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 a21eaff..b3143dc 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 @@ -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([[-6.8759e-03, -1.5809e-02],\n", + " [ 8.0131e-03, -5.1682e-05],\n", + " [ 4.1277e-03, 4.1408e-03]])\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([[-0.0037, -0.0188],\n", + " [-0.0083, 0.0147],\n", + " [-0.0029, -0.0061],\n", + " [-0.0123, 0.0162]])\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([[-0.0037, -0.0083, -0.0029, -0.0123],\n", + " [-0.0188, 0.0147, -0.0061, 0.0162]])\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([[ 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" ] } ], @@ -415,17 +415,17 @@ "tensor([[1.],\n", " [2.]], requires_grad=True)\n", "权重：\n", - "tensor([[1.],\n", - " [2.],\n", - " [3.]])\n", + "tensor([[-0.3470],\n", + " [-0.4461],\n", + " [-0.8590]])\n", "偏置：\n", - "tensor([[1.]])\n", + "tensor([ 0.9821, 0.8701, -0.4202])\n", "My_Linear输出：\n", - "tensor([[2., 3., 4.],\n", - " [3., 5., 7.]], grad_fn=)\n", + "tensor([[ 0.6351, 0.4240, -1.2792],\n", + " [ 0.2882, -0.0221, -2.1383]], grad_fn=)\n", "nn.Linear输出：\n", - "tensor([[2., 3., 4.],\n", - " [3., 5., 7.]], grad_fn=)\n" + "tensor([[ 0.6351, 0.4240, -1.2792],\n", + " [ 0.2882, -0.0221, -2.1383]], grad_fn=)\n" ] } ], @@ -455,12 +455,7 @@ "# 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, my_linear.bias = nn_linear.weight, nn_linear.bias\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", @@ -608,26 +603,7 @@ "execution_count": 10, "id": "5612661e-2809-4d46-96c2-33ee9f44116d", "metadata": {}, - "outputs": [ - { - "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" - ] - } - ], + "outputs": [], "source": [ "learning_rate = 5e-2\n", "num_epochs = 10\n", @@ -692,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "fa121afd-a1af-4193-9b54-68041e0ed068", "metadata": {}, "outputs": [], @@ -718,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "93b0fdb6-be8b-4663-b59e-05ed19a9ea09", "metadata": {}, "outputs": [ @@ -826,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "e605f1b0-1d32-410f-bddf-402a85ccc9ff", "metadata": {}, "outputs": [ @@ -890,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "759a3bb2-b5f4-4ea5-a2d7-15f0c4cdd14b", "metadata": {}, "outputs": [ @@ -952,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "74322629-8325-4823-b80f-f28182d577c1", "metadata": {}, "outputs": [ @@ -1013,7 +989,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "bb31a75e-464c-4b94-b927-b219a765e35d", "metadata": {}, "outputs": [], @@ -1073,7 +1049,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "d816dae1-5fbe-4c29-9597-19d66b5eb6b4", "metadata": {}, "outputs": [ @@ -1177,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "0163b9f7-1019-429c-8c29-06436d0a4c98", "metadata": {}, "outputs": [], @@ -1204,7 +1180,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "6d241c05-b153-4f56-a845-0f2362f6459b", "metadata": {}, "outputs": [ @@ -1321,7 +1297,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.12" } }, "nbformat": 4,