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Sylvain Gugger committed 2020-04-28 10:12:59 -07:00
1 parent b7f3f0d750
commit 5b70a64d66
6 files changed
+1411 -422
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05_pet_breeds.ipynb
+89 -77
View File
@@ -285,9 +285,9 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {
@@ -350,7 +350,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
@@ -434,12 +434,12 @@
"evalue": "invalid argument 0: Sizes of tensors must match except in dimension 0. Got 414 and 375 in dimension 2 at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/TH/generic/THTensor.cpp:612",
"output_type": "error",
"traceback": [
"\u001b[0;31m----------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-8c0a3d421ca2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msplitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRandomSplitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'))\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpets1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m\"images\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_fail_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Make sure all parts of your samples are tensors of the same size\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mwhy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'noop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nCollating items in a batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mcreate_batch\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa_convert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprebatched\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, show_batch, **kwargs)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_fail_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Make sure all parts of your samples are tensors of the same size\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mwhy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'noop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, show_batch, **kwargs)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nCollating items in a batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mcreate_batch\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa_convert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprebatched\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m else default_collate(t))\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m else default_collate(t))\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0m\u001b[1;32m 46\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m else default_collate(t))\n",
@@ -540,10 +540,10 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.491732</td>\n",
" <td>0.337355</td>\n",
" <td>0.108254</td>\n",
" <td>00:18</td>\n",
" <td>1.551305</td>\n",
" <td>0.322132</td>\n",
" <td>0.106225</td>\n",
" <td>00:19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -571,17 +571,17 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.503154</td>\n",
" <td>0.293404</td>\n",
" <td>0.096076</td>\n",
" <td>0.529473</td>\n",
" <td>0.312148</td>\n",
" <td>0.095399</td>\n",
" <td>00:23</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.314759</td>\n",
" <td>0.225316</td>\n",
" <td>0.066306</td>\n",
" <td>00:23</td>\n",
" <td>0.330207</td>\n",
" <td>0.245883</td>\n",
" <td>0.080514</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -665,8 +665,8 @@
{
"data": {
"text/plain": [
"TensorCategory([11, 0, 0, 5, 20, 4, 22, 31, 23, 10, 20, 2, 3, 27, 18, 23, 33, 5, 24, 7, 6, 12, 9, 11, 35, 14, 10, 15, 3, 3, 21, 5, 19, 14, 12, 15, 27, 1, 17, 10, 7, 6, 15, 23, 36, 1, 35, 6,\n",
" 4, 29, 24, 32, 2, 14, 26, 25, 21, 0, 29, 31, 18, 7, 7, 17], device='cuda:5')"
"TensorCategory([ 0, 5, 23, 36, 5, 20, 29, 34, 33, 32, 31, 24, 12, 36, 8, 26, 30, 2, 12, 17, 7, 23, 12, 29, 21, 4, 35, 33, 0, 20, 26, 30, 3, 6, 36, 2, 17, 32, 11, 6, 3, 30, 5, 26, 26, 29, 7, 36,\n",
" 31, 26, 26, 8, 13, 30, 11, 12, 36, 31, 34, 20, 15, 8, 8, 23], device='cuda:5')"
]
},
"execution_count": null,
@@ -703,9 +703,9 @@
{
"data": {
"text/plain": [
"tensor([7.9069e-04, 6.2350e-05, 3.7607e-05, 2.9260e-06, 1.3032e-05, 2.5760e-05, 6.2341e-08, 3.6400e-07, 4.1311e-06, 1.3310e-04, 2.3090e-03, 9.9281e-01, 4.6494e-05, 6.4266e-07, 1.9780e-06, 5.7005e-07,\n",
" 3.3448e-06, 3.5691e-03, 3.4385e-06, 1.1578e-05, 1.5916e-06, 8.5567e-08, 5.0773e-08, 2.2978e-06, 1.4150e-06, 3.5459e-07, 1.4599e-04, 5.6198e-08, 3.4108e-07, 2.0813e-06, 8.0568e-07, 4.3381e-07,\n",
" 1.0069e-05, 9.1020e-07, 4.8714e-06, 1.2734e-06, 2.4735e-06])"
"tensor([9.9911e-01, 5.0433e-05, 3.7515e-07, 8.8590e-07, 8.1794e-05, 1.8991e-05, 9.9280e-06, 5.4656e-07, 6.7920e-06, 2.3486e-04, 3.7872e-04, 2.0796e-05, 4.0443e-07, 1.6933e-07, 2.0502e-07, 3.1354e-08,\n",
" 9.4115e-08, 2.9782e-06, 2.0243e-07, 8.5262e-08, 1.0900e-07, 1.0175e-07, 4.4780e-09, 1.4285e-07, 1.0718e-07, 8.1411e-07, 3.6618e-07, 4.0950e-07, 3.8525e-08, 2.3660e-07, 5.3747e-08, 2.5448e-07,\n",
" 6.5860e-08, 8.0937e-05, 2.7464e-07, 5.6760e-07, 1.5462e-08])"
]
},
"execution_count": null,
@@ -775,7 +775,7 @@
"outputs": [
{
"data": {
"image/png": 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"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1086,46 +1086,46 @@
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"<table ><thead> <tr> <th class=\"col_heading level0 col0\" >3</th> <th class=\"col_heading level0 col1\" >7</th> <th class=\"col_heading level0 col2\" >targ</th> <th class=\"col_heading level0 col3\" >idx</th> <th class=\"col_heading level0 col4\" >loss</th> </tr></thead><tbody>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col0\" class=\"data row0 col0\" >0.602469</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col1\" class=\"data row0 col1\" >0.397531</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col2\" class=\"data row0 col2\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col3\" class=\"data row0 col3\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col4\" class=\"data row0 col4\" >0.602469</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col0\" class=\"data row0 col0\" >0.602469</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col1\" class=\"data row0 col1\" >0.397531</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col2\" class=\"data row0 col2\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col3\" class=\"data row0 col3\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col4\" class=\"data row0 col4\" >0.602469</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col0\" class=\"data row1 col0\" >0.502065</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col1\" class=\"data row1 col1\" >0.497935</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col2\" class=\"data row1 col2\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col3\" class=\"data row1 col3\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col4\" class=\"data row1 col4\" >0.497935</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col0\" class=\"data row1 col0\" >0.502065</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col1\" class=\"data row1 col1\" >0.497935</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col2\" class=\"data row1 col2\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col3\" class=\"data row1 col3\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col4\" class=\"data row1 col4\" >0.497935</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col0\" class=\"data row2 col0\" >0.133188</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col1\" class=\"data row2 col1\" >0.866811</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col2\" class=\"data row2 col2\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col3\" class=\"data row2 col3\" >2</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col4\" class=\"data row2 col4\" >0.133188</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col0\" class=\"data row2 col0\" >0.133188</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col1\" class=\"data row2 col1\" >0.866811</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col2\" class=\"data row2 col2\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col3\" class=\"data row2 col3\" >2</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col4\" class=\"data row2 col4\" >0.133188</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col0\" class=\"data row3 col0\" >0.99664</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col1\" class=\"data row3 col1\" >0.00336017</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col2\" class=\"data row3 col2\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col3\" class=\"data row3 col3\" >3</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col4\" class=\"data row3 col4\" >0.00336017</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col0\" class=\"data row3 col0\" >0.99664</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col1\" class=\"data row3 col1\" >0.00336017</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col2\" class=\"data row3 col2\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col3\" class=\"data row3 col3\" >3</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col4\" class=\"data row3 col4\" >0.00336017</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col0\" class=\"data row4 col0\" >0.595949</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col1\" class=\"data row4 col1\" >0.404051</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col2\" class=\"data row4 col2\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col3\" class=\"data row4 col3\" >4</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col4\" class=\"data row4 col4\" >0.404051</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col0\" class=\"data row4 col0\" >0.595949</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col1\" class=\"data row4 col1\" >0.404051</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col2\" class=\"data row4 col2\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col3\" class=\"data row4 col3\" >4</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col4\" class=\"data row4 col4\" >0.404051</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col0\" class=\"data row5 col0\" >0.366118</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col1\" class=\"data row5 col1\" >0.633882</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col2\" class=\"data row5 col2\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col3\" class=\"data row5 col3\" >5</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col4\" class=\"data row5 col4\" >0.366118</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col0\" class=\"data row5 col0\" >0.366118</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col1\" class=\"data row5 col1\" >0.633882</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col2\" class=\"data row5 col2\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col3\" class=\"data row5 col3\" >5</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col4\" class=\"data row5 col4\" >0.366118</td>\n",
" </tr>\n",
" </tbody></table>"
],
@@ -1232,7 +1232,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1434,7 +1434,7 @@
},
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
@@ -1467,7 +1467,10 @@
"data": {
"text/plain": [
"[('american_pit_bull_terrier', 'staffordshire_bull_terrier', 10),\n",
" ('Ragdoll', 'Birman', 6)]"
" ('Ragdoll', 'Birman', 8),\n",
" ('Siamese', 'Birman', 6),\n",
" ('Bengal', 'Egyptian_Mau', 5),\n",
" ('american_pit_bull_terrier', 'american_bulldog', 5)]"
]
},
"execution_count": null,
@@ -1541,9 +1544,9 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>8.946717</td>\n",
" <td>47.954632</td>\n",
" <td>0.893775</td>\n",
" <td>2.778816</td>\n",
" <td>5.150732</td>\n",
" <td>0.504060</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1572,9 +1575,9 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>7.231843</td>\n",
" <td>4.119265</td>\n",
" <td>0.954668</td>\n",
" <td>4.354680</td>\n",
" <td>3.003533</td>\n",
" <td>0.834235</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1624,7 +1627,7 @@
},
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1649,7 +1652,7 @@
"name": "stdout",
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"text": [
"Minimum/10: 8.32e-03, steepest point: 6.31e-03\n"
"Minimum/10: 1.00e-02, steepest point: 5.25e-03\n"
]
}
],
@@ -1694,10 +1697,10 @@
" <tbody>\n",
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" <td>1.071820</td>\n",
" <td>0.427476</td>\n",
" <td>0.133965</td>\n",
" <td>00:19</td>\n",
" <td>1.328591</td>\n",
" <td>0.344678</td>\n",
" <td>0.114344</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -1725,16 +1728,16 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.738273</td>\n",
" <td>0.541828</td>\n",
" <td>0.150880</td>\n",
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" <td>0.420945</td>\n",
" <td>0.127876</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.401544</td>\n",
" <td>0.266623</td>\n",
" <td>0.081867</td>\n",
" <td>0.329827</td>\n",
" <td>0.248813</td>\n",
" <td>0.083221</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1807,6 +1810,15 @@
"So let's try doing this manually ourselves. First of all we will train the randomly added layers for three epochs, using `fit_one_cycle`. As mentioned in <<chapter_intro>>, `fit_one_cycle` is the suggested way to train models without using `fine_tune`. We'll see why later in the book; in short, what `fit_one_cycle` does is to start training at a low learning rate, gradually increase it for the first section of training, and then gradually decrease it again for the last section of training."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn.fine_tune??"
