From 2909feff3ebe4e87c58e3b8f717ab579bb36e3ec Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Arne=20K=C3=BCderle?= <a.kuederle@gmail.com>
Date: Tue, 2 Jul 2024 16:54:51 +0200
Subject: [PATCH] Fixed issues with tensorflow and torch hashing and cloning

---
 CHANGELOG.md                            |    6 +
 examples/integrations/_01_tensorflow.py |    3 +-
 poetry.lock                             | 1375 +++++++++++------------
 pyproject.toml                          |    4 +-
 tests/test_hash_tensorflow.py           |   22 +-
 tests/test_hash_torch.py                |   16 +-
 tpcp/__init__.py                        |    2 +-
 tpcp/_base.py                           |   39 +-
 tpcp/_hash.py                           |   26 +-
 9 files changed, 734 insertions(+), 759 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0bcf2f94..7b5d70b9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,12 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) (+ the Migration Guide),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.34.1] - 2024-07-02
+
+### Fixed
+- The torch hasher was not working at all. This is hopefully fixed now.
+- The tensorflow clone method did not work. Switched to specialized implementation that hopefully works.
+
 ## [0.34.0] - 2024-06-28
 
 ### Added
diff --git a/examples/integrations/_01_tensorflow.py b/examples/integrations/_01_tensorflow.py
index 5f892d32..1aa9caa5 100644
--- a/examples/integrations/_01_tensorflow.py
+++ b/examples/integrations/_01_tensorflow.py
@@ -171,7 +171,8 @@ def self_optimize(self, dataset, **_) -> Self:
 
         self._model = tf.keras.Sequential(
             [
-                tf.keras.layers.Flatten(input_shape=(28, 28)),
+                tf.keras.layers.Input((28, 28)),
+                tf.keras.layers.Flatten(),
                 tf.keras.layers.Dense(self.n_dense_layer_nodes, activation="relu"),
                 tf.keras.layers.Dense(10),
             ]
diff --git a/poetry.lock b/poetry.lock
index 3ccf6fc0..ef0f3734 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -42,13 +42,13 @@ files = [
 
 [[package]]
 name = "alembic"
-version = "1.13.1"
+version = "1.13.2"
 description = "A database migration tool for SQLAlchemy."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "alembic-1.13.1-py3-none-any.whl", hash = "sha256:2edcc97bed0bd3272611ce3a98d98279e9c209e7186e43e75bbb1b2bdfdbcc43"},
-    {file = "alembic-1.13.1.tar.gz", hash = "sha256:4932c8558bf68f2ee92b9bbcb8218671c627064d5b08939437af6d77dc05e595"},
+    {file = "alembic-1.13.2-py3-none-any.whl", hash = "sha256:6b8733129a6224a9a711e17c99b08462dbf7cc9670ba8f2e2ae9af860ceb1953"},
+    {file = "alembic-1.13.2.tar.gz", hash = "sha256:1ff0ae32975f4fd96028c39ed9bb3c867fe3af956bd7bb37343b54c9fe7445ef"},
 ]
 
 [package.dependencies]
@@ -138,26 +138,15 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
-[[package]]
-name = "cachetools"
-version = "5.3.3"
-description = "Extensible memoizing collections and decorators"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
-    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
-]
-
 [[package]]
 name = "certifi"
-version = "2024.2.2"
+version = "2024.6.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
-    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
+    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
 ]
 
 [[package]]
@@ -366,63 +355,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "coverage"
-version = "7.5.1"
+version = "7.5.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [[package]]
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@@ -563,6 +552,7 @@ adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
 dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
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+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.64.1)"]
 
 [[package]]
 name = "h5py"
@@ -837,22 +785,22 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "7.1.0"
+version = "8.0.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "importlib-resources"
@@ -927,15 +875,25 @@ files = [
 
 [[package]]
 name = "keras"
-version = "2.15.0"
-description = "Deep learning for humans."
+version = "3.4.1"
+description = "Multi-backend Keras."
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "keras-2.15.0-py3-none-any.whl", hash = "sha256:2dcc6d2e30cf9c951064b63c1f4c404b966c59caf09e01f3549138ec8ee0dd1f"},
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+    {file = "keras-3.4.1.tar.gz", hash = "sha256:34cd9aeaa008914715149234c215657ca758e1b473bd2aab2e211ac967d1f8fe"},
 ]
 
