From 294fe26277186b5631a7d507b9522f6732e7c620 Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Mon, 8 Dec 2025 17:44:03 +0800 Subject: [PATCH 1/8] =?UTF-8?q?ops=E5=8F=98=E6=9B=B4=E4=B8=BAmint?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../0_linear-regression.ipynb | 2278 ++++++++++------- 1 file changed, 1341 insertions(+), 937 deletions(-) diff --git a/chapter_03_linear-networks/0_linear-regression.ipynb b/chapter_03_linear-networks/0_linear-regression.ipynb index 54e6e99..c5eaa42 100644 --- a/chapter_03_linear-networks/0_linear-regression.ipynb +++ b/chapter_03_linear-networks/0_linear-regression.ipynb @@ -2,878 +2,1282 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", + "%matplotlib inline\n", + "import math\n", + "import time\n", + "import numpy as np\n", + "import mindspore\n", + "from d2l import mindspore as d2l\n", + "import mindspore.mint as mint\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Tensor(shape=[10000], dtype=Float32, value= [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00 ... 1.00000000e+00, 1.00000000e+00, 1.00000000e+00])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = 10000\n", + "a = mint.ones(n)\n", + "b = mint.ones(n)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class Timer: #@save\n", + " \"\"\"记录多次运行时间\"\"\"\n", + " def __init__(self):\n", + " self.times = []\n", + " self.start()\n", + "\n", + " def start(self):\n", + " \"\"\"启动计时器\"\"\"\n", + " self.tik = time.time()\n", + "\n", + " def stop(self):\n", + " \"\"\"停止计时器并将时间记录在列表中\"\"\"\n", + " self.times.append(time.time() - self.tik)\n", + " return self.times[-1]\n", + "\n", + " def avg(self):\n", + " \"\"\"返回平均时间\"\"\"\n", + " return sum(self.times) / len(self.times)\n", + "\n", + " def sum(self):\n", + " \"\"\"返回时间总和\"\"\"\n", + " return sum(self.times)\n", + "\n", + " def cumsum(self):\n", + " \"\"\"返回累计时间\"\"\"\n", + " return np.array(self.times).cumsum().tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.28191 sec'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = mint.zeros(n)\n", + "timer = Timer()\n", + "for i in range(n):\n", + " c[i] = a[i] + b[i]\n", + "f'{timer.stop():.5f} sec'" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.00063 sec'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timer.start()\n", + "d = a + b\n", + "f'{timer.stop():.5f} sec'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def normal(x, mu, sigma):\n", + " p = 1 / math.sqrt(2 * math.pi * sigma**2)\n", + " return p * np.exp(-0.5 / sigma**2 * (x - mu)**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-12-08T16:33:08.987997\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.10.7, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 再次使用numpy进行可视化\n", + "x = np.arange(-7, 7, 0.01)\n", + "\n", + "# 均值和标准差对\n", + "params = [(0, 1), (0, 2), (3, 1)]\n", + "d2l.plot(x, [normal(x, mu, sigma) for mu, sigma in params], xlabel='x',\n", + " ylabel='p(x)', figsize=(4.5, 2.5),\n", + " legend=[f'mean {mu}, std {sigma}' for mu, sigma in params])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10", + "language": "python", + "name": "py310" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", "%matplotlib inline\n", "import math\n", "import time\n", "import numpy as np\n", "import mindspore\n", "from d2l import mindspore as d2l\n", - 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"execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = 10000\n", - "a = ops.ones(n)\n", - "b = ops.ones(n)\n", + "a = mint.ones(n)\n", + "b = mint.ones(n)\n", "a" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -907,22 +1311,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'13.19949 sec'" + "'1.28191 sec'" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "c = ops.zeros(n)\n", + "c = mint.zeros(n)\n", "timer = Timer()\n", "for i in range(n):\n", " c[i] = a[i] + b[i]\n", @@ -931,16 +1335,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.00122 sec'" + "'0.00063 sec'" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -953,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -964,7 +1368,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -978,11 +1382,11 @@ " \n", " \n", " \n", - " 