NNDL_HW2_4.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "qwFdxnaKtLsW",
        "colab_type": "text"
      },
      "source": [
        "# Auto Encoder"
      ]
    },
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      "metadata": {
        "id": "iKiJdKlc5po3",
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        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from tqdm.notebook import tqdm_notebook\n",
        "import  matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import confusion_matrix , accuracy_score, plot_confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "np.random.seed(1234)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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          "height": 35
        }
      },
      "source": [
        "# choose GPU/CPU\n",
        "if torch.cuda.is_available():\n",
        "    device = torch.device(\"cuda:0\") \n",
        "    print(\"Running on the GPU\")\n",
        "else:\n",
        "    device = torch.device(\"cpu\")\n",
        "    print(\"Running on the CPU\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running on the GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "source": [
        "import torchvision\n",
        "fmnist_train = torchvision.datasets.FashionMNIST('/content/', download=True)\n",
        "fmnist_test  = torchvision.datasets.FashionMNIST('/content/', download=True, train=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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          "text": [
            "Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "\n"
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          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QrZ6StG5vjq",
        "colab_type": "code",
        "outputId": "0b9730cc-a34a-48e0-e63b-46ebf36ea4a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "for i in range(16):\n",
        "    f, t = fmnist_train[i]\n",
        "    plt.subplot(4, 4, i+1)\n",
        "    plt.imshow(f, cmap='gray')\n",
        "    plt.title(fmnist_train.classes[t])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2mzUyk89pW8A2/WDDpiRkXpYsYK11W8t3rcFWnCIrO7nfxtnyeJ2pT2s128Gm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GIxenp6iMfjRKPRcx7P5/Nx2223EY1GGRoa4uWXXyYUCi3ClazhYoBRu3U6nXg8HmpqanC5XCQS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\nNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6U1otTt72g_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FMNIST_DS(Dataset):\n",
        "    def __init__(self, ds):\n",
        "        super().__init__()\n",
        "        self.ds = ds\n",
        "        self.len = len(ds)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        image , target = self.ds[index] \n",
        "\n",
        "        feature = np.array(image).flatten()/256.0\n",
        "        feature = torch.tensor(feature , dtype=torch.float)\n",
        "\n",
        "        target  = torch.tensor(target)\n",
        "        \n",
        "        return  feature, target\n",
        "       \n",
        "    def __len__(self):\n",
        "        return self.len\n",
        "\n",
        "class ENCODE_DS(Dataset):\n",
        "    def __init__(self, ds):\n",
        "        super().__init__()\n",
        "        self.ds = ds\n",
        "        self.len = len(ds)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        image , target = self.ds[index] \n",
        "\n",
        "        feature = np.array(image).flatten()/256.0\n",
        "        feature = torch.tensor(feature , dtype=torch.float)\n",
        "        \n",
        "        return  feature, feature\n",
        "       \n",
        "    def __len__(self):\n",
        "        return self.len\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfU_9Aq8AHYN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_ds = ENCODE_DS(fmnist_train)\n",
        "test_ds  = ENCODE_DS(fmnist_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsiECfcWCAui",
        "colab_type": "code",
        "outputId": "62a16d18-d972-4acb-cadf-7ea0c5029fa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "f , t = next(iter(train_ds))\n",
        "f.shape , t.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([784]), torch.Size([784]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CboG6-sJCDpp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 256\n",
        "train_dl = DataLoader(train_ds , batch_size=batch_size , shuffle= True , num_workers = 1)\n",
        "test_dl = DataLoader(test_ds , batch_size=batch_size , shuffle= False , num_workers = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS5BtSGWCbL5",
        "colab_type": "code",
        "outputId": "b9bbec1a-2242-4a1e-b321-8b1b2cda6a7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "fs , ts = next(iter(train_dl))\n",
        "fs.shape , ts.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([100, 784]), torch.Size([100, 784]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1nzbVlNCdJc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MLP0(nn.Module):\n",
        "    def __init__(self, input_dim = 200 , output_dim = 10):\n",
        "        super().__init__()\n",
        "        self.n = input_dim\n",
        "        self.o = output_dim\n",
        "\n",
        "        self.fc1  = nn.Linear(self.n, self.o)\n",
        "        self.act1 = nn.Tanh()\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.fc1(x)\n",
        "        out = self.act1(out)\n",
        "\n",
        "        return out\n",
        "\n",
        "class ENCODER(nn.Module):\n",
        "    def __init__(self, input_dim = 784, hidden1 = 500, hidden2 = 300, output_dim = 200):\n",
        "        super().__init__()\n",
        "        self.n = input_dim\n",
        "        self.hidden1 = hidden1\n",