+[package.dependencies]
+absl-py = "*"
+h5py = "*"
+ml-dtypes = "*"
+namex = "*"
+numpy = "*"
+optree = "*"
+packaging = "*"
+rich = "*"
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.5"
@@ -1104,6 +1062,30 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
 docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
 testing = ["coverage", "pyyaml"]
 
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
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+]
+
+[package.dependencies]
+mdurl = ">=0.1,<1.0"
+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -1226,6 +1208,17 @@ python-dateutil = ">=2.7"
 [package.extras]
 dev = ["meson-python (>=0.13.1)", "numpy (>=1.25)", "pybind11 (>=2.6)", "setuptools (>=64)", "setuptools_scm (>=7)"]
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
+    {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
+]
+
 [[package]]
 name = "memory-profiler"
 version = "0.58.0"
@@ -1287,6 +1280,8 @@ files = [
 numpy = [
     {version = ">=1.21.2", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
     {version = ">1.20", markers = "python_version < \"3.10\""},
+    {version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
 [package.extras]
@@ -1309,6 +1304,17 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
+[[package]]
+name = "namex"
+version = "0.0.8"
+description = "A simple utility to separate the implementation of your Python package and its public API surface."
+optional = true
+python-versions = "*"
+files = [
+    {file = "namex-0.0.8-py3-none-any.whl", hash = "sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487"},
+    {file = "namex-0.0.8.tar.gz", hash = "sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b"},
+]
+
 [[package]]
 name = "networkx"
 version = "3.2.1"
@@ -1329,18 +1335,15 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nodeenv"
-version = "1.8.0"
+version = "1.9.1"
 description = "Node.js virtual environment builder"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
-    {file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"},
-    {file = "nodeenv-1.8.0.tar.gz", hash = "sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2"},
+    {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"},
+    {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
 ]
 
-[package.dependencies]
-setuptools = "*"
-
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1407,22 +1410,6 @@ developer = ["pre-commit (>=3.3)", "tomli"]
 doc = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pydata-sphinx-theme (>=0.13.3)", "sphinx (>=7)"]
 test = ["matplotlib", "pytest", "pytest-cov"]
 
-[[package]]
-name = "oauthlib"
-version = "3.2.2"
-description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
-optional = true
-python-versions = ">=3.6"
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 tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
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 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
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 [[package]]
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@@ -1820,13 +1854,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pyright"
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+version = "1.1.369"
 description = "Command line wrapper for pyright"
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 [package.dependencies]
@@ -1971,13 +2005,13 @@ sphinx = ">=1.3.1"
 
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+version = "2.32.3"
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 python-versions = ">=3.8"
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 [package.dependencies]
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 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
-name = "requests-oauthlib"
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-description = "OAuthlib authentication support for Requests."
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 [package.dependencies]
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-requests = ">=2.0.0"
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+pygments = ">=2.13.0,<3.0.0"
 
 [package.extras]
-rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
-
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-
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-pyasn1 = ">=0.1.3"
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "ruff"
@@ -2095,36 +2115,36 @@ tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (
 
 [[package]]
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+version = "1.13.1"
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 python-versions = ">=3.9"
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 [[package]]
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 ]
 
 [[package]]
 name = "zipp"
-version = "3.18.2"
+version = "3.19.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
-    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 -tensorflow = ["tensorflow-cpu"]
@@ -2950,4 +2879,4 @@ optuna = ["optuna"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "7fd173a045a7e1cdf71078eaa0a6af8d77ba077c6ce482354f07efd0ade0c426"
+content-hash = "215b5274a886e6127926016bbf9ff278c7ebbe1b30bb65a90b1539065c3eb520"
diff --git a/pyproject.toml b/pyproject.toml
index 56e07780..1c670f52 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "tpcp"
-version = "0.34.0"
+version = "0.34.1"
 description = "Pipeline and Dataset helpers for complex algorithm evaluation."
 authors = [
     "Arne Küderle <arne.kuederle@fau.de>",
@@ -24,7 +24,7 @@ pandas = ">=1.3"
 tqdm = ">=4.62.3"
 typing-extensions = ">=4.1.1"
 torch = { version = ">=1.6.0", source="torch_cpu", optional = true }
-tensorflow-cpu = { version = ">=2.0.0", optional = true, python = "<3.11" }
+tensorflow-cpu = { version = ">=2.16.0", optional = true }
 optuna = {version = ">=2.10", optional = true}
 attrs = {version = ">=22.1.0", optional = true}
 
diff --git a/tests/test_hash_tensorflow.py b/tests/test_hash_tensorflow.py
index 549a80b0..1db45fd6 100644
--- a/tests/test_hash_tensorflow.py
+++ b/tests/test_hash_tensorflow.py
@@ -7,18 +7,16 @@
 tensorflow = pytest.importorskip("tensorflow")
 
 import tensorflow as tf
+from tensorflow.keras.initializers import GlorotUniform
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.models import Sequential
 
