From 22b33d63de76dd31a3ee51dfd303e2ba55f48e5b Mon Sep 17 00:00:00 2001 From: kejingfan Date: Wed, 11 Oct 2023 22:46:49 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E6=89=8B=E5=8A=A8=E5=AE=9E?= =?UTF-8?q?=E7=8E=B0=E7=BA=BF=E6=80=A7=E5=B1=82=E7=9A=84=E6=B5=8B=E8=AF=95?= =?UTF-8?q?=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Pytorch基本操作实验报告-checkpoint.ipynb | 154 ++++++++-------- Lab1/Pytorch基本操作实验报告.ipynb | 164 ++++++++++-------- .../code/.ipynb_checkpoints/1.1-checkpoint.py | 39 +++++ 3 files changed, 206 insertions(+), 151 deletions(-) create mode 100644 Lab1/code/.ipynb_checkpoints/1.1-checkpoint.py diff --git a/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb b/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb index a21eaff..ac0b0e3 100644 --- a/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb +++ b/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb @@ -147,7 +147,7 @@ "metadata": {}, "source": [ "## 题目2\n", - "1. **利用Tensor创建两个大小分别$3\\times 2$和$4\\times 2$的随机数矩阵P和Q，要求服从均值为$0$，标准差$0.01$为的正态分布；**\n", + "1. **利用Tensor创建两个大小分别$3\\times 2$和$4\\times 2$的随机数矩阵$P$和$Q$，要求服从均值为$0$，标准差$0.01$为的正态分布；**\n", "2. **对第二步得到的矩阵$Q$进行形状变换得到$Q$的转置$Q^T$；**\n", "3. **对上述得到的矩阵$P$和矩阵$Q^T$求矩阵相乘。**" ] @@ -163,21 +163,21 @@ "output_type": "stream", "text": [ "矩阵 P:\n", - "tensor([[ 0.0043, 0.0009],\n", - " [-0.0008, 0.0021],\n", - " [-0.0012, -0.0091]])\n", + "tensor([[ 0.0069, 0.0082],\n", + " [-0.0052, -0.0124],\n", + " [ 0.0055, -0.0014]])\n", "矩阵 Q:\n", - "tensor([[ 0.0016, 0.0073],\n", - " [-0.0092, 0.0024],\n", - " [ 0.0026, 0.0171],\n", - " [ 0.0101, -0.0038]])\n", + "tensor([[ 0.0050, 0.0075],\n", + " [ 0.0161, 0.0070],\n", + " [-0.0009, -0.0014],\n", + " [-0.0003, 0.0024]])\n", "矩阵 QT:\n", - "tensor([[ 0.0016, -0.0092, 0.0026, 0.0101],\n", - " [ 0.0073, 0.0024, 0.0171, -0.0038]])\n", + "tensor([[ 0.0050, 0.0161, -0.0009, -0.0003],\n", + " [ 0.0075, 0.0070, -0.0014, 0.0024]])\n", "矩阵相乘的结果:\n", - "tensor([[ 1.3472e-05, -3.7060e-05, 2.7148e-05, 3.9682e-05],\n", - " [ 1.3877e-05, 1.2322e-05, 3.3492e-05, -1.6069e-05],\n", - " [-6.8047e-05, -1.1357e-05, -1.5886e-04, 2.3463e-05]])\n" + "tensor([[ 9.6016e-05, 1.6860e-04, -1.7451e-05, 1.8011e-05],\n", + " [-1.1894e-04, -1.7065e-04, 2.1900e-05, -2.8712e-05],\n", + " [ 1.6918e-05, 7.8455e-05, -2.7165e-06, -4.9904e-06]])\n" ] } ], @@ -415,17 +415,17 @@ "tensor([[1.],\n", " [2.]], requires_grad=True)\n", "权重：\n", - "tensor([[1.],\n", - " [2.],\n", - " [3.]])\n", + "tensor([[ 0.4240],\n", + " [-0.2577],\n", + " [ 0.4972]])\n", "偏置：\n", - "tensor([[1.]])