TD4_WeightsInitialization.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/thinhngo-x/deep-learning/blob/master/TD4_WeightsInitialization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "CacYAxPaALxh",
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      },
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        "import torch\n",
        "from sklearn.datasets import make_circles\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import TensorDataset\n",
        "import math\n",
        "import numpy as np"
      ],
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      "outputs": []
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        "X,y = make_circles(5000)\n",
        "training_size = int(5000 * 0.8)\n",
        "dataset = TensorDataset(torch.tensor(X),torch.tensor(y))\n",
        "train_data = dataset[0:training_size]\n",
        "test_data = dataset[(training_size+1):-1]\n",
        "X_train, y_train = train_data\n",
        "X_test, y_test = test_data\n",
        "plt.scatter(X_train[:,0], X_train[:,1], c=y_train)\n",
        "print(type(X_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'torch.Tensor'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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l/Ws0mvoxko+3NypUF4fo+glXWJZX7axW1rcbjmAj0Nbvcxvfsf8ghOgF3AX0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\nox2BRqPRRDn/B3FFKb7moNVnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FD8-LakYeY3",
        "colab_type": "text"
      },
      "source": [
        "$\\nabla_{\\mathbf{X}} g(f(\\mathbf{X})) = \\nabla_{\\mathbf{X}} f^T \\nabla_{f}g$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGK_TCW8_8Vr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sigmoid = lambda x : 1 / (1+torch.exp(-x))\n",
        "d_sigmoid = lambda y : y * (1-y)\n",
        "\n",
        "class Neural_Network(nn.Module):\n",
        "    def __init__(self, lr=1.0, activation = sigmoid, d_activation = d_sigmoid):\n",
        "        super(Neural_Network, self).__init__()\n",
        "        self.inputSize = 2\n",
        "        self.hiddenSize = 10\n",
        "        self.outputSize = 1\n",
        "        self.lr = lr\n",
        "\n",
        "        # weights\n",
        "        self.W1 = torch.randn(self.inputSize, self.hiddenSize)\n",
        "        self.W2 = torch.randn(self.hiddenSize, self.outputSize)\n",
        "        self.b1 = torch.randn(1, self.hiddenSize)\n",
        "        self.b2 = torch.randn(1, self.outputSize)\n",
        "        self.activation = activation\n",
        "        self.d_activation = d_activation\n",
        "\n",
        "\n",
        "    def forward(self, X):\n",
        "        self.z1 = torch.matmul(X, self.W1)+self.b1\n",
        "        self.a1 = self.activation(self.z1)\n",
        "        self.z2 = torch.matmul(self.a1, self.W2) +self.b2\n",
        "        self.a2 = self.activation(self.z2)\n",
        "        return self.a2\n",
        "\n",
        "    def backpropagate(self, X, a2, y_target):\n",
        "        # commencer par:\n",
        "        dE_da2 = a2 - y_target\n",
        "        dE_dz2 = self.d_activation(a2) * dE_da2\n",
        "        dE_dW2 = torch.matmul(torch.t(self.a1), dE_dz2)\n",
        "        dE_da1 = torch.matmul(dE_dz2, torch.t(self.W2))\n",
        "        dE_db2 = torch.sum(dE_dz2, dim=0)\n",
        "\n",
        "        #update W2, b2\n",
        "        self.W2 = self.W2 - self.lr * dE_dW2\n",
        "        self.b2 = self.b2 - self.lr * dE_db2\n",
        "\n",
        "        dE_dz1 = self.d_activation(self.a1) * dE_da1\n",
        "        dE_dW1 = torch.matmul(torch.t(X), dE_dz1)\n",
        "        dE_db1 = torch.sum(dE_dz1, dim=0)\n",
        "        \n",
        "        #update W1, b1\n",
        "        self.W1 = self.W1 - self.lr * dE_dW1\n",
        "        self.b1 = self.b1 - self.lr * dE_db1\n",
        "        # completer ici\n",
        "        pass\n",
        "\n",
        "    def train(self, X, y, epoch=20, batch_size=16):\n",
        "      data = torch.utils.data.DataLoader(TensorDataset(X,y), batch_size=batch_size)\n",
        "      for i in tqdm(range(epoch)):\n",
        "        for X,y in  data:\n",
        "          tmp = self.forward(X)\n",
        "          E = 0.5 * torch.matmul(torch.t(tmp-y), tmp-y)\n",
        "          #print(E)\n",
        "          self.backpropagate(X, tmp, y)\n",
        "      pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bo7DFzeOkJOo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def success_rate(model, test_data):\n",
        "  X_test, y_test = test_data\n",
        "  y_pred = model.forward(X_test.float())\n",
        "  check = [1 for i,j in zip(y_pred, y_test) if (abs(i-j)<0.5)]\n",