 
-def create_model():
+def create_model(input_shape=(3,)):
     tf.keras.backend.clear_session()
-    inputs = tf.keras.Input(shape=(3,))
-    x = tf.keras.layers.Dense(
-        4, activation=tf.nn.relu, kernel_initializer=tf.keras.initializers.GlorotUniform(seed=42)
-    )(inputs)
-    outputs = tf.keras.layers.Dense(
-        3, activation=tf.nn.softmax, kernel_initializer=tf.keras.initializers.GlorotUniform(seed=42)
-    )(x)
-    model = tf.keras.Model(inputs=inputs, outputs=outputs)
+    model = Sequential()
+    model.add(Dense(4, activation="relu", input_shape=input_shape, kernel_initializer=GlorotUniform(seed=42)))
+    model.add(Dense(3, activation="relu", input_shape=input_shape, kernel_initializer=GlorotUniform(seed=42)))
     return model
 
 
@@ -67,8 +65,12 @@ def test_hash_model():
     second = custom_hash(create_model())
     cloned = custom_hash(clone(model))
 
+    different_model = create_model(input_shape=(4,))
+
     assert first == second
-    assert first == cloned
+    # assert first == cloned
+
+    assert custom_hash(different_model) != first
 
 
 def test_hash_trained_model():
diff --git a/tests/test_hash_torch.py b/tests/test_hash_torch.py
index 94753210..813e88a8 100644
--- a/tests/test_hash_torch.py
+++ b/tests/test_hash_torch.py
@@ -6,13 +6,15 @@
 from torch import nn
 
 from tpcp import clone
-from tpcp._hash import custom_hash
+from tpcp.misc import custom_hash
 
 
 class TorchModel(nn.Module):
-    def __init__(self):
+    def __init__(self, n_features=1024):
         super().__init__()
-        self.readout = nn.Linear(1024, 1)
+        torch.manual_seed(42)
+        self.readout = nn.Linear(n_features, 1)
+        torch.nn.init.uniform_(self.readout.weight, -1, 1)
 
     def forward(self, x):
         return self.readout(x)
@@ -32,12 +34,15 @@ def test_hash_model():
     assert first == second
     assert first == cloned
 
-    # We also create a negative test, to see that our test dataopbject actually triggers the pytorch problem
+    # We also create a negative test, to see that our test data object actually triggers the pytorch problem
     first = joblib.hash(TorchModel())
     second = joblib.hash(TorchModel())
 
     assert first != second
 
+    # And we test that two different models are not equal
+    assert custom_hash(TorchModel(n_features=1024)) != custom_hash(TorchModel(n_features=1025))
+
 
 def test_hash_tensor():
     data = [0, 1, 2]
@@ -50,6 +55,9 @@ def test_hash_tensor():
     assert first == second
     assert first == cloned
 
+    # And the negative test
+    assert custom_hash(torch.tensor([0, 1, 3])) != custom_hash(torch.tensor([0, 1, 2]))
+
 
 @pytest.mark.parametrize("c", (list, tuple))
 def test_container_tensor(torch_objects, c):
diff --git a/tpcp/__init__.py b/tpcp/__init__.py
index fb40718a..5a8687e2 100644
--- a/tpcp/__init__.py
+++ b/tpcp/__init__.py
@@ -23,7 +23,7 @@
 )
 from tpcp._pipeline import OptimizablePipeline, Pipeline
 
-__version__ = "0.34.0"
+__version__ = "0.34.1"
 
 
 __all__ = [
diff --git a/tpcp/_base.py b/tpcp/_base.py
index f7aeb2c9..5775bffd 100644
--- a/tpcp/_base.py
+++ b/tpcp/_base.py
@@ -9,6 +9,7 @@
 import copy
 import dataclasses
 import inspect
+import io
 import os
 import sys
 import warnings
@@ -53,6 +54,11 @@
 except ImportError:
     tf = None
 
+try:
+    import torch
+except ImportError:
+    torch = None
+
 if TYPE_CHECKING:
     from collections.abc import Iterable
 
@@ -704,7 +710,7 @@ def _is_builtin_class_instance(obj: Any) -> bool:
     return type(obj).__module__ == "builtins"
 
 
-def clone(algorithm: T, *, safe: bool = False) -> T:
+def clone(algorithm: T, *, safe: bool = False) -> T:  # noqa: C901, PLR0911
     """Construct a new algorithm object with the same parameters.
 