]
},
{
"cell_type": "code",
"execution_count": null,
06_multicat.ipynb
+518 -76
View File
@@ -196,6 +196,37 @@
"You can access rows and columns of a DataFrame with the `iloc` property, which lets you access rows and columns as if it is a matrix:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 000005.jpg\n",
"1 000007.jpg\n",
"2 000009.jpg\n",
"3 000012.jpg\n",
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" ... \n",
"5006 009954.jpg\n",
"5007 009955.jpg\n",
"5008 009958.jpg\n",
"5009 009959.jpg\n",
"5010 009961.jpg\n",
"Name: fname, Length: 5011, dtype: object"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[:,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -216,7 +247,6 @@
}
],
"source": [
"df.iloc[:,0]\n",
"df.iloc[0,:]\n",
"# Trailing ‘:’s are always optional (in numpy, PyTorch, pandas, etc),\n",
"# so this is equivalent:\n",
@@ -295,23 +325,18 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -319,11 +344,9 @@
"</div>"
],
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"0 1\n",
"1 2\n",
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"0 1 3\n",
"1 2 4"
]
},
"execution_count": null,
@@ -332,9 +355,8 @@
}
],
"source": [
"df1 = pd.DataFrame()\n",
"df1['a'] = [1,2,3,4]\n",
"df1"
"tmp_df = pd.DataFrame({'a':[1,2], 'b':[3,4]})\n",
"tmp_df"
]
},
{
@@ -344,12 +366,51 @@
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>c</th>\n",
" </tr>\n",
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" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>4</td>\n",
" <td>6</td>\n",
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"</table>\n",
"</div>"
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"text/plain": [
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"1 2 4 6"
]
},
"execution_count": null,
@@ -358,8 +419,8 @@
}
],
"source": [
"df1['b'] = [10, 20, 30, 40]\n",
"df1['a'] + df1['b']"
"tmp_df['c'] = tmp_df['a']+tmp_df['b']\n",
"tmp_df"
]
},
{
@@ -399,15 +460,267 @@
"As we have seen, PyTorch and fastai have two main classes for representing and accessing a training set or validation set:\n",
"\n",
"- `Dataset`:: a collection which returns a tuple of your independent and dependent variable for a single item\n",
"- `DataLoader`:: an iterator which provides a stream of mini batches, where each mini batch is a couple of a batch of independent variables and a batch of dependent variables\n",
"\n",
"- `DataLoader`:: an iterator which provides a stream of mini batches, where each mini batch is a couple of a batch of independent variables and a batch of dependent variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((0, 'a'), 26)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = list(enumerate(string.ascii_lowercase))\n",
"a[0],len(a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([25, 11, 4, 1, 7, 21, 19, 0]),\n",
" ('z', 'l', 'e', 'b', 'h', 'v', 't', 'a'))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dl_a = DataLoader(a, batch_size=8, shuffle=True)\n",
"b = first(dl_a)\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(tensor(25), 'z'),\n",
" (tensor(11), 'l'),\n",
" (tensor(4), 'e'),\n",
" (tensor(1), 'b'),\n",
" (tensor(7), 'h'),\n",
" (tensor(21), 'v'),\n",
" (tensor(19), 't'),\n",
" (tensor(0), 'a')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(zip(b[0],b[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(tensor(25), 'z'),\n",
" (tensor(11), 'l'),\n",
" (tensor(4), 'e'),\n",
" (tensor(1), 'b'),\n",
" (tensor(7), 'h'),\n",
" (tensor(21), 'v'),\n",
" (tensor(19), 't'),\n",
" (tensor(0), 'a')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(zip(*b))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On top of these, fastai provides two classes for bringing your training and validation sets together:\n",
"\n",
"- `Datasets`:: an object which contains a training `Dataset` and a validation `Dataset`\n",
"- `DataLoaders`:: an object which contains a training `DataLoader` and a validation `DataLoader`\n",
"\n",
"Since a `DataLoader` builds on top of a `Dataset`, and adds additional functionality to it (collating multiple items into a mini batch), it’s often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after that’s working.\n",
"\n",
"Since a `DataLoader` builds on top of a `Dataset`, and adds additional functionality to it (collating multiple items into a mini batch), it’s often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after that’s working."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('a', 26)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = list(string.ascii_lowercase)\n",
"a[0],len(a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('a',)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a)\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f1(o): return o+'a'\n",
"def f2(o): return o+'b'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('aa',)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a, [[f1]])\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('aab',)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a, [[f1,f2]])\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('aa', 'ab')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a, [[f1],[f2]])\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dls = DataLoaders.from_dsets(dss, batch_size=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(('da', 'aa', 'ea', 'na'), ('db', 'ab', 'eb', 'nb'))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first(dls.train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we create a `DataBlock`, we build up gradually, step-by-step, and use the notebook to check our data along the way. This is a great way to make sure that you maintain momentum as you are coding, and that you keep an eye out for any problems. It’s easy to debug, because you know that if there are any problems, it is in the line of code you just typed!\n",
"\n",
"Let’s start with the simplest case, which is a data block created with no parameters:"
@@ -442,7 +755,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"this contains a `train` and a “valid” dataset, which we can index into:"
"this contains a `train` and a `valid` dataset, which we can index into:"
]
},
{
@@ -453,13 +766,7 @@
{
"data": {
"text/plain": [
"(fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object, fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object)"
"(4009, 1002)"
]
},
"execution_count": null,
@@ -468,7 +775,34 @@
}
],
"source": [
"dsets.train[0]"
"len(dsets.train),len(dsets.valid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(fname 000224.jpg\n",
" labels tvmonitor bottle\n",
" is_valid True\n",
" Name: 113, dtype: object, fname 000224.jpg\n",
" labels tvmonitor bottle\n",
" is_valid True\n",
" Name: 113, dtype: object)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x,y = dsets.train[0]\n",
"x,y"
]
},
{
@@ -486,7 +820,27 @@
{
"data": {
"text/plain": [
"('005620.jpg', 'aeroplane')"
"'000224.jpg'"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x['fname']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('009879.jpg', 'car person')"
]
},
"execution_count": null,
@@ -515,7 +869,7 @@
{
"data": {
"text/plain": [
"('002549.jpg', 'tvmonitor')"
"('006350.jpg', 'aeroplane')"
]
},
"execution_count": null,
@@ -545,6 +899,35 @@
"We can see that the independent variable will need to be converted into a complete path, so that we can open it as an image, and the second will need to be split on the space character (which is the default for Python’s split function) so that it becomes a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Path.BASE_PATH = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Path('/home/jhoward/.fastai/data/pascal_2007')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -595,8 +978,8 @@
{
"data": {
"text/plain": [
"(PILImage mode=RGB size=500x375,\n",
" TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]))"
"(PILImage mode=RGB size=500x374,\n",
" TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
]
},
"execution_count": null,
@@ -640,7 +1023,7 @@
{
"data": {
"text/plain": [
"(#1) ['dog']"
"(#1) ['chair']"
]
},
"execution_count": null,
@@ -729,7 +1112,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
@@ -1351,7 +1734,7 @@
{
"data": {
"text/plain": [
"(#50) [Path('13.obj'),Path('07.obj'),Path('06.obj'),Path('13'),Path('10'),Path('02'),Path('11'),Path('01'),Path('20.obj'),Path('17')...]"
"(#50) [Path('01'),Path('01.obj'),Path('02'),Path('02.obj'),Path('03'),Path('03.obj'),Path('04'),Path('04.obj'),Path('05'),Path('05.obj')...]"
]
},
"execution_count": null,
@@ -1360,7 +1743,7 @@
}
],
"source": [
"path.ls()"
"path.ls().sorted()"
]
},
{
@@ -1378,7 +1761,7 @@
{
"data": {
"text/plain": [
"(#1000) [Path('01/frame_00281_pose.txt'),Path('01/frame_00078_pose.txt'),Path('01/frame_00349_rgb.jpg'),Path('01/frame_00304_pose.txt'),Path('01/frame_00207_pose.txt'),Path('01/frame_00116_rgb.jpg'),Path('01/frame_00084_rgb.jpg'),Path('01/frame_00070_rgb.jpg'),Path('01/frame_00125_pose.txt'),Path('01/frame_00324_rgb.jpg')...]"
"(#1000) [Path('01/depth.cal'),Path('01/frame_00003_pose.txt'),Path('01/frame_00003_rgb.jpg'),Path('01/frame_00004_pose.txt'),Path('01/frame_00004_rgb.jpg'),Path('01/frame_00005_pose.txt'),Path('01/frame_00005_rgb.jpg'),Path('01/frame_00006_pose.txt'),Path('01/frame_00006_rgb.jpg'),Path('01/frame_00007_pose.txt')...]"
