From f54965d2661eab8bc133d04357ae123875a5f304 Mon Sep 17 00:00:00 2001 From: Jim Slattery Date: Tue, 19 Mar 2024 00:07:49 -0400 Subject: [PATCH] chapter4: compared standard SGD with Adam Optimizer Made a little graph to compare plain SGD (lr=2e-5) with plain Adam optimizer, on a very simple quaddratic function with 3 parameters. It's like a "guess the 3 numbers I'm thinking of" game. Adam was a good improvement, but I wonder if there isn't some way to do much better. --- 04_mnist_basics.ipynb | 3141 +++++++++++++++++++++++++---------------- 1 file changed, 1887 insertions(+), 1254 deletions(-) diff --git a/04_mnist_basics.ipynb b/04_mnist_basics.ipynb index 675bb5b..7da5c69 100644 --- a/04_mnist_basics.ipynb +++ b/04_mnist_basics.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/jim/.fastai/data/mnist_sample'" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.path.abspath(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +131,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "fastai.data.external.URLs" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "URLs" + ] + }, + { + "cell_type": "code", + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -128,16 +168,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 114, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { "text/plain": [ - "(#9) [Path('cleaned.csv'),Path('item_list.txt'),Path('trained_model.pkl'),Path('models'),Path('valid'),Path('labels.csv'),Path('export.pkl'),Path('history.csv'),Path('train')]" + "(#3) [Path('labels.csv'),Path('valid'),Path('train')]" ] }, - "execution_count": null, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -155,16 +201,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(#2) [Path('train/7'),Path('train/3')]" + "(#2) [Path('train/3'),Path('train/7')]" ] }, - "execution_count": null, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -182,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -191,7 +237,7 @@ "(#6131) [Path('train/3/10.png'),Path('train/3/10000.png'),Path('train/3/10011.png'),Path('train/3/10031.png'),Path('train/3/10034.png'),Path('train/3/10042.png'),Path('train/3/10052.png'),Path('train/3/1007.png'),Path('train/3/10074.png'),Path('train/3/10091.png')...]" ] }, - "execution_count": null, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -211,17 +257,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA9ElEQVR4nM3Or0sDcRjH8c/pgrfBVBjCgibThiKIyTWbWF1bORhGwxARxH/AbtW0JoIGwzXRYhJhtuFY2q1ocLgbe3sGReTuuWbwkx6+r+/zQ/pncX6q+YOldSe6nG3dn8U/rTQ70L8FCGJUewvxl7NTmezNb8xIkvKugr1HSeMP6SrWOVkoTEuSyh0Gm2n3hQyObMnXnxkempRrvgD+gokzwxFAr7U7YXHZ8x4A/Dl7rbu6D2yl3etcw/F3nZgfRVI7rXM7hMUUqzzBec427x26rkmlkzEEa4nnRqnSOH2F0UUx0ePzlbuqMXAHgN6GY9if5xP8dmtHFfwjuQAAAABJRU5ErkJggg==\n", + "image/jpeg": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA9UlEQVR4AWNgGGSAEe4e2Upda8b/mwSvnloAF4MyDJY9+Pv31ZG/QPASTS72y8+/u/W4OFi4DiFLsoCV8XEyvCy9BGT++cfAsBlNJ7OICD9YSPvB369eaJJw7s+/X+vgHBQGX+r9vz9qUIRgHO5l74FO3S0H46PQAj9+gTzy4mo+E4o4lKOdlHQBJL9bDJssAwOncTlQ1ge7JAMD4/a/f7uhkhjm///PwHAXl87Qn3//quCQtLn29+9abuySSd///n3EiVVOa9ofYKSYostpFWvZFM//8Pfvr/WS6HIMu0GeB4KjYRhSDAzpYKkXzohUg0URtYQA/HZrR+ekLi0AAAAASUVORK5CYII=", "text/plain": [ - 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1477,7 +1012,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -1486,7 +1021,7 @@ "torch.Size([6131, 28, 28])" ] }, - "execution_count": null, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -1508,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -1517,7 +1052,7 @@ "3" ] }, - "execution_count": null, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -1544,7 +1079,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -1553,7 +1088,7 @@ "3" ] }, - "execution_count": null, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -1562,6 +1097,36 @@ "stacked_threes.ndim" ] }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_image(stacked_threes[1])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1573,19 +1138,159 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 127, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEQAAABECAYAAAA4E5OyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAE1klEQVR4nO2byU8jPRTEf2En7AgQO4gDi9hO8P9fOIE4AGIR+xoU1kAgQAKZA6o4eUMU6O7R983IdbE66XYHv3K98rOJ5fN5PByq/usf8H+DHxADPyAGfkAM/IAY1FT4/l9OQbGvPvQMMfADYuAHxMAPiIEfEINKWSYSaL1kW/t9MWKx2JfX5T6PCp4hBpEwxEb+/f0dgFwuB0A2mwXg6emppH1+fgbg9fWVj48PAGprawGIx+MANDc3A9DU1ARAfX19yX3V1dVAeQb9FJ4hBqEYIkZYJmQyGQBub28BuLi4AGBnZweAvb09AM7PzwFIJpOFPsSEvr4+AMbHxwGYnZ0FYHR0FICuri7AMUiMqar6jHFQpniGGARiSDmtkDZcXV0BsL+/D8Da2hoAu7u7ABwcHACOOalUipeXl5J3tLW1AXB6elrS58LCAgBTU1MA9Pf3A44pYkhQeIYYhGKIMoMYoigXZw+AmprP13R2dgJuvo+NjQGf2qNnbm5uSvp4e3sD4O7uDnC6I41Rn8pK+m1eQyJCJFlG0VDkW1tbARgaGgKgo6MDcFEX5CFyuVwhIx0dHQFwcnICOF3SO8RGsbOc+w0KzxCDUAyRoivSmse6lvIrGymqgqKayWRIJBIAXF9fl/Ste6RD8il6V11dXcn93qlGjEAMURQUFUVP19ISO8/FFGWfx8dH4NNjbGxsALC1tQU4DWloaAAc2wYGBgCXXRobGwHHyrDwDDGIREMsY8QMtVrjKMtcXl4CsLm5CcDq6irr6+uAc7fqc35+HoDe3l7AOVNlsqjWMIW/KdTT/yBCaUglVyjNSKVSgIv+0tISACsrKwAsLy8XHKhYJScqBrS0tABOU6JmhuAZYhBKQyxTBF0rm2ilKp3Q6lcMSSQSBWbIX6gyJv2RPxHburu7S+6zlbOgiLTIbBd9tjygHytBnJ6eBmBkZKTQh50KelZlABWZ2tvbATeFoiol+iljEMnizk4Zu9iTiVIZUNcyZvl8vsAmlR+TySRAwdI/PDwAcHh4CMDw8DDgmGItfFCj5hliEKpAZDWjXDnAFnGkGcXPSyskmipEizlKy/peDBJTrFELWijyDDH4EUMsI2xrmaPoKDVqnlsUM0T3SDO03NcC0pYrldpticFrSEQIpCG2uKxWURLEEEVLGcBuFcRisd88ixiirKN32ixiWesXdxEjlIbYTWw7nxUt6YLdqC4uHIsRcqTb29uA8yF6l5yptEV9e6f6hxDKh2i+q/CjzSQ5UGUCRdEu3IR0Os3x8THgmCEfoj61ua1FXU9PD+BKi9apBoVniMGPGGLnp90qSKfTgCsEyV1KY3SfXcmmUqlCiUDPaAtzcHAQcI50cnIScAUkOVT5FJ9lIkYgDZGiKyrSBm0JCPf394DTA2UQfS6NyWazBdboGMTMzAwAc3NzACwuLgIwMTEBOA0pVw8JCs8Qg0AaomhK2VUA1haBXcvY+8/OzgDnQuPxeEEjdERCtZNymmFLh2Gzi+AZYhCrcIzgyy8rHcOUY1V2kQtVK99SfBRTkbetdMkew4xg+8H/e8h3EIghZW8uc4S70tFu+N3jVGojgGfIdxApQ/4yeIZ8B35ADPyAGFRyqtH+d85fAM8QAz8gBn5ADPyAGPgBMfADYvALMumtb+Vr5kIAAAAASUVORK5CYII=\n", "text/plain": [ - "