\n", + "tensor([0.6298, 0.6243, 0.8217])\n", "My_Linear输出：\n", - "tensor([[2., 3., 4.],\n", - " [3., 5., 7.]], grad_fn=)\n", + "tensor([[1.0539, 0.3666, 1.3189],\n", + " [1.4779, 0.1089, 1.8161]], grad_fn=)\n", "nn.Linear输出：\n", - "tensor([[2., 3., 4.],\n", - " [3., 5., 7.]], grad_fn=)\n" + "tensor([[1.0539, 0.3666, 1.3189],\n", + " [1.4779, 0.1089, 1.8161]], grad_fn=)\n" ] } ], @@ -455,12 +455,8 @@ "# 测试\n", "my_linear = My_Linear(1, 3)\n", "nn_linear = nn.Linear(1, 3)\n", - "weight = torch.nn.Parameter(torch.tensor([[1.],\n", - " [2.],\n", - " [3.]]), requires_grad=True)\n", - "bias = torch.nn.Parameter(torch.tensor([[1.]]), requires_grad=True)\n", - "nn_linear.weight, my_linear.weight = weight, weight\n", - "nn_linear.bias, my_linear.bias = bias, bias\n", + "my_linear.weight = nn_linear.weight.clone().requires_grad_()\n", + "my_linear.bias = nn_linear.bias.clone().requires_grad_()\n", "x = torch.tensor([[1.], [2.]], requires_grad=True)\n", "print(f\"输入：\\n{x}\")\n", "print(f\"权重：\\n{my_linear.weight.data}\")\n", @@ -613,18 +609,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 696.7355516552925, Acc: 0.9442648464635192\n", - "Epoch 2/10, Loss: 680.1393249630928, Acc: 0.9911232759674347\n", - "Epoch 3/10, Loss: 677.770676612854, Acc: 0.9956893804390976\n", - "Epoch 4/10, Loss: 677.294788479805, Acc: 0.9982881159072501\n", - "Epoch 5/10, Loss: 677.1979722976685, Acc: 0.9991794744511796\n", - "Epoch 6/10, Loss: 677.1792464852333, Acc: 0.999493084950588\n", - "Epoch 7/10, Loss: 677.1751466989517, Acc: 0.9998704799602793\n", - "Epoch 8/10, Loss: 677.1746656894684, Acc: 0.9999325569195194\n", - "Epoch 9/10, Loss: 677.1742008328438, Acc: 0.9999852565480795\n", - "Epoch 10/10, Loss: 677.1745100617409, Acc: 0.9999026775350377\n", - "Model weights: -0.0018169691320508718, bias: 0.018722545355558395\n", - "Prediction for test data: 0.5042262673377991\n" + "Epoch 1/10, Loss: 688.6783249974251, Acc: 0.9766838179955138\n", + "Epoch 2/10, Loss: 679.506599009037, Acc: 0.992039453911494\n", + "Epoch 3/10, Loss: 677.644762635231, Acc: 0.9961844975781526\n", + "Epoch 4/10, Loss: 677.2690716981888, Acc: 0.998395304269398\n", + "Epoch 5/10, Loss: 677.1928514242172, Acc: 0.9993592246184307\n", + "Epoch 6/10, Loss: 677.1781670451164, Acc: 0.9996570376204033\n", + "Epoch 7/10, Loss: 677.1744618415833, Acc: 0.9998465339227576\n", + "Epoch 8/10, Loss: 677.1738814711571, Acc: 0.9998001679325041\n", + "Epoch 9/10, Loss: 677.1742851734161, Acc: 0.9998804348705138\n", + "Epoch 10/10, Loss: 677.1740592718124, Acc: 0.9999446971149187\n", + "Model weights: -0.0037125118542462587, bias: 0.017451055347919464\n", + "Prediction for test data: 0.5034345984458923\n" ] } ], @@ -726,18 +722,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 638.0160498773107, Acc: 0.9937490349019101\n", - "Epoch 2/10, Loss: 583.3716011882699, Acc: 0.9804301403247839\n", - "Epoch 