. untarred and unremoved).
+        """
+        for open_file in self.open_files:
+            open_file.close()
+        self.open_files = []
+
+        if tar and nicely:
+            # tar directory into directory.exp.tar
+            with tarfile.open(self.tar_name, "w:gz") as archive:
+                for name in os.listdir(self.directory):
+                    archive.add("%s/%s" % (self.directory, name), arcname=name)
+        if nicely:
+            shutil.rmtree(self.directory, ignore_errors=True)
+
+    def open(self):
+        """Reads experiment data from a previous run.
+
+        In general, this is called after run() and close(), or when doing an
+        analyze-only execution of previous experimental results.
+        """
+        # Create the extraction directory.
+        shutil.rmtree(self.directory, ignore_errors=True)
+        os.mkdir(self.directory)
+
+        # Extract the tar file.
+        with tarfile.open(self.tar_name, "r:*") as archive:
+            archive.extractall(self.directory)
+
+        self.artifacts = self.read_csv("artifacts")
+        # Re-open and re-fill the CSV file so we can keep writing to it.
+        self.artifacts_csv = self.begin_csv("artifacts", ["key", "type", "path"])
+        # TODO(masotoud): look into a way to open the file for appending.
+        # instead of truncating + re-adding.
+        for artifact in self.artifacts:
+            self.write_csv(self.artifacts_csv, artifact)
+
+    def has_archive(self):
+        """True if the experiment seems to have already been run.
+        """
+        return os.path.exists(self.tar_name)
+
+    def remove_archive(self):
+        """Removes an existing archive.
+        """
+        return os.remove(self.tar_name)
+
+    def __del__(self):
+        """Close file handles in case of unexpected exit.
+
+        Normal exits should call .close(nicely=True).
+        """
+        self.close(nicely=False)
+
+    @staticmethod
+    def load_network(network_name, maxify_acas=True):
+        """Loads an experiment network given by @network_name.
+
+        Currently supports models of the form:
+        - acas_#_# (ACAS Xu models translated from the ReluPlex format)
+        - {cifar10,mnist}_relu_#_# (fully-connected ReLU models from ERAN)
+        - {cifar10,mnist}_relu_conv{small,medium,big}{_diffai,_pgd}
+          (convolutional ReLU models from ERAN).
+
+        And should be referenced in BUILD rule experiments:models.
+
+        maxify_acas controlls whether the ACAS model is "cleaned" before
+        returned; cleaning removes the unnecessary ReLU layer at the end as
+        well as inverts the outputs so the recommended action becomes the
+        maximal score.
+        """
+        if "acas_" in network_name:
+            _, i, j = network_name.split("_")
+            network = Network.from_file("experiments/models/acas_%s_%s.eran"
+                                        % (i, j))
+            if maxify_acas:
+                # We remove ReLU layers from the end of the model as they don't
+                # actually change the classification (when one exists).
+                assert not hasattr(network.layers[:-1], "weights")
+                network.layers = network.layers[:-1]
+
+                # ACAS Xu networks use the minimal score as the class instead
+                # of the more-standard maximum score; this inverts the last
+                # layer so the minimal score becomes the max.
+                network.layers[-1].weights *= -1.0
+                network.layers[-1].biases *= -1.0
+            return network
+        if "squeezenet" in network_name:
+            return Network.from_file("external/onnx_squeezenet/squeezenet1.1.onnx")
+        return Network.from_file(
+            "external/%s_model/file/model.eran" % (network_name))
+
+    @staticmethod
+    def load_input_data(name_or_path, is_eran_conv_model=False):
+        """Gets a dataset and/or its metadata.
+
+        Currently supports three datasets:
+        - acas (empty dataset which returns preprocessing info for ACAS)
+        - cifar10_test (100 test images from ERAN)
+        - mnist_test (100 test images from ERAN)
+
+        Returns a dictionary with four items:
+        - process(np_array) will process a raw (uint8) Numpy array image into a
+          format that can be passed to the Network.
+        - reset(np_array) will invert process(...). This may not always be
+          possible if process(...) is non-invertible, but it should at least
+          work on all valid images (i.e., uint8 pixel values).
+        - raw_inputs holds (flattened) uint8 Numpy arrays for each input image.
+        - labels holds the corresponding label for each input image.
+        """
+        if name_or_path == "acas":
+            mins = np.array([0.0, -3.141593, -3.141593, 100.0, 0.0])
+            maxes = np.array([60760.0, 3.141593, 3.141593, 1200.0, 1200.0])
+            means = np.array([1.9791091e+04, 0.0, 0.0, 650.0, 600.0])
+            std_deviations = np.array([60261.0, 6.28318530718, 6.28318530718,
+                                       1100.0, 1200.0])
+            return {
+                "name": name_or_path,
+                "process": lambda i: ((np.clip(i, mins, maxes) - means) / std_deviations),
+                "reset": lambda i: ((i * std_deviations) + means),
+                "raw_inputs": [],
+                "labels": [],
+            }
+        inputs_file_path = "external/%s_data/file/data.csv" % name_or_path
+        # ERAN models
+        with open(inputs_file_path, "r", newline="") as inputs_file:
+            csv_inputs = csv.reader(inputs_file)
+            input_data = np.array(list(csv_inputs)).astype(np.float64)
+
+        # TODO(masotoud): handle this more robustly.
+        process_input = lambda i: i / 255.0
+        reset_input = lambda i: np.round(i * 255.0)
+        return {
+            "name": name_or_path,
+            "process": process_input,
+            "reset": reset_input,
+            "raw_inputs": input_data[:, 1:],
+            "labels": input_data[:, 0].astype(np.int),
+        }
+
+    def begin_csv(self, filename, column_labels, extrasaction="raise"):
+        """Opens a new CSV file with the given column labels for writing.
+
+        Returns a tuple (file_handle, csv_writer) that can be passed to
+        write_csv. These do not need to be manually flushed or closed --- that
+        is handled by Experiment.close() and Experiment.write_csv().
+
+        @filename should be a path-safe identifier for the CSV file (extension
+            and path not necessary).
+        @column_labels should be a list of (string) column labels. These will
+            correspond to dictionary keys in write_csv and read_csv.
+        """
+        dirname = os.path.dirname(filename)
+        self.artifact_directory(dirname) # Ensure the directory exists
+        csv_file = open("%s/%s.csv" % (self.directory, filename), "w",
+                        newline="")
+        csv_writer = csv.DictWriter(csv_file, column_labels,
+                                    extrasaction=extrasaction)
+        csv_writer.writeheader()
+        self.open_files.append(csv_file)
+        return (csv_file, csv_writer)
+
+    @staticmethod
+    def write_csv(csv_data, record):
+        """Writes a record to a CSV file opened with Experiment.begin_csv(...).
+
+        @csv_data should be the tuple returned by Experiment.begin_csv(...)
+        @record should be a dictionary with keys corresponding to the
+            @column_labels passed to Experiment.begin_csv(...)
+        """
+        csv_data[1].writerow(record)
+        csv_data[0].flush()
+
+    def read_csv(self, filename):
+        """Fully reads a CSV file and returns a list of the rows.
+
+        Each row is represented by a dictionary with keys corresponding to the
+        columns. Dictionary values are strings --- parsing them to a usable
+        format is left to the caller.
+        """
+        filename = "%s/%s.csv" % (self.directory, filename)
+        with open(filename, "r", newline="") as csv_file:
+            csv_reader = csv.DictReader(csv_file)
+            data = []
+            for record in csv_reader:
+                data.append(dict(record))
+        return data
+
+    def artifact_directory(self, dir_key):
+        """Creates a directory that will be included in the experiment archive.
+
+        Returns its path without trailing /.
+        """
+        name = "%s/%s" % (self.directory, dir_key)
+        os.makedirs(name, exist_ok=True)
+        return name
+
+    def record_artifact(self, artifact, key, artifact_type):
+        """Record a high-level artifact from the experiment.
+
+        Each Experiment instance has a corresponding "artifact store" which
+        allows one to easily record, store, and later reference artifacts
+        produced during the experiment. This method adds an artifact @artifact
+        to that store, using key @key under the assumption that the artifact
+        should be treated as type @artifact_type.
+        """
+        filename = "%s/%s" % (self.directory, key)
+
+        file_directory = os.path.dirname(filename)
+        if artifact_type != "rawpath" and not os.path.exists(file_directory):
+            # See notes on a possible race condition in the answer here:
+            # https://stackoverflow.com/questions/10149263
+            os.makedirs(file_directory)
+
+        def write_pb(path, pb_serialized):
+            """Writes @pb_serialized to @path.
+            """
+            with open(path, "wb") as to_file:
+                to_file.write(pb_serialized.SerializeToString())
+
+        if artifact_type == "np_array":
+            filename += ".npy"
+            np.save(filename, artifact)
+        elif artifact_type == "pickle":
+            filename += ".pickle"
+            with open(filename, "wb") as to_file:
+                pickle.dump(artifact, to_file)
+        elif artifact_type in ("network", "ddnn"):
+            filename += ".pb"
+            write_pb(filename, artifact.serialize())
+        elif artifact_type == "csv":
+            filename = artifact
+        else:
+            raise NotImplementedError
+        record = {"key": key, "type": artifact_type, "path": filename}
+        self.write_csv(self.artifacts_csv, record)
+        if self.artifacts is not None:
+            self.artifacts.append(record)
+
+    def read_artifact(self, key):
+        """Reads an artifact from the loaded artifact store indexed by @key.
+
+        Experiment.open() *MUST* be called before using read_artifact(...).
+        This method is intended to be used only by the analyze() method (not
+        run, which should be calling record_artifact).
+        """
+        assert self.artifacts is not None
+        try:
+            artifact = next(artifact for artifact in self.artifacts
+                            if artifact["key"] == key)
+        except StopIteration:
+            raise KeyError
+
+        def read_pb(path, pb_type):
+            """Deserializes protobuf data stored to a file.
+
+            @path is the file path, @pb_type is the Protobuf descriptor to
+            parse as.
+            """
+            with open(path, "rb") as from_file:
+                string_rep = from_file.read()
+            serialized = pb_type()
+            serialized.ParseFromString(string_rep)
+            return serialized
+
+        if artifact["type"] == "np_array":
+            return np.load(artifact["path"], allow_pickle=True)
+        if artifact["type"] == "pickle":
+            with open(artifact["path"], "rb") as from_file:
+                return pickle.load(from_file)
+        if artifact["type"] == "csv":
+            return self.read_csv(artifact["path"])
+        if artifact["type"] == "network":
+            return Network.deserialize(
+                read_pb(artifact["path"], syrenn_pb.Network))
+        if artifact["type"] == "ddnn":
+            return DDNN.deserialize(
+                read_pb(artifact["path"], syrenn_pb.MaskingNetwork))
+        raise NotImplementedError
+
+    def run(self):
+        """Runs the analysis on the network and inputs.
+        """
+        raise NotImplementedError
+
+    def analyze(self):
+        """Performs analysis and summarization after a run().
+
+        Experiment.read_artifact(key) should be used to recover data from the
+        experiment.
+        """
+        raise NotImplementedError
+
+    def main(self):
+        """Main experiment harness.
+        """
+        run = not self.has_archive()
+        if not run:
+            print("It seems that this experiment has already been run.")
+            choice = input("[R]e-run, [A]nalyze-only, or [D]elete and re-run? ").lower()[0]
+            assert choice in {"r", "a", "d"}
+            if choice == "d":
+                self.remove_archive()
+            run = choice in {"r", "d"}
+        if run:
+            self.run()
+            self.close()
+        self.open()
+        did_modify = self.analyze()
+        self.close(tar=did_modify)
diff --git a/experiments/imagenet_helpers.py b/experiments/imagenet_helpers.py
new file mode 100644
index 0000000..b64976a
--- /dev/null
+++ b/experiments/imagenet_helpers.py
@@ -0,0 +1,180 @@
+"""Helper methods for reading ImageNet-like datasets."""
+import os
+import pathlib
+import random
+from PIL import Image
+import numpy as np
+
+def read_imagenet_images(parent, n_labels=None, use_labels=None):
+    """Reads a particular subset of the ImageNet dataset stored in @parent.
+
+    The idea is that @parent has a number of subdirectories, each corresponding
+    to a class, and under which there are a number of images. @n_labels gives
+    the total number of labels you want. @use_labels limits it to a particular
+    subset of the labels.
+
+    Returns a pair (images, labels)
+
+    Used by imagenet_patching.py
+    """
+    subdirs = [subdir for subdir in sorted(os.listdir(parent))
+               if subdir != "README.txt"]
+    if use_labels is not None:
+        use_labels = list(map(SYNSETS.__getitem__, use_labels))
+        subdirs = [subdir for subdir in subdirs
+                   if int(subdir[1:]) in use_labels]
+    if n_labels is not None:
+        subdirs = subdirs[:n_labels]
+
+    all_images, all_labels = [], []
+    for subdir in subdirs:
+        if subdir == "README.txt":
+            continue
+        synset_id = int(subdir[1:])
+        label = SYNSETS.index(synset_id)
+        images = sorted(os.listdir(f"{parent}/{subdir}"))
+        random.shuffle(images)
+        n_read = 0
+        for image in images:
+            path = pathlib.Path(f"{parent}/{subdir}/{image}")
+            image = np.asarray(
+                Image.open(path.resolve())
+                .resize((224, 224))) / 255.
+            if len(image.shape) == 3:
+                all_images.append(image)
+                all_labels.append(label)
+                n_read += 1
+            else:
+                # Bad shape; we can de-BW it if we want.