" + "" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.mean" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([28, 28])\n", + "torch.Size([6131, 28])\n", + "torch.Size([6131, 28])\n" + ] + } + ], + "source": [ + "print(stacked_threes.mean(0).shape)\n", + "print(stacked_threes.mean(1).shape)\n", + "print(stacked_threes.mean(2).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# this is an x-ray view of all the numbers from the front\n", + "show_image(stacked_threes.mean(0))" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This is like an x-ray view of the numbers from the top\n", + "show_image(stacked_threes[0:28].mean(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# this is like an xray view of the numbers from the side\n", + "show_image(stacked_threes[0:28].mean(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, "output_type": "display_data" } ], @@ -1605,19 +1310,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 133, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1626,6 +1329,48 @@ "show_image(mean7);" ] }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# view of the numbers from the top\n", + "show_image(stacked_sevens[0:28].mean(1));" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# view of the numbers from the side\n", + "show_image(stacked_sevens[0:28].mean(2));" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1639,19 +1384,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 136, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1683,7 +1426,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -1692,7 +1435,7 @@ "(tensor(0.1114), tensor(0.2021))" ] }, - "execution_count": null, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } @@ -1705,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 138, "metadata": {}, "outputs": [ { @@ -1714,7 +1457,7 @@ "(tensor(0.1586), tensor(0.3021))" ] }, - "execution_count": null, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -1741,7 +1484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -1750,7 +1493,7 @@ "(tensor(0.1586), tensor(0.3021))" ] }, - "execution_count": null, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" } @@ -1831,7 +1574,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -1842,7 +1585,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -1852,7 +1595,7 @@ " [4, 5, 6]])" ] }, - "execution_count": null, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -1863,7 +1606,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -1873,7 +1616,7 @@ " [4, 5, 6]])" ] }, - "execution_count": null, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -1893,7 +1636,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -1902,7 +1645,7 @@ "tensor([4, 5, 6])" ] }, - "execution_count": null, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -1920,7 +1663,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -1929,7 +1672,7 @@ "tensor([2, 5])" ] }, - "execution_count": null, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -1947,7 +1690,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -1956,7 +1699,7 @@ "tensor([5, 6])" ] }, - "execution_count": null, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -1974,7 +1717,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -1984,7 +1727,7 @@ " [5, 6, 7]])" ] }, - "execution_count": null, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -2002,7 +1745,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -2011,7 +1754,7 @@ "'torch.LongTensor'" ] }, - "execution_count": null, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -2029,7 +1772,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -2039,7 +1782,7 @@ " [6.0000, 7.5000, 9.0000]])" ] }, - "execution_count": null, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -2077,7 +1820,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -2086,7 +1829,7 @@ "(torch.Size([1010, 28, 28]), torch.Size([1028, 28, 28]))" ] }, - "execution_count": null, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } @@ -2114,7 +1857,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -2123,7 +1866,7 @@ "tensor(0.1114)" ] }, - "execution_count": null, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -2146,17 +1889,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 151, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(tensor([0.1050, 0.1526, 0.1186, ..., 0.1122, 0.1170, 0.1086]),\n", + "(tensor([0.1153, 0.1118, 0.1325, ..., 0.1535, 0.1517, 0.1063]),\n", " torch.Size([1010]))" ] }, - "execution_count": null, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -2179,7 +1922,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -2188,7 +1931,7 @@ "tensor([2, 3, 4])" ] }, - "execution_count": null, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -2206,7 +1949,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -2215,7 +1958,7 @@ "torch.Size([1010, 28, 28])" ] }, - "execution_count": null, + "execution_count": 153, "metadata": {}, "output_type": "execute_result" } @@ -2248,7 +1991,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 154, "metadata": {}, "outputs": [], "source": [ @@ -2264,7 +2007,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 155, "metadata": {}, "outputs": [ { @@ -2273,7 +2016,7 @@ "(tensor(True), tensor(1.))" ] }, - "execution_count": null, + "execution_count": 155, "metadata": {}, "output_type": "execute_result" } @@ -2291,7 +2034,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -2300,7 +2043,7 @@ "tensor([True, True, True, ..., True, True, True])" ] }, - "execution_count": null, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } @@ -2318,7 +2061,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -2327,7 +2070,7 @@ "(tensor(0.9168), tensor(0.9854), tensor(0.9511))" ] }, - "execution_count": null, + "execution_count": 157, "metadata": {}, "output_type": "execute_result" } @@ -2537,7 +2280,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 158, "metadata": {}, "outputs": [], "source": [ @@ -2553,19 +2296,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 159, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2669,7 +2414,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 