3/10, Loss: 571.1401001196623, Acc: 0.9896515985806724\n", - "Epoch 4/10, Loss: 567.6159870155185, Acc: 0.9943010522600507\n", - "Epoch 5/10, Loss: 566.5014995958526, Acc: 0.9966799384882902\n", - "Epoch 6/10, Loss: 566.1252285088149, Acc: 0.998098013624422\n", - "Epoch 7/10, Loss: 565.9985610526666, Acc: 0.9988608489236236\n", - "Epoch 8/10, Loss: 565.9526960214308, Acc: 0.9993323768578708\n", - "Epoch 9/10, Loss: 565.9374750639024, Acc: 0.9995989407216784\n", - "Epoch 10/10, Loss: 565.9291789969539, Acc: 0.9997716274081613\n", - "Model weights: -3.685411369051284, bias: 1.8638604353928832\n", - "Prediction for test data: 0.1392477539282304\n" + "Epoch 1/10, Loss: 660.2008021697803, Acc: 0.9355364605682331\n", + "Epoch 2/10, Loss: 589.2025169091534, Acc: 0.9769773185253259\n", + "Epoch 3/10, Loss: 572.7106042209589, Acc: 0.9881629137259633\n", + "Epoch 4/10, Loss: 568.0903503441508, Acc: 0.9935173218188225\n", + "Epoch 5/10, Loss: 566.6528526848851, Acc: 0.9962586560919562\n", + "Epoch 6/10, Loss: 566.1778871576632, Acc: 0.9978209774304773\n", + "Epoch 7/10, Loss: 566.0143385848835, Acc: 0.9987369762885633\n", + "Epoch 8/10, Loss: 565.9605239629793, Acc: 0.9992563563084009\n", + "Epoch 9/10, Loss: 565.9402079010808, Acc: 0.9995321069396558\n", + "Epoch 10/10, Loss: 565.9281422200424, Acc: 0.9997496312356398\n", + "Model weights: -3.6833968323036084, bias: 1.8628376037952126\n", + "Prediction for test data: 0.13936666014014443\n" ] } ], @@ -899,15 +895,15 @@ "output_type": "stream", "text": [ "输入：\n", - "tensor([[ 1.0624, 1.7008, -1.2849, 0.4049, -0.3993],\n", - " [ 0.0757, 1.0636, 0.3586, -0.0252, -1.1431],\n", - " [ 0.4754, -1.9538, 0.6616, -1.0363, 0.6049]], requires_grad=True)\n", + "tensor([[ 0.7600, 0.4269, 0.7948, -0.6086, 1.2527],\n", + " [-0.4749, 0.5720, -0.0164, -0.2126, -0.0410],\n", + " [ 1.3269, 1.8524, -0.9815, 0.0156, 1.6971]], requires_grad=True)\n", "标签：\n", - "tensor([[0., 0., 0., 0., 1.],\n", - " [0., 1., 0., 0., 0.],\n", - " [0., 0., 0., 0., 1.]])\n", - "My_CrossEntropyLoss损失值: 1.5949448347091675\n", - "nn.CrossEntropyLoss损失值: 1.594944953918457\n" + "tensor([[0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 0.]])\n", + "My_CrossEntropyLoss损失值: 1.7417106628417969\n", + "nn.CrossEntropyLoss损失值: 1.7417105436325073\n" ] } ], @@ -1081,16 +1077,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 84.3017807006836, Acc: 0.5087000131607056\n", - "Epoch 2/10, Loss: 37.02857208251953, Acc: 0.5946999788284302\n", - "Epoch 3/10, Loss: 30.553579330444336, Acc: 0.6287999749183655\n", - "Epoch 4/10, Loss: 27.279203414916992, Acc: 0.6550999879837036\n", - "Epoch 5/10, Loss: 25.244386672973633, Acc: 0.6694999933242798\n", - "Epoch 6/10, Loss: 23.713878631591797, Acc: 