+                pass
+    return np.array(all_images), np.array(all_labels)
+
+# https://gist.github.com/fnielsen/4a5c94eaa6dcdf29b7a62d886f540372
+SYNSETS = list([
+    1440764, 1443537, 1484850, 1491361, 1494475, 1496331, 1498041, 1514668,
+    1514859, 1518878, 1530575, 1531178, 1532829, 1534433, 1537544, 1558993,
+    1560419, 1580077, 1582220, 1592084, 1601694, 1608432, 1614925, 1616318,
+    1622779, 1629819, 1630670, 1631663, 1632458, 1632777, 1641577, 1644373,
+    1644900, 1664065, 1665541, 1667114, 1667778, 1669191, 1675722, 1677366,
+    1682714, 1685808, 1687978, 1688243, 1689811, 1692333, 1693334, 1694178,
+    1695060, 1697457, 1698640, 1704323, 1728572, 1728920, 1729322, 1729977,
+    1734418, 1735189, 1737021, 1739381, 1740131, 1742172, 1744401, 1748264,
+    1749939, 1751748, 1753488, 1755581, 1756291, 1768244, 1770081, 1770393,
+    1773157, 1773549, 1773797, 1774384, 1774750, 1775062, 1776313, 1784675,
+    1795545, 1796340, 1797886, 1798484, 1806143, 1806567, 1807496, 1817953,
+    1818515, 1819313, 1820546, 1824575, 1828970, 1829413, 1833805, 1843065,
+    1843383, 1847000, 1855032, 1855672, 1860187, 1871265, 1872401, 1873310,
+    1877812, 1882714, 1883070, 1910747, 1914609, 1917289, 1924916, 1930112,
+    1943899, 1944390, 1945685, 1950731, 1955084, 1968897, 1978287, 1978455,
+    1980166, 1981276, 1983481, 1984695, 1985128, 1986214, 1990800, 2002556,
+    2002724, 2006656, 2007558, 2009229, 2009912, 2011460, 2012849, 2013706,
+    2017213, 2018207, 2018795, 2025239, 2027492, 2028035, 2033041, 2037110,
+    2051845, 2056570, 2058221, 2066245, 2071294, 2074367, 2077923, 2085620,
+    2085782, 2085936, 2086079, 2086240, 2086646, 2086910, 2087046, 2087394,
+    2088094, 2088238, 2088364, 2088466, 2088632, 2089078, 2089867, 2089973,
+    2090379, 2090622, 2090721, 2091032, 2091134, 2091244, 2091467, 2091635,
+    2091831, 2092002, 2092339, 2093256, 2093428, 2093647, 2093754, 2093859,
+    2093991, 2094114, 2094258, 2094433, 2095314, 2095570, 2095889, 2096051,
+    2096177, 2096294, 2096437, 2096585, 2097047, 2097130, 2097209, 2097298,
+    2097474, 2097658, 2098105, 2098286, 2098413, 2099267, 2099429, 2099601,
+    2099712, 2099849, 2100236, 2100583, 2100735, 2100877, 2101006, 2101388,
+    2101556, 2102040, 2102177, 2102318, 2102480, 2102973, 2104029, 2104365,
+    2105056, 2105162, 2105251, 2105412, 2105505, 2105641, 2105855, 2106030,
+    2106166, 2106382, 2106550, 2106662, 2107142, 2107312, 2107574, 2107683,
+    2107908, 2108000, 2108089, 2108422, 2108551, 2108915, 2109047, 2109525,
+    2109961, 2110063, 2110185, 2110341, 2110627, 2110806, 2110958, 2111129,
+    2111277, 2111500, 2111889, 2112018, 2112137, 2112350, 2112706, 2113023,
+    2113186, 2113624, 2113712, 2113799, 2113978, 2114367, 2114548, 2114712,
+    2114855, 2115641, 2115913, 2116738, 2117135, 2119022, 2119789, 2120079,
+    2120505, 2123045, 2123159, 2123394, 2123597, 2124075, 2125311, 2127052,
+    2128385, 2128757, 2128925, 2129165, 2129604, 2130308, 2132136, 2133161,
+    2134084, 2134418, 2137549, 2138441, 2165105, 2165456, 2167151, 2168699,
+    2169497, 2172182, 2174001, 2177972, 2190166, 2206856, 2219486, 2226429,
+    2229544, 2231487, 2233338, 2236044, 2256656, 2259212, 2264363, 2268443,
+    2268853, 2276258, 2277742, 2279972, 2280649, 2281406, 2281787, 2317335,
+    2319095, 2321529, 2325366, 2326432, 2328150, 2342885, 2346627, 2356798,
+    2361337, 2363005, 2364673, 2389026, 2391049, 2395406, 2396427, 2397096,
+    2398521, 2403003, 2408429, 2410509, 2412080, 2415577, 2417914, 2422106,
+    2422699, 2423022, 2437312, 2437616, 2441942, 2442845, 2443114, 2443484,
+    2444819, 2445715, 2447366, 2454379, 2457408, 2480495, 2480855, 2481823,
+    2483362, 2483708, 2484975, 2486261, 2486410, 2487347, 2488291, 2488702,
+    2489166, 2490219, 2492035, 2492660, 2493509, 2493793, 2494079, 2497673,
+    2500267, 2504013, 2504458, 2509815, 2510455, 2514041, 2526121, 2536864,
+    2606052, 2607072, 2640242, 2641379, 2643566, 2655020, 2666196, 2667093,
+    2669723, 2672831, 2676566, 2687172, 2690373, 2692877, 2699494, 2701002,
+    2704792, 2708093, 2727426, 2730930, 2747177, 2749479, 2769748, 2776631,
+    2777292, 2782093, 2783161, 2786058, 2787622, 2788148, 2790996, 2791124,
+    2791270, 2793495, 2794156, 2795169, 2797295, 2799071, 2802426, 2804414,
+    2804610, 2807133, 2808304, 2808440, 2814533, 2814860, 2815834, 2817516,
+    2823428, 2823750, 2825657, 2834397, 2835271, 2837789, 2840245, 2841315,
+    2843684, 2859443, 2860847, 2865351, 2869837, 2870880, 2871525, 2877765,
+    2879718, 2883205, 2892201, 2892767, 2894605, 2895154, 2906734, 2909870,
+    2910353, 2916936, 2917067, 2927161, 2930766, 2939185, 2948072, 2950826,
+    2951358, 2951585, 2963159, 2965783, 2966193, 2966687, 2971356, 2974003,
+    2977058, 2978881, 2979186, 2980441, 2981792, 2988304, 2992211, 2992529,
+    2999410, 3000134, 3000247, 3000684, 3014705, 3016953, 3017168, 3018349,
+    3026506, 3028079, 3032252, 3041632, 3042490, 3045698, 3047690, 3062245,
+    3063599, 3063689, 3065424, 3075370, 3085013, 3089624, 3095699, 3100240,
+    3109150, 3110669, 3124043, 3124170, 3125729, 3126707, 3127747, 3127925,
+    3131574, 3133878, 3134739, 3141823, 3146219, 3160309, 3179701, 3180011,
+    3187595, 3188531, 3196217, 3197337, 3201208, 3207743, 3207941, 3208938,
+    3216828, 3218198, 3220513, 3223299, 3240683, 3249569, 3250847, 3255030,
+    3259280, 3271574, 3272010, 3272562, 3290653, 3291819, 3297495, 3314780,
+    3325584, 3337140, 3344393, 3345487, 3347037, 3355925, 3372029, 3376595,
+    3379051, 3384352, 3388043, 3388183, 3388549, 3393912, 3394916, 3400231,
+    3404251, 3417042, 3424325, 3425413, 3443371, 3444034, 3445777, 3445924,
+    3447447, 3447721, 3450230, 3452741, 3457902, 3459775, 3461385, 3467068,
+    3476684, 3476991, 3478589, 3481172, 3482405, 3483316, 3485407, 3485794,
+    3492542, 3494278, 3495258, 3496892, 3498962, 3527444, 3529860, 3530642,
+    3532672, 3534580, 3535780, 3538406, 3544143, 3584254, 3584829, 3590841,
+    3594734, 3594945, 3595614, 3598930, 3599486, 3602883, 3617480, 3623198,
+    3627232, 3630383, 3633091, 3637318, 3642806, 3649909, 3657121, 3658185,
+    3661043, 3662601, 3666591, 3670208, 3673027, 3676483, 3680355, 3690938,
+    3691459, 3692522, 3697007, 3706229, 3709823, 3710193, 3710637, 3710721,
+    3717622, 3720891, 3721384, 3724870, 3729826, 3733131, 3733281, 3733805,
+    3742115, 3743016, 3759954, 3761084, 3763968, 3764736, 3769881, 3770439,
+    3770679, 3773504, 3775071, 3775546, 3776460, 3777568, 3777754, 3781244,
+    3782006, 3785016, 3786901, 3787032, 3788195, 3788365, 3791053, 3792782,
+    3792972, 3793489, 3794056, 3796401, 3803284, 3804744, 3814639, 3814906,
+    3825788, 3832673, 3837869, 3838899, 3840681, 3841143, 3843555, 3854065,
+    3857828, 3866082, 3868242, 3868863, 3871628, 3873416, 3874293, 3874599,
+    3876231, 3877472, 3877845, 3884397, 3887697, 3888257, 3888605, 3891251,
+    3891332, 3895866, 3899768, 3902125, 3903868, 3908618, 3908714, 3916031,
+    3920288, 3924679, 3929660, 3929855, 3930313, 3930630, 3933933, 3935335,
+    3937543, 3938244, 3942813, 3944341, 3947888, 3950228, 3954731, 3956157,
+    3958227, 3961711, 3967562, 3970156, 3976467, 3976657, 3977966, 3980874,
+    3982430, 3983396, 3991062, 3992509, 3995372, 3998194, 4004767, 4005630,
+    4008634, 4009552, 4019541, 4023962, 4026417, 4033901, 4033995, 4037443,
+    4039381, 4040759, 4041544, 4044716, 4049303, 4065272, 4067472, 4069434,
+    4070727, 4074963, 4081281, 4086273, 4090263, 4099969, 4111531, 4116512,
+    4118538, 4118776, 4120489, 4125021, 4127249, 4131690, 4133789, 4136333,
+    4141076, 4141327, 4141975, 4146614, 4147183, 4149813, 4152593, 4153751,
+    4154565, 4162706, 4179913, 4192698, 4200800, 4201297, 4204238, 4204347,
+    4208210, 4209133, 4209239, 4228054, 4229816, 4235860, 4238763, 4239074,
+    4243546, 4251144, 4252077, 4252225, 4254120, 4254680, 4254777, 4258138,
+    4259630, 4263257, 4264628, 4265275, 4266014, 4270147, 4273569, 4275548,
+    4277352, 4285008, 4286575, 4296562, 4310018, 4311004, 4311174, 4317175,
+    4325704, 4326547, 4328186, 4330267, 4332243, 4335435, 4336792, 4344873,
+    4346328, 4347754, 4350905, 4355338, 4355933, 4356056, 4357314, 4366367,
+    4367480, 4370456, 4371430, 4371774, 4372370, 4376876, 4380533, 4389033,
+    4392985, 4398044, 4399382, 4404412, 4409515, 4417672, 4418357, 4423845,
+    4428191, 4429376, 4435653, 4442312, 4443257, 4447861, 4456115, 4458633,
+    4461696, 4462240, 4465501, 4467665, 4476259, 4479046, 4482393, 4483307,
+    4485082, 4486054, 4487081, 4487394, 4493381, 4501370, 4505470, 4507155,
+    4509417, 4515003, 4517823, 4522168, 4523525, 4525038, 4525305, 4532106,
+    4532670, 4536866, 4540053, 4542943, 4548280, 4548362, 4550184, 4552348,
+    4553703, 4554684, 4557648, 4560804, 4562935, 4579145, 4579432, 4584207,
+    4589890, 4590129, 4591157, 4591713, 4592741, 4596742, 4597913, 4599235,
+    4604644, 4606251, 4612504, 4613696, 6359193, 6596364, 6785654, 6794110,
+    6874185, 7248320, 7565083, 7579787, 7583066, 7584110, 7590611, 7613480,
+    7614500, 7615774, 7684084, 7693725, 7695742, 7697313, 7697537, 7711569,
+    7714571, 7714990, 7715103, 7716358, 7716906, 7717410, 7717556, 7718472,
+    7718747, 7720875, 7730033, 7734744, 7742313, 7745940, 7747607, 7749582,
+    7753113, 7753275, 7753592, 7754684, 7760859, 7768694, 7802026, 7831146,
+    7836838, 7860988, 7871810, 7873807, 7875152, 7880968, 7892512, 7920052,
+    7930864, 7932039, 9193705, 9229709, 9246464, 9256479, 9288635, 9332890,
+    9399592, 9421951, 9428293, 9468604, 9472597, 9835506, 10148035, 10565667,
+    11879895, 11939491, 12057211, 12144580, 12267677, 12620546, 12768682,
+    12985857, 12998815, 13037406, 13040303, 13044778, 13052670, 13054560,
+    13133613, 15075141,
+])
diff --git a/experiments/mnist_ft.py b/experiments/mnist_ft.py
new file mode 100644
index 0000000..c14730c
--- /dev/null
+++ b/experiments/mnist_ft.py
@@ -0,0 +1,159 @@
+"""Experiment to patch an MNIST image-recognition model."""
+from collections import defaultdict
+import random
+from timeit import default_timer as timer
+import numpy as np
+from pysyrenn import Network
+from pysyrenn import ReluLayer
+from experiments.mnist_repair import MNISTRepair
+from prdnn import FTRepair
+
+class MNISTFT(MNISTRepair):
+    """Attempts to patch networks to be resillient to corruptions."""
+    # zigzag, fog, brightness
+    corruption = "fog"
+    def run(self):
+        """Runs the corruption-patching experiment."""
+        network = self.load_network("mnist_relu_3_100")
+
+        assert isinstance(network.layers[-1], ReluLayer)
+        network = Network(network.layers[:-1])
+
+        self.record_artifact(network, "original", "network")
+
+        self.which_params = int(input("Which fine-tuning params? (1 or 2): "))
+        assert self.which_params in {1, 2}
+        n_rows = int(input("How many rows of Table 2 to generate (1, 2, 3, or 4): "))
+        for n_lines in [10, 25, 50, 100][:n_rows]:
+            print(f"Running with {n_lines} lines")
+            self.run_for(network, n_lines)
+
+    def run_for(self, network, n_lines):
+        """Runs experiments for a particular # of lines."""
+        experiment = f"{n_lines}_lines"
+
+        # Get the training lines. Only use lines where the original image is
+        # correctly classified.
+        train_lines, train_labels = self.get_corrupted(
+            "train", n_lines, only_correct_on=network, corruption=self.corruption)
+        # Compute SyReNN for each line.
+        start = timer()
+        train_syrenn = network.exactlines(
+            train_lines, compute_preimages=True, include_post=False)
+        syrenn_time = timer() - start
+
+        # Record the SyReNNs and the labels.
+        self.record_artifact(
+            train_syrenn, f"{experiment}/train_syrenn", "pickle")
+        self.record_artifact(
+            train_labels, f"{experiment}/train_labels", "pickle")
+
+        # Unpack the SyReNNs and associated labels to points for the patcher.
+        points, labels = self.sample_like_syrenn(train_syrenn, train_labels)
+
+        layer = -1
+        patcher = FTRepair(network, points, labels)
+        patcher.batch_size = 16
+        # This is just a maximum epoch timeout, it will stop once all
+        # constraints are met.
+        patcher.epochs = 1000
+        patcher.momentum = 0.9
+        if self.which_params == 1:
+            patcher.lr = 0.05
+        else:
+            patcher.lr = 0.01
+        patched = patcher.compute()
+        patcher.timing["syrenn_time"] = syrenn_time
+
+        self.record_artifact(
+            len(points), f"{experiment}/n_repair_points", "pickle")
+        self.record_artifact(
+            patcher.epoched_out, f"{experiment}/epoched_out", "pickle")
+        self.record_artifact(
+            patched, f"{experiment}/patched_{layer}",
+            "pickle" if patched is None else "network")
+        self.record_artifact(
+            patcher.timing, f"{experiment}/timing_{layer}", "pickle")
+
+    def sample_like_syrenn(self, train_syrenn, train_labels):
+        points, labels = [], []
+        for line, label in zip(train_syrenn, train_labels):
+            start, end = line[0], line[-1]
+            points.extend([start, end])
+            # We always want to include the start/end
+            alphas = np.random.uniform(low=0.0, high=1.0, size=(len(line) - 2))
+            interpolated = start + np.outer(alphas, end - start)
+            points.extend(interpolated)
+            labels.extend(label for _ in range(len(interpolated) + 2))
+        return points, labels
+
+    def analyze(self):
+        """Analyze the patched MNIST networks.
+
+        Reports: Time, Drawdown, and Generalization
+        """
+        experiments = defaultdict(list)
+        for artifact in self.artifacts:
+            if "timing" not in artifact["key"]:
+                continue
+            # 10_lines/timing_2
+            n_lines, layer = artifact["key"].split("/")
+            experiments[n_lines].append(int(layer.split("_")[1]))
+
+        original_network = self.read_artifact("original")
+
+        test_lines, test_labels = self.get_corrupted("test", None, corruption=self.corruption)
+        test_images = list(map(np.array, zip(*test_lines)))
+        print("Size of drawdown, generalization sets:", len(test_lines))
+
+        timing_cols = ["layer", "total", "syrenn", "jacobian", "solver",
+                       "did_timeout", "drawdown", "generalization"]
+        for experiment in sorted(experiments.keys(), key=lambda n: int(n.split("_")[0])):
+            print(f"~~~~ Analyzing: {experiment} ~~~~")
+            # Get the patched data.
+            train_syrenn = self.read_artifact(f"{experiment}/train_syrenn")
+            train_labels = self.read_artifact(f"{experiment}/train_labels")
+            n_repair_points = self.read_artifact(f"{experiment}/n_repair_points")
+            epoched_out = self.read_artifact(f"{experiment}/epoched_out")
+            print("Size of repair set:", n_repair_points)
+            train_images = list(map(
+                np.array, zip(*[(l[0], l[-1]) for l in train_syrenn])))
+            print("Size of drawdown, generalization sets:", len(train_images))
+            print("Number of f-hat vertex points:",
+                  sum((2*len(l)) - 2 for l in train_syrenn))
+
+            before = self.compute_accuracies(original_network,
+                train_images, train_labels, test_images, test_labels)
+
+            results = self.begin_csv(f"{experiment}/analyzed", timing_cols)
+            for layer in sorted(experiments[experiment]):
+                assert int(layer) == -1
+                timing = self.read_artifact(f"{experiment}/timing_{layer}")
+                patched = self.read_artifact(f"{experiment}/patched_{layer}")
+
+                record = timing.copy()
+                record["layer"] = layer
+                record["syrenn"] = record["syrenn_time"]
+                del record["syrenn_time"]
+
+                after = self.compute_accuracies(patched,
+                    train_images, train_labels, test_images, test_labels)
+                print("\tTime (seconds):", timing["total"])
+                if epoched_out:
+                    print("\t(Timed Out)")
+
+                record["drawdown"] = (before["test_identity"]
+                                      - after["test_identity"])
+                record["generalization"] = (after["test_corrupted"]
+                                            - before["test_corrupted"])
+
+                print("\tDrawdown:", record["drawdown"])
+                print("\tGeneralization:", record["generalization"])
+
+                self.write_csv(results, record)
+        return True
+
+if __name__ == "__main__":
+    np.random.seed(24)
+    random.seed(24)
+    MNISTFT("mnist_ft").main()
diff --git a/experiments/mnist_mft.py b/experiments/mnist_mft.py
new file mode 100644
index 0000000..a3eb469
--- /dev/null
+++ b/experiments/mnist_mft.py
@@ -0,0 +1,170 @@
+"""Experiment to Modified Fine-Tune an MNIST image-recognition model."""
+from collections import defaultdict
+import random
+from timeit import default_timer as timer
+import numpy as np
+from pysyrenn import Network
+from pysyrenn import ReluLayer
+from experiments.mnist_repair import MNISTRepair
+from prdnn import FTRepair
+
+class MNISTMFT(MNISTRepair):
+    """Attempts to MFT networks to be resillient to corruptions."""
+    def run(self):
+        """Runs the corruption-fine-tuning experiment."""
+        network = self.load_network("mnist_relu_3_100")
+
+        assert isinstance(network.layers[-1], ReluLayer)
+        network = Network(network.layers[:-1])
+
+        self.record_artifact(network, "original", "network")
+
+        self.which_params = int(input("Which fine-tuning params? (1 or 2): "))
+        assert self.which_params in {1, 2}
+        n_rows = int(input("How many rows of Table 2 to generate (1, 2, 3, or 4): "))
+        for n_lines in [10, 25, 50, 100][:n_rows]:
+            print(f"Running with {n_lines} lines")
+            self.run_for(network, n_lines)
+
+    def run_for(self, network, n_lines):
+        """Runs experiments for a particular # of lines."""