2023-02-01T17:02:15.438359\n", + " 2025-12-08T16:33:08.987997\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.6.3, https://matplotlib.org/\n", + " Matplotlib v3.10.7, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -1014,16 +1418,16 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1069,7 +1473,7 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1077,11 +1481,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1109,7 +1513,7 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1117,11 +1521,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1154,7 +1558,7 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1162,11 +1566,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1203,11 +1607,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1221,11 +1625,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1239,11 +1643,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1282,16 +1686,16 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1307,8 +1711,8 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1316,11 +1720,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1343,8 +1747,8 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1352,19 +1756,19 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1372,11 +1776,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1417,8 +1821,8 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1426,19 +1830,19 @@ " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1500,9 +1904,9 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1574,7 +1978,7 @@ "L 277.104042 139.5 \n", "L 283.467614 139.5 \n", "L 283.467614 139.5 \n", - "\" clip-path=\"url(#p14bb0d3922)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #bf00bf; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p1701bb5fc0)\" style=\"fill: none; stroke-dasharray: 9.6,2.4,1.5,2.4; stroke-dashoffset: 0; stroke: #008000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1972,18 +2376,18 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1996,25 +2400,25 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2042,9 +2446,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:mindspore2.0]", + "display_name": "Python 3.10", "language": "python", - "name": "conda-env-mindspore2.0-py" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -2056,7 +2460,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.10.14" } }, "nbformat": 4, From 6b144204e01e1f6c0b3619b39ee1af44998d0378 Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Mon, 8 Dec 2025 17:44:37 +0800 Subject: [PATCH 2/8] =?UTF-8?q?ops=E5=8F=98=E6=9B=B4=E4=B8=BAmint?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../0_linear-regression.ipynb | 1234 ----------------- 1 file changed, 1234 deletions(-) diff --git a/chapter_03_linear-networks/0_linear-regression.ipynb b/chapter_03_linear-networks/0_linear-regression.ipynb index c5eaa42..d62d61a 100644 --- a/chapter_03_linear-networks/0_linear-regression.ipynb +++ b/chapter_03_linear-networks/0_linear-regression.ipynb @@ -1232,1237 +1232,3 @@ "nbformat": 4, "nbformat_minor": 4 } -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append('..')\n", - "\n", - "%matplotlib inline\n", - "import math\n", - "import time\n", - "import numpy as np\n", - "import mindspore\n", - "from d2l import mindspore as d2l\n", - "import mindspore.mint as mint\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Tensor(shape=[10000], dtype=Float32, value= [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00 ... 1.00000000e+00, 1.00000000e+00, 1.00000000e+00])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n = 10000\n", - "a = mint.ones(n)\n", - "b = mint.ones(n)\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "class Timer: #@save\n", - " \"\"\"记录多次运行时间\"\"\"\n", - " def __init__(self):\n", - " self.times = []\n", - " self.start()\n", - "\n", - " def start(self):\n", - " \"\"\"启动计时器\"\"\"\n", - " self.tik = time.time()\n", - "\n", - " def stop(self):\n", - " \"\"\"停止计时器并将时间记录在列表中\"\"\"\n", - " self.times.append(time.time() - self.tik)\n", - " return self.times[-1]\n", - "\n", - " def avg(self):\n", - " \"\"\"返回平均时间\"\"\"\n", - " return sum(self.times) / len(self.times)\n", - "\n", - " def