0.6798999905586243\n", - "Epoch 7/10, Loss: 22.5694580078125, Acc: 0.6924999952316284\n", - "Epoch 8/10, Loss: 21.611900329589844, Acc: 0.6965000033378601\n", - "Epoch 9/10, Loss: 20.85039520263672, Acc: 0.7014999985694885\n", - "Epoch 10/10, Loss: 20.116191864013672, Acc: 0.7102000117301941\n" + "Epoch 1/10, Loss: 87.64246368408203, Acc: 0.45329999923706055\n", + "Epoch 2/10, Loss: 42.025726318359375, Acc: 0.5523999929428101\n", + "Epoch 3/10, Loss: 34.06425094604492, Acc: 0.5947999954223633\n", + "Epoch 4/10, Loss: 30.135021209716797, Acc: 0.620199978351593\n", + "Epoch 5/10, Loss: 27.43822479248047, Acc: 0.6401000022888184\n", + "Epoch 6/10, Loss: 25.72039031982422, Acc: 0.6525999903678894\n", + "Epoch 7/10, Loss: 24.28335952758789, Acc: 0.6638999581336975\n", + "Epoch 8/10, Loss: 23.18214988708496, Acc: 0.671999990940094\n", + "Epoch 9/10, Loss: 22.18520164489746, Acc: 0.680899977684021\n", + "Epoch 10/10, Loss: 21.393451690673828, Acc: 0.6875999569892883\n" ] } ], @@ -1212,16 +1208,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 19.19444465637207, Acc: 0.7229999899864197\n", - "Epoch 2/10, Loss: 12.180685043334961, Acc: 0.7491999864578247\n", - "Epoch 3/10, Loss: 10.77286148071289, Acc: 0.7608999609947205\n", - "Epoch 4/10, Loss: 10.058968544006348, Acc: 0.7716000080108643\n", - "Epoch 5/10, Loss: 9.58817195892334, Acc: 0.7815999984741211\n", - "Epoch 6/10, Loss: 9.245816230773926, Acc: 0.7861999869346619\n", - "Epoch 7/10, Loss: 8.98766040802002, Acc: 0.7924000024795532\n", - "Epoch 8/10, Loss: 8.778538703918457, Acc: 0.7949999570846558\n", - "Epoch 9/10, Loss: 8.59365177154541, Acc: 0.795699954032898\n", - "Epoch 10/10, Loss: 8.442872047424316, Acc: 0.7998999953269958\n" + "Epoch 1/10, Loss: 19.15451431274414, Acc: 0.7202000021934509\n", + "Epoch 2/10, Loss: 12.260371208190918, Acc: 0.7486000061035156\n", + "Epoch 3/10, Loss: 10.835549354553223, Acc: 0.7615999579429626\n", + "Epoch 4/10, Loss: 10.09542179107666, Acc: 0.7701999545097351\n", + "Epoch 5/10, Loss: 9.626176834106445, Acc: 0.777899980545044\n", + "Epoch 6/10, Loss: 9.264442443847656, Acc: 0.7854999899864197\n", + "Epoch 7/10, Loss: 9.017412185668945, Acc: 0.7879999876022339\n", + "Epoch 8/10, Loss: 8.786051750183105, Acc: 0.7915999889373779\n", + "Epoch 9/10, Loss: 8.613431930541992, Acc: 0.79749995470047\n", + "Epoch 10/10, Loss: 8.462657928466797, Acc: 0.7996999621391296\n" ] } ], diff --git a/Lab1/Pytorch基本操作实验报告.ipynb b/Lab1/Pytorch基本操作实验报告.ipynb index b3143dc..ac0b0e3 100644 --- a/Lab1/Pytorch基本操作实验报告.ipynb +++ b/Lab1/Pytorch基本操作实验报告.ipynb @@ -163,21 +163,21 @@ "output_type": "stream", "text": [ "矩阵 P:\n", - "tensor([[-6.8759e-03, -1.5809e-02],\n", - " [ 8.0131e-03, -5.1682e-05],\n", - " [ 