+        experiment = f"{n_lines}_lines"
+
+        # Get the training lines. Only use lines where the original image is
+        # correctly classified.
+        train_lines, train_labels = self.get_corrupted(
+            "train", n_lines, only_correct_on=network, corruption=self.corruption)
+        # Compute SyReNN for each line.
+        start = timer()
+        train_syrenn = network.exactlines(
+            train_lines, compute_preimages=True, include_post=False)
+        syrenn_time = timer() - start
+
+        # Record the SyReNNs and the labels.
+        self.record_artifact(
+            train_syrenn, f"{experiment}/train_syrenn", "pickle")
+        self.record_artifact(
+            train_labels, f"{experiment}/train_labels", "pickle")
+
+        # Unpack the SyReNNs and associated labels to points for the patcher.
+        points, labels = self.sample_like_syrenn(train_syrenn, train_labels)
+
+        self.record_artifact(
+            len(points), f"{experiment}/n_repair_points", "pickle")
+
+        for layer in [2, 4]:
+            print("::::", "Layer:", layer, "::::")
+            patcher = FTRepair(network, points, labels)
+            patcher.layer = layer
+            patcher.norm_objective = True
+            # Don't stop when full repair-set accuracy is reached, only when
+            # holdout accuracy gets worse.
+            patcher.auto_stop = False
+            patcher.make_holdout_set()
+            patcher.batch_size = 16
+            # This is just a maximum epoch timeout, it will stop once all
+            # constraints are met.
+            patcher.epochs = 1000
+            patcher.momentum = 0.9
+            if self.which_params == 1:
+                patcher.lr = 0.05
+            else:
+                patcher.lr = 0.01
+            patched = patcher.compute()
+            patcher.timing["syrenn_time"] = syrenn_time
+
+            self.record_artifact(
+                patcher.epoched_out, f"{experiment}/epoched_out_{layer}", "pickle")
+            self.record_artifact(
+                patched, f"{experiment}/patched_{layer}",
+                "pickle" if patched is None else "network")
+            self.record_artifact(
+                patcher.timing, f"{experiment}/timing_{layer}", "pickle")
+            self.record_artifact(
+                patcher.accuracy_on_repair_set(patched),
+                f"{experiment}/patched_efficacy_{layer}", "pickle")
+
+    def sample_like_syrenn(self, train_syrenn, train_labels):
+        points, labels = [], []
+        for line, label in zip(train_syrenn, train_labels):
+            start, end = line[0], line[-1]
+            points.extend([start, end])
+            # We always want to include the start/end
+            alphas = np.random.uniform(low=0.0, high=1.0, size=(len(line) - 2))
+            interpolated = start + np.outer(alphas, end - start)
+            points.extend(interpolated)
+            labels.extend(label for _ in range(len(interpolated) + 2))
+        return points, labels
+
+    def analyze(self):
+        """Analyze the patched MNIST networks.
+
+        Reports: Time, Drawdown, and Generalization
+        """
+        experiments = defaultdict(list)
+        for artifact in self.artifacts:
+            if "timing" not in artifact["key"]:
+                continue
+            # 10_lines/timing_2
+            n_lines, layer = artifact["key"].split("/")
+            experiments[n_lines].append(int(layer.split("_")[1]))
+
+        original_network = self.read_artifact("original")
+
+        test_lines, test_labels = self.get_corrupted("test", None, corruption=self.corruption)
+        test_images = list(map(np.array, zip(*test_lines)))
+        print("Size of drawdown, generalization sets:", len(test_lines))
+
+        timing_cols = ["layer", "total", "syrenn", "jacobian", "solver",
+                       "did_timeout", "drawdown", "generalization"]
+        for experiment in sorted(experiments.keys(), key=lambda n: int(n.split("_")[0])):
+            print(f"~~~~ Analyzing: {experiment} ~~~~")
+            # Get the patched data.
+            train_syrenn = self.read_artifact(f"{experiment}/train_syrenn")
+            train_labels = self.read_artifact(f"{experiment}/train_labels")
+            n_repair_points = self.read_artifact(f"{experiment}/n_repair_points")
+            print("Size of repair set:", n_repair_points)
+            train_images = list(map(
+                np.array, zip(*[(l[0], l[-1]) for l in train_syrenn])))
+            print("Size of drawdown, generalization sets:", len(train_images))
+            print("Number of f-hat vertex points:",
+                  sum((2*len(l)) - 2 for l in train_syrenn))
+
+            before = self.compute_accuracies(original_network,
+                train_images, train_labels, test_images, test_labels)
+
+            results = self.begin_csv(f"{experiment}/analyzed", timing_cols)
+            for layer in sorted(experiments[experiment]):
+                print("Layer:", layer)
+                timing = self.read_artifact(f"{experiment}/timing_{layer}")
+                patched = self.read_artifact(f"{experiment}/patched_{layer}")
+                efficacy = 100 * self.read_artifact(f"{experiment}/patched_efficacy_{layer}")
+                epoched_out = self.read_artifact(f"{experiment}/epoched_out_{layer}")
+
+                record = timing.copy()
+                record["layer"] = layer
+                record["syrenn"] = record["syrenn_time"]
+                del record["syrenn_time"]
+
+                after = self.compute_accuracies(patched,
+                    train_images, train_labels, test_images, test_labels)
+                print("\tTime (seconds):", timing["total"])
+                if epoched_out:
+                    print("\t(Timed Out)")
+
+                record["drawdown"] = (before["test_identity"]
+                                      - after["test_identity"])
+                record["generalization"] = (after["test_corrupted"]
+                                            - before["test_corrupted"])
+
+                print("\tDrawdown:", record["drawdown"])
+                print("\tGeneralization:", record["generalization"])
+                print("\tEfficacy:", efficacy, "%")
+
+                self.write_csv(results, record)
+        return True
+
+if __name__ == "__main__":
+    np.random.seed(24)
+    random.seed(24)
+    MNISTMFT("mnist_mft").main()
diff --git a/experiments/mnist_repair.py b/experiments/mnist_repair.py
new file mode 100644
index 0000000..b947aea
--- /dev/null
+++ b/experiments/mnist_repair.py
@@ -0,0 +1,206 @@
+"""Experiment to patch an MNIST image-recognition model."""
+from collections import defaultdict
+import random
+from timeit import default_timer as timer
+import numpy as np
+from pysyrenn import Network
+from pysyrenn import ReluLayer
+from experiments.experiment import Experiment
+from prdnn import ProvableRepair
+
+class MNISTRepair(Experiment):
+    """Attempts to patch networks to be resillient to corruptions."""
+    corruption = "fog"
+    def run(self):
+        """Runs the corruption-patching experiment."""
+        network = self.load_network("mnist_relu_3_100")
+
+        assert isinstance(network.layers[-1], ReluLayer)
+        network = Network(network.layers[:-1])
+
+        self.record_artifact(network, "original", "network")
+
+        n_rows = int(input("How many rows of Table 2 to generate (1, 2, 3, or 4): "))
+        for n_lines in [10, 25, 50, 100][:n_rows]:
+            print(f"Running with {n_lines} lines")
+            self.run_for(network, n_lines)
+
+    def run_for(self, network, n_lines):
+        """Runs experiments for a particular # of lines."""
+        experiment = f"{n_lines}_lines"
+
+        # Get the training lines. Only use lines where the original image is
+        # correctly classified.
+        train_lines, train_labels = self.get_corrupted(
+            "train", n_lines, only_correct_on=network)
+        # Compute SyReNN for each line.
+        start = timer()
+        train_syrenn = network.exactlines(
+            train_lines, compute_preimages=True, include_post=False)
+        syrenn_time = timer() - start
+
+        # Record the SyReNNs and the labels.
+        self.record_artifact(
+            train_syrenn, f"{experiment}/train_syrenn", "pickle")
+        self.record_artifact(
+            train_labels, f"{experiment}/train_labels", "pickle")
+
+        # Unpack the SyReNNs and associated labels to points for the patcher.
+        points, representatives, labels = self.syrenn_to_points(
+            train_syrenn, train_labels)
+
+        for layer in [2, 4]:
+            print("::::", "Layer:", layer, "::::")
+            patcher = ProvableRepair(network, layer, points, labels,
+                                 representatives=representatives)
+            patcher.constraint_bufer = 0.001
+            patcher.gurobi_crossover = 0
+            patcher.gurobi_timelimit = 90 * 60
+            patched = patcher.compute()
+            patcher.timing["syrenn_time"] = syrenn_time
+            patcher.timing["total"] += syrenn_time
+
+            self.record_artifact(
+                patched, f"{experiment}/patched_{layer}",
+                "pickle" if patched is None else "ddnn")
+            self.record_artifact(
+                patcher.timing, f"{experiment}/timing_{layer}", "pickle")
+
+    def analyze(self):
+        """Analyze the patched MNIST networks.
+
+        Reports: Time, Drawdown, and Generalization
+        """
+        experiments = defaultdict(list)
+        for artifact in self.artifacts:
+            if "timing" not in artifact["key"]:
+                continue
+            # 10_lines/timing_2
+            n_lines, layer = artifact["key"].split("/")
+            experiments[n_lines].append(int(layer.split("_")[1]))
+
+        original_network = self.read_artifact("original")
+
+        test_lines, test_labels = self.get_corrupted("test", None)
+        test_images = list(map(np.array, zip(*test_lines)))
+        print("Size of drawdown, generalization sets:", len(test_lines))
+
+        timing_cols = ["layer", "total", "syrenn", "jacobian", "solver",
+                       "did_timeout", "drawdown", "generalization"]
+        for experiment in sorted(experiments.keys(), key=lambda n: int(n.split("_")[0])):
+            print(f"~~~~ Analyzing: {experiment} ~~~~")
+            # Get the patched data.
+            train_syrenn = self.read_artifact(f"{experiment}/train_syrenn")
+            train_labels = self.read_artifact(f"{experiment}/train_labels")
+            train_images = list(map(
+                np.array, zip(*[(l[0], l[-1]) for l in train_syrenn])))
+            print("Size of repair set:", len(train_images[0]))
+            print("Number of f-hat vertex points:",
+                  sum((2*len(l)) - 2 for l in train_syrenn))
+
+            before = self.compute_accuracies(original_network,
+                train_images, train_labels, test_images, test_labels)
+
+            results = self.begin_csv(f"{experiment}/analyzed", timing_cols)
+            for layer in sorted(experiments[experiment]):
+                timing = self.read_artifact(f"{experiment}/timing_{layer}")
+                patched = self.read_artifact(f"{experiment}/patched_{layer}")
+
+                record = timing.copy()
+                record["layer"] = layer
+                record["syrenn"] = record["syrenn_time"]
+                del record["syrenn_time"]
+
+                if patched is None:
+                    record["drawdown"], record["generalization"] = "", ""
+                else:
+                    after = self.compute_accuracies(patched,
+                        train_images, train_labels, test_images, test_labels)
+                    print("Layer:", layer)
+                    print("\tTime (seconds):", timing["total"])
+
+                    assert after["train_identity"] == 100.
+                    assert after["train_corrupted"] == 100.
+
+                    record["drawdown"] = (before["test_identity"]
+                                          - after["test_identity"])
+                    record["generalization"] = (after["test_corrupted"]
+                                                - before["test_corrupted"])
+                    print("\tDrawdown:", record["drawdown"])
+                    print("\tGeneralization:", record["generalization"])
+
+                self.write_csv(results, record)
+        return True
+
+    def compute_accuracies(self, network, train, train_labels, test,
+                           test_labels):
+        """Compture train, test accuracy for a network."""
+        return dict({
+            "train_identity": self.accuracy(network, train[0], train_labels),
+            "train_corrupted": self.accuracy(network, train[1], train_labels),
+            "test_identity": self.accuracy(network, test[0], test_labels),
+            "test_corrupted": self.accuracy(network, test[1], test_labels),
+        })
+
+    @staticmethod
+    def accuracy(network, inputs, labels):
+        """Measures network accuracy."""
+        net_labels = np.argmax(network.compute(inputs), axis=1)
+        return 100. * (np.count_nonzero(np.equal(net_labels, labels))
+                       / len(labels))
+
+    @classmethod
+    def syrenn_to_points(cls, syrenn, line_labels):
+        """Lists all endpoints in an ExactLine/SyReNN representation.
+
+        Returns (points, representatives, labels). Representatives are
+        non-vertex points which should have the same activation pattern in the
+        network as the corresponding point.
+        """
+        points, representatives, labels = [], [], []
+        for line, label in zip(syrenn, line_labels):
+            for start, end in zip(line, line[1:]):
+                points.extend([start, end])
+                labels.extend([label, label])
+                representative = (start + end) / 2.
+                representatives.extend([representative, representative])
+        return points, representatives, labels
+
+    @staticmethod
+    def get_corrupted(split, max_count, only_correct_on=None, corruption="fog"):
+        """Returns the desired dataset."""
+        random.seed(24)
+        np.random.seed(24)
+
+        all_images = [
+            np
+            .load(f"external/mnist_c/{corruption}/{split}_images.npy")
+            .reshape((-1, 28 * 28))
+            for corruption in ("identity", corruption)
+        ]
+        labels = np.load(f"external/mnist_c/identity/{split}_labels.npy")
+
+        indices = list(range(len(labels)))
+        random.shuffle(indices)
+        labels = labels[indices]
+        all_images = [images[indices] / 255. for images in all_images]
+
+        if only_correct_on is not None:
+            outputs = only_correct_on.compute(all_images[0])
+            outputs = np.argmax(outputs, axis=1)
+
+            correctly_labelled = (outputs == labels)
+
+            all_images = [images[correctly_labelled] for images in all_images]
+            labels = labels[correctly_labelled]
+
+        lines = list(zip(*all_images))
+        if max_count is not None:
+            lines = lines[:max_count]
+            labels = labels[:max_count]
+        return lines, labels
+
+if __name__ == "__main__":
+    np.random.seed(24)
+    random.seed(24)
+    MNISTRepair("mnist_repair").main()
diff --git a/experiments/models/README.md b/experiments/models/README.md
new file mode 100644
index 0000000..c4fd6e1
--- /dev/null
+++ b/experiments/models/README.md
@@ -0,0 +1,4 @@
+See https://github.com/guykatzz/ReluplexCav2017/blob/186476f942829ed233e72a67a6e68adc6ad0815d/COPYING
+ACAS models are licensed under CCBY 4.0
+I modified them into the ERAN format using the code at https://github.com/95616ARG/SyReNN/blob/84cef595769608a393649630b9888b6e131ddae7/models/translate_acas_model.py
+The ERAN conversion script is licensed under MIT/Expat.