]
},
"execution_count": null,
@@ -1387,7 +1770,7 @@
}
],
"source": [
"(path/'01').ls()"
"(path/'01').ls().sorted()"
]
},
{
@@ -1454,9 +1837,9 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
"text/plain": [
"<PIL.Image.Image image mode=RGB size=160x120 at 0x7FA45C869B10>"
"<PIL.Image.Image image mode=RGB size=160x120 at 0x7F4213FAD350>"
]
},
"execution_count": null,
@@ -1564,7 +1947,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAckAAAFUCAYAAABPx8fsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9eZBl113n+fmdc+597+XLtaqyVpVKm7V4kXdsQI1tjMHgHsaGwWyNu1k9wzLAdMQw9GaChiCmu5npmYCYnp6I7umYhomYhukB02AMBmxDY2PJi2RbkqUqqaTaqzIrl5dvufec85s/zrn3vSxJJQnLLv7IX0RWVr58ed+9Z/kt39/39zuiquzJnuzJnuzJnuzJM8Xc6BvYkz3Zkz3Zkz35myp7RnJP9mRP9mRP9uQ5ZM9I7sme7Mme7MmePIfsGck92ZM92ZM92ZPnkD0juSd7sid7sid78hyyZyT3ZE/2ZE/2ZE+eQ9z1fnn+3LqGEACDRsUWnl5nnqsb2+zft59f+MWf56d+8mdwrkuMkYMHl9naHNCdK1FVytICYK3FGINz1/24Z4iIMJlMKMvyr/2As7KzDcPJWeYXDnJ14wzHDt/G9vY2RVEQQmBSBcJogy89+iinnz7F9vaIulaqyZDB9oCra+tMxiM2trbp9IWlfSvE2EV1nv2rh7nt9uMgkbmusDi/j/e+9zv4jd/495y/cIUYlfW1i5w9/SQxToh+wvb2gF7Z4ejNB/E+snrwCM6ViDGAQUTAGozm/wMiiqogIvgYEaMQIs5aogZiFGKsiSEwHo+oqprB1gaHbrqd0kYUA1FQVaKCarqeqhBjBCDNOfzcP/wH8pIM/IuUGKNCzM+cviIQAY3KYGfIcDji5KOn2bi6AZrWisYRUcdErYg+UtfKeOKZDK8y3NlhMprQ6S3Q6Tq63YJebxFnC4qywFpLp2uxxuFsiZQGa7sYCjauPsbyykFiGNHrLHP4yFEWl5bo9udxzuX5EkKIxBiJQVHA1wEfIyFEQgicP3+GgwcPEbwnxECMimokakBjJMQI6on5d6ik96CgypmzT3Po0CrjcUWv10PyeKhqGiZAY6Qp61JVRKbzahA0KpNqG1eUiBgg3bNGIYYJIUbGoxH1ZIet7R1uu+tVhGpM8qdNXjcRVNJnAaqSftaQ15OmtRgKPvbRj/HWt92HSEQkrWNjHBCR7KKLSH49ff+Gt73jq77u/vWv/IJaa4A0dh2xqEAUEGzagyiI4mWCNRHRDqKKEgCPxA4Aqmm1pnnIDxk8IunvVdN7JK+ZEAPeK9YViLFUdU1UMCbt8RgjiiLGEjW9z4eAGMNfPvA5vvDwozz2xJPcfPQIb/26r+Oee25jaLs88IkHOXf+NB1GvObu21nedxNbVy+z/+BBfu9P/jMd4/mub30L9dikezcTgsklgaqIF1b2H8TaAiXkudY8j2nNO2fBZN1kDM5anHN0ul1ULMYkXRNckd6jYDCICsbujtGMzE67gAgxQlVVrF25yMblCxitqMKEYVA2L495zSvm6a/exaiCqI5ez2HE4L3HGJP0pbGIDWgArFDHCAKFGAgKLvDev/v+51xz17VatY+EIAiBalyzb3WOK2vrhKCMxjt8+MMf5md++u9T1zVGhNF4RIiKryK2SMrWGNNuHO891tpW4T+feO9f0PteqOyMxizMLxJ2LCtLi6gqc3NzWYkIxgK9ZW5/+Wu5+WWvZjK+wMXzl/jSYyfpLcyzb3U/k/GAjatb7OyMGWwP2NxYY3HFcOXiBdYuP0YIcOzELayuDvnn//O/ZGNri4Or+/nZn/1ZPvCBD7C0tEz0gQvnziDuHKoVjz9+muiVzY1tFhcW2X9wFecczpWgQsCACEYsIh5IzodRJapisEQVokZULaii6kHyl4IPI0qbNrGIMFsf++ylsjeufnZcCRtXhzz11Dk2NjYZbGxTe0+n41CdEGJNCDXVeIu6nrBxdZ3haMh4YOh2LQuLXcq+YWlhjrluwf7Fg1jnKIse1qaxc4XDljbvRcEaS+kKenNzHFhdpd9foNPrYazlTz90kVe++o1sXl3n2PFb0prOQ9Qs5QBgDdaSNEEybQgO8R4ChKCUZYcag4mNoRREIUpSCiFoUh4S0QjWJiMnGinLAmsd4/EW8/PzaIyIkWxoNU+kJGUMgLSGEoAIIg5rC6xxaV9iEImoRIQCocYYA66kcEMuXTrP4YOH8HVoLo8gaJyukOS47V4vIoIQue+++4gBrAMlG1PAqAENIIIg7Zp8obrhpZbSOBSf7zsZR0hTKSaCRIiKACUFxJD9Ek/UkOaLqh0UVYsxNhs7gxoIIeJDJIqg2OxcQTATorEEtYTgefz0Wf7oo3/O6bPnOHboIHffcRtv+fo3YlTbVSUKpXXc98Y38PWvex0hBqwYrEbqaPncQw9z9coanVCx2i/pF30e+NSn6M71+cKFAXOFcqjfIU5AMGA8Ig5DMiA+BsrSgTXUKmkMUIIoIpGOdpOh12RnrDEYBef6lEWJwRLUQFTA46J9UfPRrANf1xAnDDauYKRm5CfUVc2pU2c4dCSyuPxuVDoszBm+9NhjnLj1FqKJXFm7wk3HbmI8GWNNSdRtDA4QCrHJSItJqlSL697L9Y1kHQghbeSq9igl42rM0vwhhsMtfus//L+UZZfJeECn32V7a4S1BSEqWkF0ijEQo2CtZK/CJ+/7BWyGwWDA4uLiCx3X55UzF87y2nuP8cCDD/O6N70SiYoxJj0jyQvyqnQ6XURqRA5z9Ph+Vg7cRl3X1PUao+3A2TMn2d7eYDQcAbC1ucnO9oDBYMhga5tTO1d5yhXYTokizM8v85M/9VOMR2OstSyvLPFz//B/4F/8s3+Or4XRaJML504zHK2ztnWFM0+fxVeRI8cPMd/vs7C8RKfTAeNQiaABNYJVEI1EIhIlRxUVMYCqAS1Q9Smq8R4tSkSVKIrKTBSCpvcTUU3IwY2U/+VXf4m6rhgNBiwsLDGsJqzu289ir5+81F5Jx1kWl/dhjXDb8VtSJOIUk71abMHCwgK9uTnOPvU0b3zTm5hfXAQkeeXtp0lr6fSa527ec/e9r2ZuYRnXmSfptZiiOEmKXxBmVYA4IYakHEUVFUGMYWlxhbIoibFGYgFBMBIJKhgJRGMwIoTokyGSZPfEpO+HDx3l0uXzLCwstghLukeDQVFivoPGiMd2n6kqapQYa2zRxfsJ3U4X7z0Rl/apCxjvcM4RY6Ryls31y9x09CjB+/SsGcXAASFdt7WemHwPKQpGFOsMIURMdIiZRsXpWsnTjxozWpJm4UZItBUuuOT4mLRGEGnvJuSxlUjWmj3E1BC6BPUoEWMsxho++cBn+dBHPs7pc+d53Svu4bWvuoevee29adVZSyRHpSiiisSIeI8xBaUV7jlxjHt+4LsgR5CoQkxrU8QQ6kBhLOIjFpvWrUmLpNLARCxXL55nMrnK8aUuPgbGdLn1tuN88fRFxpMh9x6Z45V33cnALFIwQWNayZGIC4alziLF/AKqihHFxGRInEgyqjY57YjBChQ2oTHdTrddc9YYVCPGOqIGRGx+BkHMdAfORpCqCXms65oYA6dOnSJMhthYESeeELv8xX/+MD/4Iz+JMx1ssZidE2Wh36frSqrK03Vd8IqqRUyNkxKygywmRfCtSH3dtXFdI1lVFSDJWPrIZDzGGAVxbGytY21N4W5BGdDtdrl0cYN+v6Cua4rCEapIUabFkAYgfQ8hPG9E2cBEL6VnefDwYcDgEaIBG/IvrOAQKq+IEaJXjLVICFjrKMvk4Sr76PTHHLvlHqLf5pEHHwYrLBclywf2MRlPGA2HbG1uMh6OWb+0TtEFUw05uXWFGA2uKHnZnffwP/7SrwJKf9FSq+H2O1+BryPnzp7BcA6jFVub61w4d5mlxXn6C31WVw9QdEuKokyOLRYiqA2YhIXkwQONDfQliASqqsL054lhZkByxINKVlYeWq/+JRv2Fy3HD91ELAyuKFjp9fFYjAHnBKMRYw1RlcWlBXq9OY4cPczi4iJl2ZuGdu0mFO68++W7rr/bpM28fu0z50sszi9iBQgB1LWvP+fK1AZCTMrQmAhq2Ldvkdr7FCmqoEaIGExUosnK0iR4PRrNiqsx6QYngY519DsFw1HAmhKN9UwEluBChaR8jUmwbWs8k6MaY6T2ijBJDqtacpyX4HtjMc5S2g51AeAwEgiqGKGFXCXfHaRoksaAiqAhZKcYfvd3P8h73vPt6fqtQ6JJLUQQ69qB0/jS7fcXIyEaaJx3gcpXRFWKosBaw1998kE+87nP8+nPf5Gbjh3hnW9/O298/cvzmkkjIShW4M2vu5evufeVoGBNcqKMemJUQu0JxPx6kigQDCA1sQaLpFQIZgYdEKLqFALOmYhI8keiRoJG6HZ54NMPMdrZplTHa95wJ+oX+cInP0q1dDs72yO6wXPvPd9AFS2F+jaSBxCxRDHMzS9SP6vDIs/4n6jDmZIiQ6qq2iKIkGB5Y5Jb8OwGMhleyWie9xGNjnMXLsFkAxcFP/GMK+XPPvkf+Yn3/TAUPZwr0QhgQSMHDxwCFVzhWFxYZHswoJwvqOsqRcUz+75RE+m5v4xIcjSeEIPifU23o1y4MGZcOT72sb/goYc+ww/94I/y5NNn6PYqQjyIWIePQvRgTMTYiPeKc0IGpAAlxtgayucyljFGiuL6N/9CpYFyJoMRw84884srPPzoKe592a1A8vinnj9EiaCKNYZoklJ2aonRIXRSPtA7tNPlzrvuYmcwIMSa8XhE9J6NzcsEXxGqmqqq2NoYMN4ZcXn9CiGM+NzgEp2yQwBEOhw9dhy33MNY4ZY7buaxR8acuOkEdTCcP3eK8c5FBjtXuHz5PFoJy/tXmFuc48D+/VjnMNESjW1zRFFjNpI+5ylD8pxUUbFJyc04ICqaFpta0Gle5kbJfW/Line truncated
"text/plain": [
"<Figure size 576x432 with 9 Axes>"
]
@@ -1614,6 +1997,38 @@
"source": [
"Make sure that you understand *why* these are the shapes for our mini-batches.\n",
"\n",
"Tip: A colour picture is a rank-3 tensor. The first axis contains the channels: red, green, and blue:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"im = image2tensor(Image.open('images/grizzly.jpg'))\n",
"_,axs = subplots(1,3)\n",
"for bear,ax,color in zip(im,axs,('Reds','Greens','Blues')):\n",
" show_image(255-bear, ax=ax, cmap=color)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's an example of one row from the dependent variable:"
]
},
@@ -1625,7 +2040,7 @@
{
"data": {
"text/plain": [
"tensor([[0.0111, 0.1810]], device='cuda:5')"
"tensor([[-0.0753, 0.0237]], device='cuda:5')"
]
},
"execution_count": null,
@@ -1706,7 +2121,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1776,7 +2191,17 @@
},
{
"data": {
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truncated
"text/plain": [
"SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.03981071710586548)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1795,7 +2220,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll try an LR of `2e-2`:"
"We'll try an LR of `1e-2`:"
]
},
{
@@ -1818,33 +2243,50 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.045840</td>\n",
" <td>0.012957</td>\n",
" <td>00:36</td>\n",
" <td>0.049488</td>\n",
" <td>0.022839</td>\n",
" <td>00:39</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.008415</td>\n",
" <td>0.005187</td>\n",
" <td>00:54</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.006369</td>\n",
" <td>0.001853</td>\n",
" <td>00:36</td>\n",
" <td>0.003400</td>\n",
" <td>0.000343</td>\n",
" <td>00:55</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.003000</td>\n",
" <td>0.000496</td>\n",
" <td>00:37</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.001963</td>\n",
" <td>0.000360</td>\n",
" <td>00:37</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.001584</td>\n",
" <td>0.000116</td>\n",
" <td>00:36</td>\n",
" <td>0.001462</td>\n",
" <td>0.000100</td>\n",
" <td>00:55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -1858,8 +2300,8 @@
}
],
"source": [
"lr = 2e-2\n",
"learn.fit_one_cycle(5, lr)"
"lr = 1e-2\n",
"learn.fine_tune(3, lr)"
]
},
{
08_collab.ipynb
+110 -61
View File
@@ -580,63 +580,63 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>207</td>\n",
" <td>Four Weddings and a Funeral (1994)</td>\n",
" <td>3</td>\n",
" <td>542</td>\n",
" <td>My Left Foot (1989)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>565</td>\n",
" <td>Remains of the Day, The (1993)</td>\n",
" <td>5</td>\n",
" <td>422</td>\n",
" <td>Event Horizon (1997)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>506</td>\n",
" <td>Kids (1995)</td>\n",
" <td>1</td>\n",
" <td>311</td>\n",
" <td>African Queen, The (1951)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>845</td>\n",
" <td>595</td>\n",
" <td>Face/Off (1997)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>617</td>\n",
" <td>Evil Dead II (1987)</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>158</td>\n",
" <td>Jurassic Park (1993)</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>836</td>\n",
" <td>Chasing Amy (1997)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>798</td>\n",
" <td>Being Human (1993)</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>500</td>\n",
" <td>Down by Law (1986)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>409</td>\n",
" <td>Much Ado About Nothing (1993)</td>\n",
" <th>7</th>\n",
" <td>474</td>\n",
" <td>Emma (1996)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>721</td>\n",