4.1277e-03, 4.1408e-03]])\n", + "tensor([[ 0.0069, 0.0082],\n", + " [-0.0052, -0.0124],\n", + " [ 0.0055, -0.0014]])\n", "矩阵 Q:\n", - "tensor([[-0.0037, -0.0188],\n", - " [-0.0083, 0.0147],\n", - " [-0.0029, -0.0061],\n", - " [-0.0123, 0.0162]])\n", + "tensor([[ 0.0050, 0.0075],\n", + " [ 0.0161, 0.0070],\n", + " [-0.0009, -0.0014],\n", + " [-0.0003, 0.0024]])\n", "矩阵 QT:\n", - "tensor([[-0.0037, -0.0083, -0.0029, -0.0123],\n", - " [-0.0188, 0.0147, -0.0061, 0.0162]])\n", + "tensor([[ 0.0050, 0.0161, -0.0009, -0.0003],\n", + " [ 0.0075, 0.0070, -0.0014, 0.0024]])\n", "矩阵相乘的结果:\n", - "tensor([[ 3.2228e-04, -1.7527e-04, 1.1701e-04, -1.7221e-04],\n", - " [-2.8839e-05, -6.7375e-05, -2.2812e-05, -9.9158e-05],\n", - " [-9.3070e-05, 2.6567e-05, -3.7366e-05, 1.6558e-05]])\n" + "tensor([[ 9.6016e-05, 1.6860e-04, -1.7451e-05, 1.8011e-05],\n", + " [-1.1894e-04, -1.7065e-04, 2.1900e-05, -2.8712e-05],\n", + " [ 1.6918e-05, 7.8455e-05, -2.7165e-06, -4.9904e-06]])\n" ] } ], @@ -415,17 +415,17 @@ "tensor([[1.],\n", " [2.]], requires_grad=True)\n", "权重：\n", - "tensor([[-0.3470],\n", - " [-0.4461],\n", - " [-0.8590]])\n", + "tensor([[ 0.4240],\n", + " [-0.2577],\n", + " [ 0.4972]])\n", "偏置：\n", - "tensor([ 0.9821, 0.8701, -0.4202])\n", + "tensor([0.6298, 0.6243, 0.8217])\n", "My_Linear输出：\n", - "tensor([[ 0.6351, 0.4240, -1.2792],\n", - " [ 0.2882, -0.0221, -2.1383]], grad_fn=)\n", + "tensor([[1.0539, 0.3666, 1.3189],\n", + " [1.4779, 0.1089, 1.8161]], grad_fn=)\n", "nn.Linear输出：\n", - "tensor([[ 0.6351, 0.4240, -1.2792],\n", - " [ 0.2882, -0.0221, -2.1383]], grad_fn=)\n" + "tensor([[1.0539, 0.3666, 1.3189],\n", + " [1.4779, 0.1089, 1.8161]], grad_fn=)\n" ] } ], @@ -455,7 +455,8 @@ "# 测试\n", "my_linear = My_Linear(1, 3)\n", "nn_linear = nn.Linear(1, 3)\n", - "my_linear.weight, my_linear.bias = nn_linear.weight, nn_linear.bias\n", + "my_linear.weight = nn_linear.weight.clone().requires_grad_()\n", + "my_linear.bias = nn_linear.bias.clone().requires_grad_()\n", "x = torch.tensor([[1.], [2.]], requires_grad=True)\n", "print(f\"输入：\\n{x}\")\n", "print(f\"权重：\\n{my_linear.weight.data}\")\n", @@ -603,7 +604,26 @@ "execution_count": 10, "id": "5612661e-2809-4d46-96c2-33ee9f44116d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10, Loss: 688.6783249974251, Acc: 0.9766838179955138\n", + "Epoch 2/10, Loss: 679.506599009037, Acc: 0.992039453911494\n", + "Epoch 3/10, Loss: 677.644762635231, Acc: 0.9961844975781526\n", + "Epoch 4/10, Loss: 677.2690716981888, Acc: 0.998395304269398\n", + "Epoch 5/10, Loss: 677.1928514242172, Acc: 0.9993592246184307\n", + "Epoch 6/10, Loss: 677.1781670451164, Acc: 0.9996570376204033\n", + "Epoch 7/10, Loss: 677.1744618415833, Acc: 0.9998465339227576\n", + "Epoch 