161, "metadata": {}, "outputs": [], "source": [ @@ -2689,7 +2434,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 162, "metadata": {}, "outputs": [ { @@ -2698,7 +2443,7 @@ "tensor(9., grad_fn=)" ] }, - "execution_count": null, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } @@ -2717,7 +2462,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 163, "metadata": {}, "outputs": [], "source": [ @@ -2740,7 +2485,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -2749,7 +2494,7 @@ "tensor(6.)" ] }, - "execution_count": null, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -2769,7 +2514,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -2778,7 +2523,7 @@ "tensor([ 3., 4., 10.], requires_grad=True)" ] }, - "execution_count": null, + "execution_count": 165, "metadata": {}, "output_type": "execute_result" } @@ -2797,7 +2542,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 166, "metadata": {}, "outputs": [ { @@ -2806,7 +2551,7 @@ "tensor(125., grad_fn=)" ] }, - "execution_count": null, + "execution_count": 166, "metadata": {}, "output_type": "execute_result" } @@ -2827,7 +2572,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 167, "metadata": {}, "outputs": [ { @@ -2836,7 +2581,7 @@ "tensor([ 6., 8., 20.])" ] }, - "execution_count": null, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } @@ -2847,10 +2592,202 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 188, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(50., grad_fn=)" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "The gradients only tell us the slope of our function, they don't actually tell us exactly how far to adjust the parameters. But it gives us some idea of how far; if the slope is very large, then that may suggest that we have more adjustments to do, whereas if the slope is very small, that may suggest that we are close to the optimal value." + "xt = tensor([3.,4.,5.]).requires_grad_()\n", + "def f(x): return (x**2).sum() # without sum, the backward() will yield RuntimeError: grad can be implicitly created only for scalar outputs \n", + "yt = f(xt)\n", + "yt" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor([ 6., 8., 10.])" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(xt.grad)\n", + "yt.backward()\n", + "xt.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([3., 4., 5.], requires_grad=True), tensor([10.8000, 14.4000, 18.0000]))" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xt, xt.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1.9200, 2.5600, 3.2000], requires_grad=True)" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xt_2 = tensor(tensor([3.,4.,5.]) - xt.grad *0.1).requires_grad_()\n", + "xt_2" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(50., grad_fn=)\n", + "tensor(20.4800, grad_fn=)\n" + ] + } + ], + "source": [ + "print(f(xt))\n", + "yt_2 = f(xt_2)\n", + "print(yt_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([3.8400, 5.1200, 6.4000])" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yt_2.backward()\n", + "xt_2.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.7680, 1.0240, 1.2800], requires_grad=True)" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xt_3 = xt_2.detach().clone() - xt_2.grad * 0.3\n", + "xt_3.requires_grad_()\n", + "xt_3" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([3.0720, 4.0960, 5.1200])" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yt_3 = f(xt_3)\n", + "yt_3.backward()\n", + "xt_3.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([3., 4., 5.], requires_grad=True) tensor([10.8000, 14.4000, 18.0000]) tensor(50., grad_fn=)\n", + "tensor([1.9200, 2.5600, 3.2000], requires_grad=True) tensor([3.8400, 5.1200, 6.4000]) tensor(20.4800, grad_fn=)\n", + "tensor([0.7680, 1.0240, 1.2800], requires_grad=True) tensor([3.0720, 4.0960, 5.1200]) tensor(3.2768, grad_fn=)\n" + ] + } + ], + "source": [ + "print(xt, xt.grad, yt)\n", + "print(xt_2, xt_2.grad, yt_2)\n", + "print(xt_3, xt_3.grad, yt_3)" ] }, { @@ -2935,7 +2872,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 213, "metadata": {}, "outputs": [ { @@ -2944,7 +2881,7 @@ "tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.])" ] }, - "execution_count": null, + "execution_count": 213, "metadata": {}, "output_type": "execute_result" } @@ -2955,19 +2892,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 219, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "(1, 20)" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time.ndim, len(time)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.scatter(time,torch.randn(20)*3 + .75*(time-9.5)**2 + 1);" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA0xBEAACAaQgiAADANAQRAABgGoIIAAAwDUEEAACYpo/ZBQAAEK1aWg2V72vQgUaP+qfYlDskTfFxFrPLiioEEQAAOqF0p1OL1lbJ6fL4tjnsNhUVZCk/22FiZdGFrhkAAEJUutOpeasq/EKIJO13eTRvVYVKdzpNqiz6EEQAAAhBS6uhRWurZATZ5922aG2VWlqDtcCZCCIAAISgfF9DwJ2Q0xmSnC6Pyvc1dF9RUYwgAgBACA40th1COtOut2OwahRilDYAmKd/ii2s7Xo7gkiUYZQ2AJgrd0iaHHab9rs8QceJWCSl20/9kYj20TUTRRilDQDmi4+zqKggS9Kp0HE6789FBVncqe4ggkiUYJQ2APQc+dkOLS8cqXS7f/dLut2m5YUjuUMdArpmokQoo7RHDz2/+woDgF4qP9uhSVnpjNnrIoJIlGCUNgD0PPFxFv746yK6ZqIEo7QBALGIIBIlvKO027rhZ9Gp2TOM0gYARBOCSJRglDYAIBYRRKIIo7QBALGGwapRhlHaAIBYQhCJQozSBgDECrpmAACAabocRJ555hlZLBZlZ2cH7Nu0aZPGjBmjpKQkpaena/78+WpqaurqRwIAgBjRpa6Z2tpaLV68WOecc07AvsrKSk2YMEGXX365nn/+edXW1mrJkiXas2eP3nvvva58LAAAiBFdCiKPPfaYrrvuOrW0tOjQoUN++5544gmdd955KisrU2pqqiQpMzNT9913nz744ANNnjy5Kx8NAABiQKe7Zv7yl79ozZo1+sUvfhGwz+12a/369SosLPSFEEmaPXu2kpOTVVJS0tmPBQAAMaRTQaSlpUUPPvigvv/97+uKK64I2L9jxw6dPHlSo0aN8tuekJCgnJwcbd++vXPVAgCAmNKprplf//rX+uqrr7Rhw4ag+51OpyTJ4QhcYMvhcOiTTz5p872bm5vV3Nzs+9ntdnemRAAAEAVCviNy+PBh/fSnP9XChQvVr1+/oG2OHTsmSbJarQH7bDabb38wxcXFstvtvldGRkaoJQIAgCgRchB58sknlZaWpgcffLDNNomJiZLkd2fDy+Px+PYHs2DBArlcLt+rpqYm1BIBAECUCKlrZs+ePVqxYoV+8YtfqL6+3rfd4/HoxIkT+vLLL5WamurrkvF20ZzO6XRqwIABbX6G1WoNeicFAADEnpDuiNTV1am1tVXz58/XkCFDfK8tW7aourpaQ4YM0VNPPaXs7Gz16dNH27Zt8zv++PHjqqysVE5OTjjPAQAARKmQ7ohkZ2fr7bffDtj+5JNPqrGxUS+88IKGDh0qu92uiRMnatWqVVq4cKFSUlIkSa+++qqampo0Y8aM8FQPAACimsUwDKOrb3LDDTfo0KFD2rlzp29bRUWF8vLylJWVpblz56q2tlZLly7V2LFj9f7773f4vd1ut+x2u1wul9+aJAAAoOfq6Pd3xB56N3LkSG3YsEGJiYl65JFHtGLFCs2ZM0dr1qyJ1EcCAIAoE5Y7IpHEHREAAKKP6XdEAAAA2kMQAQAApiGIAAAA0xBEAACAaQgiAADANAQRAABgGoIIAAAwDUEEAACYhiACAABMQxABAACmIYgAAADTEEQAAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJiGIAIAAExDEAEAAKYhiAAAANMQRAAAgGn6mF0Aep6WVkPl+xp0oNGj/ik25Q5JU3ycxeyyAAAxiCACP6U7nVq0tkpOl8e3zWG3qaggS/nZDhMrAwDEIrpm4FO606l5qyr8Qogk7Xd5NG9VhUp3Ok2qDAAQqwgikHSqO2bR2ioZQfZ5ty1aW6WW1mAtAADoHIIIJEnl+xoC7oSczpDkdHlUvq+h+4oCAMQ8gggkSQca2w4hnWkHAEBHEEQgSeqfYgtrOwAAOoIgAklS7pA0Oew2tTVJ16JTs2dyh6R1Z1kAgBhHEIEkKT7OoqKCLEkKCCPen4sKslhPBAAQVgQR+ORnO7S8cKTS7f7dL+l2m5YXjmQdEQBRp6XV0Oa9h/Wnyjpt3nuYmX89EAuawU9+tkOTstJZWRVA1GOBxuhgMQyjR8dDt9stu90ul8ul1NRUs8sBAEQB7wKNZ37Bef+k4i5v5HX0+5uuGQBATGGBxugSUhD529/+phkzZuiiiy5SUlKSLrjgAo0dO1Zr164NaLtr1y7l5+crOTlZaWlpuuuuu3Tw4MGwFQ4AQDAs0BhdQhoj8tVXX6mxsVF33323BgwYoG+//VZvvfWWpk6dqt/85jeaO3euJKm2tlZjx46V3W7X4sWL1dTUpCVLlmjHjh0qLy9XQkJCRE4GAAAWaIwuXR4j0tLSoquvvloej0e7d++WJP3gBz/QypUrtXv3bg0ePFiStGHDBk2aNMkvsHQEY0QAAKHYvPewZv2/z9pt94f7rtPooed3Q0W9U7eNEYmPj1dGRoaOHDni2/bWW2/plltu8YUQSZo4caIuueQSlZSUdPUjAQBoEws0RpdOBZGjR4/q0KFD2rt3r37+85/rvffe04QJEyRJdXV1OnDggEaNGhVwXG5urrZv337W925ubpbb7fZ7AQDQUSzQGF06FUR+/OMfq1+/fho2bJgee+wx3XbbbVq2bJkkyel0SpIcjsBpUQ6HQw0NDWpubm7zvYuLi2W3232vjIyMzpQIAOjFWKAxenRqQbOHH35Y06dPV319vUpKStTS0qLjx49Lko4dOyZJslqtAcfZbDZfm2D7JWnBggV69NFHfT+73W7CCAAgZCzQGB06FUQuu+wyXXbZZZKk2bNna/LkySooKNCWLVuUmJgoSUHveng8p0Yoe9sEY7Va2wwpAACEIj7OwoDUHi4sC5pNnz5dW7duVXV1ta9LxttFczqn06m0tDSCBgAAkBSmIOLtjnG5XBo4cKD69eunbdu2BbQrLy9XTk5OOD4SAADEgJCCyIEDBwK2nThxQr///e+VmJiorKxTo5SnTZumdevWqaamxtfuww8/VHV1tWbMmNHFkgEAQKwIaUGz2267TW63W2PHjtXAgQO1f/9+rV69Wrt379bSpUt9g0xramo0YsQInXvuuXrooYfU1NSk5557ToMGDdLWrVtD6pphQTMAAKJPR7+/Qwoir7/+ul566SXt2LFDhw8fVkpKiq6++mo9+OCDmjp1ql/bv/3tb3r00Ue1ceNGJSQkaMqUKVq6dKkuvPDCiJwIAADoOSISRMxAEAEAIPp02xLvAAAAnUUQAQAApiGIAAAA0xBEAACAaQgiAADANAQRAABgGoIIAAAwDUEEAACYhiACAABMQxABAACmIYgAAADT9DG7APQuLa2Gyvc16ECjR/1TbModkqb4OIvZZQEATEIQQbcp3enUorVVcro8vm0Ou01FBVnKz3aYWBkAwCx0zaBblO50at6qCr8QIkn7XR7NW1Wh0p1OkyoDAJiJIIKIa2k1tGhtlYwg+7zbFq2tUktrsBYAgFhGEEHEle9rCLgTcjpDktPlUfm+hu4rCgDQIxBEEHEHGtsOIZ1pBwCIHQQRRFz/FFtY2wEAYgezZhBxuUPS5LDbtN/lCTpOxCIp3X5qKi+A3oUp/SCIIOLi4ywqKsjSvFUVskh+YcT7z01RQRb/+AC9DFP6IdE1g26Sn+3Q8sKRSrf7d7+k221aXjiSf3SAXoYp/fDijgi6TX62Q5Oy0rkNC/Ry7U3pt+jUlP5JWen8+9ALEETQreLjLBo99HyzywBgolCm9PPvReyjawYA0K2Y0o/TEUQAAN2KKf04HUEEANCtvFP62xr9YdGp2TNM6e8dCCIAgG7lndIvKSCMMKW/9yGIAAC6HVP64cWsGQCAKZjSD4kgAgAwEVP6QdcMAAAwDUEEAACYJqQgsnXrVv3oRz/S8OHDdc4552jw4MGaOXOmqqurA9ru2rVL+fn5Sk5OVlpamu666y4dPHgwbIUDAIDoF9IYkWeffVaffvqpZsyYoSuvvFL79+/XsmXLNHLkSH322WfKzs6WJNXW1mrs2LGy2+1avHixmpqatGTJEu3YsUPl5eVKSEiIyMkAAIDoElIQefTRR/Xaa6/5BYnbb79dV1xxhX72s59p1apVkqTFixfr6NGj+vzzzzV48GBJUm5uriZNmqSVK1dq7ty5YTwFAAAQrSyGYQR7AGJIrr76aknS559/Lkm68MILNW7cOJWUlPi1u/TSS5WRkaENGzZ0+L3dbrfsdrtcLpdSU1O7WioAAOgGHf3+7vJgVcMw9PXXX+uCCy6QJNXV1enAgQMaNWpUQNvc3Fxt3779rO/X3Nwst9vt9wIAALGpy0Fk9erVqqur0+233y5JcjqdkiSHI3BVPIfDoYaGBjU3N7f5fsXFxbLb7b5XRkZGV0sEAAA9VJeCyO7du/XDH/5Qo0eP1t133y1JOnbsmCTJarUGtLfZbH5tglmwYIFcLpfvVVNT05USgbBqaTW0ee9h/amyTpv3HlZLa5d7NgGgV+v0yqr79+/XlClTZLfbtWbNGsXHx0uSEhMTJSnoXQ+Px+PXJhir1Ro0xABmK93p1KK1VXK6PL5tDrtNRQVZPBcDADqpU3dEXC6XbrrpJh05ckSlpaUaMGCAb5+3S8bbRXM6p9OptLQ0ggaiTulOp+atqvALIZK03+XRvFUVKt0Z+PsOAGhfyEHE4/GooKBA1dXVWrdunbKysvz2Dxw4UP369dO2bdsCji0vL1dOTk6niwXM0NJqaNHaKgXrhPFuW7S2im4aAOiEkIJIS0uLbr/9dm3evFlvvvmmRo8eHbTdtGnTtG7dOr/xHR9++KGqq6s1Y8aMrlUMdLPyfQ0Bd0JOZ0hyujwq39fQfUUBQIwIaYzIj3/8Y73zzjsqKChQQ0ODbwEzr8LCQknSE088oTfffFPjx4/XQw89pKamJj333HO64oordO+994aveqAbHGhsO4R0ph0A4B9CCiKVlZWSpLVr12rt2rUB+71BJCMjQ3/+85/16KOP6t/+7d+UkJCgKVOmaOnSpYwPQdTpn2ILazsAwD+EFETKyso63Hb48OF6//33Q60H6HFyh6TJYbdpv8sTdJyIRVK63abcIWndXRoARL0uL2gGxLr4OIuKCk4Nyracsc/7c1FBluLjztwLAGgPQQTogPxsh5YXjlS63b/7Jd1u0/LCkawjAgCd1OkFzYCeqKXVUPm+Bh1o9Kh/yqnuknDdqcjPdmhSVnrE3h8AeiOCCGJGd6x8Gh9n0eih54flvYBoEcmADxBEEBO8K5+eOZjUu/Ip3SdA5/BoA0QaY0QQ9Vj5FIgMHm2A7kAQQdRj5VMg/Aj46C4EEUQ9Vj4Fwo+Aj+5CEEHUY+VTIPwI+OguBBFEPe/Kp22N4bfo1OA6Vj4FOo6Aj+5CEEHUY+VTIPwI+OguBBHEBFY+BcKLgI/uYjEMo0cPeXa73bLb7XK5XEpNTTW7HPRwLLwEhBfriKCzOvr9TRABAJwVAR+d0dHvb1ZWBQCcFY82QCQxRgQAAJiGIAIAAExDEAEAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBqCCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA0xBEAACAaQgiAADANAQRAABgGoIIAAAwDUEEAACYJuQg0tTUpKKiIuXn5ystLU0Wi0UrV64M2nbXrl3Kz89XcnKy0tLSdNddd+ngwYNdrRkAAMSIPqEecOjQIT311FMaPHiwrrrqKpWVlQVtV1tbq7Fjx8put2vx4sVqamrSkiVLtGPHDpWXlyshIaGrtQMAgCgXchBxOBxyOp1KT0/Xtm3bdM011wRtt3jxYh09elSff/65Bg8eLEnKzc3VpEmTtHLlSs2dO7drlQMAgKgXcteM1WpVenp6u+3eeust3XLLLb4QIkkTJ07UJZdcopKSklA/FgAAxKCQ74h0RF1dnQ4cOKBRo0YF7MvNzdW7777b5rHNzc1qbm72/ex2uyNRIgAA6AEiMmvG6XRKOtWNcyaHw6GGhga/sHG64uJi2e123ysjIyMSJQIAgB4gIkHk2LFjkk5145zJZrP5tTnTggUL5HK5fK+amppIlAgAAHqAiHTNJCYmSlLQux4ej8evzZmsVmvQAAMAAGJPRO6IeLtkvF00p3M6nUpLSyNsAACAyNwRGThwoPr166dt27YF7CsvL1dOTk4kPhYAeqWWVkPl+xp0oNGj/ik25Q5JU3ycxeyygA6JSBCRpGnTpumVV15RTU2Nb8Dphx9+qOrqaj3yyCOR+lgA6FVKdzq1aG2VnC6Pb5vDblNRQZbyswMnDAA9jcUwDCPUg5YtW6YjR46ovr5ey5cv1/e+9z2NGDFCkvTggw/KbrerpqZGI0aM0LnnnquHHnpITU1Neu655zRo0CBt3bq1w10zbrdbdrtdLpdLqampoZYKAO2K1jsKpTudmreqQmf+I+6tfHnhSMIITNPR7+9OBZHMzEx99dVXQfft27dPmZmZkqS//e1vevTRR7Vx40YlJCRoypQpWrp0qS688MIOfxZBBEAkResdhZZWQ2Oe/civ7tNZJKXbbdr4+HejIlQh9kQ0iHQnggiASInmOwqb9x7WrP/3Wbvt/nDfdRo99PxuqAjw19Hv74jMmgGAnq6l1dCitVUBIUSSb9uitVVqae2Zf6sdaAx+J6Sz7QCzEEQA9Erl+xra7NaQToURp8uj8n0N3VdUCPqn2MLaDjALQQRArxTtdxRyh6TJYbeprdEfFp0a65I7JK07ywJCRhAB0CtF+x2F+DiLigqyJCkgjHh/LirIYqAqejyCCIBeKRbuKORnO7S8cKTS7f5hKd1u69EDbYHTRWxBMwDoybx3FOatqpBF8hu0Gk13FPKzHZqUlR6V66AAEtN3AfRy0bqOCNDTdfT7mzsiAHo17igA5iKIAOj14uMsLPoFmITBqgAAwDQEEQAAYBq6ZgAgwqL16b5AdyCIAEAEMSsHODu6ZgAgQrxP9z3zmTb7XR7NW1Wh0p1OkyoDeg6CCABEQLQ/3RfoLgQRAIiAaH+6L9BdCCIAEAHR/nRfoLswWBXoQSI5u4KZG90r2p/uC3QXggjQQ0RydgUzN7qf9+m++12eoONELDr1lNye/HRfoDvQNQP0AJGcXcHMDXN4n+4r/eNpvl7R9HRfINIIIoDJIjm7gpkb5srPdmh54Uil2/27X9LtNi0vHMndKEB0zQCmC2V2RagPZovke6NjeLovcHYEEcBkkZxdEUszN6J5sC1P9wXaRhABTBbJ2RWxMnODwbZA7GKMCGAy7+yKtv62t+jUl25nZldE8r27C4NtgdhGEAFMFsnZFdE+c4PBtkDsI4gAPUAkZ1dE88wNlkkHYh9jRIAeIpKzK6J15kYsDbYFEBxBBOhBIjm7IhpnbsTKYFsAbaNrBkCPFQuDbQGcHUEEQI8V7YNtAbSPIAKgR4vmwbYA2hfRMSLNzc366U9/qldffVXffPONrrzySj399NOaNGlSJD8WQIyJ1sG2ANoX0SByzz33aM2aNXr44Yd18cUXa+XKlbr55pv18ccfa8yYMZH8aAAxJhoH2wJon8UwjIisBFReXq5rr71Wzz33nB577DFJksfjUXZ2tvr3769NmzZ16H3cbrfsdrtcLpdSU1MjUSoAAAizjn5/R2yMyJo1axQfH6+5c+f6ttlsNs2ZM0ebN29WTU1NpD4aAABEiYh1zWzfvl2XXHJJQArKzc2VJFVWViojIyPguObmZjU3N/t+drvdkSoRAACYLGJ3RJxOpxyOwNHs3m319fVBjysuLpbdbve9goUVAAAQGyIWRI4dOyar1Rqw3Waz+fYHs2DBArlcLt+LLhwAAGJXxLpmEhMT/bpYvDwej29/MFarNWiAAQAAsSdid0QcDoecTmfAdu+2AQMGROqjAQBAlIhYEMnJyVF1dXXAYNMtW7b49gMAgN4tYkFk+vTpamlp0YoVK3zbmpub9fLLL+vaa69lECoAAIjcGJFrr71WM2bM0IIFC3TgwAENGzZMr7zyir788ku99NJLkfpYAAAQRSK6xPvvf/97LVy40O9ZM+vWrdPYsWMj+bEAACBKRGyJ93BhiXcgOrS0GjyUDoBPR7+/I3pHBEDvULrTqUVrq+R0eXzbHHabigqylJ8duLAhAHhFbLAqgN6hdKdT81ZV+IUQSdrv8mjeqgqV7gycxg8AXgQRAJ3W0mpo0doqBevf9W5btLZKLa09ugcYgIkIIgA6rXxfQ8CdkNMZkpwuj8r3NXRfUQCiCkEEQKcdaGw7hHSmHYDehyACoNP6p9jC2g5A70MQAdBpuUPS5LDb1NYkXYtOzZ7JHZLWnWUBiCIEEQCdFh9nUVFBliQFhBHvz0UFWawnAqBNBBEAXZKf7dDywpFKt/t3v6TbbVpeOJJ1RACcFQuaAeiy/GyHJmWls7IqgJARRACERXycRaOHnm92GQCiDF0zAADANAQRAABgGoIIAAAwDUEEAACYhiACAABMQxABAACmIYgAAADTEEQAAIBpCCIAAMA0PX5lVcMwJElut9vkSgAAQEd5v7e93+Nt6fFBpLGxUZKUkZFhciUAACBUjY2Nstvtbe63GO1FFZO1traqvr5eKSkpsljC9wAtt9utjIwM1dTUKDU1NWzv21P1pvPlXGNXbzpfzjV29ZbzNQxDjY2NGjBggOLi2h4J0uPviMTFxWnQoEERe//U1NSY/kU4U286X841dvWm8+VcY1dvON+z3QnxYrAqAAAwDUEEAACYptcGEavVqqKiIlmtVrNL6Ra96Xw519jVm86Xc41dve1829PjB6sCAIDY1WvviAAAAPMRRAAAgGkIIgAAwDQEEQAAYJqYCyLNzc16/PHHNWDAACUmJuraa6/V+vXrO3RsXV2dZs6cqXPPPVepqam69dZb9cUXX0S44s7bunWrfvSjH2n48OE655xzNHjwYM2cOVPV1dXtHrty5UpZLJagr/3793dD9aEpKytrs97PPvus3eOj7drec889bZ6vxWJRXV1dm8f++7//e9BjbDZbN55BcE1NTSoqKlJ+fr7S0tJksVi0cuXKoG137dql/Px8JScnKy0tTXfddZcOHjzY4c965513NHLkSNlsNg0ePFhFRUU6efJkmM6kfR0519bWVq1cuVJTp05VRkaGzjnnHGVnZ+vpp5+Wx+Pp0OfccMMNQa93fn5+BM6qbR29tm39bl922WUd/qxouLaSzvrf8KRJk9r9nMzMzKDHPvDAAxE4K/P0+JVVQ3XPPfdozZo1evjhh3XxxRdr5cqVuvnmm/Xxxx9rzJgxbR7X1NSk8ePHy+Vy6YknnlDfvn3185//XOPGjVNlZaXOP//8bjyLjnn22Wf16aefasaMGbryyiu1f/9+LVu2TCNHjtRnn32m7Ozsdt/jqaee0pAhQ/y2nXvuuRGquOvmz5+va665xm/bsGHDznpMNF7b+++/XxMnTvTbZhiGHnjgAWVmZmrgwIHtvsfy5cuVnJzs+zk+Pj7sdYbq0KFDeuqppzR48GBdddVVKisrC9qutrZWY8eOld1u1+LFi9XU1KQlS5Zox44dKi8vV0JCwlk/57333tM//dM/6YYbbtAvf/lL7dixQ08//bQOHDig5cuXR+DMAnXkXL/99lvde++9uu666/TAAw+of//+2rx5s4qKivThhx/qo48+6tCjLQYNGqTi4mK/bQMGDAjXqXRIR6+tdGr66m9/+1u/bR1ZgVOKnmsrSa+++mrAtm3btumFF17Q5MmTO/RZOTk5+vGPf+y37ZJLLgm55h7NiCFbtmwxJBnPPfecb9uxY8eMoUOHGqNHjz7rsc8++6whySgvL/dt27VrlxEfH28sWLAgYjV3xaeffmo0Nzf7bauurjasVqtx5513nvXYl19+2ZBkbN26NZIlhs3HH39sSDLefPPNkI+NxmsbzCeffGJIMp555pmztisqKjIkGQcPHuymyjrO4/EYTqfTMAzD2Lp1qyHJePnllwPazZs3z0hMTDS++uor37b169cbkozf/OY37X5OVlaWcdVVVxknTpzwbfvJT35iWCwWY9euXV0/kQ7oyLk2Nzcbn376acCxixYtMiQZ69evb/dzxo0bZwwfPjwsNXdFR6/t3XffbZxzzjmd/pxoubZtmTNnjmGxWIyampp2237nO98xpkyZ0pVSo0JMdc2sWbNG8fHxmjt3rm+bzWbTnDlztHnzZtXU1Jz12Guuucbvr+3LLrtMEyZMUElJSUTr7qy8vLyAvwwvvvhiDR8+XLt27erw+zQ2NqqlpSXc5UVMY2NjSLdho/HaBvPaa6/JYrHojjvu6FB7wzDkdrvbfQR3d7JarUpPT2+33VtvvaVbbrlFgwcP9m2bOHGiLrnkknavWVVVlaqqqjR37lz16fOPm74/+MEPZBiG1qxZ0/kTCEFHzjUhIUF5eXkB22+77TZJCum/45MnT6qpqSm0IsOoo9fWq6WlxfeY+I6KpmsbTHNzs9566y2NGzcupGeoHT9+XEePHg3586JFTAWR7du365JLLgl4iFBubq4kqbKyMuhxra2t+p//+R+NGjUqYF9ubq727t2rxsbGsNcbCYZh6Ouvv9YFF1zQofbjx49XamqqkpKSNHXqVO3ZsyfCFXbNvffeq9TUVNlsNo0fP17btm07a/tYubYnTpxQSUmJ8vLylJmZ2aFjLrroItntdqWkpKiwsFBff/11ZIsMk7q6Oh04cKDNa7Z9+/azHu/df+bxAwYM0KBBg9o9vifwjtPq6H/H1dXVOuecc5SSkqL09HQtXLhQJ06ciGSJXfLtt98qNTVVdrtdaWlp+uEPf9ihEBXt1/bdd9/VkSNHdOedd3b4mI8++khJSUlKTk5WZmamXnjhhQhWaI6YGiPidDrlcDgCtnu31dfXBz2uoaFBzc3N7R576aWXhrHayFi9erXq6ur01FNPnbVdUlKS7rnnHl8Q+fzzz/X8888rLy9PFRUVysjI6KaKOyYhIUHTpk3TzTffrAsuuEBVVVVasmSJrr/+em3atEkjRowIelysXNv3339fhw8f7tA/YOedd55+9KMfafTo0bJarfrkk0/0X//1XyovL9e2bdt6/NM+nU6nJLV5zbzXtK3lsds7vq1/B3qS//zP/1RqaqpuuummdtsOHTpU48eP1xVXXKGjR49qzZo1evrpp1VdXa033nijG6oNjcPh0L/+679q5MiRam1tVWlpqX71q1/pr3/9q8rKyvzudJwp2q/t6tWrZbVaNX369A61v/LKKzVmzBhdeumlOnz4sFauXKmHH35Y9fX1evbZZyNcbTcytWMozC666CLjpptuCti+d+9eQ5Lx85//POhxf//73w1JxrPPPhuw76WXXjIkGdu3bw9zteG3a9cuIzU11Rg9erRx8uTJkI//5JNPDIvFYtx///0RqC789uzZYyQmJho33nhjm21i5drOmjXL6Nu3r3Ho0KFOHb969WpDklFcXBzmyjqvrb71v/zlL4Yk44033gg4ZuHChYYk45tvvmnzfZ966ilDkvH1118H7Lv++uuNq666qouVhy6UcQTPPPOMIcn41a9+1enPu++++wxJxubNmzv9Hl0R6rgJ7zn/4Q9/OGu7aL62LpfLsNlsxm233dbpz2ptbTVuvPFGo0+fPh0aYxItYqprJjExUc3NzQHbvdPgEhMT2zxOUqeO7Sn279+vKVOmyG63+8bKhGrMmDG69tprtWHDhghUGH7Dhg3Trbfeqo8//rjNMS6xcG2bmpr0pz/9STfeeGOnZ/jccccdSk9Pj4pr29Vr1t7xPfl6v/HGG3ryySc1Z84czZs3r9Pv451lEQ3XW5IeeeQRxcXFtVtvNF/bt956Sx6PJ6RumTNZLBY98sgjOnny5FlnJUWbmAoiDofDd+vudN5tbU1nS0tLk9Vq7dSxPYHL5dJNN92kI0eOqLS0tEu1ZmRkqKGhIYzVRVZGRsZZB3JF+7WVpD/+8Y/69ttvu/QPmBQ919Z7272ta+a9pp09vqde7/Xr12v27NmaMmWKfv3rX3fpvbxdq9FwvaVTAeP8889vt95ovbbSqW4Zu92uW265pUvvE23XtiNiKojk5OSouro6YCT2li1bfPuDiYuL0xVXXBF04OOWLVt00UUXKSUlJez1hoPH41FBQYGqq6u1bt06ZWVlden9vvjiC/Xr1y9M1UXeF198IZvN5rdexumi+dp6rV69WsnJyZo6dWqn38MwDH355ZdRcW0HDhyofv36Bb1m5eXlbf537OXdf+bx9fX1qq2tbfd4M2zZskW33XabRo0apZKSkrOOk+gI72J90XC9pVMz4Q4dOtRuvdF4baVTIenjjz/WtGnTzhqiOyLarm1HxFQQmT59ulpaWrRixQrftubmZr388su69tprfUny73//u3bv3h1w7NatW/1+wf/3f/9XH330kWbMmNE9JxCilpYW3X777dq8ebPefPNNjR49Omg7p9Op3bt3+42iD7ZC5bvvvqvPP/+821dk7Ihg9f71r3/VO++8o8mTJysu7tSvcqxcW6+DBw9qw4YNuu2225SUlBSwP9j5Bvv/1fLly3Xw4MEeeW2DmTZtmtatW+c35f7DDz9UdXW13zU7ceKEdu/e7fcX8vDhw3XZZZdpxYoVfl12y5cvl8Vi6fBAwe6ya9cuTZkyRZmZmVq3bt1Zuxd2796tv//9776f3W53QDeFYRh6+umnJUk33nhjZIruJI/HE3SW2n/8x3/IMAy/389YuLZer7/+ulpbW9u8qxnsXBsaGgK6nE+cOKGf/exnSkhI0Pjx4yNac3eyGEYPWmQgDGbOnKm3335bjzzyiIYNG6ZXXnlF5eXl+vDDDzV27FhJp5ZE/vOf/+y3vkJjY6NGjBihxsZGPfbYY+rbt6+ef/55tbS0qLKyskemz4cfflgvvPCCCgoKNHPmzID9hYWFkk6tNvvKK69o3759vqmfF198sUaMGKFRo0bJbreroqJCv/vd7+RwOLR161ZdeOGF3Xkq7frud7+rxMRE5eXlqX///qqqqtKKFSvUt29fbd68WZdffrmk2Lm2XsuWLdODDz6o0tLSoF8qwc43KSlJt99+u6644grZbDZt3LhRr7/+uq666ip9+umnQQNNd1q2bJmOHDmi+vp6LV++XN/73vd8s54efPBB2e121dTUaMSIETr33HP10EMPqampSc8995wGDRqkrVu3+v6q/PLLLzVkyBDdfffdfktsr1u