" <td>Braveheart (1995)</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>316</td>\n",
" <td>Psycho (1960)</td>\n",
" <td>2</td>\n",
" <td>466</td>\n",
" <td>Jackie Chan's First Strike (1996)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>883</td>\n",
" <td>Judgment Night (1993)</td>\n",
" <td>5</td>\n",
" <td>554</td>\n",
" <td>Scream (1996)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -661,6 +661,27 @@
"In order to represent collaborative filtering in PyTorch we can't just use the crosstab representation directly, especially if we want it to fit into our deep learning framework. We can represent our movie and user latent factor tables as simple matrices:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'user': (#944) ['#na#',1,2,3,4,5,6,7,8,9...],\n",
" 'title': (#1635) ['#na#',\"'Til There Was You (1997)\",'1-900 (1994)','101 Dalmatians (1996)','12 Angry Men (1957)','187 (1997)','2 Days in the Valley (1996)','20,000 Leagues Under the Sea (1954)','2001: A Space Odyssey (1968)','3 Ninjas: High Noon At Mega Mountain (1998)'...]}"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dls.classes"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -684,6 +705,35 @@
"It turns out that we can represent *look up in an index* as a matrix product! The trick is to replace our indices with one hot encoded vectors. Here is an example of what happens if we multiply a vector by a one hot encoded vector representing the index three:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"one_hot_3 = one_hot(3, n_users).float()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([944, 5])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"user_factors.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -701,7 +751,6 @@
}
],
"source": [
"one_hot_3 = one_hot(3, n_users).float()\n",
"user_factors.t() @ one_hot_3"
]
},
@@ -918,32 +967,32 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.326261</td>\n",
" <td>1.295701</td>\n",
" <td>0.993168</td>\n",
" <td>0.990168</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1.091352</td>\n",
" <td>1.091475</td>\n",
" <td>00:11</td>\n",
" <td>0.884821</td>\n",
" <td>0.911269</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.961574</td>\n",
" <td>0.977690</td>\n",
" <td>00:11</td>\n",
" <td>0.671865</td>\n",
" <td>0.875679</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.829995</td>\n",
" <td>0.893122</td>\n",
" <td>0.471727</td>\n",
" <td>0.878200</td>\n",
" <td>00:11</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.781661</td>\n",
" <td>0.876511</td>\n",
" <td>0.361314</td>\n",
" <td>0.884209</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1006,33 +1055,33 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.976380</td>\n",
" <td>1.001455</td>\n",
" <td>0.973745</td>\n",
" <td>0.993206</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.875964</td>\n",
" <td>0.919960</td>\n",
" <td>0.869132</td>\n",
" <td>0.914323</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.685377</td>\n",
" <td>0.870664</td>\n",
" <td>0.676553</td>\n",
" <td>0.870192</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.483701</td>\n",
" <td>0.874071</td>\n",
" <td>0.485377</td>\n",
" <td>0.873865</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.385249</td>\n",
" <td>0.878055</td>\n",
" <td>00:12</td>\n",
" <td>0.377866</td>\n",
" <td>0.877610</td>\n",
" <td>00:11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
clean/05_pet_breeds.ipynb
+89 -77
View File
@@ -144,9 +144,9 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {
@@ -192,7 +192,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
@@ -267,12 +267,12 @@
"evalue": "invalid argument 0: Sizes of tensors must match except in dimension 0. Got 414 and 375 in dimension 2 at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/TH/generic/THTensor.cpp:612",
"output_type": "error",
"traceback": [
"\u001b[0;31m----------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-8c0a3d421ca2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msplitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRandomSplitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'))\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpets1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m\"images\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_fail_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Make sure all parts of your samples are tensors of the same size\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mwhy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'noop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nCollating items in a batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, show_batch, **kwargs)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_fail_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Make sure all parts of your samples are tensors of the same size\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mwhy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'noop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, show_batch, **kwargs)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nCollating items in a batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mcreate_batch\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa_convert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprebatched\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m else default_collate(t))\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m else default_collate(t))\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0m\u001b[1;32m 46\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m else default_collate(t))\n",
@@ -310,10 +310,10 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.491732</td>\n",
" <td>0.337355</td>\n",
" <td>0.108254</td>\n",
" <td>00:18</td>\n",
" <td>1.551305</td>\n",
" <td>0.322132</td>\n",
" <td>0.106225</td>\n",
" <td>00:19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -341,17 +341,17 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.503154</td>\n",
" <td>0.293404</td>\n",
" <td>0.096076</td>\n",
" <td>0.529473</td>\n",
" <td>0.312148</td>\n",
" <td>0.095399</td>\n",
" <td>00:23</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.314759</td>\n",
" <td>0.225316</td>\n",
" <td>0.066306</td>\n",
" <td>00:23</td>\n",
" <td>0.330207</td>\n",
" <td>0.245883</td>\n",
" <td>0.080514</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -400,8 +400,8 @@
{
"data": {
"text/plain": [
"TensorCategory([11, 0, 0, 5, 20, 4, 22, 31, 23, 10, 20, 2, 3, 27, 18, 23, 33, 5, 24, 7, 6, 12, 9, 11, 35, 14, 10, 15, 3, 3, 21, 5, 19, 14, 12, 15, 27, 1, 17, 10, 7, 6, 15, 23, 36, 1, 35, 6,\n",
" 4, 29, 24, 32, 2, 14, 26, 25, 21, 0, 29, 31, 18, 7, 7, 17], device='cuda:5')"
"TensorCategory([ 0, 5, 23, 36, 5, 20, 29, 34, 33, 32, 31, 24, 12, 36, 8, 26, 30, 2, 12, 17, 7, 23, 12, 29, 21, 4, 35, 33, 0, 20, 26, 30, 3, 6, 36, 2, 17, 32, 11, 6, 3, 30, 5, 26, 26, 29, 7, 36,\n",
" 31, 26, 26, 8, 13, 30, 11, 12, 36, 31, 34, 20, 15, 8, 8, 23], device='cuda:5')"
]
},
"execution_count": null,
@@ -431,9 +431,9 @@
{
"data": {
"text/plain": [
"tensor([7.9069e-04, 6.2350e-05, 3.7607e-05, 2.9260e-06, 1.3032e-05, 2.5760e-05, 6.2341e-08, 3.6400e-07, 4.1311e-06, 1.3310e-04, 2.3090e-03, 9.9281e-01, 4.6494e-05, 6.4266e-07, 1.9780e-06, 5.7005e-07,\n",
" 3.3448e-06, 3.5691e-03, 3.4385e-06, 1.1578e-05, 1.5916e-06, 8.5567e-08, 5.0773e-08, 2.2978e-06, 1.4150e-06, 3.5459e-07, 1.4599e-04, 5.6198e-08, 3.4108e-07, 2.0813e-06, 8.0568e-07, 4.3381e-07,\n",
" 1.0069e-05, 9.1020e-07, 4.8714e-06, 1.2734e-06, 2.4735e-06])"
"tensor([9.9911e-01, 5.0433e-05, 3.7515e-07, 8.8590e-07, 8.1794e-05, 1.8991e-05, 9.9280e-06, 5.4656e-07, 6.7920e-06, 2.3486e-04, 3.7872e-04, 2.0796e-05, 4.0443e-07, 1.6933e-07, 2.0502e-07, 3.1354e-08,\n",
" 9.4115e-08, 2.9782e-06, 2.0243e-07, 8.5262e-08, 1.0900e-07, 1.0175e-07, 4.4780e-09, 1.4285e-07, 1.0718e-07, 8.1411e-07, 3.6618e-07, 4.0950e-07, 3.8525e-08, 2.3660e-07, 5.3747e-08, 2.5448e-07,\n",
" 6.5860e-08, 8.0937e-05, 2.7464e-07, 5.6760e-07, 1.5462e-08])"
]
},
"execution_count": null,
@@ -480,7 +480,7 @@
"outputs": [
{
"data": {
"image/png": 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"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -674,46 +674,46 @@