        "        self.hidden2 = hidden2\n",
        "        self.o = output_dim\n",
        "\n",
        "        # encode\n",
        "        self.fc1  = nn.Linear(self.n, self.hidden1)\n",
        "        self.act1 = nn.Tanh()\n",
        "        self.fc2  = nn.Linear(self.hidden1, self.hidden2)\n",
        "        self.act2 = nn.Tanh()\n",
        "        self.fc3  = nn.Linear(self.hidden2, self.o)\n",
        "        self.act3 = nn.Tanh()\n",
        "\n",
        "        #decode\n",
        "        self.fc4  = nn.Linear(self.o, self.hidden2)\n",
        "        self.act4 = nn.Tanh()\n",
        "        self.fc5  = nn.Linear(self.hidden2, self.hidden1)\n",
        "        self.act5 = nn.Tanh()\n",
        "        self.fc6  = nn.Linear(self.hidden1, self.n)\n",
        "        self.act6 = nn.Tanh()\n",
        "\n",
        "    def encode(self, x):\n",
        "        out = self.fc1(x)\n",
        "        out = self.act1(out)\n",
        "        out = self.fc2(out)\n",
        "        out = self.act2(out)\n",
        "        out = self.fc3(out)\n",
        "        out = self.act3(out)\n",
        "        return out\n",
        "\n",
        "    def decode(self, x):\n",
        "        out = self.fc4(x)\n",
        "        out = self.act4(out)\n",
        "        out = self.fc5(out)\n",
        "        out = self.act5(out)\n",
        "        out = self.fc6(out)\n",
        "        out = self.act6(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        #encode\n",
        "        out = self.encode(x)\n",
        "        #decode\n",
        "        out = self.decode(out)\n",
        "        return out\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdEW559VD8x6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder = ENCODER().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYK1rfvCEG9G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss().to(device)\n",
        "optimizer = optim.SGD(net.parameters(),lr=0.5)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.999)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3KyXdE665hK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plt_conf_mat(y_true , y_pred, labels, normalize = 'true'  , include_values = True, \n",
        "                 xticks_rotation='horizontal',\n",
        "                 values_format=None,\n",
        "                 cmap='winter'):\n",
        "    plt.figure(figsize=[9, 9])\n",
        "    ax = plt.subplot(1, 1, 1)\n",
        "    cm = confusion_matrix(y_true, y_pred, normalize=normalize)\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "    return disp.plot(include_values=include_values,\n",
        "                     cmap=cmap, ax=ax, xticks_rotation=xticks_rotation,\n",
        "                     values_format=values_format)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2t-dKAI2ELW0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_step(model, train_dl, criterion, optimizer):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    \n",
        "    # for i, (train_inputs, train_labels) in tqdm_notebook(enumerate(train_dl), \n",
        "    #                                                      desc='Training', \n",
        "    #                                                      total=len(train_dl)):\n",
        "    for i, (train_inputs, train_labels) in enumerate(train_dl):\n",
        "        # move to device\n",
        "        train_inputs , train_labels = train_inputs.to(device) , train_labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output = model(train_inputs)\n",
        "        loss = criterion(output, train_labels)\n",
        "        loss.backward()     \n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        # with torch.no_grad():\n",
        "        #     mx , argmx = torch.max(output, dim=1)\n",
        "        #     total_correct += torch.sum(argmx == train_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(train_dl), total_correct/len(train_dl)\n",
        "\n",
        "\n",
        "def validate_step(model, valid_dl, criterion):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    with torch.no_grad():\n",
        "        # for i, (test_inputs, test_labels) in tqdm_notebook(enumerate(valid_dl), \n",
        "        #                                                 desc='Validation', \n",
        "        #                                                 total=len(valid_dl)):\n",
        "        for i, (test_inputs, test_labels) in enumerate(valid_dl):\n",
        "            # move to device\n",
        "            test_inputs , test_labels = test_inputs.to(device) , test_labels.to(device)\n",
        "\n",
        "            output = model(test_inputs)\n",
        "            loss = criterion(output, test_labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            # mx , argmx = torch.max(output, dim=1)\n",
        "            # total_correct += torch.sum(argmx == test_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(test_dl), total_correct/len(test_dl)\n",
        "\n",
        "\n",
        "def train(model, train_dl, valid_dl, criterion, optimizer, scheduler, num_epochs, train_loss, test_loss, train_acc, test_acc):\n",
        "    \n",
        "    best_wts = None\n",
        "    best_loss = float('inf')\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        ## perform one epoch of training and validation\n",
        "        trn_loss, trn_acc = train_step(model, train_dl, criterion, optimizer)\n",
        "        val_loss, val_acc = validate_step(model, valid_dl, criterion)\n",
        "        scheduler.step()\n",
        "\n",
        "        train_loss += [trn_loss]\n",
        "        test_loss  += [val_loss]\n",
        "        train_acc  += [trn_acc ]\n",
        "        test_acc   += [val_acc ]\n",