sum(self):\n", - " \"\"\"返回时间总和\"\"\"\n", - " return sum(self.times)\n", - "\n", - " def cumsum(self):\n", - " \"\"\"返回累计时间\"\"\"\n", - " return np.array(self.times).cumsum().tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.28191 sec'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c = mint.zeros(n)\n", - "timer = Timer()\n", - "for i in range(n):\n", - " c[i] = a[i] + b[i]\n", - "f'{timer.stop():.5f} sec'" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.00063 sec'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timer.start()\n", - "d = a + b\n", - "f'{timer.stop():.5f} sec'" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def normal(x, mu, sigma):\n", - " p = 1 / math.sqrt(2 * math.pi * sigma**2)\n", - " return p * np.exp(-0.5 / sigma**2 * (x - mu)**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2025-12-08T16:33:08.987997\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.10.7, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 再次使用numpy进行可视化\n", - "x = np.arange(-7, 7, 0.01)\n", - "\n", - "# 均值和标准差对\n", - "params = [(0, 1), (0, 2), (3, 1)]\n", - "d2l.plot(x, [normal(x, mu, sigma) for mu, sigma in params], xlabel='x',\n", - " ylabel='p(x)', figsize=(4.5, 2.5),\n", - " legend=[f'mean {mu}, std {sigma}' for mu, sigma in params])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10", - "language": "python", - "name": "py310" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 19966cd5881067e566567e5d0ca98f1c600b680a Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Mon, 8 Dec 2025 17:45:54 +0800 Subject: [PATCH 3/8] =?UTF-8?q?ops=E5=8F=98=E6=9B=B4=E4=B8=BAmint,?= =?UTF-8?q?=E4=BF=9D=E7=95=99ops.assign?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../1_linear-regression-scratch.ipynb | 2249 ++++++++--------- 1 file changed, 1106 insertions(+), 1143 deletions(-) diff --git a/chapter_03_linear-networks/1_linear-regression-scratch.ipynb b/chapter_03_linear-networks/1_linear-regression-scratch.ipynb index 475ac08..eca24a6 100644 --- a/chapter_03_linear-networks/1_linear-regression-scratch.ipynb +++ b/chapter_03_linear-networks/1_linear-regression-scratch.ipynb @@ -2,29 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", "%matplotlib inline\n", "import random\n", "import mindspore\n", "import mindspore.ops as ops\n", + "import mindspore.mint as mint\n", "from mindspore import Tensor\n", - "from d2l import mindspore as d2l\n" + "from d2l import mindspore as d2l" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def synthetic_data(w, b, num_examples): #@save\n", " \"\"\"生成y=Xw+b+噪声\"\"\"\n", - " X = ops.normal((num_examples, len(w)), Tensor(0, mindspore.int32), Tensor(1, mindspore.int32))\n", - " y = ops.matmul(X, w) + b\n", - " y += ops.normal(y.shape, Tensor(0, mindspore.int32), Tensor(0.01, mindspore.float32))\n", + " X = mint.randn((num_examples, len(w)))\n", + " y = mint.matmul(X, w) + b\n", + " y += mint.randn(y.shape) * 0.01\n", " return X, y.reshape((-1, 1))\n", "\n", "true_w = mindspore.Tensor([2, -3.4])\n", @@ -34,15 +38,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "features: [ 0.38652942 -0.73049617] \n", - "label: [7.455265]\n" + "features: [ 0.5784863 -0.12856144] \n", + "label: [5.7876496]\n" ] } ], @@ -52,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -61,16 +65,16 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " 2023-02-01T17:01:39.686551\n", + " 2025-12-08T16:37:25.433505\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.6.3, https://matplotlib.org/\n", + " Matplotlib v3.10.7, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -81,25 +85,25 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -1413,7 +1383,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -1430,33 +1400,33 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[-0.54448646 -1.952117 ]\n", - " [-0.33652925 0.8121438 ]\n", - " [ 0.8297916 1.3013457 ]\n", - " [ 2.2724445 0.42148796]\n", - " [-1.3854086 -0.20155674]\n", - " [-1.1028225 0.31749418]\n", - " [ 0.5637778 0.04631535]\n", - " [ 0.88368213 0.22705936]\n", - " [ 1.5013051 -0.5921145 ]\n", - " [-1.2514561 -0.14155853]] \n", - " [[9.748452 ]\n", - " [0.75043386]\n", - " [1.4231086 ]\n", - " [7.320481 ]\n", - " [2.114023 ]\n", - " [0.9228111 ]\n", - " [5.1760674 ]\n", - " [5.191453 ]\n", - " [9.209715 ]\n", - " [2.1723204 ]]\n" + "[[ 0.13969123 -1.962415 ]\n", + " [ 1.3944087 0.59370184]\n", + " [-0.3421929 -1.7562478 ]\n", + " [-0.14319316 1.5355211 ]\n", + " [ 1.2545266 0.28453276]\n", + " [-0.6407034 0.86420107]\n", + " [-1.0695428 -0.06114257]\n", + " [-1.4124746 -0.66168445]\n", + " [-0.69476527 0.35402057]\n", + " [-0.945358 -0.43053 ]] \n", + " [[11.14591 ]\n", + " [ 4.981207 ]\n", + " [ 9.493603 ]\n", + " [-1.3017813 ]\n", + " [ 5.7478967 ]\n", + " [-0.02166818]\n", + " [ 2.2814643 ]\n", + " [ 3.6188238 ]\n", + " [ 1.5994323 ]\n", + " [ 3.7644536 ]]\n" ] } ], @@ -1477,7 +1447,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -1491,17 +1461,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def linreg(x, w, b):\n", - " return ops.matmul(x, w) + b" + " return mint.matmul(x, w) + b" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1512,7 +1482,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1540,7 +1510,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1569,16 +1539,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.035895\n", - "epoch 2, loss 0.000129\n", - "epoch 3, loss 0.000048\n" + ".epoch 1, loss 0.046992\n", + "epoch 2, loss 0.000203\n", + "epoch 3, loss 0.000049\n" ] } ], @@ -1601,15 +1571,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "w的估计误差: [-0.0002172 0.00028515]\n", - "b的估计误差: [0.00030804]\n" + "w的估计误差: [ 0.00078046 -0.0007298 ]\n", + "b的估计误差: [0.00019407]\n" ] } ], @@ -1617,20 +1587,13 @@ "print(f'w的估计误差: {true_w - net.trainable_params()[0].reshape(true_w.shape)}')\n", "print(f'b的估计误差: {true_b - net.trainable_params()[1]}')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:mindspore2.0]", + "display_name": "Python 3.10", "language": "python", - "name": "conda-env-mindspore2.0-py" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -1642,7 +1605,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.10.14" } }, "nbformat": 4, From 8bc78ee5ef8f7f75f918342546a8e9d9f5bd54d2 Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Mon, 8 Dec 2025 17:47:15 +0800 Subject: [PATCH 4/8] =?UTF-8?q?ops=E5=8F=98=E6=9B=B4=E4=B8=BAmint?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../2_linear-regression-concise.ipynb | 82 +++++++++---------- 1 file changed, 41 insertions(+), 41 deletions(-) diff --git a/chapter_03_linear-networks/2_linear-regression-concise.ipynb b/chapter_03_linear-networks/2_linear-regression-concise.ipynb index 35b28a1..6d6c0b7 100644 --- a/chapter_03_linear-networks/2_linear-regression-concise.ipynb +++ b/chapter_03_linear-networks/2_linear-regression-concise.ipynb @@ -2,14 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", "import numpy as np\n", "import mindspore\n", "import mindspore.dataset as ds\n", - "import mindspore.ops as ops\n", "from mindspore import Tensor\n", "from d2l import mindspore as d2l\n", "\n", @@ -20,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -47,31 +49,37 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Tensor(shape=[10, 2], dtype=Float32, value=\n", - " [[ 1.73414111e-01, -1.24616718e+00],\n", - " [ 1.02213132e+00, -6.50639892e-01],\n", - " [ 1.30825520e+00, -1.22844315e+00],\n", - " ...\n", - " [-1.60054636e+00, 1.20353259e-01],\n", - " [ 7.61325806e-02, 7.83914328e-01],\n", - " [ 1.73925209e+00, -3.65485922e-02]]),\n", + " [[-0.22888152, 0.9795823 ],\n", + " [-0.6006927 , 2.0978146 ],\n", + " [-0.3551168 , 0.17106538],\n", + " [ 0.225816 , 0.89234185],\n", + " [ 0.7022703 , 0.37690485],\n", + " [ 0.09781982, 0.58985835],\n", + " [-1.6895691 , -0.71199685],\n", + " [-0.9362989 , 0.08117686],\n", + " [-1.1256968 , -0.26578853],\n", + " [-0.10324197, -0.25843388]]),\n", " Tensor(shape=[10, 1], dtype=Float32, value=\n", - " [[ 8.76539135e+00],\n", - " [ 8.46442604e+00],\n", - " [ 1.09844027e+01],\n", - " ...