TensorFlowDeepAutoencoder
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cmgreen210
MNIST Digit Classification Using Stacked Autoencoder And TensorFlow
Bumps [urllib3](https://github.com/urllib3/urllib3) from 1.7.1 to 1.26.5. Release notes Sourced from urllib3's releases. 1.26.5 :warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap Fixed...
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Bumps [numpy](https://github.com/numpy/numpy) from 1.11.2 to 1.22.0. Release notes Sourced from numpy's releases. v1.22.0 NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread...
Bumps [html5lib](https://github.com/html5lib/html5lib-python) from 0.999 to 0.999999999. Changelog Sourced from html5lib's changelog. Commits 6a73efa Yes, another release, already. :( e0dc25f Fix attribute order to the treebuilder to be document order a3b8252...
Bumps [requests](https://github.com/psf/requests) from 2.2.1 to 2.20.0. Changelog Sourced from requests's changelog. 2.20.0 (2018-10-18) Bugfixes Content-Type header parsing is now case-insensitive (e.g. charset=utf8 v Charset=utf8). Fixed exception leak where certain redirect...
Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 3.0.0b2 to 3.18.3. Release notes Sourced from protobuf's releases. Protocol Buffers v3.18.3 C++ Reduce memory consumption of MessageSet parsing This release addresses a Security Advisory for C++...
Bumps [wheel](https://github.com/pypa/wheel) from 0.24.0 to 0.38.1. Changelog Sourced from wheel's changelog. Release Notes UNRELEASED Updated vendored packaging to 22.0 0.38.4 (2022-11-09) Fixed PKG-INFO conversion in bdist_wheel mangling UTF-8 header values...
Hello, I found a performance issue in the definition of `main_unsupervised`, code/ae/autoencoder.py, [`tf.reshape`](https://github.com/cmgreen210/TensorFlowDeepAutoencoder/blob/5298ec437689ba7ecb59229599141549ef6a6a1d/code/ae/autoencoder.py#L336) will be created repeatedly during program execution, resulting in reduced efficiency. I think it should be created...
Hello, thank you for your contribution. What I wonder is the propose of your code. Is the propose focusing on binary classification problem? Thank you.
I have a question about the code here when I have read it. I think the line 181: [https://github.com/cmgreen210/TensorFlowDeepAutoencoder/blob/5298ec437689ba7ecb59229599141549ef6a6a1d/code/ae/autoencoder.py#L181-L182](url) out = self._activate(last_output, self._w(n), self._b(n, "_out"), transpose_w=True) the second param shouldn't...
In the File autoencoder.py line 407, the training times is epochs(which is 56 )*num_train. Why steps of training are those
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MNIST Digit Classification Using Stacked Autoencoder And TensorFlow