        "\n",
        "        # save weights\n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_wts = model.state_dict().copy()\n",
        "            \n",
        "# #             torch.save(best_wts, 'models/lstm-{}-{}-{}-{}-{}-{}-{:.5f}.pth'.format(\n",
        "# #                 epoch, max_len, min_count, model.embed_size, model.hidden_size, model.num_layers, best_acc))\n",
        "\n",
        "        print(f'[Epoch:{epoch+1}/{num_epochs}] Training Loss: %.4f| Testing Loss: %.4f| Training ACC: %.4f| Testing ACC: %.4f|'\n",
        "              % (trn_loss* 10000, val_loss* 10000 , trn_acc*100, val_acc*100))\n",
        "\n",
        "    model.load_state_dict(best_wts)\n",
        "    return train_loss, test_loss, train_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeukQhoCTJTL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loss , test_loss, train_acc, test_acc = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNe4NtxGV1sz",
        "colab_type": "code",
        "outputId": "2b530ca5-5d73-439d-955f-3b0eca360021",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_loss, test_loss, train_acc, test_acc = train(encoder, train_dl, test_dl, criterion, optimizer, scheduler, 100, train_loss, test_loss, train_acc, test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch:1/100] Training Loss: 137.1034| Testing Loss: 138.8043| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:2/100] Training Loss: 137.0878| Testing Loss: 138.7752| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:3/100] Training Loss: 137.0934| Testing Loss: 138.7568| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:4/100] Training Loss: 137.0262| Testing Loss: 138.7177| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:5/100] Training Loss: 137.0143| Testing Loss: 138.6927| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:6/100] Training Loss: 136.9866| Testing Loss: 138.6593| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:7/100] Training Loss: 136.9575| Testing Loss: 138.6276| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:8/100] Training Loss: 136.9021| Testing Loss: 138.5962| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:9/100] Training Loss: 136.8780| Testing Loss: 138.5753| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:10/100] Training Loss: 136.8675| Testing Loss: 138.5407| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:11/100] Training Loss: 136.8436| Testing Loss: 138.5120| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:12/100] Training Loss: 136.8174| Testing Loss: 138.4796| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:13/100] Training Loss: 136.7766| Testing Loss: 138.4510| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:14/100] Training Loss: 136.7648| Testing Loss: 138.4270| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:15/100] Training Loss: 136.7051| Testing Loss: 138.3920| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:16/100] Training Loss: 136.7000| Testing Loss: 138.3643| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:17/100] Training Loss: 136.6515| Testing Loss: 138.3322| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:18/100] Training Loss: 136.6398| Testing Loss: 138.3120| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:19/100] Training Loss: 136.6133| Testing Loss: 138.2750| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:20/100] Training Loss: 136.6013| Testing Loss: 138.2482| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:21/100] Training Loss: 136.5185| Testing Loss: 138.2187| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:22/100] Training Loss: 136.5211| Testing Loss: 138.1872| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:23/100] Training Loss: 136.4758| Testing Loss: 138.1714| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:24/100] Training Loss: 136.4570| Testing Loss: 138.1300| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:25/100] Training Loss: 136.4514| Testing Loss: 138.1024| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:26/100] Training Loss: 136.4296| Testing Loss: 138.0793| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:27/100] Training Loss: 136.3558| Testing Loss: 138.0461| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:28/100] Training Loss: 136.3368| Testing Loss: 138.0239| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:29/100] Training Loss: 136.3458| Testing Loss: 137.9946| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:30/100] Training Loss: 136.2891| Testing Loss: 137.9557| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:31/100] Training Loss: 136.2467| Testing Loss: 137.9260| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:32/100] Training Loss: 136.2425| Testing Loss: 137.9048| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:33/100] Training Loss: 136.2043| Testing Loss: 137.8668| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:34/100] Training Loss: 136.2068| Testing Loss: 137.8406| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:35/100] Training Loss: 136.1412| Testing Loss: 137.8164| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:36/100] Training Loss: 136.0896| Testing Loss: 137.7835| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:37/100] Training Loss: 136.0600| Testing Loss: 137.7545| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:38/100] Training Loss: 136.0203| Testing Loss: 137.7276| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:39/100] Training Loss: 136.0403| Testing Loss: 137.6960| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:40/100] Training Loss: 135.9887| Testing Loss: 137.6727| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:41/100] Training Loss: 135.9563| Testing Loss: 137.6394| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:42/100] Training Loss: 