diff --git a/experiments/models/acas_2_9.eran b/experiments/models/acas_2_9.eran
new file mode 100644
index 0000000..1572ab7
--- /dev/null
+++ b/experiments/models/acas_2_9.eran
@@ -0,0 +1,21 @@
+ReLU
+[[3.17590e-02, -1.73163e+00, -8.74419e-02, 2.46198e-01, 1.70159e-01], [2.04788e-01, 1.54263e+00, -1.44915e-01, 3.42657e-01, -4.20385e-01], [-7.20303e-03, -6.60776e-02, -1.34737e-02, -1.02165e-03, -1.92418e+00], [4.26224e-03, 6.57441e-01, 5.05933e-01, -2.43405e-01, 1.39503e-01], [-2.38069e+00, 3.64433e-02, 6.25100e-03, -3.72337e-02, -6.21462e-03], [-1.01592e-01, -2.10203e+00, -1.68368e+00, 5.28628e-01, -3.22604e-01], [-8.56859e-03, 1.28800e-01, -3.06258e+00, 2.26857e-01, -6.43075e-03], [-1.62130e-01, -9.51543e-03, -6.66873e-02, -6.97469e-01, 4.54756e-01], [-1.94758e-03, -2.45621e+00, -2.56437e-01, 3.96768e-01, -2.69345e-01], [-7.03167e-01, 2.06452e-02, 3.32877e-03, -3.33520e-02, -1.92472e-01], [-6.19844e-01, 1.02498e-01, -8.92096e-02, 7.77859e-01, -8.87706e-01], [8.82727e-02, -6.06744e-01, -1.74252e+00, -5.01028e-01, 5.50622e-01], [2.25161e-02, 1.31116e+00, 5.54675e-02, 1.80400e-01, 9.52956e-02], [3.26263e-01, -1.39139e+00, 1.23378e-01, 3.01085e-01, -5.45245e-01], [-1.23880e-01, 5.97425e-02, 4.90169e-02, 1.36575e-01, 3.42596e-02], [-6.20247e-02, 2.63086e+00, 7.71730e-01, 9.09136e-02, -2.60298e-01], [4.79038e-01, 2.69331e-01, -8.46440e-01, -3.34050e-02, 1.46664e-01], [-2.66136e+00, 1.19223e-01, -2.45131e-01, 1.14503e-01, 2.52789e-02], [-1.11910e-01, 2.31118e-01, -1.27900e-01, -8.37781e-01, 9.03312e-01], [-5.95087e-01, 1.00181e-01, 1.07487e+00, 3.10281e-01, 4.63232e-01], [6.66975e-02, 1.04186e+00, -1.38637e+00, 1.02932e-01, -2.06311e-01], [-1.80886e-01, -1.91682e-01, 3.67809e-01, -6.41283e-01, 7.52682e-01], [9.86450e-03, -8.69041e-01, 1.15434e+00, -1.30129e-01, 3.48460e-01], [2.17290e-02, 1.28442e-01, 1.89109e-02, -1.07978e+00, -2.02482e-01], [-5.61075e-03, -7.28461e-01, -1.39993e-01, 1.77784e-01, 3.29259e-01], [-2.06801e-02, 5.25462e-01, 2.12339e-01, -6.26803e-02, 1.98268e-01], [-2.82766e-02, 9.16147e-01, -1.22178e+00, -9.08048e-02, 2.64168e-01], [2.60941e-03, 1.08946e+00, -1.06754e+00, -1.17726e-02, 2.50136e-01], [2.91987e-02, 2.82137e-01, -3.22542e-01, -1.26966e-01, -6.84722e-01], [-6.29533e-05, -1.42837e+00, 1.49373e+00, 4.15997e-01, -6.11340e-01], [-2.21228e-02, 1.13180e+00, -2.66133e-01, 7.51714e-02, -6.59726e-01], [-7.27327e-01, -1.49128e-01, 2.77102e-03, 1.33677e-01, 9.50372e-02], [-1.43035e-02, -5.84230e-01, 7.82375e-01, 1.56485e-01, -4.34127e-01], [1.47155e-02, 8.66103e-01, -1.08741e+00, -9.59969e-02, 1.99876e-01], [2.55551e-02, -5.48825e-01, 1.54026e+00, 6.15600e-02, 2.38038e-01], [-3.22285e-02, -4.21609e-01, -3.23992e-01, -2.28876e-01, -1.13297e-01], [1.61573e-02, 1.84644e+00, 2.55636e-01, 2.25635e-01, -1.05505e-01], [6.09760e-02, 8.77586e-01, -1.40493e+00, 2.02471e-02, 7.83561e-01], [5.55735e-02, -4.34014e-01, -3.48002e-02, -1.67393e-01, 2.04044e-01], [4.37442e-01, 1.05189e+00, -7.68403e-01, -1.56163e-01, 3.14424e-01], [5.79094e-02, 2.23822e-01, 1.38849e-01, -1.86365e-01, 1.71985e-01], [-1.59981e-01, 9.03545e-01, -8.33153e-01, 8.06955e-02, -3.70409e-01], [-2.52766e-01, -1.67361e+00, 2.16148e+00, -2.08214e-01, 7.98023e-01], [-2.87291e-01, 1.07820e+00, -1.80353e+00, -1.15986e-01, 3.82090e-01], [1.41282e-01, 1.93135e+00, -2.17608e-01, 3.88677e-01, -5.18071e-01], [1.07432e-01, 2.56495e-01, -3.30009e-01, -4.61143e-01, 4.23958e-01], [-1.70512e-02, -1.01881e-01, -1.72361e+00, 7.07845e-01, -6.57099e-01], [3.98667e-02, 5.59591e-01, 8.94113e-01, 4.79703e-01, 2.77834e-02], [-2.25216e-02, -1.27270e+00, 1.51476e+00, -5.82114e-02, 3.88037e-01], [2.04429e+00, 2.12589e-02, -4.82790e-03, 3.08448e-03, 3.87546e-02]]
+[3.13369e-01, -2.29538e-01, -6.81796e-01, 9.37459e-02, -3.70911e-01, -8.98740e-02, 2.41037e-01, -8.99729e-02, 1.20231e-02, 8.36056e-02, -7.15250e-02, 1.09455e-01, 2.11034e-01, -2.01085e-01, 3.70529e-02, -2.83455e-01, 1.36668e-01, -6.37612e-01, 9.05210e-02, -1.72603e-02, -3.15357e-01, 8.52187e-02, -4.78976e-01, -4.07736e-01, -1.19194e-01, 1.14991e-01, -4.63243e-01, -3.48663e-01, -3.51308e-01, -2.64336e-01, -2.13971e-01, -7.35065e-02, 1.38533e-01, -4.97442e-01, 1.47239e-01, 1.21170e-01, 4.69269e-02, 2.26245e-01, 2.55251e-02, 4.39761e-02, 3.86366e-02, -2.08809e-02, -3.26913e-01, -2.04925e-01, -1.19119e-01, 1.08432e-01, 2.08443e-01, -6.08107e-03, -5.05891e-01, 5.78389e-01, ]
+ReLU
+[[-2.74312e-01, 4.53244e-01, -2.64590e-01, -2.09235e-01, 2.62021e-01, -6.82389e-03, -1.92727e-02, -3.02871e-01, -4.34269e-01, 3.74813e-01, 6.06781e-01, -1.64066e-01, -3.66329e-01, -2.00209e-01, -2.45032e-03, 6.39766e-03, 5.53460e-01, 2.55258e-01, -1.20655e-01, 5.66855e-01, -5.08450e-01, -6.58648e-01, -1.52196e-02, -4.74349e-01, 2.30758e-02, -6.34321e-01, -6.99678e-01, 1.04320e+00, 6.60976e-01, -1.19919e-01, -9.11582e-02, 1.30008e-01, 5.07275e-01, -3.93132e-01, 1.73094e-01, -7.93119e-01, -7.14824e-01, 6.35318e-02, 1.15503e-02, 2.19275e-02, 2.15136e-01, -1.39338e-01, 1.78261e-01, 3.85188e-01, -8.27580e-02, 2.95069e-01, 2.80843e-01, -5.99651e-02, 6.84920e-02, -1.54697e+00], [2.57361e-02, -2.67017e-02, 4.22067e-03, 3.61900e-02, 3.81616e-02, -4.72989e-02, 9.91837e-04, 3.85129e-02, 1.22661e-02, -4.57941e-03, 2.80929e-02, 2.25725e-02, 5.40007e-03, -9.91005e-03, -5.90786e-04, 1.16036e-02, -2.61224e-02, -4.32062e-02, 2.99120e-02, -1.41413e-02, 1.07733e-02, -4.05311e-02, -1.18146e-02, 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+ReLU
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-1.49565e+00, 4.43003e-01, -3.16333e-01, -1.39926e+00, 1.09158e+00, 2.71072e-01, 1.10867e+00, 3.63415e-01, -8.23858e-02, -8.36159e-02, 2.93691e-01, 1.55476e+00, 1.04637e+00, 2.28413e+00, -2.03023e+00, 3.13201e+00, 1.73756e-01, 1.06944e+00, -4.02672e-02, 1.74001e+00, 1.63903e+00]]
+[-4.45462e-01, 5.62400e-03, 5.89068e+00, 2.77841e-01, 1.40840e+00, 1.46208e-02, 3.39046e+00, -5.82072e+00, -7.14864e+00, 4.25952e+00, -5.89017e+00, -6.12832e+00, 1.92275e-01, 1.21389e-01, -5.73660e+00, 3.08365e+00, 7.83543e-01, -5.58092e+00, -1.27750e+00, -9.13020e+00, -1.60800e+00, 3.39128e+00, -3.99809e+00, 1.56087e+00, -1.04304e+01, -8.98217e+00, -9.91012e+00, -4.88063e+00, -1.47045e+00, -3.56399e+00, -4.89964e+00, -6.07058e+00, 1.18896e+00, 5.69209e-02, -3.31694e+00, -5.30129e+00, -3.09108e-02, -7.21790e+00, 3.53086e+00, -1.27792e+01, -3.35543e+00, 1.12941e-01, 3.32683e+00, -6.00712e+00, 1.03079e+00, 4.65157e+00, -7.62273e+00, 1.97126e+00, -9.84769e-01, -9.08274e+00, ]
+ReLU
+[[-9.51356e-02, -2.22979e-02, -6.33151e-03, -8.40396e-03, 2.45446e-02, 5.31824e-03, -8.86438e-03, 8.35088e-03, 3.64966e-03, -1.39195e-02, 5.94725e-03, 3.46741e-03, 1.01027e-03, 3.42597e-03, 3.21230e-03, -6.59967e-03, 6.82855e-03, 5.31019e-03, -2.26668e-02, 2.48221e-03, 1.30637e-03, -1.11098e-02, 5.20208e-03, -5.20106e-03, 1.89907e-03, 4.80087e-03, 3.38263e-03, 7.33350e-03, -5.85999e-03, 5.07230e-03, 2.79465e-03, 4.19309e-03, -4.19862e-02, -1.69099e-02, 3.85188e-03, 5.46258e-03, 3.25345e-03, 5.74500e-03, -1.43218e-02, 2.48820e-03, 3.37939e-03, 1.63476e-04, -6.82584e-03, 3.23427e-03, -1.99525e-02, -5.58794e-03, 4.92059e-03, -1.27108e-02, 4.48274e-03, 3.99711e-03], [3.41369e-03, 1.88770e-03, -8.34984e-05, 3.40946e-04, -3.31490e-04, -4.42893e-04, 1.57528e-04, -7.68266e-04, -1.10867e-03, 8.85364e-04, -5.74967e-05, -4.45446e-04, 6.23063e-03, 3.81003e-03, 2.05502e-06, 9.53738e-04, -9.98671e-04, -1.00667e-03, -3.99822e-06, 4.94672e-05, -8.62808e-04, 8.52861e-04, -1.09531e-05, 2.37848e-04, 1.06589e-04, -5.40875e-04, -1.02445e-04, -7.07167e-04, -1.16845e-03, -1.06233e-04, -1.09839e-04, -8.72209e-04, -2.18234e-03, 2.11973e-04, 6.06344e-04, -1.09847e-04, -1.35821e-03, -1.52328e-04, 1.62783e-04, -9.69814e-04, -2.97743e-04, -3.11798e-03, 5.69821e-04, -3.23376e-05, 8.50102e-04, 4.39917e-04, -2.34037e-04, 9.60049e-04, 1.40754e-04, -7.53481e-05], [-1.25400e-03, 8.94158e-04, -3.03122e-04, -6.46678e-04, -2.61326e-04, -2.89380e-04, 3.32650e-04, 1.76162e-04, 1.93910e-03, -6.75485e-06, 1.17657e-03, 4.90595e-04, 1.11946e-02, 5.36627e-03, 1.31839e-03, -1.61391e-04, 6.40120e-04, 1.26014e-04, 6.03507e-04, -3.12422e-04, -3.16359e-04, -6.16220e-04, 3.00544e-04, 1.18252e-04, 1.58326e-04, -8.66311e-05, 1.99267e-04, 1.93484e-04, -9.66102e-05, 8.58869e-04, 1.79149e-04, -1.82584e-04, -1.83384e-03, 1.76642e-04, 1.26378e-04, -7.06107e-06, 2.06563e-02, -4.62604e-04, -3.76827e-04, 3.56520e-04, 3.35840e-04, -9.76277e-03, -2.19176e-04, 2.67810e-04, 1.19132e-03, 3.16581e-04, 7.77611e-04, -4.22035e-04, 1.73234e-05, -8.78440e-06], [-2.29016e-03, -5.36503e-04, 1.41985e-03, 1.25661e-03, -2.01187e-05, -8.19703e-05, 3.43571e-04, 5.55825e-05, -2.12435e-04, 2.17003e-03, 1.52579e-04, 8.53428e-04, 1.57278e-02, 6.49768e-03, 1.34340e-03, 1.88267e-03, 9.24013e-04, -6.28561e-04, -8.39472e-05, 2.52810e-04, 9.19614e-04, -1.02148e-03, -4.15421e-04, 1.75075e-04, 5.29421e-05, 2.15433e-04, -1.42746e-04, -2.99572e-04, 5.17548e-04, -4.53469e-04, -1.78366e-04, 7.81821e-04, -2.92129e-03, -2.95382e-04, 3.23113e-03, -9.61390e-05, 1.17189e-02, 6.67009e-04, -6.92915e-04, 2.31220e-04, 2.68844e-05, -1.10992e-02, -2.61363e-04, 8.43888e-04, 9.50246e-04, -1.80688e-04, 9.22557e-04, -2.64588e-04, 6.60628e-04, 1.44544e-03], [-1.52026e-03, 4.49468e-04, -2.22061e-04, 3.90152e-04, 1.29054e-04, -5.56167e-04, -1.43732e-04, 7.97761e-05, 7.06409e-04, 2.86401e-04, 1.44057e-04, 3.41538e-04, 1.37299e-02, 4.77681e-03, 2.20423e-04, -2.15062e-04, 9.68713e-04, 2.05602e-04, -4.48056e-04, -1.42797e-04, -2.21096e-04, -3.82816e-04, 2.77808e-04, -1.26446e-04, 7.93513e-05, -8.94536e-05, 9.92721e-04, 3.52616e-04, 8.32333e-04, 3.37465e-04, 6.89273e-04, 1.09770e-05, 9.25683e-04, -2.29256e-04, -8.79770e-05, 1.05290e-03, 2.03980e-02, -4.34820e-04, -4.44605e-04, -1.80175e-04, 1.28410e-03, -1.00219e-02, -1.94714e-04, 7.68009e-04, 1.27401e-03, 8.91767e-05, -2.09657e-04, 2.76205e-04, -2.57235e-06, 4.72037e-04]]
+[-2.10782e-02, -1.97138e-02, 1.82674e-02, -1.85579e-02, 1.82602e-02, ]
diff --git a/experiments/squeezenet_ft.py b/experiments/squeezenet_ft.py
new file mode 100644
index 0000000..70a8fb5
--- /dev/null
+++ b/experiments/squeezenet_ft.py
@@ -0,0 +1,78 @@
+"""Fine-tuning SqueezeNet on NAE dataset.
+"""
+from collections import defaultdict
+import random
+import numpy as np
+# pylint: disable=import-error
+from pysyrenn import Network
+from pysyrenn import ReluLayer, NormalizeLayer
+from pysyrenn import FullyConnectedLayer, Conv2DLayer
+from prdnn import FTRepair
+from experiments.squeezenet_repair import SqueezenetRepair
+from imagenet_helpers import read_imagenet_images
+
+class SqueezenetFT(SqueezenetRepair):
+    """Fine-tunes SqueezeNet with the NAE dataset (Hendrycks et al.)"""
+    def run(self):
+        """Fine-tune Squeezenet model and record patched versions."""
+        network = self.load_network("squeezenet")
+        assert not isinstance(network.layers[-1], ReluLayer)
+        # Add a normalize layer to the start to take the images to the
+        # Squeezenet format.
+        normalize = NormalizeLayer(
+            means=np.array([0.485, 0.456, 0.406]),
+            standard_deviations=np.array([0.229, 0.224, 0.225]))
+        network = Network([normalize] + network.layers)
+
+        # Get the trainset and record it.
+        train_inputs, train_labels = self.get_train(n_labels=9)
+
+        sorted_labels = sorted(set(train_labels))
+        train_labels = list(map(sorted_labels.index, train_labels))
+
+        self.record_artifact(train_inputs, f"train_inputs", "pickle")
+        self.record_artifact(sorted_labels, f"sorted_labels", "pickle")
+        self.record_artifact(train_labels, f"train_labels", "pickle")
+
+        # Add a final layer which maps it into the subset of classes
+        # considered.
+        final_weights = np.zeros((1000, len(sorted_labels)))
+        final_biases = np.zeros(len(sorted_labels))
+        for new_label, old_label in enumerate(sorted_labels):
+            final_weights[old_label, new_label] = 1.
+        final_layer = FullyConnectedLayer(final_weights, final_biases)
+        network = Network(network.layers + [final_layer])
+
+        # Record the network before patching.
+        self.record_artifact(network, f"pre_patching", "network")
+
+        which_params = int(input("Which fine-tuning params? (1 or 2): "))
+        assert which_params in {1, 2}
+        n_rows = int(input("How many rows of Table 1 to generate (1, 2, 3, or 4): "))
+        for n_points in [100, 200, 400, 800][:n_rows]:
+            print("~~~~", "Points:", n_points, "~~~~")
+            key = f"{n_points}_-1"
+
+            patcher = FTRepair(
+                network, train_inputs[:n_points], train_labels[:n_points])
+            patcher.lr = 0.0001
+            patcher.momentum = 0.0
+            # This is just a maximum epoch timeout, it will stop once the
+            # constraints are met.
+            patcher.epochs = 500
+            if which_params == 1:
+                patcher.batch_size = 2
+            else:
+                patcher.batch_size = 16
+
+            patched = patcher.compute()
+
+            self.record_artifact(patcher.timing, f"{key}/timing", "pickle")
+            self.record_artifact(
+                patched, f"{key}/patched",
+                "network" if patched is not None else "pickle")
+
+if __name__ == "__main__":
+    np.random.seed(24)
+    random.seed(24)
+    SqueezenetFT("squeezenet_ft").main()
diff --git a/experiments/squeezenet_mft.py b/experiments/squeezenet_mft.py
new file mode 100644
index 0000000..de4f87f
--- /dev/null
+++ b/experiments/squeezenet_mft.py
@@ -0,0 +1,86 @@
+"""Modified fine-tuning SqueezeNet on NAE dataset.