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"<table ><thead> <tr> <th class=\"col_heading level0 col0\" >3</th> <th class=\"col_heading level0 col1\" >7</th> <th class=\"col_heading level0 col2\" >targ</th> <th class=\"col_heading level0 col3\" >idx</th> <th class=\"col_heading level0 col4\" >loss</th> </tr></thead><tbody>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col0\" class=\"data row0 col0\" >0.602469</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col1\" class=\"data row0 col1\" >0.397531</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col2\" class=\"data row0 col2\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col3\" class=\"data row0 col3\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col4\" class=\"data row0 col4\" >0.602469</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col0\" class=\"data row0 col0\" >0.602469</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col1\" class=\"data row0 col1\" >0.397531</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col2\" class=\"data row0 col2\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col3\" class=\"data row0 col3\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row0_col4\" class=\"data row0 col4\" >0.602469</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col0\" class=\"data row1 col0\" >0.502065</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col1\" class=\"data row1 col1\" >0.497935</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col2\" class=\"data row1 col2\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col3\" class=\"data row1 col3\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col4\" class=\"data row1 col4\" >0.497935</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col0\" class=\"data row1 col0\" >0.502065</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col1\" class=\"data row1 col1\" >0.497935</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col2\" class=\"data row1 col2\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col3\" class=\"data row1 col3\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row1_col4\" class=\"data row1 col4\" >0.497935</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col0\" class=\"data row2 col0\" >0.133188</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col1\" class=\"data row2 col1\" >0.866811</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col2\" class=\"data row2 col2\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col3\" class=\"data row2 col3\" >2</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col4\" class=\"data row2 col4\" >0.133188</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col0\" class=\"data row2 col0\" >0.133188</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col1\" class=\"data row2 col1\" >0.866811</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col2\" class=\"data row2 col2\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col3\" class=\"data row2 col3\" >2</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row2_col4\" class=\"data row2 col4\" >0.133188</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col0\" class=\"data row3 col0\" >0.99664</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col1\" class=\"data row3 col1\" >0.00336017</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col2\" class=\"data row3 col2\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col3\" class=\"data row3 col3\" >3</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col4\" class=\"data row3 col4\" >0.00336017</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col0\" class=\"data row3 col0\" >0.99664</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col1\" class=\"data row3 col1\" >0.00336017</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col2\" class=\"data row3 col2\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col3\" class=\"data row3 col3\" >3</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row3_col4\" class=\"data row3 col4\" >0.00336017</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col0\" class=\"data row4 col0\" >0.595949</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col1\" class=\"data row4 col1\" >0.404051</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col2\" class=\"data row4 col2\" >1</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col3\" class=\"data row4 col3\" >4</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col4\" class=\"data row4 col4\" >0.404051</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col0\" class=\"data row4 col0\" >0.595949</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col1\" class=\"data row4 col1\" >0.404051</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col2\" class=\"data row4 col2\" >1</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col3\" class=\"data row4 col3\" >4</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row4_col4\" class=\"data row4 col4\" >0.404051</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col0\" class=\"data row5 col0\" >0.366118</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col1\" class=\"data row5 col1\" >0.633882</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col2\" class=\"data row5 col2\" >0</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col3\" class=\"data row5 col3\" >5</td>\n",
" <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col4\" class=\"data row5 col4\" >0.366118</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col0\" class=\"data row5 col0\" >0.366118</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col1\" class=\"data row5 col1\" >0.633882</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col2\" class=\"data row5 col2\" >0</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col3\" class=\"data row5 col3\" >5</td>\n",
" <td id=\"T_6b1e5e2c_8421_11ea_8806_bdb274b1aa15row5_col4\" class=\"data row5 col4\" >0.366118</td>\n",
" </tr>\n",
" </tbody></table>"
],
@@ -792,7 +792,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -900,7 +900,7 @@
},
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
@@ -925,7 +925,10 @@
"data": {
"text/plain": [
"[('american_pit_bull_terrier', 'staffordshire_bull_terrier', 10),\n",
" ('Ragdoll', 'Birman', 6)]"
" ('Ragdoll', 'Birman', 8),\n",
" ('Siamese', 'Birman', 6),\n",
" ('Bengal', 'Egyptian_Mau', 5),\n",
" ('american_pit_bull_terrier', 'american_bulldog', 5)]"
]
},
"execution_count": null,
@@ -972,9 +975,9 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>8.946717</td>\n",
" <td>47.954632</td>\n",
" <td>0.893775</td>\n",
" <td>2.778816</td>\n",
" <td>5.150732</td>\n",
" <td>0.504060</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1003,9 +1006,9 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>7.231843</td>\n",
" <td>4.119265</td>\n",
" <td>0.954668</td>\n",
" <td>4.354680</td>\n",
" <td>3.003533</td>\n",
" <td>0.834235</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1041,7 +1044,7 @@
},
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1066,7 +1069,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum/10: 8.32e-03, steepest point: 6.31e-03\n"
"Minimum/10: 1.00e-02, steepest point: 5.25e-03\n"
]
}
],
@@ -1095,10 +1098,10 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.071820</td>\n",
" <td>0.427476</td>\n",
" <td>0.133965</td>\n",
" <td>00:19</td>\n",
" <td>1.328591</td>\n",
" <td>0.344678</td>\n",
" <td>0.114344</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -1126,16 +1129,16 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.738273</td>\n",
" <td>0.541828</td>\n",
" <td>0.150880</td>\n",
" <td>0.540180</td>\n",
" <td>0.420945</td>\n",
" <td>0.127876</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.401544</td>\n",
" <td>0.266623</td>\n",
" <td>0.081867</td>\n",
" <td>0.329827</td>\n",
" <td>0.248813</td>\n",
" <td>0.083221</td>\n",
" <td>00:24</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1161,6 +1164,15 @@
"### Unfreezing and transfer learning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn.fine_tune??"
]
},
{
"cell_type": "code",
"execution_count": null,
clean/06_multicat.ipynb
+495 -70
View File
@@ -133,6 +133,37 @@
"### Sidebar: Pandas and DataFrames"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 000005.jpg\n",
"1 000007.jpg\n",
"2 000009.jpg\n",
"3 000012.jpg\n",
"4 000016.jpg\n",
" ... \n",
"5006 009954.jpg\n",
"5007 009955.jpg\n",
"5008 009958.jpg\n",
"5009 009959.jpg\n",
"5010 009961.jpg\n",
"Name: fname, Length: 5011, dtype: object"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[:,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -153,7 +184,6 @@
}
],
"source": [
"df.iloc[:,0]\n",
"df.iloc[0,:]\n",
"# Trailing ‘:’s are always optional (in numpy, PyTorch, pandas, etc),\n",
"# so this is equivalent:\n",
@@ -218,23 +248,18 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -242,11 +267,9 @@
"</div>"
],
"text/plain": [
" a\n",
"0 1\n",
"1 2\n",
"2 3\n",
"3 4"
" a b\n",
"0 1 3\n",
"1 2 4"
]
},
"execution_count": null,
@@ -255,9 +278,8 @@
}
],
"source": [
"df1 = pd.DataFrame()\n",
"df1['a'] = [1,2,3,4]\n",
"df1"
"tmp_df = pd.DataFrame({'a':[1,2], 'b':[3,4]})\n",
"tmp_df"
]
},
{
@@ -267,12 +289,51 @@
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"0 11\n",
"1 22\n",
"2 33\n",
"3 44\n",
"dtype: int64"
" a b c\n",
"0 1 3 4\n",
"1 2 4 6"
]
},
"execution_count": null,
@@ -281,8 +342,8 @@
}
],
"source": [
"df1['b'] = [10, 20, 30, 40]\n",
"df1['a'] + df1['b']"
"tmp_df['c'] = tmp_df['a']+tmp_df['b']\n",
"tmp_df"
]
},
{
@@ -299,6 +360,248 @@