8/10, Loss: 677.1738814711571, Acc: 0.9998001679325041\n", + "Epoch 9/10, Loss: 677.1742851734161, Acc: 0.9998804348705138\n", + "Epoch 10/10, Loss: 677.1740592718124, Acc: 0.9999446971149187\n", + "Model weights: -0.0037125118542462587, bias: 0.017451055347919464\n", + "Prediction for test data: 0.5034345984458923\n" + ] + } + ], "source": [ "learning_rate = 5e-2\n", "num_epochs = 10\n", @@ -668,7 +688,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "fa121afd-a1af-4193-9b54-68041e0ed068", "metadata": {}, "outputs": [], @@ -694,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "93b0fdb6-be8b-4663-b59e-05ed19a9ea09", "metadata": {}, "outputs": [ @@ -702,18 +722,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 638.0160498773107, Acc: 0.9937490349019101\n", - "Epoch 2/10, Loss: 583.3716011882699, Acc: 0.9804301403247839\n", - "Epoch 3/10, Loss: 571.1401001196623, Acc: 0.9896515985806724\n", - "Epoch 4/10, Loss: 567.6159870155185, Acc: 0.9943010522600507\n", - "Epoch 5/10, Loss: 566.5014995958526, Acc: 0.9966799384882902\n", - "Epoch 6/10, Loss: 566.1252285088149, Acc: 0.998098013624422\n", - "Epoch 7/10, Loss: 565.9985610526666, Acc: 0.9988608489236236\n", - "Epoch 8/10, Loss: 565.9526960214308, Acc: 0.9993323768578708\n", - "Epoch 9/10, Loss: 565.9374750639024, Acc: 0.9995989407216784\n", - "Epoch 10/10, Loss: 565.9291789969539, Acc: 0.9997716274081613\n", - "Model weights: -3.685411369051284, bias: 1.8638604353928832\n", - "Prediction for test data: 0.1392477539282304\n" + "Epoch 1/10, Loss: 660.2008021697803, Acc: 0.9355364605682331\n", + "Epoch 2/10, Loss: 589.2025169091534, Acc: 0.9769773185253259\n", + "Epoch 3/10, Loss: 572.7106042209589, Acc: 0.9881629137259633\n", + "Epoch 4/10, Loss: 568.0903503441508, Acc: 0.9935173218188225\n", + "Epoch 5/10, Loss: 566.6528526848851, Acc: 0.9962586560919562\n", + "Epoch 6/10, Loss: 566.1778871576632, Acc: 0.9978209774304773\n", + "Epoch 7/10, Loss: 566.0143385848835, Acc: 0.9987369762885633\n", + "Epoch 8/10, Loss: 565.9605239629793, Acc: 0.9992563563084009\n", + "Epoch 9/10, Loss: 565.9402079010808, Acc: 0.9995321069396558\n", + "Epoch 10/10, Loss: 565.9281422200424, Acc: 0.9997496312356398\n", + "Model weights: -3.6833968323036084, bias: 1.8628376037952126\n", + "Prediction for test data: 0.13936666014014443\n" ] } ], @@ -802,7 +822,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "e605f1b0-1d32-410f-bddf-402a85ccc9ff", "metadata": {}, "outputs": [ @@ -866,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "759a3bb2-b5f4-4ea5-a2d7-15f0c4cdd14b", "metadata": {}, "outputs": [ @@ -875,15 +895,15 @@ "output_type": "stream", "text": [ "输入：\n", - "tensor([[ 1.0624, 1.7008, -1.2849, 0.4049, -0.3993],\n", - " [ 