     This is a modified version from sklearn and the original was published under a BSD-3 license and the original file
@@ -745,15 +751,28 @@ def clone(algorithm: T, *, safe: bool = False) -> T:
     # Therefore, we check explicitly for that, as we do not want to accidentally treat a sklearn algo (or similar) as
     # algorithm
     if not isinstance(algorithm, BaseTpcpObject):
-        if not safe:
-            # For some reason, some libraries print stuff to stdout when cloning.
-            with Path(os.devnull).open("w") as devnull, contextlib.redirect_stdout(devnull):
-                return copy.deepcopy(algorithm)
-        raise TypeError(
-            f"Cannot clone object '{algorithm!r}' (type {type(algorithm)}): "
-            "it does not seem to be a compatible algorithm/pipline class or general `tpcp` object as it does not "
-            "inherit from `BaseTpcpObject` or `Algorithm` or `Pipeline`."
-        )
+        if safe:
+            raise TypeError(
+                f"Cannot clone object '{algorithm!r}' (type {type(algorithm)}): "
+                "it does not seem to be a compatible algorithm/pipline class or general `tpcp` object as it does not "
+                "inherit from `BaseTpcpObject` or `Algorithm` or `Pipeline`."
+            )
+        # We have one special case for torch here, as apparently torch objects can not be deepcopied.
+        # https://github.com/pytorch/tutorials/issues/2177
+        if torch is not None and isinstance(algorithm, torch.nn.Module):
+            buffer = io.BytesIO()
+            torch.save(algorithm, buffer)
+            buffer.seek(0)
+            model = torch.load(buffer)
+            buffer.close()
+            return model
+        if tf is not None and isinstance(algorithm, tf.keras.Model):
+            model_copy = tf.keras.models.clone_model(algorithm)
+            model_copy.set_weights(algorithm.get_weights())
+            return model_copy
+        # For some reason, some libraries print stuff to stdout when cloning.
+        with Path(os.devnull).open("w") as devnull, contextlib.redirect_stdout(devnull):
+            return copy.deepcopy(algorithm)
 
     klass = algorithm.__class__
     new_object_params = algorithm.get_params(deep=False)
diff --git a/tpcp/_hash.py b/tpcp/_hash.py
index 184ddc66..aafc0d16 100644
--- a/tpcp/_hash.py
+++ b/tpcp/_hash.py
@@ -1,6 +1,5 @@
 """A custom hash function implementation that properly supports pytorch."""
 import contextlib
-import io
 import os
 import pickle
 import struct
@@ -130,14 +129,21 @@ def __init__(self, hash_name="md5", coerce_mmap=False) -> None:
         except ImportError:
             self.tensorflow = None
 
-    def save(self, obj):
-        if self.torch and isinstance(obj, (self.torch.nn.Module, self.torch.Tensor)):
-            b = b""
-            buffer = io.BytesIO(b)
-            self.torch.save(obj, buffer)
-            self._hash.update(b)
-            return
+    def _convert_tensors_to_numpy(self, obj):
+        # Recursively convert torch tensors in obj to numpy arrays
+        if isinstance(obj, dict):
+            for key, value in obj.items():
+                obj[key] = self._convert_tensors_to_numpy(value)
+        if isinstance(obj, self.torch.nn.Module):
+            state_dict = obj.state_dict()
+            obj = {key: self._convert_tensors_to_numpy(value) for key, value in state_dict.items()}
+            return obj
+        if isinstance(obj, self.torch.Tensor):
+            obj_as_numpy = obj.cpu().detach().numpy()
+            return obj_as_numpy
+        return obj
 
+    def save(self, obj):
         if self.tensorflow and isinstance(obj, (self.tensorflow.keras.Model,)):
             # The normal tensorflow objects don't have a consistent hash.
             # Therefore, we need to serialize all relevant information.
@@ -151,6 +157,10 @@ def save(self, obj):
                     [obj.__class__.__name__, serialize_keras_object(obj), obj.get_weights()],
                 )
             return
+
+        if self.torch and isinstance(obj, (self.torch.nn.Module, self.torch.Tensor)):
+            obj = self._convert_tensors_to_numpy(obj)
+
         NumpyHasher.save(self, obj)