"### Constructing a data block"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((0, 'a'), 26)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = list(enumerate(string.ascii_lowercase))\n",
"a[0],len(a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([25, 11, 4, 1, 7, 21, 19, 0]),\n",
" ('z', 'l', 'e', 'b', 'h', 'v', 't', 'a'))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dl_a = DataLoader(a, batch_size=8, shuffle=True)\n",
"b = first(dl_a)\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(tensor(25), 'z'),\n",
" (tensor(11), 'l'),\n",
" (tensor(4), 'e'),\n",
" (tensor(1), 'b'),\n",
" (tensor(7), 'h'),\n",
" (tensor(21), 'v'),\n",
" (tensor(19), 't'),\n",
" (tensor(0), 'a')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(zip(b[0],b[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(tensor(25), 'z'),\n",
" (tensor(11), 'l'),\n",
" (tensor(4), 'e'),\n",
" (tensor(1), 'b'),\n",
" (tensor(7), 'h'),\n",
" (tensor(21), 'v'),\n",
" (tensor(19), 't'),\n",
" (tensor(0), 'a')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(zip(*b))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('a', 26)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = list(string.ascii_lowercase)\n",
"a[0],len(a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('a',)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a)\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f1(o): return o+'a'\n",
"def f2(o): return o+'b'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('aa',)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a, [[f1]])\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('aab',)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a, [[f1,f2]])\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('aa', 'ab')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dss = Datasets(a, [[f1],[f2]])\n",
"dss[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dls = DataLoaders.from_dsets(dss, batch_size=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(('da', 'aa', 'ea', 'na'), ('db', 'ab', 'eb', 'nb'))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first(dls.train)"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -325,13 +628,7 @@
{
"data": {
"text/plain": [
"(fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object, fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object)"
"(4009, 1002)"
]
},
"execution_count": null,
@@ -340,7 +637,7 @@
}
],
"source": [
"dsets.train[0]"
"len(dsets.train),len(dsets.valid)"
]
},
{
@@ -351,7 +648,54 @@
{
"data": {
"text/plain": [
"('005620.jpg', 'aeroplane')"
"(fname 000224.jpg\n",
" labels tvmonitor bottle\n",
" is_valid True\n",
" Name: 113, dtype: object, fname 000224.jpg\n",
" labels tvmonitor bottle\n",
" is_valid True\n",
" Name: 113, dtype: object)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x,y = dsets.train[0]\n",
"x,y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'000224.jpg'"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x['fname']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('009879.jpg', 'car person')"
]
},
"execution_count": null,
@@ -373,7 +717,7 @@
{
"data": {
"text/plain": [
"('002549.jpg', 'tvmonitor')"
"('006350.jpg', 'aeroplane')"
]
},
"execution_count": null,
@@ -389,6 +733,35 @@
"dsets.train[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Path.BASE_PATH = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Path('/home/jhoward/.fastai/data/pascal_2007')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -432,8 +805,8 @@
{
"data": {
"text/plain": [
"(PILImage mode=RGB size=500x375,\n",
" TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]))"
"(PILImage mode=RGB size=500x374,\n",
" TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
]
},
"execution_count": null,
@@ -456,7 +829,7 @@
{
"data": {
"text/plain": [
"(#1) ['dog']"
"(#1) ['chair']"
]
},
"execution_count": null,
@@ -522,7 +895,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAACzCAYAAAD2UgRyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9ebQl13Xe99vnnKq605tfT+hGA2h0AwRATARHSZRIyhxNiaS8JMW2TMma7CxFiZOlJF62ZQ3kkuXElpfjaEhoKYwdS6QGSxRJ0SItEgRJkAA4E2N3o4EGenz95jtW1Rnyxzn39kMbbEo0gZbI+61113v31q26p6rOsPe3v71LQghMMcUUU0wxxRTf2lBXugFTTDHFFFNMMcWVx9QgmGKKKaaYYooppgbBFFNMMcUUU0wxNQimmGKKKaaYYgqmBsEUU0wxxRRTTMHUIJhiiimmmGKKKZgaBFNMMcUVgoi8SkQeFJFaRO6+0u2Z4spARO4WkX97pdsxBZgr3YApppjiWxa/DjwAvBHoX+G2TDHFtzymDMHzDBHJr3Qbpvirj2+SfnQE+EgI4ekQwvqVbswU3zz4JhkfzzumBsGfAyLyUyLysIiUIrIiIr+fPv9bInKfiGyJyKqIfFBEbtix37UiEkTkb4vIn4hIH/ilK3YiU1wRJEr0t0Tkl1M/2RaRfysizR3f+WkReVRERiJyTET+sYiYHdufFJF3isivicga8Kn0+Y+LyCNpvzURuUdEDuzY700i8rkdfffXRKS9Y/u7ReQ/i8hPisjJ1Lb3iciur3FO3yEinxKRbnp9SURev2P7jWk89NLr/SJyOG17lYgEQAP/Lo2RH5GId4nI4yIyFJETIvJLIlJ8A27DFH/JISI/KyLnRGQ99ct2+lxE5GdSf6hS//gHl+z79Y6Pu0Tkw6mPXhCR/ygi1zyvJ/6XCSGE6esyL+AXgB7w3wE3AC8C/kna9neBNwPXA3cCfwwcA/K0/VogAKeAHwIOAddd6XOavp73PnQ3sA28C7gJ+B5gBfg/0vafB04CbwOuA94EPAW8Y8cxnkzH+PnUD28G7gIs8HbgGuBW4MeBA2mf29L2f5V+943puP9+x3HfDWwBvwO8EPi21Jb/d8d3xv34R9J7DawDv0L08o+ktr8ybW+mY/xZauNdwMeA40CeXnvTMX8q/d8kOijvBF6WfvN7gbPAL1zpezh9PefjYzP10xcAb0jvfyFt/ylgCPxk6mt/HxgBP7bjGF/P+LiZOLf/QvrdW4HfA44CjSt9Xa7IvbjSDfjL/ALaqSP+zJ/z+4tpkvv29H48kf7slT6X6evKvdKE9ySgd3z2k0CZ+tgAeMMl+7wd2Nzx/kngzy75ztvSYj77VX733wP3X/LZWwAPXJPevxu4ABQ7vvMPgbM73u8HHgXelt4vpH79qq/yuz+Wzml5x2d70lh6+47PAvBDX+Pa/Y/AsSt9D6ev5+6VxseXL/nsN4BPp/+fBv63S7b/K+DEjvdfz/h4N/CeSz4rUt9965W+LlfiNRUVXh63AA3gw8+2UUTuAH4OuANYBiRtuoZEWSXc/xy2cYq/Grg/hOB2vP8U0VN+MdE7/oNEo4+hgYaI7AohXBgf45JjfgQ4ATwhIh8BPgr8xxDCatp+S/psJz5O7Kc3E714gEdCCOWO75wmLuAAhBBOEz2o8fuNpAr/UxH5aDrmH4YQHtvxuw/vaAchhPMi8lja9lUhIj9B9OKuJRpLhmlo81sBX7zk/WngdSIyCxwA7rlk+8eB/0FEWiGEQfrsLzo+XgIcFpHeJfs1iEzEtxymA+3Ph//ikZAi0iIaCgH4UeClxA4WiBP9TkwV1FNcCrnk/fcTDcvx61bipLRTbPeMfhRC6BENircRac6/DxwXkbt2fu2r/P7Oz6tn2XZp+575hRB+gkjJfgT4LuBBEfl7X+N35TLtQUS+H/hV4L3EsMmdwC8C2eXaMsU3BZ6tD6pL3u/Es/XPv+j4UEQW7Y5LXjcA35JpkFOD4PJ4mBirev2zbLsJ2AX84xDCx0IIjxCp1MtOpFN8y+IlIqJ3vH8FcRL8IrGPHQohHH+Wl3vWoyWEEFwI4Z4Qwj8lLtBngb+VNj9EXKx34ruIk+vD/7UnFEJ4MITwKyGENwK/SQyDjH/3FhFZHn9XRPYQJ9qHLnPI7wS+kI75uRDCMSJTMMW3KEII20QN1qX9+DuBJ3awA19t/8uNj88SdTaPP8u42/jGnslfDUxDBpdBCKEnIv8S+HkRGRK9oSbRe3kXMQb80+k71wK/zGU8oCm+pbEE/KqI/GuiuPQdwLtCCFsi8kvAL4kIxD5miAzBnSGE//WrHVBE3pKOdQ9RB3AXcDUXF/v/Hfi8iPwK8H8T++i/Af5DCOGpr/dEUrbATwDvJ8Z3rwJeCXw+feW3gX8KvFdE/meikfwviDTwey9z6MeAH0vn9SBRsPt9X287p/imwT8D/qWIHCPqDV4D/LdEseFXxZ9jfPwSMczw/6VxeYE4Rt4K/OsQwolv9In8ZcfUIPja+FliR/nviUKWDeCeEMKqiPwQsbP+KPAI8A+IyuopprgUvw90gU8SQ0q/B/wvACGEd4jIGeCniQvnkEhxvvtrHHODmLHwj4AZ4uL8TuC30nG/LCLfSzQ+foqowv594Gf+Ig0XkWuBJ4C/G0J4N5GaPQK8h8iSrQEfHB83hDAUkdcRx8s49ns3UTh5KTW8E/8X0RD6f4hz0weIqvF/8xdp7xTfdPh1op7kHwG/Ruzn/zCE8JtfY7+vNT4eEZFvS5/9KVE7cJqoNdj8xp/GX35IUlZOMcUUzxEkluU9HkL48Svdlq8HIvIa4oJ/y7ei1zTFFN8qmGoIpphiiq+FNwP/fGoMTDHFNzemIYMpppjisggh/E9Xug1TTDHFc49pyGCKKaaYYoopppiGDKaYYooppphiiqlBMMUUU0wxxRRT8DU0BMfu/cXwvo+e5s0/9A6yrAGAkwIbQKRE5Q20FlRKvdcECOBEc2Fzm8ceP0FwliePH4s/JoGyu8bv/e57cV549Wu+m2oYi0t94H1/wGy7oLe9xcGD13Ht4RvJTCxQFmzFw48+xszCPIPBAJzjVd/5nXzw/X8MwAtfeAs3HLmRqw8e5Od+7p9w/fXXIxJtnb/+pjfzH377d9Da4Lxj//797Nq1i2PHjgLw0ENf4fbb72BpeYmPfexjXH/9IWxtAdje3qauK7a3tjl8/WHOr6zQaMY2bW1voVAYY5jtzNLtdvHB89KXvxyAxx9/nFE5Yt+uPWxtrHHm3BluPBKrv25vnWZ21rC63Yesg8Wzb/csmxdihVqnCg7uLVg5tcmZMxVFnvOyO2Il2XKgue8rK8zsaTE322Bxdpmt7la8/ipne7vP3/zB/wbrahYXZvnBH/h+TLqOZ86cZW11jbnlRXbvu4ayVoiOD9xrNBrEEvceEVCiCEEIkzJLnkyERq7IlUWCw9ohAJ///H387nv/kNe+7o0cuflGlmaWOPnF+2IfWltjee8sMwz4jjf86PNetOl1b3xz0KIZ9rp02h0AdBCsgqCEps5Y724wW7QAaLRaWKWoyyFVOWKhM0vfWXTqT/1ul85sB+88TgJb233ml5cAsHVNJ89xzpHnOZ1mgU11qsr+EOsc83MdbOnYrkoWOh2qQbyGs/NzDKuSYa9H1m5z5vwKKtYlYO/sHJ3FWepRSbesCC5QNBuMylhtuGg06Xb7tI2m0y6wgwGbw0E67gKrG9vMtGdozDSpe318qnW0qz1DD09dW4bbXWpbs9RsIe1YaNMHaIiiCpZmXnB2bY1mM16n+YV5jGlQ9bqsbm7QmZujqQ2DYTyfmV2LbG1t0kbYHlUsLCyyvRUzuRrNJt7CxtY6cwtzNPMGm+txW2u2TfCOhU4HpRQrW9u08xxsbLNFUTQKPEJvc5O5+Rl6o3gdMq3xVc1H/uzD9AcDvHc0iwbtdny44/r6OpvbXYpGxoE9eyjLmtMrKwBUdU2qA4FSip2h1DzTFEVBWVZsbXevSOGxGhdUCAyC8PRGvFaPnXgEW21ibEVLNylLMCrOXSrUqEabylsKb2kUbfKiTaMV71/wNUYEW40oqy6OBiaPy8HcXJuZuX1sb5csLc0wGm5hU5/Z3tok2CFzzYx9e28gBHj06JdZXroKgP37DiNBOL++xvs+/DHuvefjnHgszv+7D1zNyoUVHn/sUcreNtVoxO49VzG7Nz58MDcZob+KtQNOnV+jqize+9jeEOclgsc7G9eGdIuqqsJ7j4jgvUchscyhjmNWlIBSHDhwNbv27sVkGVke+7itKjbX1jhz6iSzs7N05uZotIrJ/beVJ8/jHDkYDLiwsjLp4wJUVYkI6NRnvLvYb6yrUUqjxFA0mswtLOJ07D6vft0bWNp3Pa985ct5yS3XsrvTQaudXesyvnoIBAFbWyprkcxwYSPWUdq9sEhDawhwYdjnS489ym3XHgJgcX6GXlXSMQ0yY561H1/WIDh7/hynL2yyMaxophprzXZBpnOMKILWSADSTVOKuIgEyFoFWZFz8snTzC8uAHD65BN86I/+CJThNa95DYcPH+bkiccB0NqwZ88+bF1zxx13kjVarK3Hqq2duTnEGEyW812vejmPHz3K2voG3/d9fwOAo489ylvf8lZ+5z3v5dChw9x0083cckssmf7oI0e55uA1OO9ZXFokz3P27dvH0aOPTX53//6reOihBymKnMOHr+fLX/oSANdddw2nT59mOOjT63dxrqbfH8X9RPA+sH//VWxubuO84/CRI3zxC18AoByNmJ2bY21jjRtvOMzZ86s4G1Owr7pqmdWN0xQtjSoUPgTqcpM9u2MnPbPiGAyE3fuW0WbI6ac2OX5iDYBGo4nOKmY6C6yubGFLzexcHOSD/ohbbrmZvfv28fjjx7jzRXeRFQW7d8Un2XZmZvmdz7+Hm5eWubCLine truncated
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
@@ -946,7 +1319,7 @@
{
"data": {
"text/plain": [
"(#50) [Path('13.obj'),Path('07.obj'),Path('06.obj'),Path('13'),Path('10'),Path('02'),Path('11'),Path('01'),Path('20.obj'),Path('17')...]"