+"""
+from collections import defaultdict
+import random
+import numpy as np
+# pylint: disable=import-error
+from pysyrenn import Network
+from pysyrenn import ReluLayer, NormalizeLayer
+from pysyrenn import FullyConnectedLayer, Conv2DLayer
+from prdnn import FTRepair
+from experiments.squeezenet_repair import SqueezenetRepair
+from imagenet_helpers import read_imagenet_images
+
+class SqueezenetMFT(SqueezenetRepair):
+    """Modified fine-tunes SqueezeNet with the NAE dataset (Hendrycks et al.)"""
+    def run(self):
+        """Modified fine-tune Squeezenet model and record patched versions."""
+        network = self.load_network("squeezenet")
+        assert not isinstance(network.layers[-1], ReluLayer)
+        # Add a normalize layer to the start to take the images to the
+        # Squeezenet format.
+        normalize = NormalizeLayer(
+            means=np.array([0.485, 0.456, 0.406]),
+            standard_deviations=np.array([0.229, 0.224, 0.225]))
+        network = Network([normalize] + network.layers)
+
+        # Get the trainset and record it.
+        train_inputs, train_labels = self.get_train(n_labels=9)
+
+        sorted_labels = sorted(set(train_labels))
+        train_labels = list(map(sorted_labels.index, train_labels))
+
+        self.record_artifact(train_inputs, f"train_inputs", "pickle")
+        self.record_artifact(sorted_labels, f"sorted_labels", "pickle")
+        self.record_artifact(train_labels, f"train_labels", "pickle")
+
+        # Add a final layer which maps it into the subset of classes
+        # considered.
+        final_weights = np.zeros((1000, len(sorted_labels)))
+        final_biases = np.zeros(len(sorted_labels))
+        for new_label, old_label in enumerate(sorted_labels):
+            final_weights[old_label, new_label] = 1.
+        final_layer = FullyConnectedLayer(final_weights, final_biases)
+        network = Network(network.layers + [final_layer])
+
+        # Record the network before patching.
+        self.record_artifact(network, f"pre_patching", "network")
+
+        patchable = [i for i, layer in enumerate(network.layers)
+                     if isinstance(layer, (FullyConnectedLayer, Conv2DLayer))]
+        which_params = int(input("Which fine-tuning params? (1 or 2): "))
+        assert which_params in {1, 2}
+        n_rows = int(input("How many rows of Table 1 to generate (1, 2, 3, or 4): "))
+        for n_points in [100, 200, 400, 800][:n_rows]:
+            print("~~~~", "Points:", n_points, "~~~~")
+            for layer in patchable:
+                print("::::", "Layer:", layer, "::::")
+                key = f"{n_points}_{layer}"
+
+                patcher = FTRepair(
+                    network, train_inputs[:n_points], train_labels[:n_points])
+                patcher.layer = layer
+                patcher.norm_objective = True
+                patcher.auto_stop = False
+                patcher.make_holdout_set()
+                patcher.lr = 0.0001
+                patcher.momentum = 0.0
+                # This is just a maximum epoch timeout, it will stop once the
+                # constraints are met.
+                patcher.epochs = 500
+                if which_params == 1:
+                    patcher.batch_size = 2
+                else:
+                    patcher.batch_size = 16
+
+                patched = patcher.compute()
+
+                self.record_artifact(patcher.timing, f"{key}/timing", "pickle")
+                self.record_artifact(
+                    patched, f"{key}/patched",
+                    "network" if patched is not None else "pickle")
+
+if __name__ == "__main__":
+    np.random.seed(24)
+    random.seed(24)
+    SqueezenetMFT("squeezenet_mft").main()
diff --git a/experiments/squeezenet_repair.py b/experiments/squeezenet_repair.py
new file mode 100644
index 0000000..deeb70d
--- /dev/null
+++ b/experiments/squeezenet_repair.py
@@ -0,0 +1,190 @@
+"""Repair SqueezeNet on the NAE dataset.
+"""
+from collections import defaultdict
+import random
+import numpy as np
+# pylint: disable=import-error
+from pysyrenn import Network
+from pysyrenn import ReluLayer, NormalizeLayer
+from pysyrenn import FullyConnectedLayer, Conv2DLayer
+from prdnn import FTRepair
+from experiments.experiment import Experiment
+from imagenet_helpers import read_imagenet_images
+
+class SqueezenetRepair(Experiment):
+    """Repairs Imagenet with the NAE dataset (Hendrycks et al.)"""
+    def run(self):
+        """Repair Squeezenet model and record patched versions."""
+        network = self.load_network("squeezenet")
+        assert not isinstance(network.layers[-1], ReluLayer)
+        # Add a normalize layer to the start to take the images to the
+        # Squeezenet format.
+        normalize = NormalizeLayer(
+            means=np.array([0.485, 0.456, 0.406]),
+            standard_deviations=np.array([0.229, 0.224, 0.225]))
+        network = Network([normalize] + network.layers)
+
+        # Get the trainset and record it.
+        train_inputs, train_labels = self.get_train(n_labels=9)
+
+        sorted_labels = sorted(set(train_labels))
+        train_labels = list(map(sorted_labels.index, train_labels))
+
+        self.record_artifact(train_inputs, f"train_inputs", "pickle")
+        self.record_artifact(sorted_labels, f"sorted_labels", "pickle")
+        self.record_artifact(train_labels, f"train_labels", "pickle")
+
+        # Add a final layer which maps it into the subset of classes
+        # considered.
+        final_weights = np.zeros((1000, len(sorted_labels)))
+        final_biases = np.zeros(len(sorted_labels))
+        for new_label, old_label in enumerate(sorted_labels):
+            final_weights[old_label, new_label] = 1.
+        final_layer = FullyConnectedLayer(final_weights, final_biases)
+        network = Network(network.layers + [final_layer])
+
+        # Record the network before patching.
+        self.record_artifact(network, f"pre_patching", "network")
+
+        # All the layers we can patch.
+        patchable = [i for i, layer in enumerate(network.layers)
+                     if isinstance(layer, (FullyConnectedLayer, Conv2DLayer))]
+        n_rows = int(input("How many rows of Table 1 to generate (1, 2, 3, or 4): "))
+        for n_points in [100, 200, 400, 800][:n_rows]:
+            print("~~~~", "Points:", n_points, "~~~~")
+            for layer in patchable:
+                print("::::", "Layer:", layer, "::::")
+                key = f"{n_points}_{layer}"
+
+                patcher = ProvableRepair(
+                    network, layer,
+                    train_inputs[:n_points], train_labels[:n_points])
+                patcher.batch_size = 8
+                patcher.gurobi_timelimit = (n_points // 10) * 60
+                patcher.gurobi_crossover = 0
+
+                patched = patcher.compute()
+
+                self.record_artifact(patcher.timing, f"{key}/timing", "pickle")
+                self.record_artifact(
+                    patched, f"{key}/patched",
+                    "ddnn" if patched is not None else "pickle")
+
+    def analyze(self):
+        """Compute drawdown statistics for patched models."""
+        print("~~~~ Results ~~~~")
+        # Get the datasets and compute pre-patching accuracy.
+        network = self.read_artifact("pre_patching")
+        train_inputs = self.read_artifact("train_inputs")
+        train_labels = self.read_artifact("train_labels")
+        sorted_labels = self.read_artifact("sorted_labels")
+
+        test_inputs, test_labels = self.get_test(sorted_labels)
+
+        original_train_accuracy = self.accuracy(
+            network, train_inputs, train_labels)
+        original_test_accuracy = self.accuracy(
+            network, test_inputs, test_labels)
+        print("Max size of repair set:", len(train_inputs))
+        print("Size of drawdown set:", len(test_inputs))
+        print("Buggy network repair set accuracy:", original_train_accuracy)
+        print("Buggy network drawdown set accuracy:", original_test_accuracy)
+
+        # Get info about the patch runs.
+        by_n_points = defaultdict(list)
+        by_layer = defaultdict(list)
+        for artifact in self.artifacts:
+            artifact = artifact["key"]
+            if "timing" not in artifact:
+                continue
+            key = artifact.split("/")[0]
+            n_points, layer = map(int, key.split("_"))
+            by_n_points[n_points].append(layer)
+            by_layer[layer].append(n_points)
+
+        timing_cols = ["total", "jacobian", "solver", "did_timeout",
+                       "efficacy", "drawdown"]
+        n_points_csvs = dict({
+            n_points:
+                self.begin_csv(f"{n_points}_points", ["layer"] + timing_cols)
+            for n_points in by_n_points.keys()
+        })
+        layer_csvs = dict({
+            layer: self.begin_csv(f"{layer}_layer", ["points"] + timing_cols)
+            for layer in by_layer.keys()
+        })
+        for n_points in sorted(by_n_points.keys()):
+            print("~~~~~", "Points:", min(int(n_points), len(train_inputs)), "~~~~~")
+            records_for_row = []
+            for layer in sorted(by_n_points[n_points]):
+                timing = self.read_artifact(f"{n_points}_{layer}/timing")
+                record = timing.copy()
+
+                patched = self.read_artifact(f"{n_points}_{layer}/patched")
+                if patched is not None:
+                    new_train_accuracy = self.accuracy(patched,
+                                                       train_inputs[:n_points],
+                                                       train_labels[:n_points])
+                    new_test_accuracy = self.accuracy(patched,
+                                                      test_inputs,
+                                                      test_labels)
+                    record["efficacy"] = new_train_accuracy
+                    record["drawdown"] = (original_test_accuracy
+                                          - new_test_accuracy)
+                    records_for_row.append(record)
+                else:
+                    record["efficacy"] = 0
+                    record["drawdown"] = 0
+
+                record["layer"] = layer
+                self.write_csv(n_points_csvs[n_points], record)
+                del record["layer"]
+                record["points"] = n_points
+                self.write_csv(layer_csvs[layer], record)
+            best_record = min(records_for_row, key=lambda record: record["drawdown"])
+            print("\tBest drawdown:", best_record["drawdown"])
+            print("\tTotal time for best drawdown (seconds):", best_record["total"])
+        return True
+
+    @staticmethod
+    def get_train(n_labels=10):
+        """Reads the training (patch) set from disk."""
+        np.random.seed(24)
+        random.seed(24)
+
+        parent = input("ImageNet-A Dataset Path: ")
+        images, labels = read_imagenet_images(parent, n_labels=n_labels)
+
+        indices = list(range(len(labels)))
+        random.shuffle(indices)
+        return images[indices], labels[indices]
+
+    @staticmethod
+    def get_test(sorted_labels):
+        """Reads the test set from disk.
+
+        @sorted_labels[i] gives the synset label corresponding to the ith
+        output of the model. Only returns images belonging to those classes.
+        """
+        np.random.seed(24)
+        random.seed(24)
+
+        parent = input("ImageNet-Val Dataset Path: ")
+        images, labels = read_imagenet_images(
+            parent, n_labels=None, use_labels=sorted_labels)
+        labels = np.array([sorted_labels.index(l) for l in labels])
+
+        indices = list(range(len(labels)))
+        random.shuffle(indices)
+        return images[indices], labels[indices]
+
+    @staticmethod
+    def accuracy(network, inputs, labels):
+        """Computes accuracy on a test set."""
+        out = np.argmax(network.compute(inputs), axis=1)
+        return 100. * np.count_nonzero(np.equal(out, labels)) / len(labels)
+
+if __name__ == "__main__":
+    np.random.seed(24)
+    random.seed(24)
+    SqueezenetRepair("squeezenet_repair").main()
diff --git a/pip_info/README.md b/pip_info/README.md
new file mode 100644
index 0000000..adb7af5
--- /dev/null
+++ b/pip_info/README.md
@@ -0,0 +1,16 @@
+# PRDNN
+Provable Repair of DNNs. See our [Github
+repository](https://github.com/95616ARG/PRDNN) for more information.
+
+## Prerequisites
+You will need to install Gurobi.
+
+If you wish to do polytope patching, you will also need a running SyReNN
+server. See [the SyReNN GitHub repository](https://github.com/95616ARG/syrenn)
+for more details. We also use [PyTorch](https://pytorch.org/), please ensure
+you have the desired version of PyTorch installed before installing this
+package.
+
+## Supported Python Versions
+We only test against Python 3.7.4, however we believe it should work for other
+versions of Python as well.
diff --git a/pip_info/__metadata__.py b/pip_info/__metadata__.py
new file mode 100644
index 0000000..f06bd4c
--- /dev/null
+++ b/pip_info/__metadata__.py
@@ -0,0 +1,11 @@
+__version__ = "1.0"
+__title__ = "prdnn"
+__description__ = "Provable Repair of DNNs"
+__uri__ = "https://github.com/95616ARG/PRDNN"
+__doc__ = __description__ + " <" + __uri__ + ">"
+
+__author__ = "Matthew Sotoudeh and Aditya V. Thakur"
+__email__ = "masotoudeh@ucdavis.edu, avthakur@ucdavis.edu"
+
+__license__ = "MIT"
+__copyright__ = "Copyright (c) 2020 Matthew Sotoudeh and Aditya V. Thakur"
diff --git a/pip_info/setup.cfg b/pip_info/setup.cfg
new file mode 100644
index 0000000..ed8a958
--- /dev/null
+++ b/pip_info/setup.cfg
@@ -0,0 +1,5 @@
+[bdist_wheel]
+universal = 1
+
+[metadata]
+license_file = LICENSE
diff --git a/pip_info/setup.py b/pip_info/setup.py
new file mode 100644
index 0000000..e2184dc
--- /dev/null
+++ b/pip_info/setup.py
@@ -0,0 +1,101 @@
+"""Setup script for prdnn.
+
+Adapted from:
+https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/
+"""
+import codecs
+import os
+import re
+
+from setuptools import setup, find_packages
+
+###################################################################
+
+NAME = "prdnn"
+PACKAGES = [
+    "prdnn",
+]
+META_PATH = "__metadata__.py"
+KEYWORDS = ["class", "attribute", "boilerplate"]
+CLASSIFIERS = [
+    "Development Status :: 5 - Production/Stable",
+    "Intended Audience :: Developers",
+    "Natural Language :: English",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 2",
+    "Programming Language :: Python :: 2.7",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.3",
+    "Programming Language :: Python :: 3.4",
+    "Programming Language :: Python :: 3.5",
+    "Programming Language :: Python :: 3.6",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Programming Language :: Python :: Implementation :: PyPy",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+]
+INSTALL_REQUIRES = ["torch"]
+with open("requirements.txt") as requirements:
+    reading = False
+    for line in requirements.readlines():
+        if line.startswith("# PRDNN"):
+            reading = True
+        elif line.startswith("# END"):
+            reading = False
+        elif line.startswith("#"):
+            pass
+        elif line.startswith("pysyrenn=="):
+            # Releases before 1.1 did not have the necessary interfaces.
+            INSTALL_REQUIRES.append("pysyrenn>=1.1")
+        elif reading:
+            INSTALL_REQUIRES.append(line.strip().split("==")[0])
+
+###################################################################
+
+HERE = os.path.abspath(os.path.dirname(__file__))
+
+def read(*parts):
+    """
+    Build an absolute path from *parts* and and return the contents of the
+    resulting file.  Assume UTF-8 encoding.
+    """
+    with codecs.open(os.path.join(HERE, *parts), "rb", "utf-8") as f:
+        return f.read()
+
+META_FILE = read(META_PATH)
+
+def find_meta(meta):
+    """Extract __*meta*__ from META_FILE.
+    """
+    meta_match = re.search(
+        r"^__{meta}__ = ['\"]([^'\"]*)['\"]".format(meta=meta),
+        META_FILE, re.M
+    )
+    if meta_match:
+        return meta_match.group(1)
+    raise RuntimeError("Unable to find __{meta}__ string.".format(meta=meta))
+
+
+if __name__ == "__main__":
+    setup(
+        name=NAME,
+        description=find_meta("description"),
+        license=find_meta("license"),
+        url=find_meta("uri"),
+        version=find_meta("version"),
+        author=find_meta("author"),
+        author_email=find_meta("email"),
+        maintainer=find_meta("author"),
+        maintainer_email=find_meta("email"),
+        keywords=KEYWORDS,
+        long_description=read("README.md"),
+        long_description_content_type="text/markdown",
+        packages=PACKAGES,
+        package_dir={"": "."},
+        package_data={"": ["prdnn/*.py"]},
+        zip_safe=False,
+        classifiers=CLASSIFIERS,
+        install_requires=INSTALL_REQUIRES,
+    )
diff --git a/prdnn/BUILD b/prdnn/BUILD
new file mode 100644
index 0000000..206e83c
--- /dev/null
+++ b/prdnn/BUILD
@@ -0,0 +1,30 @@
+py_library(
+    name = "prdnn",
+    srcs = ["__init__.py"],
+    visibility = ["//:__subpackages__"],
+    deps = [
+        ":ddnn",
+        ":ft_repair",
+        ":provable_repair",
+    ],
+)
+
+py_library(
+    name = "provable_repair",
+    srcs = ["provable_repair.py"],
+    visibility = ["//visibility:public"],
+    deps = [":ddnn"],
+)
+
+py_library(
+    name = "ft_repair",
+    srcs = ["ft_repair.py"],
+    visibility = ["//visibility:public"],
+    deps = [":provable_repair"],
+)
+
+py_library(
+    name = "ddnn",
+    srcs = ["ddnn.py"],
+    visibility = ["//visibility:public"],
+)
diff --git a/prdnn/__init__.py b/prdnn/__init__.py
new file mode 100644
index 0000000..421bfc6
--- /dev/null
+++ b/prdnn/__init__.py
@@ -0,0 +1,3 @@
+from prdnn.ddnn import DDNN
+from prdnn.ft_repair import FTRepair
+from prdnn.provable_repair import ProvableRepair
diff --git a/prdnn/ddnn.py b/prdnn/ddnn.py
new file mode 100644
index 0000000..7d0336a
--- /dev/null
+++ b/prdnn/ddnn.py
@@ -0,0 +1,133 @@
+"""Methods for describing and executing Decoupled DNNs."""
