[Lab 1 Part 1.3] Solution throws AssertionError on Apple M1?
adrianhak opened this issue · comments
Running the solution notebook for section 1.3 returns an AssertionError (pasted below)
I'm running macOS 11.5.1 on the M1 chip, using tensorflow-macos 2.5.0
Have anyone got this working on the M1 chip?
[[0.55266035 0.85308117 0.09644735]]
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/var/folders/_q/0sn2mknj461fsdfsz8xvkhpm0000gn/T/ipykernel_88430/4197002161.py in <module>
37 # test the output!
38 print(y.numpy())
---> 39 mdl.lab1.test_custom_dense_layer_output(y)
~/miniforge3/lib/python3.9/site-packages/mitdeeplearning/lab1.py in test_custom_dense_layer_output(y)
83 true_y = np.array([[0.2697859, 0.45750418, 0.66536945]],dtype='float32')
84 assert tf.shape(y).numpy().tolist() == list(true_y.shape), "[FAIL] output is of incorrect shape. expected {} but got {}".format(true_y.shape, y.numpy().shape)
---> 85 np.testing.assert_almost_equal(y.numpy(), true_y, decimal=7, err_msg="[FAIL] output is of incorrect value. expected {} but got {}".format(y.numpy(), true_y), verbose=True)
86 print("[PASS] test_custom_dense_layer_output")
87 return True
[... skipping hidden 2 frame]
~/miniforge3/lib/python3.9/site-packages/numpy/testing/_private/utils.py in assert_array_compare(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)
842 verbose=verbose, header=header,
843 names=('x', 'y'), precision=precision)
--> 844 raise AssertionError(msg)
845 except ValueError:
846 import traceback
AssertionError:
Arrays are not almost equal to 7 decimals
[FAIL] output is of incorrect value. expected [[0.55266035 0.85308117 0.09644735]] but got [[0.2697859 0.45750418 0.66536945]]
Mismatched elements: 3 / 3 (100%)
Max absolute difference: 0.5689221
Max relative difference: 1.0485145
x: array([[0.5526603, 0.8530812, 0.0964473]], dtype=float32)
y: array([[0.2697859, 0.4575042, 0.6653695]], dtype=float32)
Python packages installed:
absl-py @ file:///home/conda/feedstock_root/build_artifacts/absl-py_1606234718434/work
anyio==3.3.0
appnope==0.1.2
argon2-cffi==20.1.0
astunparse @ file:///home/conda/feedstock_root/build_artifacts/astunparse_1610696312422/work
attrs==21.2.0
Babel==2.9.1
backcall==0.2.0
bleach==4.0.0
brotlipy==0.7.0
cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
cachetools==4.2.2
certifi==2021.5.30
cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1625835350093/work
chardet @ file:///Users/runner/miniforge3/conda-bld/chardet_1610093454858/work
charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1626371162869/work
cloudpickle==1.6.0
colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1602866480661/work
conda==4.10.3
conda-package-handling @ file:///Users/runner/miniforge3/conda-bld/conda-package-handling_1618231726924/work
cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1616851494586/work
cycler==0.10.0
debugpy==1.4.1
decorator==5.0.9
defusedxml==0.7.1
entrypoints==0.3
flatbuffers==1.12
gast @ file:///home/conda/feedstock_root/build_artifacts/gast_1596839682936/work
google-auth==1.35.0
google-auth-oauthlib==0.4.5
google-pasta==0.2.0
grpcio @ file:///Users/runner/miniforge3/conda-bld/grpcio_1610588577338/work
gym==0.19.0
h5py @ file:///Users/runner/miniforge3/conda-bld/h5py_1609497507927/work
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1609836280497/work
ipykernel==6.2.0
ipython==7.26.0
ipython-genutils==0.2.0
jedi==0.18.0
Jinja2==3.0.1
json5==0.9.6
jsonschema==3.2.0
jupyter-client==6.1.12
jupyter-core==4.7.1
jupyter-server==1.10.2
jupyterlab==3.1.7
jupyterlab-pygments==0.1.2
jupyterlab-server==2.7.1
keras-nightly==2.5.0.dev2021032900
Keras-Preprocessing @ file:///home/conda/feedstock_root/build_artifacts/keras-preprocessing_1610713559828/work
kiwisolver==1.3.1
Markdown==3.3.4
MarkupSafe==2.0.1
matplotlib==3.4.3
matplotlib-inline==0.1.2
mistune==0.8.4
mitdeeplearning==0.2.0
nbclassic==0.3.1
nbclient==0.5.4
nbconvert==6.1.0
nbformat==5.1.3
nest-asyncio==1.5.1
notebook==6.4.3
numpy==1.21.2
oauthlib==3.1.1
opencv-python==4.5.3.56
opt-einsum @ file:///home/conda/feedstock_root/build_artifacts/opt_einsum_1617859230218/work
packaging==21.0
pandocfilters==1.4.3
parso==0.8.2
pexpect==4.8.0
pickleshare==0.7.5
Pillow==8.3.1
prometheus-client==0.11.0
prompt-toolkit==3.0.19
protobuf==3.17.2
ptyprocess==0.7.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1624996792650/work
pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1593275161868/work
Pygments==2.10.0
pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1608055815057/work
pyparsing==2.4.7
pyrsistent==0.18.0
PySocks @ file:///Users/runner/miniforge3/conda-bld/pysocks_1610291460269/work
python-dateutil==2.8.2
pytz==2021.1
pyzmq==22.2.1
regex==2021.8.3
requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1626393743643/work
requests-oauthlib==1.3.0
requests-unixsocket==0.2.0
rsa==4.7.2
ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1624999868738/work
scipy @ file:///Users/runner/miniforge3/conda-bld/scipy_1624824941870/work
Send2Trash==1.8.0
six @ file:///home/conda/feedstock_root/build_artifacts/six_1590081179328/work
sniffio==1.2.0
tensorboard==2.6.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow-estimator==2.5.0
tensorflow-macos==2.5.0
tensorflow-metal==0.1.2
termcolor==1.1.0
terminado==0.11.0
testpath==0.5.0
tornado==6.1
tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1627689047698/work
traitlets==5.0.5
typing-extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1602702424206/work
urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1624634538755/work
wcwidth==0.2.5
webencodings==0.5.1
websocket-client==1.2.1
Werkzeug==2.0.1
wrapt @ file:///Users/runner/miniforge3/conda-bld/wrapt_1624972047019/work
If this is MIT lab, I had the same problem because was referencing b from the context instead of self.b , but please keep this post for future generations
I am using the same machine, also referring self.b and getting the same error. Is there any way to rectify this issue?
I am using the same machine, also referring
self.band getting the same error. Is there any way to rectify this issue?
See another issue #116
If you re running locally then get the pull request suggested and install local version of mitdeeplearning library with pip3 install . from the suggested PR