"(#50) [Path('01'),Path('01.obj'),Path('02'),Path('02.obj'),Path('03'),Path('03.obj'),Path('04'),Path('04.obj'),Path('05'),Path('05.obj')...]"
]
},
"execution_count": null,
@@ -955,7 +1328,7 @@
}
],
"source": [
"path.ls()"
"path.ls().sorted()"
]
},
{
@@ -966,7 +1339,7 @@
{
"data": {
"text/plain": [
"(#1000) [Path('01/frame_00281_pose.txt'),Path('01/frame_00078_pose.txt'),Path('01/frame_00349_rgb.jpg'),Path('01/frame_00304_pose.txt'),Path('01/frame_00207_pose.txt'),Path('01/frame_00116_rgb.jpg'),Path('01/frame_00084_rgb.jpg'),Path('01/frame_00070_rgb.jpg'),Path('01/frame_00125_pose.txt'),Path('01/frame_00324_rgb.jpg')...]"
"(#1000) [Path('01/depth.cal'),Path('01/frame_00003_pose.txt'),Path('01/frame_00003_rgb.jpg'),Path('01/frame_00004_pose.txt'),Path('01/frame_00004_rgb.jpg'),Path('01/frame_00005_pose.txt'),Path('01/frame_00005_rgb.jpg'),Path('01/frame_00006_pose.txt'),Path('01/frame_00006_rgb.jpg'),Path('01/frame_00007_pose.txt')...]"
]
},
"execution_count": null,
@@ -975,7 +1348,7 @@
}
],
"source": [
"(path/'01').ls()"
"(path/'01').ls().sorted()"
]
},
{
@@ -1028,9 +1401,9 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAB4CAIAAAD6wG44AABe9UlEQVR4nM39aaxl2XUmiH1r7X3OvfdNMeYUOWcymcwkk6Q4SKJUsqzS2C6pqlpuw3C7baAHN+A//tNA223YBRsFN2DDcBvucrddqEYZ1XbJrRooUUOVVJIocSZFMpOZJHOeIjIjMmN48eIN995z9l7r84+9z7n3RUQmSamq2geBiBv3nmGfvfaavjVs+Tt/5//+8Y9/nCQAFwJQQgCHYDhWnwCAa99z/C9lPFfEV1dQiJsOAhDc+v2tRzmHEIBlGFIfKxCSJB0wM6fnnNzcPbibWWdGd3d3M6fT3Zzm7kY4Sc9Og4EA3Qk6xWkEQYcTQJ0TIyAgOfwLkKSwniOQ9e9JA+gE6YJ6DgkCZALgJoQDYHlUhjOZ5Vnb7JzcdC93EsLK7cpAyDqe8kw6SQF8OAdexgzSHXASgJ4/fyH+2Cc+/lOf+akfPNf/bR8EHHDCjTRYtmxm2VL2vk8555QTmXO2nLIzWxYzz5bdDOZkds9mZurZMplJwtWNpJs5XACS5p7pBEHSzFI6DDGCNHdhY+6guzs9DxQlYfTxM0m6F0p3QP0NFCCSdEM2AHRPZtnEYOjMTp254yNPPdb3ybKXhezOYZkVApdlQVJAIYW0gboOEITTh6sEEFJiWU0jh5RP5T8ysOcaP4JeliWcMObxtRKUdApBJ6RwNMmYDYS7uxOidLhDIDQxJ0kz9jR3p9MGhjM6zLN15ubOnLN55+65nJFp5maWU7Ls7uaFSszmvZsTQiZzKywsZqQT7jRHItw9Ox0mJJwEPdIAkg7x7EXIwN1p68sslylxUlg/ExABK4UB1pkkCWiZM5DuBmhhcTdxN2N2T+YJCYvOZie2CqncnRRAK2nLdA6cSvpAXSkPLc9yL+Qv/xb5AoHGN14/PHvm3a7LZm5Myc3K+nMj6x1inwUghKSb5Cr+6Jnu7ubm5sxkdiS3npqHNwRZ3x4kEMgq9IbVRwLueXw39+S0YTEaCALuJjKIOhI0rljGyqJjFWW9u4OxTispoiiLHS5CKQzmJgIpIg6kU9kCEGHKfdf3sYkxNnSnUUREim4olIOTIlAJlQfEKyOQFAepKiKKgT0IUVFhJEihqAtUfeLSaFgGELB2GoEgDCqhTHXlL2G5/yCiV0cZVfm+fAZUle6VuiIav/H1L1+79o57gjA4ASur2K1cQJLwOnt1aVTNSEGRZUIQCIXoAIP0JJ1F1Ii7ZzM61QNAIkEc1HFwFigiObtAGzik6KpAiYUfBFImVoSgDwzjgEIFCjoBF9EilwRJCAFERRSqIztBpYUSwVQBgQi0nIQgAg1ilmbTuFgsQzsTBIEIEEIAEJBFVCQItQxKRSCCJgMiiICUp0gluYKiKhABlTAgE+amdKeTxgSDJ/O8c/Ys3LUoa4iNclMqLVWVdFKreBWwSE3AARQCi8MpUqSIExavXX5t1vaq0vddEyey4ieoSBHnWtZv/VtFVABVFU0iQaRRBJVUlq0IIBQiqpTXh6q2ASMfyBQQgapqua2Lxja65+VysdFOIBQIoMTaVWoiI0niSjNrEIFIIXAodyYhamW0w3SrSBCoKEWAQTENs1+eI0UexMCuT7GZaWhk+AmAM6gSMIC6prhUfZCK4gPbCTwn0r1pGlE1B2kQc5pl0IliQzhgKZm1zZbWB61z5E2MW36vBqqIQBWkuANlDAIoxKVYuyJxZ/PkHafumkymi8WinVBFNMS6PBWqQUQCJARFfdOoKgBURYQiUthE2K+GJSLlFjIcZbJDFSkCoaxEX59T27TzxSKEMGlUECEi4pWDKyXIeoVwWGhOEgqwrKrlso8hNs0UIGHjtXVFU0kh+mrqQwb7AygaT7hYHIlAhRFNbKaxmWhREiAAkwAUgenKNQJD6C4DI1Wh5wgBGqKoEBAFWNSqQhwwoxjhZrnrl31uN0RER2KO4rdMI+swIIBXVSEcXBgRkAZoWcdgz5FaO2cmJ87OptON2bLZmjZBQwgNoCKF2KqiqlJEhKCQG6qqoiJRlaJ2Y383xFNnzp6tukeDig5klSqkBFEaVYpS1FVFVFTV3V94/pmNjenZ02cvX979wGMPbm+fBgjJkLi2flUg7kKHax6MEZqjeAvm6dLFdzc2Nra2dsyKV8SVn0QjM0GypddFz2pYgXRxh5AQDUHAEBhiKxoFKwclSFkcSoZ1Di40YzHBpKwbhUJoZZGD5XsQAQQkQwlxJ9x78y6bcfBMpazWgXLlc1VTGKRDIf+ayzqYxJVxx/vERx96YGMSP/zkh15/48177j93//33QyRoDBpFNMYoqkGziIZQaCJlQYtAEEUMyNd2r8Rw4tTp0ytndW1AP/Bw950zp7rDyel7u0cfvifEeLuz6OZOmlk25mwpJZK5GNbZsuWtHTt16iSAnK0cxVwsDk/xiulWDS11upLVy3amQIYQtIkKMeQQY1At0r36vjLDwFZr/4JwgMIAAZEDBQxOepEUIYAIVCl+jhBiDnFVDao5KCLgoupQhxCEQgr5WARNU59TbHQpbi7BIigJSLGgpFJdQQABaOJDDz3+kY88dfbs2XPnHjx59mTTToZ76Q+mTLW28p13PgSEkdsGI+yHoy55x50Pbp7c3OPy1LnTIUbeYi6WQ1TFXVWVEoIUl1NEzVREIRJj0zTFGDZVUzVWtMNDCO5uZqBx+BZUFl+IDBAlVWIIjULMEENUDTIAMnUp1I9c/QMoQIpoJQqEpJIuEqRacJUFy+8qg36SYboEEoIUAVseSUH1k24zE1UTjjK8LIvx1EJ3UYHE2EzvuvschCdPn2radm1yVzdmNWNvD0ARkSyo1l/4iEaRoIM39Z5HmRVVIUOMxcGvh7urxhDKYldAB7jA3M2s2Fygl2VRVku5qvCxBneVELQRIChDaKSIV9anFMdnQB44EAjFDJUKXDVF64s4EFRVNRSlPJ5eXqQskGpWFWACQaDEwKiig7O0mpOV8YXqS0ldCWVabloTjBpUo7ibhML4txOsUh2rmw4KKqP/ZYgLUAqyoJTw/mcW+hazeSB2QQ8YQrEXQmGtECjSuFe6lqtJocDNVcUpChaTrfjp5ZwYI81FVDUAonXVce0+dSQrZiiojrPyqUDEzaiqg+EDiIkOdpgUHlZZHSpSMOIiOEfciWuA062zUWjKm08oRq4IgCjF05NQnJbb34i3V6cymJd/mUMAChowCCKcEt7/lqripGqZdC/+1sCUGoIWCayqpJUv3alagCmnA0HdrfpiBZkTFyeVDKIqQngIIQRzoTgoFc7gIBkxuOOFxsU7MIdAq4hTkUhmVagKnRQDHUKgkBYqFAkm4ioQBC1GX1kCuqYBKGoFngSEUA641sD9PvizVUi4K8uyEsSVxSW3p+K/+kOa5rZW1XtfIBgpV9bvyAhF1xaSlyXrXtixak2oirtIUDiFpLh78XQAhtiIKMRVJZuF0ErBF91XtF0Tg9WZKaPSanoN54hI5eCiIkWErA7rLe9EESk+uqxuM96/eFoVR1uJ6fHi0fqpiGb5Xaoz+yNN7r/0QwRt0xQlxNWy/UEXiQAIQUII1Y0TGZGT8Zvx+3K+qpbPWkGYY2eJBNWYkgmQc54fHakq1lyO8SjCcV3mrYY9OocixSstuDGKVoFiUNgVJijMVT7SCS9+9rE7UyFKCr1ch5ueO46kjhMqCIIAqILwYoD8xWn0lzwkpdR3nYh4CQr8wAtWi3I1m+s0vunLW7+RgcgDbVU1AkJn6lKZfg2hQDrD5Vi/2y0jud04a9Cl/FEUmFMCbjZVy4rRwZ6q0cXjN5eCLWPtdW73zNGur79GkG4eBtTph3Vu/mUehINQh9EixdFqMdffcyjig/UhsjZXIqIaSJj5SI91DV0CNZWboU4C1PLWQqUUKNihDq34JQIqfFjt1io2JaCKRAddBpur4ABVwKoQHjR6DRhK9YgkYAgKCYKIClS14rIcvFvSi0FOUZJUBwFb0wrQ6h2xqgGSWmP5Thgkx+I/1LHevGr+NR3FxXR3g1NLiO1HkyhF9ha7A1VMKmByTGDefBRbpv6hiTClXlhBnBGpqWDhcWN17YZ6zNLkcXxpODgYdUVFF7BdRKAZcEEIOgUCSvBkyCAY1e549x9iMlhoDQooa9aNCN0l/OsmcOEyWlYVao1rvP+blAWuIrYWEB2h6tGlkRpiHHhucH8BHZf1MAYAAiKEqAUJFAkaAAjCQBdCbBwzORg0t7zO6JmWw2uWBjAs5bLe6ruOryQilBJkG5d3NdYKOkABKAo6Zc1JG/zven6Jr0KKUgi6vro1/BDo1b+ao2BSP+zJNWvCj315y2SPFLhJH8uaFXbsApECx45rZVDVxZe9zfCG2IqOiNUwwjHb471War0SjEARq2nQ1uuae/VHpMZqZe0pJUJ6821H9qBozux6IyRnt2z/+kU0iYOjLllPQz/ve5q8rwJmySpxjODcSMVygogA4hBCIMpBcQ7UDauoy4CcFFefIp2RoSHY9125ikrqaNkKxzw0uWlaqyXlCA4dWFKOj6oo9cF3qodXIApxZEVXmJqrQYzMTqvUlAANlOCMRAtGutIDGAglFKIsT2cUBBHX63vXL126KCqHR0f7B/v/con3wxwkLl+5AsCczvwDYxQlPWSUuusieg0cqJL//Z37dRKUTynnpm0AzGaznZ2d4W7DeWOU9fYafU1OiPhg2R1DkAY4etD9fut9hh/X+Xj1iKLAsTK0bsYyAQABKKa66KnTJ++7/153397ZXHulf52H33PPndsnToA4eXJ7Opu8zxhGcOc95J+si+410t0skNfF9fBZAEwnk9lsg2TbTmKMFVQ4tuiGC2+R/GujOca44xIEUC1zYckfHAPSay7ZuEAVogON1w8TuIgTBphoAfXWAtuihXdFCITYNHEyac0svDdU+a/0INA0QVeuyA9xyXvrth9JxaybKuMLine truncated
"text/plain": [
"<PIL.Image.Image image mode=RGB size=160x120 at 0x7FA45C869B10>"
"<PIL.Image.Image image mode=RGB size=160x120 at 0x7F4213FAD350>"
]
},
"execution_count": null,
@@ -1099,7 +1472,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<Figure size 576x432 with 9 Axes>"
