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_preview/16/build/autoencoder-090bb62dfd871d7f5545cac7f77874e5.ipynb renamed to _preview/16/build/autoencoder-d48db8855beffc5bc17d982f6d792b67.ipynb

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"if os.name == 'nt':\n",
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" import osgeo\n",
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"\n",
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"from importlib import reload\n",
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"\n",
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"# DL stuff\n",
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"import matplotlib.pyplot as plt\n",
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"import torch\n",
@@ -170,75 +168,7 @@
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"source": [
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"## Model setup\n",
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"\n",
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"Here we instantiate the autoencoder class and verify that the output shape is what we expect. Autoencoders compress input data to a latent space and then reconstruct the input from the compressed representation. Usually, we are interested in the compressed result, but during training we are trying to recreate the input exactly. Therefore, the output tensor should match the shape of the input tensor.\n",
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"\n",
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"Input tensor:\n",
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"\n",
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196-
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197-
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"\n",
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"Output tensor:\n",
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"\n",
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206-
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import dask.array.svg as svg\n",
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"from IPython.display import HTML \n",
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"\n",
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"svg.svg(((50,), (10,), (10,)), size=100).replace(\"\\n\", \"\")"
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"Here we instantiate the autoencoder class and verify that the output shape is what we expect. Autoencoders compress input data to a latent space and then reconstruct the input from the compressed representation. Usually, we are interested in the compressed result, but during training we are trying to recreate the input exactly. Therefore, the output tensor should match the shape of the input tensor.\n"
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]
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},
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{
@@ -497,63 +427,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To be clear, we started with the usual x/y/band tensor from the input dataset.\n",
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"\n",
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"<svg width=\"177\" height=\"167\" style=\"stroke:rgb(0,0,0);stroke-width:1\"> <!-- Horizontal lines -->\n",
503-
" <line x1=\"10\" y1=\"0\" x2=\"27\" y2=\"17\" style=\"stroke-width:2\" />\n",
504-
" <line x1=\"10\" y1=\"100\" x2=\"27\" y2=\"117\" style=\"stroke-width:2\" /> <!-- Vertical lines -->\n",
505-
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"100\" style=\"stroke-width:2\" />\n",
506-
" <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"117\" style=\"stroke-width:2\" /> <!-- Colored Rectangle -->\n",
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" style=\"fill:#ECB172A0;stroke-width:0\" /> <!-- Horizontal lines -->\n",
509-
" <line x1=\"10\" y1=\"0\" x2=\"110\" y2=\"0\" style=\"stroke-width:2\" />\n",
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" <line x1=\"27\" y1=\"17\" x2=\"127\" y2=\"17\" style=\"stroke-width:2\" /> <!-- Vertical lines -->\n",
511-
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525-
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"</svg>\n",
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"\n",
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"...and end up with a tensor that has singleton x/y dimensions and a new, 64-element channel dimension.\n",
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"\n",
530-
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"To be clear, we started with the usual x/y/band tensor from the input dataset, and end up with a tensor that has singleton x/y dimensions and a new, 64-element channel dimension.\n",
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"\n",
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"We can go through the same process as before to see how we put together the `predict_on_array` call. Both the `x` and `y` dimensions change size and are used by the batch generator, so they go in `resample_dims`. The remaining dimension, `channel`, is a new dimension and goes in the `new_dim` list."
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]
@@ -687,6 +561,7 @@
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"\n",
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"grisdale_similarity.plot.imshow()\n",
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"plt.scatter(grisdale[\"x\"], grisdale[\"y\"], marker=\"*\", c=\"purple\", edgecolor=\"black\", s=200)\n",
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"plt.title(\"Cosine similarity with Grisdale, WA\")\n",
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"plt.show()"
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]
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},

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