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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/indra622/AIAcademy_SpeechRecognition/blob/main/2_1_basic_MLP_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 기초 MLP 모델 실습\n"
      ],
      "metadata": {
        "id": "TU_YHyDKJ-AH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### import"
      ],
      "metadata": {
        "id": "B5cMixVg_djR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "Jz5UHKW4Kb3_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### device check"
      ],
      "metadata": {
        "id": "fN8iyjng_f6V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.is_available()\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "device"
      ],
      "metadata": {
        "id": "UPPmtLDJKi3n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "60eb58e5-2f45-4966-9ba1-610c1c16d92a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
      ],
      "metadata": {
        "id": "Fgk_ScutNyh7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHI4sjFpN65T",
        "outputId": "ca8a4275-ea79-4577-b436-6d5ac36fcf08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XOR 문제\n",
        "\n",
        "Train Sample \n",
        "\n",
        "<img src=\"https://github.com/indra622/AIAcademy_SpeechRecognition/raw/main/img/xor_input.jpg\" width=200>"
      ],
      "metadata": {
        "id": "gIv1eRoLNagO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = [[0,0], [0,1], [1,0], [1,1]]\n",
        "Y = [[0],[1],[1],[0]]"
      ],
      "metadata": {
        "id": "BAgxh7DIKoES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = torch.tensor(X,dtype=torch.float)\n",
        "Y = torch.tensor(Y,dtype=torch.float)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWHFOT8LOQyd",
        "outputId": "8bc024f8-d144-449c-d266-075c5674ffe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X,Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnisExmpOXKW",
        "outputId": "d1251d6b-5808-47fb-f659-35577fbc47d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[0., 0.],\n",
              "         [0., 1.],\n",
              "         [1., 0.],\n",
              "         [1., 1.]]), tensor([[0.],\n",
              "         [1.],\n",
              "         [1.],\n",
              "         [0.]]))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sa4UQerFOZAF",
        "outputId": "356c5cf6-ad45-4510-9763-ff4d7851fc1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float32"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Perceptron\n",
        "<img src=\"https://raw.githubusercontent.com/indra622/AIAcademy_SpeechRecognition/main/img/perceptron.jpg\" width=400>"
      ],
      "metadata": {
        "id": "VL8xs24iOcW3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = nn.Sequential(\n",
        "    nn.Linear(2,1,bias=True),\n",
        "    nn.Sigmoid()\n",
        ")"
      ],
      "metadata": {
        "id": "1rSbx7n-K2CE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmG2BPa5PXmh",
        "outputId": "061db766-e9bf-4162-948c-28326707339b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=2, out_features=1, bias=True)\n",
              "  (1): Sigmoid()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "e5Kbsm9IP9Im"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### loss function and optimizer\n"
      ],
      "metadata": {
        "id": "FHWnWrWTQ3Le"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = torch.nn.BCELoss()\n",
        "optim = torch.optim.SGD(model.parameters(),lr = 0.01)"
      ],
      "metadata": {
        "id": "9Z5ru-cLPf4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### train loop\n",
        "- epoch (전체 데이터)\n",
        "\n",
        "  - batch (전체 데이터의 부분집합)\n"
      ],
      "metadata": {
        "id": "MQti2LXhQ2nj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iME0ZBw1Rlau",
        "outputId": "296ce640-ba5b-44a3-ec89-3705db7b31dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.5034],\n",
              "        [0.6718],\n",
              "        [0.5558],\n",
              "        [0.7165]], grad_fn=<SigmoidBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "0 -> False \n",
        "\n",
        "1 -> True"
      ],
      "metadata": {
        "id": "T49qxeBHR-ky"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm #progress bar 형식으로 loop의 진행상황을 확인가능"
      ],
      "metadata": {
        "id": "_E0qxF42R8VU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "for _ in tqdm(range(100)):\n",
        "  time.sleep(0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7a9968f043b44bc4bb654e20e9fe8c66",
            "c5474365d6454273b2056e03eddd91a3",
            "46e5ab86df0e4269980bc72cd4299ec6",
            "fe778b05e6de41f796c05c33bca85dc9",
            "ebd635dd1b1f4c548c6597e222484413",
            "924a3d0d7c6141f1b5d2667a5fc2c89f",
            "2a9f71f2931c4076b29595c5b0a0f246",
            "f078ea17c9d14814a09664cf8b305535",
            "5e4f8cf3c4f848b898eaa0e401afe55a",
            "16f1a7ab79394882bdb66edfba120707",
            "a4d2e2c55c634bb79e2fec9f32890a6b"
          ]
        },
        "id": "b3f4DMbDTZiK",