+import numpy as np
+from pysyrenn.frontend.network import Network
+from pysyrenn.frontend import FullyConnectedLayer, Conv2DLayer
+from pysyrenn.frontend import ReluLayer, HardTanhLayer
+from pysyrenn.frontend import ConcatLayer, AveragePoolLayer
+from pysyrenn.frontend import MaxPoolLayer, NormalizeLayer
+import syrenn_proto.syrenn_pb2 as transformer_pb
+import torch
+
+# NOTE: We currently only have limited support for concat layers, namely when
+# the intermediate layers are all linear.
+LINEAR_LAYERS = (FullyConnectedLayer, Conv2DLayer, ConcatLayer,
+                 AveragePoolLayer, NormalizeLayer)
+
+class DDNN:
+    """Implements a DDNN.
+
+    Currently supports:
+    - Arbitrary layers as long as the activation and values parameters are
+      equal up to that layer.
+    - Once the activation and values parameters differ, only linear (see
+      above), ReLU, HardTanh, and MaxPool layers are supported. Support for
+      other layer types can be added by modifying the compute(...) method.
+    """
+    def __init__(self, activation_layers, value_layers):
+        """Constructs the new DDNN.
+
+        @activation_layers is a list of layers defining the values of the
+            activation vectors.
+        @value_layers is a list of layers defining the values of the value
+            vectors. Non-linear layers here will be re-interpreted using the
+            corresponding decoupled value-network layer.
+
+        Note that the number, types, and output sizes of the layers in
+        @activation_layers and @values_layers should match.
+        """
+        self.activation_layers = activation_layers
+        self.value_layers = value_layers
+
+        self.n_layers = len(activation_layers)
+        assert self.n_layers == len(value_layers)
+
+        try:
+            self.differ_index = next(
+                l for l in range(self.n_layers)
+                if activation_layers[l] is not value_layers[l])
+        except StopIteration:
+            self.differ_index = len(value_layers)
+
+    def compute(self, inputs, representatives=None):
+        """Computes the output of the Decoupled Network on @inputs.
+
+        @inputs should be a Numpy array of inputs.
+        """
+        differ_index = self.differ_index
+        if representatives is not None:
+            differ_index = 0
+        # Up to differ_index, the values and activation vectors are the same.
+        pre_network = Network(self.activation_layers[:differ_index])
+        mid_inputs = pre_network.compute(inputs)
+        # Now we have to actually separately handle the masking when
+        # activations != values.
+        activation_vector = mid_inputs
+        if representatives is not None:
+            activation_vector = pre_network.compute(representatives)
+        value_vector = mid_inputs
+        for layer_index in range(differ_index, self.n_layers):
+            activation_layer = self.activation_layers[layer_index]
+            value_layer = self.value_layers[layer_index]
+            if isinstance(activation_layer, LINEAR_LAYERS):
+                if isinstance(activation_layer, ConcatLayer):
+                    assert not any(
+                        isinstance(input_layer, ConcatLayer)
+                        for input_layer in activation_layer.input_layers)
+                    assert all(
+                        isinstance(input_layer, LINEAR_LAYERS)
+                        for input_layer in activation_layer.input_layers)
+                activation_vector = activation_layer.compute(activation_vector)
+                value_vector = value_layer.compute(value_vector)
+            elif isinstance(activation_layer, ReluLayer):
+                mask = np.maximum(np.sign(activation_vector), 0.0)
+                if isinstance(value_vector, np.ndarray):
+                    value_vector *= mask
+                else:
+                    # NOTE: Originally this was torch.tensor(mask,
+                    # dtype=torch.float). I changed to this to silence a
+                    # warning from Pytorch. I don't think there will be, but it
+                    # might be worth testing for a performance regression.
+                    value_vector *= mask.clone().detach().float()
+                activation_vector *= mask
+            elif isinstance(activation_layer, HardTanhLayer):
+                mask = np.ones_like(value_vector)
+                value_vector[activation_vector >= 1.0] = 1.0
+                value_vector[activation_vector <= -1.0] = -1.0
+                np.clip(activation_vector, -1.0, 1.0, out=activation_vector)
+            elif isinstance(activation_layer, MaxPoolLayer):
+                activation_vector, indices = activation_layer.compute(
+                    activation_vector, return_indices=True)
+
+                value_vector = value_layer.from_indices(value_vector, indices)
+            else:
+                raise NotImplementedError
+        return value_vector
+
+    def serialize(self):
+        """Serializes the DDNN to the Protobuf format.
+
+        Notably, the value_net only includes layers after differ_index.
+        """
+        serialized = transformer_pb.MaskingNetwork()
+        serialized.activation_layers.extend([
+            layer.serialize() for layer in self.activation_layers
+        ])
+        serialized.value_layers.extend([
+            layer.serialize()
+            for layer in self.value_layers[self.differ_index:]
+        ])
+        serialized.differ_index = self.differ_index
+        return serialized
+
+    @classmethod
+    def deserialize(cls, serialized):
+        """Deserializes the DDNN from the Protobuf format."""
+        activation_layers = serialized.activation_layers
+        activation_layers = Network.deserialize_layers(activation_layers)
+
+        value_layers = serialized.value_layers
+        value_layers = Network.deserialize_layers(value_layers)
+
+        differ_index = serialized.differ_index
+        value_layers = activation_layers[:differ_index] + value_layers
+        return cls(activation_layers, value_layers)
diff --git a/prdnn/ft_repair.py b/prdnn/ft_repair.py
new file mode 100644
index 0000000..197d9c4
--- /dev/null
+++ b/prdnn/ft_repair.py
@@ -0,0 +1,207 @@
+"""Methods for patching deep neural networks."""
+import random
+import sys
+import os
+from timeit import default_timer as timer
+import torch
+import numpy as np
+from scipy import sparse
+from tqdm import tqdm
+from pysyrenn.frontend import Network, FullyConnectedLayer
+from pysyrenn.frontend import Conv2DLayer, ReluLayer
+from pysyrenn.frontend import ConcatLayer, HardTanhLayer
+from prdnn.ddnn import DDNN, LINEAR_LAYERS
+from prdnn.provable_repair import ProvableRepair
+
+class FTRepair(ProvableRepair):
+    """Helper for patching a DDNN.
+    """
+    def __init__(self, network, inputs, labels):
+        super().__init__(network, -1, inputs, labels)
+        self.epochs = 100
+        self.batch_size = 16
+        self.lr = 0.01
+        self.momentum = 0.9
+        self.auto_stop = True
+        self.norm_objective = False
+        self.layer = None
+        self.holdout_set = None
+        self.verbose = False
+
+    def maybe_print(self, *messages):
+        if self.verbose:
+            print(*messages)
+
+    def compute(self):
+        network = Network.deserialize(self.network.serialize())
+
+        if self.layer is not None:
+            for param in self.get_parameters(network):
+                param.requires_grad = False
+
+        parameters = self.get_parameters(network, self.layer)
+        for param in parameters:
+            param.requires_grad = True
+
+        if self.norm_objective:
+            original_parameters = [param.detach().clone() for param in parameters]
+            for param in original_parameters:
+                # Do not train these, they're just for reference.
+                param.requires_grad = False
+
+        start = timer()
+        optimizer = torch.optim.SGD(parameters, lr=self.lr, momentum=self.momentum)
+        indices = list(range(len(self.inputs)))
+        random.seed(24)
+        self.epoched_out = None
+        holdout_n_correct = self.holdout_n_correct(network)
+        for epoch in range(self.epochs):
+            # NOTE: In the paper, we checked this _after_ the inner loop. It
+            # should only make a difference in the case where the network
+            # already met the specification, so should make no difference to
+            # the results.
+            if self.auto_stop and self.is_done(network):
+                self.maybe_print("100% training accuracy!")
+                self.epoched_out = False
+                break
+            random.shuffle(indices)
+            losses = []
+            for batch_start in range(0, len(self.inputs), self.batch_size):
+                batch = slice(batch_start, batch_start + self.batch_size)
+                inputs = torch.tensor([self.inputs[i] for i in indices[batch]])
+                labels = torch.tensor([self.labels[i] for i in indices[batch]])
+                # representatives = [self.representatives[i] for i in indices[batch]]
+
+                optimizer.zero_grad()
+                output = network.compute(inputs)
+                loss = torch.nn.functional.cross_entropy(output, labels)
+                if self.norm_objective:
+                    for curr_param, og_param in zip(parameters, original_parameters):
+                        delta = (curr_param - og_param).flatten()
+                        loss += torch.linalg.norm(delta, ord=2)
+                        loss += torch.linalg.norm(delta, ord=float("inf"))
+                loss.backward()
+                losses.append(loss)
+                optimizer.step()
+            self.maybe_print("Average Loss:", torch.mean(torch.tensor(losses)))
+            if self.holdout_set is not None:
+                new_holdout_n_correct = self.holdout_n_correct(network)
+                self.maybe_print("New holdout n correct:", new_holdout_n_correct, "/", len(self.holdout_set))
+                if new_holdout_n_correct < holdout_n_correct:
+                    self.maybe_print("Holdout accuracy dropped, ending!")
+                    break
+                holdout_n_correct = new_holdout_n_correct
+        else:
+            self.epoched_out = True
+        for param in parameters:
+            param.requires_grad = False
+        self.timing = dict({
+            "total": timer() - start,
+        })
+        return network
+
+    def is_done(self, network):
+        for batch_start in range(0, len(self.inputs), self.batch_size):
+            batch = slice(batch_start, batch_start + self.batch_size)
+            inputs = torch.tensor(self.inputs[batch])
+            labels = torch.tensor(self.labels[batch])
+            output = torch.argmax(network.compute(inputs), axis=1)
+            if not torch.all(output == labels):
+                return False
+        return True
+
+    def accuracy_on_repair_set(self, network):
+        n_correct = 0
+        for batch_start in range(0, len(self.inputs), self.batch_size):
+            batch = slice(batch_start, batch_start + self.batch_size)
+            inputs = torch.tensor(self.inputs[batch])
+            labels = torch.tensor(self.labels[batch])
+            output = torch.argmax(network.compute(inputs), axis=1)
+            n_correct += torch.sum(output == labels)
+        return n_correct / len(self.inputs)
+
+    def holdout_n_correct(self, network):
+        if self.holdout_set is None:
+            return None
+        n_correct = 0
+        for batch_start in range(0, len(self.holdout_set), self.batch_size):
+            batch = slice(batch_start, batch_start + self.batch_size)
+            inputs = torch.tensor(self.holdout_set[batch])
+            labels = torch.tensor(self.holdout_labels[batch])
+            output = torch.argmax(network.compute(inputs), axis=1)
+            n_correct += torch.sum(output == labels)
+        return n_correct
+
+    def make_holdout_set(self):
+        assert self.holdout_set is None
+        indices = list(range(len(self.inputs)))
+        random.shuffle(indices)
+        holdout_indices = indices[:len(indices)//4]
+        self.holdout_set = self.inputs[holdout_indices]
+        self.holdout_labels = self.labels[holdout_indices]
+        self.inputs = [x for i, x in enumerate(self.inputs)
+                       if i not in holdout_indices]
+        self.labels = [x for i, x in enumerate(self.labels)
+                       if i not in holdout_indices]
+
+    @classmethod
+    def from_planes(cls, network, planes, labels,
+                    samples_per_plane, label_fn=None):
+        """Constructs a ProvableRepair to patch 2D regions.
+
+        @planes should be a list of input 2D planes (Numpy arrays of their
+            vertices in counter-clockwise order).
+        @labels a list of the corresponding desired labels (integers).
+        """
+        points = []
+        point_labels = []
+        if labels is None:
+            labels = [0 for i in planes]
+        for vertices, label, samples in zip(planes, labels, samples_per_plane):
+            coefficients = np.random.uniform(
+                0., 1., size=(samples, len(vertices)))
+            coefficients = (coefficients.T / np.sum(coefficients, axis=1)).T
+            points.extend(list(np.matmul(coefficients, vertices)))
+            if not label_fn:
+                point_labels.extend(label for _ in range(samples))
+        if label_fn:
+            point_labels = label_fn(points)
+        return cls(network, np.array(points), np.array(point_labels))
+
+
+    @classmethod
+    def from_spec_function(cls, network, region_plane,
+                           spec_function, samples_per_plane):
+        """Constructs a ProvableRepair for an input region and "Spec Function."
+
+        @region_plane should be a single plane (Numpy array of
+            counter-clockwise vertices) that defines the "region of interest"
+            to patch over.
+        @spec_function should take a set of input points (Numpy array) and
+            return the desired corresponding labels (list/Numpy array of ints).
+        """
+        if len(np.asarray(region_plane).shape) == 2:
+            region_plane = [region_plane]
+        assert len(np.asarray(region_plane).shape) == 3
+        return cls.from_planes(network, region_plane, None,
+                               samples_per_plane, label_fn=spec_function)
+
+    @classmethod
+    def get_parameters(cls, network, layer=None):
+        if layer is not None:
+            return cls.get_parameters_layer(network.layers[layer])
+        params = []
+        for layer in network.layers:
+            params.extend(cls.get_parameters_layer(layer))
+        return params
+
+    @classmethod
+    def get_parameters_layer(cls, layer):
+        if isinstance(layer, FullyConnectedLayer):
+            return [layer.weights, layer.biases]
+        if isinstance(layer, Conv2DLayer):
+            return [layer.filter_weights, layer.biases]
+        if isinstance(layer, ConcatLayer):
+            return [param for in_layer in layer.input_layers
+                    for param in cls.get_parameters_layer(in_layer)]
+        return []
diff --git a/prdnn/provable_repair.py b/prdnn/provable_repair.py
new file mode 100644
index 0000000..387747f
--- /dev/null
+++ b/prdnn/provable_repair.py
@@ -0,0 +1,523 @@
+"""Methods for patching deep neural networks."""
+import sys
+import os
+from timeit import default_timer as timer
+import torch
+import numpy as np
+from scipy import sparse
+from tqdm import tqdm
+from pysyrenn.frontend import Network, FullyConnectedLayer
+from pysyrenn.frontend import Conv2DLayer, ReluLayer
+from pysyrenn.frontend import ConcatLayer, HardTanhLayer
+if "GUROBI_HOME" in os.environ:
+    PY_VERSION = f"{sys.version_info.major}.{sys.version_info.minor}"
+    if PY_VERSION == "3.7":
+        sys.path.append(os.environ["GUROBI_HOME"] + "/lib/python3.7")
+    elif PY_VERSION == "3.8":
+        sys.path.append(os.environ["GUROBI_HOME"] + "/lib/python3.8_utf32")
+    else:
+        # The user can figure it out.
+        pass
+from gurobipy import Model, GRB
+from prdnn.ddnn import DDNN, LINEAR_LAYERS
+
+class ProvableRepair:
+    """Helper for patching a DDNN.
+
+    An instance of a ProvableRepair represents patching a specific network on a
+    specific pointwise patching specification with specific hyperparameters.
+    Polytope patching specifications can be specified using
+    ProvableRepair.from_planes or ProvableRepair.from_spec_function, which will
+    construct an equivalent pointwise patching specification.
+    """
+    def __init__(self, network, layer_index, inputs, labels,
+                 delta_bounds=(-3., 3.), representatives=None):
+        """Initializes a new ProvableRepair.
+
+        By default, we only allow weight deltas to take values between -3. and
+        3. to prevent numerical issues. This can be relaxed in some scenarios,
+        although I have found (-GRB.INFINITY, GRB.INFINITY) causing actual
+        significant issues.
+
+        For raw feasibility, take delta_norm_type="none",
+        constraint_type="hard", slack_lb=0, slack_ub=INFTY.
+
+        To do a strict relaxation of feasibility, take delta_norm_type="none",
+        constraint_type="linf", slack_lb=0, slack_ub=INFTY. Note that if the
+        model is feasible, these settings will find the satisfying model.
+        However, the behavior is less predictable if the model is infeasible.
+        In that scenario, it may have worst-case behavior.
+
+        To do a better relaxation of feasibility, use constraint_type="l1" and
+        play with slack_*b, *_weight.
+        """
+        self.network = network
+        self.layer_index = layer_index
+        self.inputs = np.asarray(inputs, dtype=np.float32)
+        self.representatives = representatives
+        if representatives is None:
+            self.representatives = self.inputs
+        self.labels = np.asarray(labels, dtype=np.int64)
+        self.epsilon = None
+        original_layer = network.layers[layer_index]
+        # intermediates[i] is the DDNN after i patching steps, so
+        # intermediates[0] is the original network.
+        self.intermediates = [self.construct_patched(original_layer)]
+        # times[i] is the time taken to run the ith iteration.