\n", - " [ 6.07668221e-01],\n", - " [ 1.68395686e+00],\n", - " [ 7.80520296e+00]])]" + " [[ 0.39374894],\n", + " [-4.1332893 ],\n", + " [ 2.9085197 ],\n", + " [ 1.6274605 ],\n", + " [ 4.334508 ],\n", + " [ 2.3881655 ],\n", + " [ 3.2570887 ],\n", + " [ 2.0458496 ],\n", + " [ 2.8606327 ],\n", + " [ 4.8533044 ]])]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -82,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -123,16 +131,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.000214\n", - "epoch 2, loss 0.000108\n", - "epoch 3, loss 0.000108\n" + "..epoch 1, loss 0.000389\n", + "epoch 2, loss 0.000107\n", + "epoch 3, loss 0.000107\n" ] } ], @@ -154,16 +162,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "w的估计误差： [Tensor(shape=[], dtype=Float32, value= -0.00017786)\n", - " Tensor(shape=[], dtype=Float32, value= -0.000656366)]\n", - "b的估计误差： [-0.00027466]\n" + "w的估计误差： [-3.00407410e-05 -2.89583206e-04]\n", + "b的估计误差： [0.00088167]\n" ] } ], @@ -173,20 +180,13 @@ "b = net[0].bias.data\n", "print('b的估计误差：', true_b - b)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:mindspore2.0]", + "display_name": "Python 3.10", "language": "python", - "name": "conda-env-mindspore2.0-py" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -198,7 +198,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.10.14" } }, "nbformat": 4, From f6e83c8518351bd7f13387b45ff41957b3a745a4 Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Mon, 8 Dec 2025 17:48:24 +0800 Subject: [PATCH 5/8] Update 3_image-classification-dataset.ipynb --- .../3_image-classification-dataset.ipynb | 896 +++++++++--------- 1 file changed, 446 insertions(+), 450 deletions(-) diff --git a/chapter_03_linear-networks/3_image-classification-dataset.ipynb b/chapter_03_linear-networks/3_image-classification-dataset.ipynb index d9f28fc..4cfc5a7 100644 --- a/chapter_03_linear-networks/3_image-classification-dataset.ipynb +++ b/chapter_03_linear-networks/3_image-classification-dataset.ipynb @@ -2,10 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", "%matplotlib inline\n", "import mindspore\n", "import mindspore.dataset as ds\n", @@ -17,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -51,7 +54,7 @@ "(60000, 10000)" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -62,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -71,7 +74,7 @@ "[[28, 28, 1], []]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -82,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -127,16 +130,16 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " 2023-02-01T17:03:28.958753\n", + " 2025-12-08T16:44:59.379083\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.6.3, https://matplotlib.org/\n", + " Matplotlib v3.10.7, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -147,49 +150,49 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", + 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+ " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -1408,7 +1411,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -1420,16 +1423,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.12 sec'" + "'3.28 sec'" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1443,7 +1446,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1473,7 +1476,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -1492,20 +1495,13 @@ " print(X.shape, X.dtype, y.shape, y.dtype)\n", " break" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:mindspore2.0]", + "display_name": "Python 3.10", "language": "python", - "name": "conda-env-mindspore2.0-py" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -1517,7 +1513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.10.14" } }, "nbformat": 4, From 286867f617eacd3380246d32447dacc9e43da660 Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Mon, 8 Dec 2025 17:49:26 +0800 Subject: [PATCH 6/8] =?UTF-8?q?ops=E5=8F=98=E6=9B=B4=E4=B8=BAmint,?= =?UTF-8?q?=E4=BF=9D=E7=95=99ops.arange?