        "outputId": "6a38aef6-d690-4d40-af52-286434c38d41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a9968f043b44bc4bb654e20e9fe8c66"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_epoch = 10000\n",
        "loss_fn = torch.nn.BCELoss()\n",
        "optim = torch.optim.SGD(model.parameters(),lr = 0.01)\n",
        "\n",
        "model.to(device)\n",
        "X = X.to(device)\n",
        "Y = Y.to(device)\n",
        "\n",
        "for epoch in tqdm(range(n_epoch)):\n",
        "  #for batch in train_loader()\n",
        "  optim.zero_grad()    # gradient 초기화\n",
        "  pred = model(X)\n",
        "  loss = loss_fn(pred,Y) # loss(prediction, GT) \n",
        "  if epoch %1000 == 0 : \n",
        "    print(loss)\n",
        "  loss.backward()       # back propagation gradient 계산\n",
        "  optim.step()          # Weight Update\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225,
          "referenced_widgets": [
            "82efa45b35084371aaaae1c1f523d40c",
            "89d2d6d9e80a47fc82c065068ac8f108",
            "0784c576f1c04fad9fc2ceca343a4bb0",
            "336889c70790464f9afc86e3f3ce1150",
            "23ab1110dfcb4f2f91c561695d3acd07",
            "ba4917a9739249dfb4f255c5a54f6d4e",
            "913fe513cd0e4f8c88d5d8582a6168ea",
            "5407168a98744bbd98acb89b2f2d4e7d",
            "0cfbbf7a2f4c469d8fa5d9981fceb685",
            "355b4fe828da41ea99e6fe8b46504b62",
            "b111772c8dba4bb882778e344b8a194f"
          ]
        },
        "id": "dSC-K6_zPhLL",
        "outputId": "7973e70c-bc04-4c49-9d5d-631b0439f312"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82efa45b35084371aaaae1c1f523d40c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6931, device='cuda:0', grad_fn=<BinaryCrossEntropyBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7L7Mt3blT0gw",
        "outputId": "ab40db79-d7d0-462c-ae16-8aef946ca4f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.5000],\n",
              "        [0.5000],\n",
              "        [0.5000],\n",
              "        [0.5000]], device='cuda:0', grad_fn=<SigmoidBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MLP \n",
        "<img src=\"https://raw.githubusercontent.com/indra622/AIAcademy_SpeechRecognition/main/img/MLP.jpg\" width=200>"
      ],
      "metadata": {
        "id": "zxbqiRW-OQTa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MLP_model = nn.Sequential(\n",
        "    nn.Linear(2,2,bias=True),\n",
        "    nn.Sigmoid(),\n",
        "    nn.Linear(2,1,bias=True),\n",
        "    nn.Sigmoid()\n",
        ")"
      ],
      "metadata": {
        "id": "QgOhZWP0LsBZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MLP_model"
      ],
      "metadata": {
        "id": "JfsKGtLaK9VB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9732cde7-b324-45b5-e5e9-6374b47d2131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=2, out_features=2, bias=True)\n",
              "  (1): Sigmoid()\n",
              "  (2): Linear(in_features=2, out_features=1, bias=True)\n",
              "  (3): Sigmoid()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "MtwqpcPsZTst"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MLP_model = nn.Sequential(\n",
        "    nn.Linear(2,2,bias=True),\n",
        "    nn.Sigmoid(),\n",
        "    nn.Linear(2,1,bias=True),\n",
        "    nn.Sigmoid()\n",
        ")\n",
        "\n",
        "n_epoch = 10**4 # 10^5\n",
        "loss_fn = torch.nn.BCELoss()\n",
        "optim = torch.optim.Adam(MLP_model.parameters(),lr = 0.01)\n",
        "\n",
        "\n",
        "\n",
        "MLP_model.to(device)\n",
        "X = X.to(device)\n",
        "Y = Y.to(device)\n",
        "\n",
        "losses = []\n",
        "\n",
        "for epoch in tqdm(range(n_epoch)):\n",
        "  #for batch in train_loader()\n",
        "  optim.zero_grad()    # gradient 초기화\n",
        "  pred = MLP_model(X)\n",
        "  loss = loss_fn(pred,Y) # loss(prediction, GT) \n",
        "  # if epoch %10000 == 0 : \n",
        "  #   print(loss)\n",
        "  losses.append(loss.item())  \n",
        "  loss.backward()       # back propagation gradient 계산\n",
        "  optim.step()          # Weight Update\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "H8o2D9Mg7PCW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ba18ecaac90545c1801c68b162d10195",
            "76fa5d9ae85a45bbab18f011c674813f",
            "fa9c6f41525d486fa660f55fcb207930",
            "36ac302e6d774decaca62c8a526adb02",
            "f5d53c499bba4aa595135698cc242c07",
            "83ff0587518d4f09a19d7fa826904e29",
            "d9769bfaf7ca405ba8e3b79349de2dc4",
            "c3ca178873894606b66507431b4b9800",
            "8ad5f310f27a441bbfba71a849000db6",
            "62be37fcc1524c43a4219c14eef7a3eb",
            "60cefea3c7394dd7919a7f1f1c50e91d"
          ]
        },
        "outputId": "35de88c3-f744-4b43-80d8-e866b3427ea7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba18ecaac90545c1801c68b162d10195"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#losses[0].item(),type(losses[0].item())"
      ],
      "metadata": {
