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‎refs/pull/334/merge/_images/notebooks_AutoEncoders_17_0.png‎

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‎refs/pull/334/merge/_sources/notebooks/AutoEncoders.ipynb.txt‎

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‎refs/pull/334/merge/_sources/notebooks/Basic_Usage.ipynb.txt‎

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‎refs/pull/334/merge/_sources/notebooks/Benchmarks.ipynb.txt‎

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"<a href=\"https://colab.research.google.com/github/adriangb/scikeras/blob/docs-deploy/refs/heads/master/notebooks/Benchmarks.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Run in Google Colab</a>"
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{
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"cell_type": "markdown",
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"id": "418feec2",
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"# SciKeras Benchmarks\n",
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{
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"execution_count": 1,
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"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
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"E0000 00:00:1734039224.808030 7945 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
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"E0000 00:00:1734039224.813871 7945 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
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"/home/runner/work/scikeras/scikeras/scikeras/__init__.py:20: UserWarning: \n",
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" This project is now deprecated. Keras has re-introduced wrappers with a similar API to those in SciKeras, but they will be better maintained.\n",
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" SciKeras was a project to meet a specific need that was developed by a single developer.\n",
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" I no longer use Keras nor do I have the time to maintain this project, which became increasingly difficult with multiple versions of Keras and Scikit-Learn to support.\n",
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" I thank all of the users and contributors over the years and hope that the new Keras wrappers will meet your needs.\n",
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" TODO: add link to Keras docs and release here.\n",
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" \n",
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" warn(\n"
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"E0000 00:00:1734039852.622019 6721 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
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"E0000 00:00:1734039852.628823 6721 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
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"id": "833491fc",
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"Silence TensorFlow logging to keep output succinct."
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"## 2. Dataset\n",
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"## 3. Define Keras Model\n",
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"## 4. Keras benchmarks"
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"\r",
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"\u001b[1m 1/16\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 48ms/step"
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"\u001b[1m 1/16\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 78ms/step"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\u001b[1m10/16\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step "
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"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step"
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"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n"
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"As you can see, the overhead for SciKeras is <1 sec, and the accuracy is identical."

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