+        self.times = [0.0]
+
+        self.delta_bounds = delta_bounds
+        # "hard", "l1", "linf"
+        self.constraint_type = "hard"
+        # Controls how much to prioritize improving already-met constraints vs.
+        # meeting more constraints. Setting slack_lb = 0 means that, once a
+        # constraint is met, 'meeting it more' won't improve the score.
+        # Changing slack_ub is a bit weirder, basically it controls the maximum
+        # 'badness' that an unmet constraint can contribute to the score.
+        self.soft_constraint_slack_lb = -0.05
+        self.soft_constraint_slack_ub = GRB.INFINITY
+        self.soft_constraint_slack_type = GRB.CONTINUOUS
+        # Objective weights
+        self.delta_l1_weight = 1.
+        self.delta_linf_weight = 1.
+        self.soft_constraints_weight = 1.
+        self.normalize_objective = True
+        # We require Ax >= b + cb
+        self.constraint_buffer = 0.05
+        # Batch size
+        self.batch_size = 128
+        # Gurobi timeout (in seconds).
+        self.gurobi_timelimit = None
+        # Crossover parameter in Gurobi.
+        self.gurobi_crossover = -1
+        self.timing = None
+
+    def compute(self):
+        """Performs the Layer patching."""
+        patch_start = timer()
+        layer = self.network.layers[self.layer_index]
+        if isinstance(layer, FullyConnectedLayer):
+            weights = layer.weights.numpy().copy()
+            biases = layer.biases.numpy().copy()
+        elif isinstance(layer, Conv2DLayer):
+            weights = layer.filter_weights.numpy().copy()
+            biases = layer.biases.numpy().copy()
+        else:
+            raise NotImplementedError
+
+        model = Model()
+        if self.gurobi_timelimit is not None:
+            model.Params.TimeLimit = self.gurobi_timelimit
+        if self.gurobi_crossover != -1:
+            model.Params.Crossover = self.gurobi_crossover
+            model.Params.Method = 2
+
+        # Adding variables...
+        lb, ub = self.delta_bounds
+        weight_deltas = model.addVars(weights.flatten().size, lb=lb, ub=ub).select()
+        bias_deltas = model.addVars(biases.size, lb=lb, ub=ub).select()
+        all_deltas = weight_deltas + bias_deltas
+
+        if self.constraint_type == "hard":
+            soft_constraint_bounds = []
+        elif self.constraint_type == "linf":
+            soft_constraint_bounds = [model.addVar(
+                lb=self.soft_constraint_slack_lb,
+                ub=self.soft_constraint_slack_ub,
+                vtype=self.soft_constraint_slack_type)]
+        elif self.constraint_type == "l1":
+            out_dims = self.network.compute(self.inputs[:1]).shape[1]
+            soft_constraint_bounds = model.addVars(
+                len(self.inputs) * (out_dims - 1),
+                lb=self.soft_constraint_slack_lb,
+                ub=self.soft_constraint_slack_ub,
+                vtype=self.soft_constraint_slack_type).select()
+        else:
+            raise NotImplementedError
+
+        # Adding constraints...
+        jacobian_compute_time = 0.
+        for batch_start in tqdm(range(0, len(self.inputs), self.batch_size)):
+            batch_slice = slice(batch_start, batch_start + self.batch_size)
+            batch_labels = self.labels[batch_slice]
+
+            jacobian_start = timer()
+            A_batch, b_batch = self.network_jacobian(batch_slice)
+            jacobian_compute_time += (timer() - jacobian_start)
+
+            out_dims = A_batch.shape[1]
+            assert out_dims == b_batch.shape[1]
+
+            variables = None
+            if self.constraint_type == "l1":
+                variables = weight_deltas + bias_deltas + bounds
+            weight_softs = 1.
+            if self.soft_constraint_slack_type == GRB.BINARY:
+                weight_softs = 10.
+
+            full_As, full_bs = [], []
+            bounds_slice = slice(batch_slice.start * (out_dims - 1),
+                                 batch_slice.stop * (out_dims - 1))
+            constraint_bounds_batch = soft_constraint_bounds[bounds_slice]
+            for i, (A, b, label) in enumerate(zip(A_batch, b_batch, batch_labels)):
+                other_labels = [l for l in range(out_dims) if l != label]
+                # A[label]x + b[label] >= A[other]x + b[other]
+                # (A[label] - A[other])x >= b[other] - b[label]
+                As = np.expand_dims(A[label], 0) - A[other_labels]
+                bs = (b[other_labels] - np.expand_dims(b[label], 0))
+                bs += self.constraint_buffer
+
+                if self.constraint_type == "linf":
+                    As = np.concatenate((As, weight_softs * np.ones((As.shape[0], 1))), axis=1)
+                elif self.constraint_type == "l1":
+                    bounds = constraint_bounds_batch[
+                        i*(out_dims-1):((i+1)*(out_dims-1))]
+                    As = np.concatenate((As, weight_softs * np.eye(len(bounds))), axis=1)
+
+                full_As.append(As)
+                full_bs.extend(bs)
+            model.addMConstr(np.concatenate(tuple(full_As), axis=0),
+                             variables, '>', full_bs)
+
+        # Specifying objective...
+        objective = 0.
+        if self.delta_l1_weight != 0.:
+            # Penalize the L_1 norm. To do this, we must add variables which
+            # represent the absolute value of each of the deltas.  The approach
+            # used here is described at:
+            # https://optimization.mccormick.northwestern.edu/index.php/Optimization_with_absolute_values
+            n_vars = len(all_deltas)
+            abs_ub = max(abs(lb), abs(ub))
+            variable_abs = model.addVars(n_vars, lb=0., ub=abs_ub).select()
+            n_vars += n_vars
+
+            A = sparse.diags([1., -1.], [0, (n_vars // 2)],
+                             shape=(n_vars // 2, n_vars),
+                             dtype=np.float, format="lil")
+            b = np.zeros(n_vars // 2)
+            model.addMConstr(A, all_deltas + variable_abs, '<', b)
+
+            A = sparse.diags([-1., -1.], [0, (n_vars // 2)],
+                             shape=(n_vars // 2, n_vars),
+                             dtype=np.float, format="lil")
+            model.addMConstr(A, all_deltas + variable_abs, '<', b)
+
+            # Then the objective we use is just the L_1 norm. TODO: maybe we
+            # should wait until the end to weight this so the coefficients
+            # aren't too small?
+            if self.normalize_objective:
+                weight = self.delta_l1_weight / len(variable_abs)
+            else:
+                weight = self.delta_l1_weight
+            objective += (weight * sum(variable_abs))
+        if self.delta_linf_weight != 0.:
+            # Penalize the L_infty norm. We use a similar approach, except it
+            # only takes one additional variable. For some reason it throws an
+            # error if I use just addVar here.
+            l_inf = model.addVar(lb=0., ub=max(abs(lb), abs(ub)))
+            n_vars = len(weight_deltas) + len(bias_deltas) + 1
+
+            A = sparse.eye(n_vars - 1, n_vars, dtype=np.float, format="lil")
+            A[:, -1] = -1.
+            b = np.zeros(n_vars - 1)
+            model.addMConstr(A, all_deltas + [l_inf], '<', b)
+
+            A = sparse.diags([-1.], shape=(n_vars - 1, n_vars),
+                             dtype=np.float, format="lil")
+            A[:, -1] = -1.
+            model.addMConstr(A, all_deltas + [l_inf], '<', b)
+
+            # L_inf objective.
+            objective += (self.delta_linf_weight * l_inf)
+        # Soft constraint weight.
+        if self.normalize_objective:
+            weight = self.soft_constraints_weight / max(len(soft_constraint_bounds), 1)
+        else:
+            weight = self.soft_constraints_weight
+        objective += weight * sum(soft_constraint_bounds)
+        model.setObjective(objective, GRB.MINIMIZE)
+
+        # Solving...
+        gurobi_start = timer()
+        model.update()
+        model.optimize()
+        gurobi_solve_time = (timer() - gurobi_start)
+
+        self.timing = dict({
+            "jacobian": jacobian_compute_time,
+            "solver": gurobi_solve_time,
+        })
+        self.timing["did_timeout"] = (model.status == GRB.TIME_LIMIT)
+        if model.status != GRB.OPTIMAL:
+            print("Not optimal!")
+            print("Model status:", model.status)
+            self.timing["total"] = (timer() - patch_start)
+            return None
+
+        # Extracting weights...
+        weights += np.asarray([d.X for d in weight_deltas]).reshape(weights.shape)
+        biases += np.asarray([d.X for d in bias_deltas]).reshape(biases.shape)
+
+        # Returning a patched network!
+        if isinstance(layer, FullyConnectedLayer):
+            patched_layer = FullyConnectedLayer(weights.copy(), biases.copy())
+        else:
+            patched_layer = Conv2DLayer(layer.window_data, weights.copy(), biases.copy())
+
+        patched = self.construct_patched(patched_layer)
+
+        self.timing["total"] = (timer() - patch_start)
+        return patched
+
+    def network_jacobian(self, batch_slice):
+        """Returns A, b st A x delta + b = network output for a given batch.
+
+        Essentially, this method returns the Jacobian of the network output wrt
+        the given layer's weights and biases. Unfortunately, Pytorch is *really
+        bad* at computing Jacobians. It can compute gradients of a single
+        scalar, but not Jacobians of a vector output (or a matrix, as we need).
+
+        This method takes the following approach:
+        1. If the layer in question is a fully-connected layer, we can manually
+           compute the Jacobian using self.layer_jacobian(...).
+        2. Otherwise, we assume the layer is a 2D convolutional layer and use
+           Pytorch to compute the Jacobian, which takes m*n Jacobian queries
+           where m is the number of points and n is the number of output
+           classes.
+
+        TODO(masotoud, good starter project): For case (2), Pytorch recently
+        introduced a Jacobian method that does exactly that. We can probably
+        simplify this function by using their implementation.
+
+        TODO(masotoud, follow-up project): In fact, there exist a number of
+        optimized Jacobian implementations for Pytorch (see, e.g., the
+        "backpack" project). However, using them seems to require significant
+        modifications to the way we call Pytorch. Long-term, it would be nice
+        to move to one of those (or at least re-use their primitives).
+        """
+        inputs = self.inputs[batch_slice]
+        representatives = self.representatives[batch_slice]
+
+        layer = self.network.layers[self.layer_index]
+
+        original_outputs = self.network.compute(inputs)
+        n_inputs, out_dims = original_outputs.shape
+
+        if isinstance(layer, FullyConnectedLayer):
+            weight_scales, bias_scales = self.layer_jacobian(
+                inputs, representatives)
+            # weight_scales is (n_inputs, out_dims, in_dims, mid_dims)
+            # bias_scales is (n_inputs, out_dims, mid_dims)
+        else:
+            # This *SHOULD* be possible in general, but I don't know how to
+            # express it nicely in Numpy/Pytorch. Trying to run layer_jacobian
+            # will create a huge matrix, which is not what we want. The
+            # approach below is pretty slow (due primarily to limitations with
+            # Jacobian computation in Pytorch) but should work as long as we're
+            # not using representatives.
+            filters = layer.filter_weights
+            biases = layer.biases
+
+            def set_grad(v, g):
+                v.requires_grad_(g)
+            def maybe_zero(v):
+                if v is not None:
+                    v.zero_()
+
+            for v in [filters, biases]:
+                set_grad(v, True)
+            pytorch_x = torch.tensor(inputs, requires_grad=True, dtype=torch.float)
+            if self.inputs is self.representatives:
+                output = self.network.compute(pytorch_x)
+            else:
+                masknet = DDNN(self.network.layers, self.network.layers)
+                output = masknet.compute(pytorch_x, representatives)
+            assert len(output.shape) == 2
+            n_inputs, out_dims = output.shape
+
+            weight_scales = np.zeros((n_inputs, out_dims,) + filters.shape)
+            bias_scales = np.zeros((n_inputs, out_dims,) + biases.shape)
+            for i in range(output.shape[0]):
+                for j in range(output.shape[1]):
+                    maybe_zero(filters.grad)
+                    maybe_zero(biases.grad)
+                    output[i, j].backward(retain_graph=True)
+                    weight_scales[i, j, :, :, :] = filters.grad.numpy()
+                    bias_scales[i, j, :] = biases.grad.numpy()
+
+            for v in [filters, biases]:
+                set_grad(v, False)
+            weight_scales = np.array(weight_scales)
+            bias_scales = np.array(bias_scales)
+
+        # (n_inputs, out_dims, [filter_shape]) -> (out, [filter_size])
+        weight_scales = weight_scales.reshape((n_inputs, out_dims, -1))
+        # (n_inputs, out_dims, [bias_shape])
+        bias_scales = bias_scales.reshape((n_inputs, out_dims, -1))
+        return np.concatenate((weight_scales, bias_scales), axis=2), original_outputs
+
+    def layer_jacobian(self, points, representatives):
+        """Computes the Jacobian of the FULLY CONNECTED layer parameters.
+
+        Returns (n_points, n_outputs, [weight_shape])
+
+        Basically, we assume WLOG that the layer is the first layer then we get
+        something like for each point:
+        B_nB_{n-1}...B_1Ax
+
+        For each point we can collapse B_n...B_1 into a matrix B of shape
+        (n_outputs, n_A_outputs)
+        then we compute the einsum with x to get
+        (n_outputs, n_A_outputs, n_A_inputs)
+        which is what we want.
+        """
+        pre_network = Network(self.network.layers[:self.layer_index])
+        points = pre_network.compute(points)
+        n_points, A_in_dims = points.shape
+
+        representatives = pre_network.compute(representatives)
+        representatives = self.network.layers[self.layer_index].compute(representatives)
+        n_points, A_out_dims = representatives.shape
+
+        post_network = Network(self.network.layers[(self.layer_index+1):])
+        # (n_points, A_out, A_out)
+        jacobian = np.repeat([np.eye(A_out_dims)], n_points, axis=0)
+        # (A_out, n_points, A_out)
+        jacobian = jacobian.transpose((1, 0, 2))
+        for layer in post_network.layers:
+            if isinstance(layer, LINEAR_LAYERS):
+                if isinstance(layer, ConcatLayer):
+                    assert not any(isinstance(input_layer, ConcatLayer)
+                                   for input_layer in layer.input_layers)
+                    assert all(isinstance(input_layer, LINEAR_LAYERS)
+                               for input_layer in layer.input_layers)
+                representatives = layer.compute(representatives)
+                jacobian = jacobian.reshape((A_out_dims * n_points, -1))
+                jacobian = layer.compute(jacobian, jacobian=True)
+                jacobian = jacobian.reshape((A_out_dims, n_points, -1))
+            elif isinstance(layer, ReluLayer):
+                # (n_points, running_dims)
+                zero_indices = (representatives <= 0)
+                representatives[zero_indices] = 0.
+                # (A_out, n_points, running_dims)
+                jacobian[:, zero_indices] = 0.
+            elif isinstance(layer, HardTanhLayer):
+                big_indices = (representatives >= 1.)
+                small_indices = (representatives <= -1.)
+                np.clip(representatives, -1.0, 1.0, out=representatives)
+                jacobian[:, big_indices] = 0.
+                jacobian[:, small_indices] = 0.
+            else:
+                raise NotImplementedError
+        # (A_out, n_points, n_classes) -> (n_points, n_classes, A_out)
+        B = jacobian.transpose((1, 2, 0))
+        # (n_points, n_classes, n_A_in, n_A_out)
+        C = np.einsum("nco,ni->ncio", B, points)
+        return C, B
+
+    def set_points(self, inputs, labels, representatives=None):
+        """Change the pointwise patching specification."""
+        self.inputs = np.asarray(inputs, dtype=np.float32)
+        self.labels = labels
+        self.representatives = representatives
+        if representatives is None:
+            self.representatives = self.inputs
+
+    @classmethod
+    def from_planes(cls, network, layer_index, planes, labels, use_representatives=False):
+        """Constructs a ProvableRepair to repair 2D regions.
+
+        @planes should be a list of input 2D planes (Numpy arrays of their
+            vertices in counter-clockwise order).
+        @labels a list of the corresponding desired labels (integers).
+
+        Internally, SyReNN is used to lower the problem to that of finitely
+        many points.
+
+        NOTE: This function requires one to have a particularly precise
+        representation of the desired network output; in most cases,
+        ProvableRepair.from_spec_function is more useful (see below).
+        """
+        transformed = network.transform_planes(planes,
+                                               compute_preimages=True,
+                                               include_post=False)
+        all_inputs = []
+        all_labels = []
+        all_representatives = [] if use_representatives else None
+        for upolytope, label in zip(transformed, labels):
+            # include_post=False so the upolytope is just a list of Numpy
+            # arrays.
+            if use_representatives:
+                points = []
+                for vertices in upolytope:
+                    all_inputs.extend(vertices)
+                    representative = np.mean(vertices, axis=0)
+                    all_representatives.extend(representative
+                                               for _ in vertices)
+                    all_labels.extend(label for _ in vertices)
+            else:
+                points = []
+                for vertices in upolytope:
+                    points.extend(vertices)
+                # Remove duplicate points.