        "  return(len(check)/len(y_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLkxcJ6UdGiZ",
        "colab_type": "code",
        "outputId": "63fbf462-5e67-4fe6-c720-d7ecef7c9911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122,
          "referenced_widgets": [
            "f1bde4511c53406fa630c54c527d3dce",
            "194f225ff1364c4badd644945e2d8737",
            "129b16928c5a4818aa253672f48747ea",
            "c4ded6198b364e438dc15c7acb9f0c23",
            "c6267268301d4782beed40264dc9d2fb",
            "511f185501e746e59949612cebfd1ae0",
            "f370ec5381394bb5b6a1a7807d8df8c5",
            "7bd9018a8e964f359390d9017eb72cb3"
          ]
        }
      },
      "source": [
        "model = Neural_Network()\n",
        "model.train(X_train.float(), y_train.float().reshape(-1,1))\n",
        "success_rate(model, test_data)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:52: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1bde4511c53406fa630c54c527d3dce",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=20), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBFI6AMiEWg6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nzj7TzwQUVuG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torchvision import datasets, transforms\n",
        "\n",
        "training_data = datasets.FashionMNIST(\".\",download=True,train=True, transform=transforms.Compose([\n",
        "                                                                                transforms.ToTensor(),\n",
        "                                                                                lambda x: torch.flatten(x)\n",
        "]))\n",
        "test_data = datasets.FashionMNIST(\".\",download=True,train=False, transform=transforms.Compose([\n",
        "                                                                                transforms.ToTensor(),\n",
        "                                                                                lambda x: torch.flatten(x),\n",
        "]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVdVDQfopPzE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def trainer(dataset,model,loss_fn,epoch=10,batch_size=1,rate=1e-4):\n",
        "  loss_eval = list()\n",
        "  data = torch.utils.data.DataLoader(dataset, batch_size)\n",
        "  optimizer = optim.Adam(model.parameters(),lr=rate)\n",
        "  j = 0\n",
        "  for i in tqdm(range(epoch)):\n",
        "    for imgs, labels in data:\n",
        "      j += 1\n",
        "      imgs = imgs\n",
        "      labels = labels\n",
        "      optimizer.zero_grad()\n",
        "      labels = labels.float()\n",
        "      output = model(imgs)\n",
        "      loss = loss_fn(output, labels)\n",
        "      if(j%10 == 0):\n",
        "        loss_eval.append(loss.item())\n",
        "      loss.backward()\n",
        "\n",
        "      optimizer.step()\n",
        "  return loss_eval"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75Z6neczWXJl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def xavier(weights):\n",
        "  n_in = weights.size(1)\n",
        "  n_out = weights.size(0)\n",
        "  print(n_in, n_out)\n",
        "  a = math.sqrt(6/(n_in + n_out))\n",
        "  with torch.no_grad():\n",
        "    return weights.uniform_(-a, a)\n",
        "\n",
        "\n",
        "def init_weights_kaiming(m):\n",
        "    #print(m)\n",
        "    if type(m) == nn.Linear:\n",
        "      nn.init.kaiming_uniform_(m.weight)\n",
        "      #print(m.weight)\n",
        "\n",
        "\n",
        "def init_weights_xavier(m):\n",
        "  if type(m) == nn.Linear:\n",
        "    xavier(m.weight)\n",
        "\n",
        "def init_weights_constant(m):\n",
        "       #print(m)\n",
        "       if type(m) == nn.Linear:\n",
        "          nn.init.constant_(m.weight,0)\n",
        "          #print(m.weight)\n",
        "\n",
        "def graph(title, **kwargs):\n",
        "  colors = \"bgrcmykw:\"\n",
        "  fig, ax = plt.subplots()\n",
        "  \n",
        "  for name,color in zip(kwargs,colors):\n",
        "    ax.plot(kwargs[name],color,label=name)\n",
        "  ax.set_title(title)\n",
        "  ax.legend()\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7cg3LoZkf6h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net_Tanh = nn.Sequential(nn.Linear(784, 128),\n",
        "                    nn.Tanh(),\n",
        "                    nn.Linear(128, 10),\n",
        "                    nn.Linear(10,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpFfVNtkY0P-",
        "colab_type": "code",
        "outputId": "823a89f2-a177-4ded-9685-1b17814392c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354,
          "referenced_widgets": [