3TlOnTtX48eP1z//8z9q5c6eWLVumOXPm+P2hYva5xsXFafjw4aqrq9PixYsDVswdOnSo3x8YFotF48aN840PKCsr06xZszRr1iwNGzZMx44d09tvv61PP/1Uc+fO1W9+85tuO1ep/fP95ptvNGLECM2aNcu3pPv777+vd999V/n5+frv//5v3x8V0X5tT18pdtSoUXI6naqpqfGd3+mCnevKlSv19NNPa/r06RoyZIgaGhr02muvaefOnVq8eLEWLFjQLefZLcwbJxsZx44dMx577DEjPT3dsFqtxjXXXGOUlpb6tRk3bpwR7NRramqM6dOnG6mpqUZycrJxyy23GHv27Omu0kPmPY+2Xl533323IcnYt2+fb9tPfvITIycnx7Db7Ubfvn2NwYMHG/PmzTP2799vwpm074UXXjByc3ONtLQ0o0+fPobD4TAKCwsDrk+sXFuv6667zujfv3+bs6CCne/3v/99Iysry0hJSTH69u1rDBs2zHj88ccNt9vdHSW36zvf+U6bv7On/47u3LnTmDx5spGUlGSce+65xp133hnw+7lv3z5DknH33XcHfM7bb79t5OTkGFar1Rg0aJDx5JNPGsePH4/w2flr71y99bf1OvO8JBnjxo3z/fzFF18YM2bMMDIzMw2bzWYkJSUZV199tfHrX//aaG1t7dZzNYz2z/ebb74xCgsLjWHDhhlJSUmG1Wo1hg8fbixevDjg2kT7tfXavXu3Icl49NFH23yvYOe6bds2o6CgwBg4cKCRkJBgJCcnG2PGjDFKSkoieFbmiLk7IgAAIHrE1BgRAAAQXQgiAADANAQRAABgGoIIAAAwDUEEAACYhiACAABMQxABAACmIYgAAADTEEQAAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJjm/wN8A114uzETkAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, "output_type": "display_data" } ], @@ -2987,7 +2963,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 240, "metadata": {}, "outputs": [], "source": [ @@ -3009,13 +2985,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 241, "metadata": {}, "outputs": [], "source": [ "def mse(preds, targets): return ((preds-targets)**2).mean()" ] }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.2500)" + ] + }, + "execution_count": 246, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mse(tensor(3.), tensor(3.5))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3039,7 +3035,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 291, "metadata": {}, "outputs": [], "source": [ @@ -3048,12 +3044,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 292, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([-0.0126, 1.1940, 1.1201], requires_grad=True)\n", + "tensor([-0.0126, 1.1940, 1.1201], grad_fn=)\n" + ] + } + ], "source": [ "#hide\n", - "orig_params = params.clone()" + "orig_params = params.clone()\n", + "print(params)\n", + "print(orig_params)" ] }, { @@ -3072,7 +3079,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 252, "metadata": {}, "outputs": [], "source": [ @@ -3088,7 +3095,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 293, "metadata": {}, "outputs": [], "source": [ @@ -3101,19 +3108,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 295, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3144,16 +3149,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 256, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor(25823.8086, grad_fn=)" + "tensor(24894.9180, grad_fn=)" ] }, - "execution_count": null, + "execution_count": 256, "metadata": {}, "output_type": "execute_result" } @@ -3186,16 +3191,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 257, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([-53195.8594, -3419.7146, -253.8908])" + "tensor([52188.5312, 3342.7866, 208.7529])" ] }, - "execution_count": null, + "execution_count": 257, "metadata": {}, "output_type": "execute_result" } @@ -3207,16 +3212,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 258, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([-0.5320, -0.0342, -0.0025])" + "tensor([0.5219, 0.0334, 0.0021])" ] }, - "execution_count": null, + "execution_count": 258, "metadata": {}, "output_type": "execute_result" } @@ -3234,22 +3239,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 261, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([-0.7658, -0.7506, 1.3525], requires_grad=True)" + "(tensor([ 1.0836, -0.1926, -1.0489], requires_grad=True),\n", + " tensor([52188.5312, 3342.7866, 208.7529]),\n", + " tensor([52188.5312, 3342.7866, 208.7529]))" ] }, - "execution_count": null, + "execution_count": 261, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "params" + "params, params.grad, params.grad.data" ] }, { @@ -3268,7 +3275,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 262, "metadata": {}, "outputs": [], "source": [ @@ -3293,16 +3300,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 263, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor(5435.5366, grad_fn=)" + "tensor(5272.2061, grad_fn=)" ] }, - "execution_count": null, + "execution_count": 263, "metadata": {}, "output_type": "execute_result" } @@ -3321,19 +3328,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 264, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3350,7 +3355,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 265, "metadata": {}, "outputs": [], "source": [ @@ -3380,38 +3385,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([-0.7496, -0.4951, 1.3849], requires_grad=True)" + ] + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = torch.randn(3).requires_grad_()\n", + "params" + ] + }, + { + "cell_type": "code", + "execution_count": 334, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5435.53662109375\n", - "1577.4495849609375\n", - "847.3780517578125\n", - "709.22265625\n", - "683.0757446289062\n", - "678.12451171875\n", - "677.1839599609375\n", - "677.0025024414062\n", - "676.96435546875\n", - "676.9537353515625\n" + "14.66791820526123\n", + "13.921719551086426\n", + "13.276110649108887\n", + "12.670208930969238\n", + "12.1411771774292\n", + "11.65521240234375\n", + "11.19435977935791\n", + "10.773752212524414\n", + "10.462143898010254\n", + "10.165071487426758\n" ] } ], "source": [ - "for i in range(10): apply_step(params)" + "lr = 2e-5\n", + "for i in range(100_000): apply_step(params, i % 10_000 == 0)\n", + "#for i in range(1000): apply_step(params)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 313, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([nan, nan, nan], requires_grad=True)" + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#hide\n", - "params = orig_params.detach().requires_grad_()" + "params = orig_params.detach().requires_grad_()\n", + "params" ] }, { @@ -3423,19 +3463,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 335, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3459,6 +3497,453 @@ "We just decided to stop after 10 epochs arbitrarily. In practice, we would watch the training and validation losses and our metrics to decide when to stop, as we've discussed." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Side experiment: Compare to Adam Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_preds(speed)" + ] + }, + { + "cell_type": "code", + "execution_count": 389, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.8925, 1.0355, 0.8071], requires_grad=True)" + ] + }, + "execution_count": 389, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = torch.randn(3).requires_grad_()\n", + "params" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam([params])" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 0.8071, 2.7351, 6.4483, 11.9465, 19.2297, 28.2980, 39.1514, 51.7899, 66.2134, 82.4220, 100.4157, 120.1944, 141.7582, 165.1071, 190.2410, 217.1600, 245.8640, 276.3531, 308.6273,\n", + " 342.6866], grad_fn=)" + ] + }, + "execution_count": 411, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply step:\n", + "preds = f(time, params)\n", + "preds" + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(19740.8027, grad_fn=)" + ] + }, + "execution_count": 412, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loss = mse(speed, preds)\n", + "loss" + ] + }, + { + "cell_type": "code", + "execution_count": 413, + "metadata": {}, + "outputs": [], + "source": [ + "# backpropagate from loss to calc gradients\n", + "optimizer.zero_grad()\n", + "loss.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 414, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([0.8925, 1.0355, 0.8071], requires_grad=True),\n", + " tensor([46328.8320, 2991.5833, 188.6014]))" + ] + }, + "execution_count": 414, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params, params.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 415, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([0.8915, 1.0345, 0.8061], requires_grad=True),\n", + " tensor([46328.8320, 2991.5833, 188.6014]))" + ] + }, + "execution_count": 415, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimizer.step()\n", + "params, params.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 439, + "metadata": {}, + "outputs": [], + "source": [ + "def apply_step_adam(params, prn=True):\n", + " preds = f(time, params)\n", + " loss = mse(preds, speed)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " #params.data -= lr * params.grad.data\n", + " #params.grad = None\n", + " if prn: print(loss.item())\n", + " return loss.item()" + ] + }, + { + "cell_type": "code", + "execution_count": 420, + "metadata": {}, + "outputs": [], + "source": [ + "params = torch.randn(3).requires_grad_()\n", + "optimizer = torch.optim.Adam([params])" + ] + }, + { + "cell_type": "code", + "execution_count": 423, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "481.08148193359375\n", + "336.9613037109375\n", + "219.37503051757812\n", + "128.1016082763672\n", + "62.873207092285156\n", + "23.2652530670166\n", + "7.924426078796387\n", + "7.1908278465271\n", + "7.19083309173584\n", + "7.190825462341309\n" + ] + } + ], + "source": [ + "for i in range(100_000): apply_step_adam(params, i % 10_000 == 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 467, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "70965.8984375\n", + "591.1617431640625\n", + "415.2744140625\n", + "282.7113342285156\n", + "176.57345581054688\n", + "96.61905670166016\n", + "42.587989807128906\n", + "13.799138069152832\n", + "7.192816257476807\n", + "7.191018104553223\n" + ] + } + ], + "source": [ + "losses_adam = []\n", + "params = torch.randn(3).requires_grad_()\n", + "optimizer = torch.optim.Adam([params])\n", + "for i in range(100_000):\n", + " loss = apply_step_adam(params, i % 10_000 == 0)\n", + " losses_adam.append(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 468, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100000" + ] + }, + "execution_count": 468, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(losses_adam)" + ] + }, + { + "cell_type": "code", + "execution_count": 469, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70965.8984375" + ] + }, + "execution_count": 469, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "losses_adam[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 473, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 473, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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18.7779], requires_grad=True)\n", + "loss:328.3970947265625, params:tensor([ 0.3182, -4.2525, 21.3679], requires_grad=True)\n", + "loss:296.82818603515625, params:tensor([ 0.3403, -4.7650, 23.8274], requires_grad=True)\n", + "loss:268.363037109375, params:tensor([ 0.3612, -5.2518, 26.1627], requires_grad=True)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 461, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lr = 2e-5\n", + "def apply_step(params, prn=True):\n", + " preds = f(time, params)\n", + " loss = mse(preds, speed)\n", + " #optimizer.zero_grad()\n", + " loss.backward()\n", + " #optimizer.step()\n", + " params.data -= lr * params.grad.data\n", + " params.grad = None\n", + " if prn:\n", + " print(f\"loss:{loss.item()}, params:{params}\")\n", + " return loss.item()\n", + "\n", + "losses = []\n", + "params = torch.randn(3).requires_grad_()\n", + "for i in range(100_000):\n", + " loss = apply_step(params, i % 10_000 == 0)\n", + " losses.append(loss)\n", + "\n", + "plt.scatter(range(len(losses)), losses)" + ] + }, + { + "cell_type": "code", + "execution_count": 480, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "90000 90000\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 480, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "skip = 10_000\n", + "ys = losses[skip:]\n", + "ys_adam = losses_adam[skip:]\n", + "print(len(ys), len(ys_adam))\n", + "plt.scatter(range(len(ys)), ys, label=\"gradient descent (lr=2e-5)\")\n", + "plt.scatter(range(len(ys_adam)), ys_adam, label=\"Adam optimizer\")\n", + "plt.title('Loss - Comparing standard gradient descent vs. Adam')\n", + "plt.legend() # Display the legend to differentiate between the two series\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cool, the Adam optimizer seemed to do better. Still, even faster would be nicer. Not sure if there's a inherent challenge to these \"guess the 3 numbers I'm thinking of\" situations, comopared to other deep learning scenarios.