]
@@ -1143,8 +1516,33 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"text/plain": [
"tensor([[0.0111, 0.1810]], device='cuda:5')"
"<Figure size 648x216 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"im = image2tensor(Image.open('images/grizzly.jpg'))\n",
"_,axs = subplots(1,3)\n",
"for bear,ax,color in zip(im,axs,('Reds','Greens','Blues')):\n",
" show_image(255-bear, ax=ax, cmap=color)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-0.0753, 0.0237]], device='cuda:5')"
]
},
"execution_count": null,
@@ -1188,7 +1586,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1240,7 +1638,17 @@
},
{
"data": {
"image/png": 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truncated
"text/plain": [
"SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.03981071710586548)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1275,33 +1683,50 @@
" <tbody>\n",
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" <td>0</td>\n",
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" <td>00:36</td>\n",
" <td>0.049488</td>\n",
" <td>0.022839</td>\n",
" <td>00:39</td>\n",
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" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.008415</td>\n",
" <td>0.005187</td>\n",
" <td>00:54</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.006369</td>\n",
" <td>0.001853</td>\n",
" <td>00:36</td>\n",
" <td>0.003400</td>\n",
" <td>0.000343</td>\n",
" <td>00:55</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.003000</td>\n",
" <td>0.000496</td>\n",
" <td>00:37</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.001963</td>\n",
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" <tr>\n",
" <td>4</td>\n",
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@@ -1315,8 +1740,8 @@
}
],
"source": [
"lr = 2e-2\n",
"learn.fit_one_cycle(5, lr)"
"lr = 1e-2\n",
"learn.fine_tune(3, lr)"
]
},
{
clean/08_collab.ipynb
+110 -61
View File
@@ -405,63 +405,63 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>207</td>\n",
" <td>Four Weddings and a Funeral (1994)</td>\n",
" <td>3</td>\n",
" <td>542</td>\n",
" <td>My Left Foot (1989)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>565</td>\n",
" <td>Remains of the Day, The (1993)</td>\n",
" <td>5</td>\n",
" <td>422</td>\n",
" <td>Event Horizon (1997)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>506</td>\n",
" <td>Kids (1995)</td>\n",
" <td>1</td>\n",
" <td>311</td>\n",
" <td>African Queen, The (1951)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>845</td>\n",
" <td>595</td>\n",
" <td>Face/Off (1997)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>617</td>\n",
" <td>Evil Dead II (1987)</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>158</td>\n",
" <td>Jurassic Park (1993)</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>836</td>\n",
" <td>Chasing Amy (1997)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>798</td>\n",
" <td>Being Human (1993)</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>500</td>\n",
" <td>Down by Law (1986)</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>409</td>\n",
" <td>Much Ado About Nothing (1993)</td>\n",
" <th>7</th>\n",
" <td>474</td>\n",
" <td>Emma (1996)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>721</td>\n",
" <td>Braveheart (1995)</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>316</td>\n",
" <td>Psycho (1960)</td>\n",
" <td>2</td>\n",
" <td>466</td>\n",
" <td>Jackie Chan's First Strike (1996)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>883</td>\n",
" <td>Judgment Night (1993)</td>\n",
" <td>5</td>\n",
" <td>554</td>\n",
" <td>Scream (1996)</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
@@ -479,6 +479,27 @@
"dls.show_batch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'user': (#944) ['#na#',1,2,3,4,5,6,7,8,9...],\n",
" 'title': (#1635) ['#na#',\"'Til There Was You (1997)\",'1-900 (1994)','101 Dalmatians (1996)','12 Angry Men (1957)','187 (1997)','2 Days in the Valley (1996)','20,000 Leagues Under the Sea (1954)','2001: A Space Odyssey (1968)','3 Ninjas: High Noon At Mega Mountain (1998)'...]}"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dls.classes"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -493,6 +514,35 @@
"movie_factors = torch.randn(n_movies, n_factors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"one_hot_3 = one_hot(3, n_users).float()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([944, 5])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"user_factors.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -510,7 +560,6 @@
}
],
"source": [
"one_hot_3 = one_hot(3, n_users).float()\n",
"user_factors.t() @ one_hot_3"
]
},
@@ -641,32 +690,32 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.326261</td>\n",
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" <td>0.993168</td>\n",
" <td>0.990168</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1.091352</td>\n",
" <td>1.091475</td>\n",
" <td>00:11</td>\n",
" <td>0.884821</td>\n",
" <td>0.911269</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.961574</td>\n",
" <td>0.977690</td>\n",
" <td>00:11</td>\n",
" <td>0.671865</td>\n",
" <td>0.875679</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.829995</td>\n",
" <td>0.893122</td>\n",
" <td>0.471727</td>\n",
" <td>0.878200</td>\n",
" <td>00:11</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.781661</td>\n",
" <td>0.876511</td>\n",
" <td>0.361314</td>\n",
" <td>0.884209</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -722,33 +771,33 @@
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.976380</td>\n",
" <td>1.001455</td>\n",
" <td>0.973745</td>\n",
" <td>0.993206</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.875964</td>\n",
" <td>0.919960</td>\n",
" <td>0.869132</td>\n",
" <td>0.914323</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.685377</td>\n",
" <td>0.870664</td>\n",
" <td>0.676553</td>\n",
" <td>0.870192</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.483701</td>\n",
" <td>0.874071</td>\n",
" <td>0.485377</td>\n",
" <td>0.873865</td>\n",
" <td>00:12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.385249</td>\n",
" <td>0.878055</td>\n",
" <td>00:12</td>\n",
" <td>0.377866</td>\n",
" <td>0.877610</td>\n",
" <td>00:11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"