            "61f70ddf13aa4517be75a58c7a65ea71",
            "d18711139b9f43589df1615954bdd293",
            "ca4aaf8cade242f5baa88e48a9ed4793",
            "c19351d492de49dc9cf308fc8f804651",
            "ea881bdac6d44f5a9a2d91344da083fc",
            "0dbd5a091dca4e79ac6b6baea84e03d5",
            "87b722f8845d44dca15ce931d5d7a289",
            "17a9f14a83574a0980b0697b782e62c0",
            "dfdd90bfc0364c42bbc1da7f0c51c6ae",
            "3b34ad9a59074645837bfcf378d7ee0c",
            "9c44465d27d640359bfddc2e962860cb",
            "5df2152cb215454c91dff2429ee22180",
            "59535ad047444d84b4f928fe33be9fca",
            "24e68bb75ece4852868558d10a744281",
            "88e0617d747d4bcdb35394a3b76d5898",
            "85f13776b60d45298bf460dbd8d41c87",
            "4e43bbc0ee884fee88d9b7835c62bff5",
            "a1125fbc67214deb8809b53d5b6cfabd",
            "cbc20343803d423baf1ea3d7410ea3f9",
            "7fb489ae5a634993a0e162408e80feb6",
            "658a25f6720f4eacaf07b317a3d168d0",
            "bedb69082a02498d9e481c589cded3c6",
            "efc63bcfba0a422594801324fba3e166",
            "feb2e3858be54181bcee2a22ea4a2736"
          ]
        }
      },
      "source": [
        "net_Tanh.apply(init_weights_xavier)\n",
        "loss_xavier = trainer(training_data, net_Tanh, nn.MSELoss(), batch_size=256, epoch=3)\n",
        "\n",
        "net_Tanh.apply(init_weights_kaiming)\n",
        "loss_kaiming = trainer(training_data, net_Tanh, nn.MSELoss(), batch_size=256, epoch=3)\n",
        "\n",
        "net_Tanh.apply(init_weights_constant)\n",
        "loss_constant = trainer(training_data, net_Tanh, nn.MSELoss(), batch_size=256, epoch=3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "784 128\n",
            "128 10\n",
            "10 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "61f70ddf13aa4517be75a58c7a65ea71",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([256])) that is different to the input size (torch.Size([256, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([96])) that is different to the input size (torch.Size([96, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dfdd90bfc0364c42bbc1da7f0c51c6ae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e43bbc0ee884fee88d9b7835c62bff5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnXG-Ex0hrKB",
        "colab_type": "code",
        "outputId": "8d9e7598-1956-4bae-8d3e-58ae4499bb1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "graph(xavier=loss_xavier[0:40],kaiming=loss_kaiming[0:40], constant=loss_constant[0:40],\n",
        "      title='Initialization of weights with the activation Tanh()')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ORn/xxYHHjAFnp1+40I2IE0wpJ0+62Pe33Za8eeX++12Z77+HDh1S7uu115xr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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daXvNai7ktiX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net_Sigmoid = nn.Sequential(nn.Linear(784, 128),\n",
        "                            nn.Sigmoid(),\n",
        "                            nn.Linear(128, 10),\n",
        "                            nn.Linear(10,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0it39epk2Zp",
        "colab_type": "code",
        "outputId": "11b688ce-6f8c-4a60-add9-fd3ed13043ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354,
          "referenced_widgets": [
            "7d84b792d475416c9da719b187c16d0f",
            "9d2f697d0b5b45efa07b585e3bc5cbdf",
            "67f85e451f0a4e2ba2495f99a1e80ff3",
            "985e697dd6df46108b0e86fd916b1ef2",
            "697d644e9fde4fb5a58affd9d0c2aad4",
            "e5f62863c46745848c8340068fd50c5f",
            "bb56bd3022d84ad9a9239b4477b9e6eb",
            "07ceeab43c314160b10312cdea66d50c",
            "39997b4540a0489882b204406d371d3c",
            "54b6dae9703f468e9c9c4861f28f9441",
            "39c5d0d6bbc74495a81f640c5e87b3eb",
            "5a5b9933d7ee4d5e88e467f034bed0d8",
            "49876c0937c34445b1102d2f642c1040",
            "7ab9d7bf2f8a4e4b92c9c0f978788339",
            "ba06b1b81d84417d8fe1e5077eca4806",
            "fb19b2d48bf245d788feabf151faa7e0",
            "c298c72af1c04ce68b5fe212258ef8bb",
            "70bb15e45b4546bcb9f323e8fab0b704",
            "75ee741b848443849f9cdea0aae29211",
            "b9097660738d46638aea6f004b4cc14f",
            "8df40e1e41b24ae493eed3aaf6002d81",
            "fc19134c0c2d4bd7aa8c4c2caf275753",
            "b0a82963409f495885a6ed397a123a20",
            "3f2581aaba774de6a039a6e0282dfee1"
          ]
        }
      },
      "source": [
        "net_Sigmoid.apply(init_weights_xavier)\n",
        "loss_xavier = trainer(training_data, net_Sigmoid, nn.MSELoss(), batch_size=256, epoch=2)\n",
        "\n",
        "net_Sigmoid.apply(init_weights_kaiming)\n",