+                points = list(set(map(tuple, points)))
+                all_inputs.extend(points)
+                all_labels.extend(label for _ in points)
+        if not use_representatives:
+            all_inputs, indices = np.unique(all_inputs, return_index=True, axis=0)
+            all_labels = np.asarray(all_labels)[indices]
+        return cls(network, layer_index, all_inputs, all_labels,
+                   representatives=all_representatives)
+
+    @classmethod
+    def from_spec_function(cls, network, layer_index, region_plane,
+                           spec_function, use_representatives=False):
+        """Constructs a ProvableRepair for an input region and "Spec Function."
+
+        @region_plane should be a single plane (Numpy array of
+            counter-clockwise vertices) that defines the "region of interest"
+            to patch over.
+        @spec_function should take a set of input points (Numpy array) and
+            return the desired corresponding labels (list/Numpy array of ints).
+
+        Here we use a slightly in-exact algorithm; we get all partition
+        endpoints using SyReNN, then use those for the ProvableRepair.
+
+        If the @spec_function classifies all points on a linear partition the
+        same way, then this exactly encodes the corresponding problem for the
+        ProvableRepair (i.e., if the ProvableRepair reports all constraints met
+        then the repaired network exactly matches the @spec_function).
+
+        If the @spec_function classifies some points on a linear partition
+        differently than others, the encoding may not be exact (i.e.,
+        ProvableRepair may report all constraints met even when some input has
+        a different output in the patched network than the @spec_function).
+        However, in practice, this works *very* well and is significantly more
+        efficient than computing the exact encoding and resulting patches.
+        """
+        if len(np.asarray(region_plane).shape) == 2:
+            region_plane = [region_plane]
+        assert len(np.asarray(region_plane).shape) == 3
+        upolytopes = network.transform_planes(region_plane,
+                                              compute_preimages=True,
+                                              include_post=False)
+        inputs = []
+        representatives = [] if use_representatives else None
+        for upolytope in upolytopes:
+            for polytope in upolytope:
+                inputs.extend(list(polytope))
+                if use_representatives:
+                    representative = np.mean(polytope, axis=0)
+                    representatives.extend(representative for _ in polytope)
+        if not use_representatives:
+            inputs = np.unique(np.asarray(inputs, dtype=np.float32), axis=0)
+        labels = spec_function(inputs)
+        return cls(network, layer_index, inputs, labels,
+                   representatives=representatives)
+
+    def construct_patched(self, patched_layer):
+        """Constructs a DDNN given the patched layer."""
+        activation_layers = self.network.layers
+        value_layers = activation_layers.copy()
+        value_layers[self.layer_index] = patched_layer
+        return DDNN(activation_layers, value_layers)
diff --git a/prdnn/tests/BUILD b/prdnn/tests/BUILD
new file mode 100644
index 0000000..5740d71
--- /dev/null
+++ b/prdnn/tests/BUILD
@@ -0,0 +1,29 @@
+py_test(
+    name = "test_provable_repair",
+    size = "small",
+    srcs = ["test_provable_repair.py"],
+    deps = [
+        "//prdnn:provable_repair",
+        "@bazel_python//:pytest_helper",
+    ],
+)
+
+py_test(
+    name = "test_ft_repair",
+    size = "small",
+    srcs = ["test_ft_repair.py"],
+    deps = [
+        "//prdnn:ft_repair",
+        "@bazel_python//:pytest_helper",
+    ],
+)
+
+py_test(
+    name = "test_ddnn",
+    size = "small",
+    srcs = ["test_ddnn.py"],
+    deps = [
+        "//prdnn:ddnn",
+        "@bazel_python//:pytest_helper",
+    ],
+)
diff --git a/prdnn/tests/test_ddnn.py b/prdnn/tests/test_ddnn.py
new file mode 100644
index 0000000..0b926f8
--- /dev/null
+++ b/prdnn/tests/test_ddnn.py
@@ -0,0 +1,166 @@
+"""Tests the methods in ddnn.py."""
+# pylint: disable=import-error
+import numpy as np
+import torch
+from pysyrenn import ReluLayer, FullyConnectedLayer, ArgMaxLayer
+from pysyrenn import HardTanhLayer, MaxPoolLayer, StridedWindowData
+try:
+    from external.bazel_python.pytest_helper import main
+    IN_BAZEL = True
+except ImportError:
+    IN_BAZEL = False
+from prdnn.ddnn import DDNN
+
+def test_compute():
+    """Tests that it works for a simple example."""
+    activation_layers = [
+        FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
+        ReluLayer(),
+        FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ]
+    value_layers = activation_layers[:2] + [
+        FullyConnectedLayer(3.0 * np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ]
+    network = DDNN(activation_layers, value_layers)
+    assert network.differ_index == 2
+    output = network.compute([[-2.0, 1.0]])
+    assert np.allclose(output, [[0.0, 6.0]])
+    output = network.compute(torch.tensor([[-2.0, 1.0]])).numpy()
+    assert np.allclose(output, [[0.0, 6.0]])
+
+    activation_layers = [
+        FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
+        HardTanhLayer(),
+    ]
+    value_layers = [
+        FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
+        HardTanhLayer(),
+    ]
+    network = DDNN(activation_layers, value_layers)
+    output = network.compute([[0.5, -0.9]])
+    assert np.allclose(output, [[1.0, -1.8]])
+
+    # Test HardTanh
+    activation_layers = [
+        FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
+        HardTanhLayer(),
+    ]
+    value_layers = [
+        FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
+        HardTanhLayer(),
+    ]
+    network = DDNN(activation_layers, value_layers)
+    output = network.compute([[0.5, -0.9]])
+    assert np.allclose(output, [[1.0, -1.8]])
+
+    # Test MaxPool
+    width, height, channels = 2, 2, 2
+    window_data = StridedWindowData((height, width, channels),
+                                    (2, 2), (2, 2), (0, 0), channels)
+    maxpool_layer = MaxPoolLayer(window_data)
+    activation_layers = [
+        FullyConnectedLayer(np.eye(8), np.ones(shape=(8,))),
+        maxpool_layer,
+    ]
+    value_layers = [
+        FullyConnectedLayer(-1. * np.eye(8), np.zeros(shape=(8,))),
+        maxpool_layer,
+    ]
+    network = DDNN(activation_layers, value_layers)
+    output = network.compute([[1.0, 2.0, -1.0, -2.5, 0.0, 0.5, 1.5, -3.5]])
+    # NHWC, so the two channels are: [1, -1, 0, 1.5] and [2, -2.5, 0.5, -3.5]
+    # So the maxes are 1.5 and 2.0, so the value layer outputs -1.5, -2.0
+    assert np.allclose(output, [[-1.5, -2.0]])
+
+def test_compute_representatives():
+    """Tests that the linear-region endpoints work."""
+    activation_layers = [
+        FullyConnectedLayer(np.eye(1), np.zeros(shape=(1,))),
+        ReluLayer(),
+    ]
+    value_layers = [
+        FullyConnectedLayer(np.eye(1), np.ones(shape=(1,))),
+        ReluLayer(),
+    ]
+    network = DDNN(activation_layers, value_layers)
+    assert network.differ_index == 0
+    points = np.array([[0.0], [0.0]])
+    representatives = np.array([[1.0], [-1.0]])
+    output = network.compute(points, representatives=representatives)
+    assert np.array_equal(output, [[1.], [0.]])
+
+def test_nodiffer():
+    """Tests the it works if activation and value layers are identical."""
+    activation_layers = [
+        FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
+        ReluLayer(),
+        FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ]
+    value_layers = activation_layers
+    network = DDNN(activation_layers, value_layers)
+    assert network.differ_index == 4
+    output = network.compute([[-2.0, 1.0]])
+    assert np.allclose(output, [[0.0, 4.0]])
+
+def test_bad_layer():
+    """Tests that unspported layers after differ_index fail."""
+    # It should work if it's before the differ_index.
+    activation_layers = [
+        FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
+        ReluLayer(),
+        FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
+        ArgMaxLayer(),
+    ]
+    value_layers = activation_layers
+    network = DDNN(activation_layers, value_layers)
+    assert network.differ_index == 4
+    output = network.compute([[-2.0, 1.0]])
+    assert np.allclose(output, [[1.0]])
+    # But not after the differ_index.
+    activation_layers = [
+        FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
+        ReluLayer(),
+        FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
+        ArgMaxLayer(),
+    ]
+    value_layers = activation_layers[:2] + [
+        FullyConnectedLayer(3.0 * np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ]
+    network = DDNN(activation_layers, value_layers)
+    assert network.differ_index == 2
+    try:
+        output = network.compute([[-2.0, 1.0]])
+        assert False
+    except NotImplementedError:
+        pass
+
+def test_serialization():
+    """Tests that it correctly (de)serializes."""
+    activation_layers = [
+        FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
+        ReluLayer(),
+        FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ]
+    value_layers = activation_layers[:2] + [
+        FullyConnectedLayer(3.0 * np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ]
+    network = DDNN(activation_layers, value_layers)
+    serialized = network.serialize()
+    assert all(serialized == layer.serialize()
+               for serialized, layer in zip(serialized.activation_layers,
+                                            activation_layers))
+    assert all(serialized == layer.serialize()
+               for serialized, layer in zip(serialized.value_layers,
+                                            value_layers[2:]))
+    assert serialized.differ_index == 2
+
+    assert DDNN.deserialize(serialized).serialize() == serialized
+
+if IN_BAZEL:
+    main(__name__, __file__)
diff --git a/prdnn/tests/test_ft_repair.py b/prdnn/tests/test_ft_repair.py
new file mode 100644
index 0000000..7f75614
--- /dev/null
+++ b/prdnn/tests/test_ft_repair.py
@@ -0,0 +1,54 @@
+"""Tests the methods in netpatch.py
+"""
+import numpy as np
+import torch
+import pytest
+from pysyrenn.frontend import Network, ReluLayer, FullyConnectedLayer
+try:
+    from external.bazel_python.pytest_helper import main
+    IN_BAZEL = True
+except ImportError:
+    IN_BAZEL = False
+from prdnn.ft_repair import FTRepair
+
+def test_already_good():
+    """Point-wise test case where all constraints are already met.
+
+    We want to make sure that it doesn't change any weights unnecessarily.
+    """
+    network = Network([
+        FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ])
+    layer_index = 0
+    points = [[1.0, 0.5], [2.0, -0.5], [4.0, 5.0]]
+    labels = [0, 0, 1]
+    patcher = FTRepair(network, points, labels)
+    patcher.layer = layer_index
+    patched = patcher.compute()
+    patched_layer = patched.layers[0]
+    assert np.allclose(patched_layer.weights.numpy(), np.eye(2))
+    assert np.allclose(patched_layer.biases.numpy(), np.zeros(shape=(2,)))
+
+def test_one_point():
+    """Point-wise test case with only one constraint.
+
+    This leads to weight bounds that are unbounded-on-one-side.
+    """
+    # Where the weight is too big.
+    network = Network([
+        FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+        FullyConnectedLayer(np.array([[1.0, 0.0], [1.0, 0.0]]),
+                            np.array([0.0, 0.0])),
+    ])
+    layer_index = 0
+    points = [[1.0, 0.5]]
+    labels = [1]
+    patcher = FTRepair(network, points, labels)
+    patcher.epochs = 10000
+    patched = patcher.compute()
+    assert np.argmax(patched.compute(points)) == 1
+
+if IN_BAZEL:
+    main(__name__, __file__)
diff --git a/prdnn/tests/test_provable_repair.py b/prdnn/tests/test_provable_repair.py
new file mode 100644
index 0000000..bf97a44
--- /dev/null
+++ b/prdnn/tests/test_provable_repair.py
@@ -0,0 +1,150 @@
+"""Tests the methods in netpatch.py
+"""
+import numpy as np
+import torch
+import pytest
+from pysyrenn.frontend import Network, ReluLayer, FullyConnectedLayer
+try:
+    from external.bazel_python.pytest_helper import main
+    IN_BAZEL = True
+except ImportError:
+    IN_BAZEL = False
+from prdnn.provable_repair import ProvableRepair
+
+def test_already_good():
+    """Point-wise test case where all constraints are already met.
+
+    We want to make sure that it doesn't change any weights unnecessarily.
+    """
+    network = Network([
+        FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ])
+    layer_index = 0
+    points = [[1.0, 0.5], [2.0, -0.5], [4.0, 5.0]]
+    labels = [0, 0, 1]
+    patcher = ProvableRepair(network, layer_index, points, labels)
+    patched = patcher.compute()
+    patched_layer = patched.value_layers[0]
+    assert np.allclose(patched_layer.weights.numpy(), np.eye(2))
+    assert np.allclose(patched_layer.biases.numpy(), np.zeros(shape=(2,)))
+
+def test_one_point():
+    """Point-wise test case with only one constraint.
+
+    This leads to weight bounds that are unbounded-on-one-side.
+    """
+    # Where the weight is too big.
+    network = Network([
+        FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+        FullyConnectedLayer(np.array([[1.0, 0.0], [1.0, 0.0]]),
+                            np.array([0.0, 0.0])),
+    ])
+    layer_index = 0
+    points = [[1.0, 0.5]]
+    labels = [1]
+    patcher = ProvableRepair(network, layer_index, points, labels)
+    patched = patcher.compute()
+    assert np.argmax(patched.compute(points)) == 1
+
+    # Where the weight is too small.
+    network = Network([
+        FullyConnectedLayer(np.eye(2), np.array([0.0, 0.0])),
+        ReluLayer(),
+        FullyConnectedLayer(np.array([[1.0, 0.0], [1.0, 0.0]]), np.array([0.0, 2.0])),
+    ])
+    layer_index = 0
+    points = [[1.0, 0.0]]
+    labels = [0]
+    patcher = ProvableRepair(network, layer_index, points, labels)
+    patched = patcher.compute()
+    assert np.argmax(patched.compute(points)) == 0
+
+def test_optimal():
+    """Point-wise test case that the greedy algorithm can solve in 1 step.
+
+    All it needs to do is triple the second component.
+    """
+    network = Network([
+        FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,))),
+        ReluLayer(),
+    ])
+    layer_index = 0
+    points = [[1.0, 0.5], [2.0, -0.5], [5.0, 4.0]]
+    labels = [1, 0, 1]
+    patcher = ProvableRepair(network, layer_index, points, labels)
+    patched = patcher.compute()
+    assert patched.differ_index == 0
+    assert np.count_nonzero(
+            np.argmax(patched.compute(points), axis=1) == labels) == 3
+
+def test_from_planes():
+    """Test case to load key points from a set of labeled 2D polytopes.
+    """
+    if not Network.has_connection():
+        pytest.skip("No server connected.")
+
+    network = Network([
+        ReluLayer(),
+        FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,)))
+    ])
+    layer_index = 1
+    planes = [
+        np.array([[-1.0, -3.0], [-0.5, -3.0], [-0.5, 9.0], [-1.0, 9.0]]),
+        np.array([[8.0, -2.0], [16.0, -2.0], [16.0, 6.0], [8.0, 6.0]]),
+    ]
+    labels = [1, 0]
+    patcher = ProvableRepair.from_planes(network, layer_index, planes, labels)
+    assert patcher.network is network
+    assert patcher.layer_index is layer_index
+    assert len(patcher.inputs) == (4 + 2) + (4 + 2)
+    true_key_points = list(planes[0])
+    true_key_points += [np.array([-1.0, 0.0]), np.array([-0.5, 0.0])]
+    true_key_points += list(planes[1])
+    true_key_points += [np.array([8.0, 0.0]), np.array([16.0, 0.0])]
+    true_labels = ([1] * 6) + ([0] * 6)
+    for true_point, true_label in zip(true_key_points, true_labels):
+        try:
+            i = next(i for i, point in enumerate(patcher.inputs)
+                     if np.allclose(point, true_point))
+        except StopIteration:
+            assert False
+        assert true_label == patcher.labels[i]
+
+def test_from_spec():
+    """Test case to load key points from a spec function.
+    """
+    if not Network.has_connection():
+        pytest.skip("No server connected.")
+
+    network = Network([
+        ReluLayer(),
+        FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,)))
+    ])
+    layer_index = 1
+    region_of_interest = np.array([
+        [0.5, -3.0],
+        [1.0, -3.0],
+        [1.0, 9.0],
+        [0.5, 9.0],
+    ])
+    spec_fn = lambda i: np.isclose(i[:, 0], 1.0).astype(np.float32)
+    patcher = ProvableRepair.from_spec_function(network, layer_index,
+                                            region_of_interest, spec_fn)
+    assert patcher.network is network
+    assert patcher.layer_index is layer_index
+    assert len(patcher.inputs) == (4 + 2)
+    true_key_points = list(region_of_interest)
+    true_key_points += [np.array([0.5, 0.0]), np.array([1.0, 0.0])]
+    true_labels = [0, 1, 1, 0, 0, 1]
+    for true_point, true_label in zip(true_key_points, true_labels):
+        try:
+            i = next(i for i, point in enumerate(patcher.inputs)
+                     if np.allclose(point, true_point))
+        except StopIteration:
+            assert False
+        assert true_label == patcher.labels[i]
+
+if IN_BAZEL:
+    main(__name__, __file__)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..9a0bffa
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+# PRDNN
+pysyrenn==1.1
+scipy==1.5.2
+# END
+numpy==1.17.2
+imageio==2.5.0
+Pillow==7.1.0
+# For building the package.
+setuptools==41.2.0
+wheel==0.33.6
+# For testing
+pytest==5.2.0
+coverage==4.5.4