fixed smaller issues
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parent
31aed1f58c
commit
7664c5a6a0
@ -1357,7 +1357,7 @@
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"err_source = vx_ref - vx_src \n",
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"err_source = vx_ref - vx_src \n",
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"err_hybrid = vx_ref - vx_hyb \n",
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"err_hybrid = vx_ref - vx_hyb \n",
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"v = np.concatenate([err_source,err_hybrid], axis=1)\n",
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"v = np.concatenate([err_source,err_hybrid], axis=1)\n",
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"axes[3].imshow( v , origin='lower', cmap='magma')\n",
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"axes[3].imshow( v , origin='lower', cmap='cividis')\n",
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"axes[3].set_title(f\" Errors: Source & Learned\")\n",
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"axes[3].set_title(f\" Errors: Source & Learned\")\n",
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"\n",
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"\n",
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"pylab.tight_layout()\n"
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"pylab.tight_layout()\n"
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@ -20,9 +20,11 @@ python3.7 json-cleanup-for-pdf.py
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/Users/thuerey/Library/Python/3.7/bin/jupyter-book build . --builder pdflatex
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/Users/thuerey/Library/Python/3.7/bin/jupyter-book build . --builder pdflatex
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cd _build/latex
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cd _build/latex
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#mv book.pdf book-xetex.pdf # failed anyway
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#mv book.pdf book-xetex.pdf # not necessary, failed anyway
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# this generates book.tex
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rm -f book-in.tex sphinxmessages-in.sty book-in.aux book-in.toc
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rm -f book-in.tex sphinxmessages-in.sty book-in.aux book-in.toc
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# rename book.tex -> book-in.tex (this is the original output!)
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mv book.tex book-in.tex
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mv book.tex book-in.tex
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mv sphinxmessages.sty sphinxmessages-in.sty
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mv sphinxmessages.sty sphinxmessages-in.sty
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mv book.aux book-in.aux
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mv book.aux book-in.aux
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@ -30,9 +32,10 @@ mv book.toc book-in.toc
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#mv sphinxmanual.cls sphinxmanual-in.cls
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#mv sphinxmanual.cls sphinxmanual-in.cls
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python3.7 ../../fixup-latex.py
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python3.7 ../../fixup-latex.py
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# generates book-in2.tex
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# reads book-in.tex -> writes book-in2.tex
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# remove unicode chars via unix iconv
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# remove unicode chars via unix iconv
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# reads book-in2.tex -> writes book.tex
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iconv -c -f utf-8 -t ascii book-in2.tex > book.tex
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iconv -c -f utf-8 -t ascii book-in2.tex > book.tex
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# finally run pdflatex, now it should work:
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# finally run pdflatex, now it should work:
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@ -28,7 +28,7 @@ $$ (learn-l2)
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We typically optimize, i.e. _train_,
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We typically optimize, i.e. _train_,
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with a stochastic gradient descent (SGD) optimizer of choice, e.g. Adam {cite}`kingma2014adam`.
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with a stochastic gradient descent (SGD) optimizer of choice, e.g. Adam {cite}`kingma2014adam`.
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We'll rely on auto-diff to compute the gradient w.r.t. weights, $\partial f / \partial \theta$,
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We'll rely on auto-diff to compute the gradient of a scalar loss $L$ w.r.t. the weights, $\partial L / \partial \theta$,
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We will also assume that $e$ denotes a _scalar_ error function (also
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We will also assume that $e$ denotes a _scalar_ error function (also
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called cost, or objective function).
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called cost, or objective function).
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It is crucial for the efficient calculation of gradients that this function is scalar.
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It is crucial for the efficient calculation of gradients that this function is scalar.
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