        "loss_kaiming = trainer(training_data, net_Sigmoid, nn.MSELoss(), batch_size=256, epoch=2)\n",
        "\n",
        "net_Sigmoid.apply(init_weights_constant)\n",
        "loss_constant = trainer(training_data, net_Sigmoid, nn.MSELoss(), batch_size=256, epoch=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "784 128\n",
            "128 10\n",
            "10 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d84b792d475416c9da719b187c16d0f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([256])) that is different to the input size (torch.Size([256, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([96])) that is different to the input size (torch.Size([96, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "39997b4540a0489882b204406d371d3c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c298c72af1c04ce68b5fe212258ef8bb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpDEmzDjm4eB",
        "colab_type": "code",
        "outputId": "af0b22c1-62fa-43c9-d265-6f73cfb72cf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "graph(xavier=loss_xavier[0:40],kaiming=loss_kaiming[0:40], constant=loss_constant[0:40],\n",
        "      title='Initialization of weights with the activation Sigmoid()')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BPd9/r8P+b74ZXnoJhg9P+WnCYkkj5cqV48iRI4SEhLjaFLfD19eXcuXKOV3e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\nIDPzfQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlT8PFP_nJ9A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net_ReLU = nn.Sequential(nn.Linear(784, 128),\n",
        "                         nn.ReLU(),\n",
        "                         nn.Linear(128, 10),\n",
        "                         nn.Linear(10,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8AXhXtRnX_S",
        "colab_type": "code",
        "outputId": "6d2c38af-5234-449d-c930-a7409f9386e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354,
          "referenced_widgets": [
            "b0f1c2032220493d8c7400abe017e8f6",
            "f1fda63d6c274dd69db795efa4651c07",
            "b2a112c3437f49a09fc30fddfa264993",
            "1c273246b07b4cd483a450dd0dd8e788",
            "ae504a5757a94c2ba257c58b8950f3af",
            "26f17275249243b1a30cbb9190d77619",
            "f0941c41dccd4837a3413f12de67996b",
            "fd2c2aee794b45aa8df58383032362bf",
            "bf632b1aa7244d7f9024e0fca61aaa57",
            "a4cfd61cfd9a42feba21efc863ddef2a",
            "b6c1bfa54b6a4924922779986fc5bbe0",
            "d87f374656934e378d6a09676d32e26f",
            "c6243f2caa3a451581ef23529954b1ca",
            "1e6604a7150c4730a38768620507f6bd",
            "3f2064709b874459b014367cd22f3084",
            "c6ef4048d2834dc09dd63ffb8f2846eb",
            "8a90b29a8840471bb13b1fd2e0f4f716",
            "75106dedff65493e874ec16a0a7749c0",
            "fdb5dbf6ef80417c8404d9e20243979d",
            "458efcdf6b9048d2ac632e0815034fcf",
            "126c6144debf4b8eaa8d514a9679f5e7",
            "3724fcf7a3a14727a396c6f011e506e9",
            "81acac5ef5cb49c1980b6c45a4c56022",
            "e84a168742c34e3699786150e3a91c6d"
          ]
        }
      },
      "source": [
        "net_ReLU.apply(init_weights_xavier)\n",
        "loss_xavier = trainer(training_data, net_ReLU, nn.MSELoss(), batch_size=256, epoch=2)\n",
        "\n",
        "net_ReLU.apply(init_weights_kaiming)\n",
        "loss_kaiming = trainer(training_data, net_ReLU, nn.MSELoss(), batch_size=256, epoch=2)\n",
        "\n",
        "net_ReLU.apply(init_weights_constant)\n",
        "loss_constant = trainer(training_data, net_ReLU, nn.MSELoss(), batch_size=256, epoch=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "784 128\n",
            "128 10\n",
            "10 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0f1c2032220493d8c7400abe017e8f6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([256])) that is different to the input size (torch.Size([256, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:431: UserWarning: Using a target size (torch.Size([96])) that is different to the input size (torch.Size([96, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf632b1aa7244d7f9024e0fca61aaa57",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a90b29a8840471bb13b1fd2e0f4f716",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPMvbzJFntbp",
        "colab_type": "code",
        "outputId": "affd43e3-e159-463e-cd72-516a1be983d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "graph(xavier=loss_xavier[0:40],kaiming=loss_kaiming[0:40], constant=loss_constant[0:40],\n",
        "      title='Initialization of weights with the activation ReLU()')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m5QBcFZk33minjPLII7ZBz7hxdlCK7t3tgBGvvw7OaCG8/DK88w7072+XM0KJ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}