0.0757, 1.0636, 0.3586, -0.0252, -1.1431],\n", - " [ 0.4754, -1.9538, 0.6616, -1.0363, 0.6049]], requires_grad=True)\n", + "tensor([[ 0.7600, 0.4269, 0.7948, -0.6086, 1.2527],\n", + " [-0.4749, 0.5720, -0.0164, -0.2126, -0.0410],\n", + " [ 1.3269, 1.8524, -0.9815, 0.0156, 1.6971]], requires_grad=True)\n", "标签：\n", - "tensor([[0., 0., 0., 0., 1.],\n", - " [0., 1., 0., 0., 0.],\n", - " [0., 0., 0., 0., 1.]])\n", - "My_CrossEntropyLoss损失值: 1.5949448347091675\n", - "nn.CrossEntropyLoss损失值: 1.594944953918457\n" + "tensor([[0., 1., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 0.]])\n", + "My_CrossEntropyLoss损失值: 1.7417106628417969\n", + "nn.CrossEntropyLoss损失值: 1.7417105436325073\n" ] } ], @@ -928,7 +948,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "74322629-8325-4823-b80f-f28182d577c1", "metadata": {}, "outputs": [ @@ -989,7 +1009,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "bb31a75e-464c-4b94-b927-b219a765e35d", "metadata": {}, "outputs": [], @@ -1049,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "d816dae1-5fbe-4c29-9597-19d66b5eb6b4", "metadata": {}, "outputs": [ @@ -1057,16 +1077,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 84.3017807006836, Acc: 0.5087000131607056\n", - "Epoch 2/10, Loss: 37.02857208251953, Acc: 0.5946999788284302\n", - "Epoch 3/10, Loss: 30.553579330444336, Acc: 0.6287999749183655\n", - "Epoch 4/10, Loss: 27.279203414916992, Acc: 0.6550999879837036\n", - "Epoch 5/10, Loss: 25.244386672973633, Acc: 0.6694999933242798\n", - "Epoch 6/10, Loss: 23.713878631591797, Acc: 0.6798999905586243\n", - "Epoch 7/10, Loss: 22.5694580078125, Acc: 0.6924999952316284\n", - "Epoch 8/10, Loss: 21.611900329589844, Acc: 0.6965000033378601\n", - "Epoch 9/10, Loss: 20.85039520263672, Acc: 0.7014999985694885\n", - "Epoch 10/10, Loss: 20.116191864013672, Acc: 0.7102000117301941\n" + "Epoch 1/10, Loss: 87.64246368408203, Acc: 0.45329999923706055\n", + "Epoch 2/10, Loss: 42.025726318359375, Acc: 0.5523999929428101\n", + "Epoch 3/10, Loss: 34.06425094604492, Acc: 0.5947999954223633\n", + "Epoch 4/10, Loss: 30.135021209716797, Acc: 0.620199978351593\n", + "Epoch 5/10, Loss: 27.43822479248047, Acc: 0.6401000022888184\n", + "Epoch 6/10, Loss: 25.72039031982422, Acc: 0.6525999903678894\n", + "Epoch 7/10, Loss: 24.28335952758789, Acc: 0.6638999581336975\n", + "Epoch 8/10, Loss: 23.18214988708496, Acc: 0.671999990940094\n", + "Epoch 9/10, Loss: 22.18520164489746, Acc: 0.680899977684021\n", + "Epoch 10/10, Loss: 21.393451690673828, Acc: 0.6875999569892883\n" ] } ], @@ -1153,7 +1173,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "0163b9f7-1019-429c-8c29-06436d0a4c98", "metadata": {}, "outputs": [], @@ -1180,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "6d241c05-b153-4f56-a845-0f2362f6459b", "metadata": {}, "outputs": [ @@ -1188,16 +1208,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 19.19444465637207, Acc: 0.7229999899864197\n", - "Epoch 2/10, Loss: 12.180685043334961, Acc: 0.7491999864578247\n", - "Epoch 3/10, Loss: 10.77286148071289, Acc: 0.7608999609947205\n", - "Epoch 4/10, Loss: 10.058968544006348, Acc: 0.7716000080108643\n", - "Epoch 5/10, Loss: 9.58817195892334, Acc: 0.7815999984741211\n", - "Epoch 6/10, Loss: 9.245816230773926, Acc: 0.7861999869346619\n", - "Epoch 7/10, Loss: 8.98766040802002, Acc: 0.7924000024795532\n", - "Epoch 8/10, Loss: 8.778538703918457, Acc: 0.7949999570846558\n", - "Epoch 9/10, Loss: 8.59365177154541, Acc: 0.795699954032898\n", - "Epoch 10/10, Loss: 8.442872047424316, Acc: 0.7998999953269958\n" + "Epoch 1/10, Loss: 19.15451431274414, Acc: 0.7202000021934509\n", + "Epoch 2/10, Loss: 12.260371208190918, Acc: 0.7486000061035156\n", + "Epoch 3/10, Loss: 10.835549354553223, Acc: 0.7615999579429626\n", + "Epoch 4/10, Loss: 10.09542179107666, Acc: 0.7701999545097351\n", + "Epoch 5/10, Loss: 9.626176834106445, Acc: 0.777899980545044\n", + "Epoch 6/10, Loss: 9.264442443847656, Acc: 0.7854999899864197\n", + "Epoch 7/10, Loss: 9.017412185668945, Acc: 0.7879999876022339\n", + "Epoch 8/10, Loss: 8.786051750183105, Acc: 0.7915999889373779\n", + "Epoch 9/10, Loss: 8.613431930541992, Acc: 0.79749995470047\n", + "Epoch 10/10, Loss: 8.462657928466797, Acc: 0.7996999621391296\n" ] } ], @@ -1297,7 +1317,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Lab1/code/.ipynb_checkpoints/1.1-checkpoint.py b/Lab1/code/.ipynb_checkpoints/1.1-checkpoint.py new file mode 100644 index 0000000..0720510 --- /dev/null +++ b/Lab1/code/.ipynb_checkpoints/1.1-checkpoint.py @@ -0,0 +1,39 @@ +import torch + +A = torch.tensor([[1, 2, 3]]) + +B = torch.tensor([[4], + [5]]) + +# 方法1: 使用PyTorch的减法操作符 +result1 = A - B + +# 方法2: 使用PyTorch的sub函数 +result2 = torch.sub(A, B) + +# 方法3: 手动实现广播机制并作差 +def my_sub(a:torch.Tensor, b:torch.Tensor): + if not ( + (a.size(0) == 1 and b.size(1) == 1) + or + (a.size(1) == 1 and b.size(0) == 1) + ): + raise ValueError("输入的张量大小无法满足广播机制的条件。") + else: + target_shape = torch.Size([max(A.size(0), B.size(0)), max(A.size(1), B.size(1))]) + A_broadcasted = A.expand(target_shape) + B_broadcasted = B.expand(target_shape) + result = torch.zeros(target_shape, dtype=torch.int64).to(device=A_broadcasted.device) + for i in range(target_shape[0]): + for j in range(target_shape[1]): + result[i, j] = A_broadcasted[i, j] - B_broadcasted[i, j] + return result + +result3 = my_sub(A, B) + +print("方法1的结果:") +print(result1) +print("方法2的结果:") +print(result2) +print("方法3的结果:") +print(result3)