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../4_softmax-regression-scratch.ipynb | 768 +++++++++++++++++- 1 file changed, 741 insertions(+), 27 deletions(-) diff --git a/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb b/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb index 8570ad8..e572b24 100644 --- a/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb +++ b/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb @@ -2,11 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", "import mindspore\n", + "import mindspore.mint as mint\n", "import mindspore.ops as ops\n", "from mindspore import Tensor\n", "from IPython import display\n", @@ -48,7 +52,7 @@ ], "source": [ "X = mindspore.Tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], mindspore.float64)\n", - "X.sum(0, keepdims=True), X.sum(1, keepdims=True)" + "mint.sum(X, 0, keepdim=True), mint.sum(X, 1, keepdim=True)" ] }, { @@ -58,8 +62,8 @@ "outputs": [], "source": [ "def softmax(X):\n", - " X_exp = ops.exp(X)\n", - " partition = X_exp.sum(1, keepdims=True)\n", + " X_exp = mint.exp(X)\n", + " partition = mint.sum(X_exp, 1, keepdim=True)\n", " return X_exp / partition" ] }, @@ -72,9 +76,9 @@ "data": { "text/plain": [ "(Tensor(shape=[2, 5], dtype=Float32, value=\n", - " [[ 1.23927899e-01, 4.60792333e-01, 4.21655998e-02, 2.91563839e-01, 8.15503001e-02],\n", - " [ 2.28272960e-01, 2.19555587e-01, 1.69258676e-02, 3.50688905e-01, 1.84556618e-01]]),\n", - " Tensor(shape=[2], dtype=Float32, value= [ 1.00000000e+00, 9.99999881e-01]))" + " [[ 1.18672863e-01, 2.49089330e-01, 4.82115030e-01, 4.17001210e-02, 1.08422577e-01],\n", + " [ 8.18764046e-02, 2.21757680e-01, 3.63770872e-02, 3.11983824e-01, 3.48005027e-01]]),\n", + " Tensor(shape=[2], dtype=Float32, value= [ 9.99999940e-01, 1.00000000e+00]))" ] }, "execution_count": 5, @@ -83,7 +87,7 @@ } ], "source": [ - "X = mindspore.Tensor(ops.normal((2, 5),Tensor(0, mindspore.float32), Tensor(1, mindspore.float32)))\n", + "X = mindspore.Tensor(mint.randn((2, 5)))\n", "X_prob = softmax(X)\n", "X_prob, X_prob.sum(1)" ] @@ -105,7 +109,7 @@ " self.b = Parameter(initializer(Zero(), num_outputs, mindspore.float32))\n", "\n", " def construct(self, X):\n", - " return softmax(ops.matmul(X.reshape((-1, self.W.shape[0])), self.W) + self.b)" + " return softmax(mint.matmul(X.reshape((-1, self.W.shape[0])), self.W) + self.b)" ] }, { @@ -148,7 +152,7 @@ ], "source": [ "def cross_entropy(y_hat, y):\n", - " return -(ops.log(y_hat[ops.arange(len(y_hat)), y]))\n", + " return -(mint.log(y_hat[ops.arange(len(y_hat)), y]))\n", "\n", "cross_entropy(y_hat, y)" ] @@ -230,7 +234,7 @@ { "data": { "text/plain": [ - "0.0616" + "0.0927" ] }, "execution_count": 13, @@ -288,7 +292,7 @@ " self.config_axes = lambda: d2l.set_axes(\n", " self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n", " self.X, self.Y, self.fmts = None, None, fmts\n", - "\n", + " \n", " def add(self, x, y):\n", " if not hasattr(y, \"__len__\"):\n", " y = [y]\n", @@ -334,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -345,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1141,7 +1145,7 @@ } ], "source": [ - "num_epochs = 10\n", + "num_epochs = 20\n", "train_iter = mnist_train.create_tuple_iterator()\n", "test_iter = mnist_test.create_tuple_iterator()\n", "train_ch3(net, mnist_train, test_iter, cross_entropy, num_epochs, optim)" @@ -1149,9 +1153,726 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-12-08T17:31:40.578728\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.10.7, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def predict_ch3(net, test_iter, n=6): \n", " \"\"\"预测标签（定义见第3章）。\"\"\"\n", @@ -1165,20 +1886,13 @@ "\n", "predict_ch3(net, test_iter)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "MindSpore", + "display_name": "Python 3.10", "language": "python", - "name": "mindspore" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -1190,7 +1904,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.10.14" } }, "nbformat": 4, From aa48ce5aa646de3a27b312b277d298d8d957afb3 Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Mon, 8 Dec 2025 17:50:25 +0800 Subject: [PATCH 7/8] =?UTF-8?q?ops=E5=8F=98=E6=9B=B4=E4=B8=BAmint?