\n", + "I wish such a simple set of 3 parameters could have a solution converge in just a handful of steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### End Side experiment" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3612,7 +4097,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 336, "metadata": {}, "outputs": [], "source": [ @@ -3628,7 +4113,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 345, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([12396])" + ] + }, + "execution_count": 345, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_y = tensor([1]*len(threes) + [0]*len(sevens))\n", + "train_y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 346, "metadata": {}, "outputs": [ { @@ -3637,7 +4143,7 @@ "(torch.Size([12396, 784]), torch.Size([12396, 1]))" ] }, - "execution_count": null, + "execution_count": 346, "metadata": {}, "output_type": "execute_result" } @@ -3656,7 +4162,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 350, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12396" + ] + }, + "execution_count": 350, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dset = list(zip(train_x,train_y))\n", + "len(dset)" + ] + }, + { + "cell_type": "code", + "execution_count": 351, "metadata": {}, "outputs": [ { @@ -3665,20 +4192,19 @@ "(torch.Size([784]), tensor([1]))" ] }, - "execution_count": null, + "execution_count": 351, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dset = list(zip(train_x,train_y))\n", "x,y = dset[0]\n", "x.shape,y" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 352, "metadata": {}, "outputs": [], "source": [ @@ -3696,7 +4222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 353, "metadata": {}, "outputs": [], "source": [ @@ -3705,11 +4231,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 361, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([-0.3475, 0.6820, -1.5155])\n", + "tensor([[-0.1630],\n", + " [-1.0171],\n", + " [-0.8578]])\n" + ] + } + ], "source": [ - "weights = init_params((28*28,1))" + "print(torch.randn(3))\n", + "print(torch.randn((3,1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 358, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([784, 1])" + ] + }, + "execution_count": 358, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights = init_params((28*28,1))\n", + "weights.shape" ] }, { @@ -3721,7 +4280,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 362, "metadata": {}, "outputs": [], "source": [ @@ -3751,16 +4310,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 363, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([20.2336], grad_fn=)" + "tensor([-2.9236], grad_fn=)" ] }, - "execution_count": null, + "execution_count": 363, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(train_x[0]*weights.T).sum() + bias" + ] + }, + { + "cell_type": "code", + "execution_count": 368, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([784]), torch.Size([784, 1]), torch.Size([1]))" + ] + }, + "execution_count": 368, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_x[0].shape, weights.shape, (train_x[0]@weights).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([-2.9236], grad_fn=)" + ] + }, + "execution_count": 370, "metadata": {}, "output_type": "execute_result" } @@ -3796,22 +4395,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 371, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[20.2336],\n", - " [17.0644],\n", - " [15.2384],\n", + "tensor([[-2.9236],\n", + " [ 0.7668],\n", + " [ 1.0439],\n", " ...,\n", - " [18.3804],\n", - " [23.8567],\n", - " [28.6816]], grad_fn=)" + " [10.7635],\n", + " [ 1.8304],\n", + " [ 6.3583]], grad_fn=)" ] }, - "execution_count": null, + "execution_count": 371, "metadata": {}, "output_type": "execute_result" } @@ -3822,6 +4421,26 @@ "preds" ] }, + { + "cell_type": "code", + "execution_count": 372, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([12396, 1])" + ] + }, + "execution_count": 372, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds.shape" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3838,13 +4457,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 373, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[ True],\n", + "tensor([[False],\n", " [ True],\n", " [ True],\n", " ...,\n", @@ -3853,7 +4472,7 @@ " [False]])" ] }, - "execution_count": null, + "execution_count": 373, "metadata": {}, "output_type": "execute_result" } @@ -3865,16 +4484,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 374, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.4912068545818329" + "0.2155534029006958" ] }, - "execution_count": null, + "execution_count": 374, "metadata": {}, "output_type": "execute_result" } @@ -3892,7 +4511,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 375, "metadata": {}, "outputs": [], "source": [ @@ -3901,16 +4520,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 376, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.4912068545818329" + "0.2155534029006958" ] }, - "execution_count": null, + "execution_count": 376, "metadata": {}, "output_type": "execute_result" } @@ -4076,7 +4695,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "### Sigmoid" ] @@ -4111,7 +4732,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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" ] @@ -5593,7 +6214,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -5856,6 +6477,18 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" } }, "nbformat": 4,