        "id": "naEepA3MZoLH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "8dx0IpDfaeM6",
        "outputId": "8c1ffa82-9d62-406a-b750-d7f76e3e32e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4abe09b250>]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MLP_model(X)"
      ],
      "metadata": {
        "id": "5VUTvKNqLEDm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53999169-52f7-44a2-aa67-08af3284dcfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[3.0583e-05],\n",
              "        [9.9999e-01],\n",
              "        [4.9999e-01],\n",
              "        [5.0001e-01]], device='cuda:0', grad_fn=<SigmoidBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "9.3806e-04 # [0,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0H2UjgB5bPbJ",
        "outputId": "445d5edd-5b5f-4888-ecfc-2d42171d75e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00093806"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "9.9870e-01 #[0,1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpnlnLa1bRNe",
        "outputId": "0c7fefc5-3ea2-4799-e4c0-f89f7268d032"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9987"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "9.9913e-01 #[1,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sg4RK6J0bSzA",
        "outputId": "e2cb16e6-dc23-4835-8edd-5ccef27e8109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.99913"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "8.1598e-04 #[1,1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmQcK_pHbbfN",
        "outputId": "30acb261-6d46-4b43-8c41-2d7c32ca6b57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00081598"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fearture space transformation check?"
      ],
      "metadata": {
        "id": "J4ekBOdA_rzt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "activation = {}\n",
        "def get_activation(name):\n",
        "    def hook(model, input, output):\n",
        "        activation[name] = output.detach()\n",
        "    return hook"
      ],
      "metadata": {
        "id": "2LvfP75gLzlr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MLP_model[1].register_forward_hook(get_activation('layer1'))\n",
        "MLP_model(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uagmwcrqdn8F",
        "outputId": "52d35a52-f7f0-4e4e-91fe-ac9e87aa9751"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[3.0583e-05],\n",
              "        [9.9999e-01],\n",
              "        [4.9999e-01],\n",
              "        [5.0001e-01]], device='cuda:0', grad_fn=<SigmoidBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wKhsfL_eF4C",
        "outputId": "e4b2c079-93eb-4632-adf5-c60a134b0039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 0.],\n",
              "        [0., 1.],\n",
              "        [1., 0.],\n",
              "        [1., 1.]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = activation['layer1'][:,0]\n",
        "x2 = activation['layer1'][:,1]"
      ],
      "metadata": {
        "id": "pvTi-5NWdq3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "activation['layer1']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0mkO2HIeVgm",
        "outputId": "f9bb8105-17dd-4e44-dd5b-b50f9a04cb09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[5.0438e-02, 5.5562e-03],\n",
              "        [9.8663e-01, 8.9997e-01],\n",
              "        [1.0000e+00, 1.4383e-09],\n",
              "        [1.0000e+00, 2.3160e-06]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1,x2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4r5A-eweT-T",
        "outputId": "c228dbae-0d60-47ef-c070-ad389846c58f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([0.0504, 0.9866, 1.0000, 1.0000], device='cuda:0'),\n",
              " tensor([5.5562e-03, 8.9997e-01, 1.4383e-09, 2.3160e-06], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "id": "Y1r77VBsepdG",
        "outputId": "485a5e41-3565-449e-bd91-9c5179e89e25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.],\n",
              "        [1.],\n",
              "        [1.],\n",
              "        [0.]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x1.cpu(),x2.cpu(),c=Y.cpu())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Tz7Qesbr8Y7k",
        "outputId": "a00621c8-db49-4d66-a9dc-e4a90dcda5ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4abdef6550>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "B9hHBZ1s8_o8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "h0Ytwfn3_UeE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "M-8oKwFj9Dis"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3fvOj36H9HvW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Vz0E2jra_g6B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "Q4SczfFpHwqj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "lQLPP1vCEGqC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "HzpvncP0I1Jn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "HWyKZ-LNHc4-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "XkVllrX_E5p-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "we8bIImfFOFo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}