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../5_softmax-regression-concise.ipynb | 191 +++++++++--------- 1 file changed, 97 insertions(+), 94 deletions(-) diff --git a/chapter_03_linear-networks/5_softmax-regression-concise.ipynb b/chapter_03_linear-networks/5_softmax-regression-concise.ipynb index fc2f6fd..e096971 100644 --- a/chapter_03_linear-networks/5_softmax-regression-concise.ipynb +++ b/chapter_03_linear-networks/5_softmax-regression-concise.ipynb @@ -2,12 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", "import mindspore\n", - "from mindspore import nn, ops\n", + "from mindspore import nn\n", "from d2l import mindspore as d2l\n", "\n", "batch_size = 256\n", @@ -60,11 +63,11 @@ " \n", " \n", " \n", - " 2023-02-01T16:27:57.573903\n", + " 2025-12-08T17:36:40.446006\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.6.3, https://matplotlib.org/\n", + " Matplotlib v3.10.7, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -96,16 +99,16 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -145,11 +148,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -184,11 +187,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -234,11 +237,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -293,11 +296,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -341,7 +344,7 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -463,10 +466,10 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -475,16 +478,16 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -500,8 +503,8 @@ "\" transform=\"scale(0.015625)\"/>\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -509,19 +512,19 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -529,61 +532,61 @@ " \n", " \n", + "\" clip-path=\"url(#p3902a5af0b)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -794,14 +797,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -814,20 +817,20 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -849,9 +852,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:mindspore2.0]", + "display_name": "Python 3.10", "language": "python", - "name": "conda-env-mindspore2.0-py" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -863,7 +866,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.10.14" } }, "nbformat": 4, From a12993db7f8e990e6d2b70b0b496c9067b55a2c7 Mon Sep 17 00:00:00 2001 From: Y-yyyyq <648203301@qq.com> Date: Tue, 6 Jan 2026 18:28:10 +0800 Subject: [PATCH 8/8] =?UTF-8?q?=E5=B0=86ops.arange=E6=9B=BF=E6=8D=A2?= =?UTF-8?q?=E4=B8=BAmint.arange?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../4_softmax-regression-scratch.ipynb | 53 +++++++++---------- 1 file changed, 26 insertions(+), 27 deletions(-) diff --git a/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb b/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb index e572b24..51b6a89 100644 --- a/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb +++ b/chapter_03_linear-networks/4_softmax-regression-scratch.ipynb @@ -11,7 +11,6 @@ "\n", "import mindspore\n", "import mindspore.mint as mint\n", - "import mindspore.ops as ops\n", "from mindspore import Tensor\n", "from IPython import display\n", "from d2l import mindspore as d2l\n", @@ -76,8 +75,8 @@ "data": { "text/plain": [ "(Tensor(shape=[2, 5], dtype=Float32, value=\n", - " [[ 1.18672863e-01, 2.49089330e-01, 4.82115030e-01, 4.17001210e-02, 1.08422577e-01],\n", - " [ 8.18764046e-02, 2.21757680e-01, 3.63770872e-02, 3.11983824e-01, 3.48005027e-01]]),\n", + " [[ 3.75246108e-01, 2.04125911e-01, 2.79982001e-01, 4.02570777e-02, 1.00388817e-01],\n", + " [ 2.57526249e-01, 1.64709106e-01, 1.66897565e-01, 3.88683647e-01, 2.21834648e-02]]),\n", " Tensor(shape=[2], dtype=Float32, value= [ 9.99999940e-01, 1.00000000e+00]))" ] }, @@ -152,7 +151,7 @@ ], "source": [ "def cross_entropy(y_hat, y):\n", - " return -(mint.log(y_hat[ops.arange(len(y_hat)), y]))\n", + " return -(mint.log(y_hat[mint.arange(len(y_hat)), y]))\n", "\n", "cross_entropy(y_hat, y)" ] @@ -234,7 +233,7 @@ { "data": { "text/plain": [ - "0.0927" + "0.0683" ] }, "execution_count": 13, @@ -349,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1153,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1167,11 +1166,11 @@ " \n", " \n", " \n", - " 2025-12-08T17:31:40.578728\n", + " 2026-01-06T18:14:55.229456\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.10.7, https://matplotlib.org/\n", + " Matplotlib v3.10.8, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -1198,9 +1197,9 @@ "z\n", "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", + " \n", " \n", + "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\" id=\"image47fb78937f\" transform=\"scale(1 -1) translate(0 -72)\" x=\"7.2\" y=\"-35.498357\" width=\"72\" height=\"72\"/>\n", " \n", " \n", " \n", " \n", - 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