135.9123| Testing Loss: 137.6094| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:43/100] Training Loss: 135.9078| Testing Loss: 137.5854| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:44/100] Training Loss: 135.9078| Testing Loss: 137.5525| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:45/100] Training Loss: 135.8269| Testing Loss: 137.5232| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:46/100] Training Loss: 135.8103| Testing Loss: 137.4951| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:47/100] Training Loss: 135.7887| Testing Loss: 137.4648| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:48/100] Training Loss: 135.7312| Testing Loss: 137.4369| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:49/100] Training Loss: 135.6884| Testing Loss: 137.4085| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:50/100] Training Loss: 135.7112| Testing Loss: 137.3792| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:51/100] Training Loss: 135.6860| Testing Loss: 137.3742| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:52/100] Training Loss: 135.6356| Testing Loss: 137.3257| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:53/100] Training Loss: 135.6534| Testing Loss: 137.2924| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:54/100] Training Loss: 135.6248| Testing Loss: 137.2651| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:55/100] Training Loss: 135.5536| Testing Loss: 137.2357| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:56/100] Training Loss: 135.5007| Testing Loss: 137.2114| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:57/100] Training Loss: 135.4942| Testing Loss: 137.1798| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:58/100] Training Loss: 135.4588| Testing Loss: 137.1508| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:59/100] Training Loss: 135.4552| Testing Loss: 137.1286| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:60/100] Training Loss: 135.4066| Testing Loss: 137.0973| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:61/100] Training Loss: 135.3607| Testing Loss: 137.0813| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:62/100] Training Loss: 135.3804| Testing Loss: 137.0407| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:63/100] Training Loss: 135.3643| Testing Loss: 137.0117| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:64/100] Training Loss: 135.3482| Testing Loss: 136.9833| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:65/100] Training Loss: 135.3021| Testing Loss: 136.9530| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:66/100] Training Loss: 135.2635| Testing Loss: 136.9247| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:67/100] Training Loss: 135.2620| Testing Loss: 136.8992| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:68/100] Training Loss: 135.1921| Testing Loss: 136.8714| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:69/100] Training Loss: 135.1665| Testing Loss: 136.8423| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:70/100] Training Loss: 135.0996| Testing Loss: 136.8127| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:71/100] Training Loss: 135.1199| Testing Loss: 136.7831| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:72/100] Training Loss: 135.0721| Testing Loss: 136.7576| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:73/100] Training Loss: 135.0537| Testing Loss: 136.7288| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:74/100] Training Loss: 135.0299| Testing Loss: 136.7052| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:75/100] Training Loss: 134.9815| Testing Loss: 136.6740| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:76/100] Training Loss: 134.9869| Testing Loss: 136.6435| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:77/100] Training Loss: 134.9209| Testing Loss: 136.6212| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:78/100] Training Loss: 134.9344| Testing Loss: 136.5918| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:79/100] Training Loss: 134.8610| Testing Loss: 136.5614| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:80/100] Training Loss: 134.8343| Testing Loss: 136.5356| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:81/100] Training Loss: 134.7867| Testing Loss: 136.5020| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:82/100] Training Loss: 134.7999| Testing Loss: 136.4788| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:83/100] Training Loss: 134.7929| Testing Loss: 136.4505| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:84/100] Training Loss: 134.7267| Testing Loss: 136.4229| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:85/100] Training Loss: 134.6953| Testing Loss: 136.3913| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:86/100] Training Loss: 134.6730| Testing Loss: 136.3644| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:87/100] Training Loss: 134.6257| Testing Loss: 136.3364| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:88/100] Training Loss: 134.6076| Testing Loss: 136.3123| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:89/100] Training Loss: 134.5954| Testing Loss: 136.2794| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:90/100] Training Loss: 134.5691| Testing Loss: 136.2576| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:91/100] Training Loss: 134.5295| Testing Loss: 136.2282| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:92/100] Training Loss: 134.5229| Testing Loss: 136.1968| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:93/100] Training Loss: 134.4921| Testing Loss: 136.1710| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:94/100] Training Loss: 134.4755| Testing Loss: 136.1498| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:95/100] Training Loss: 134.4246| Testing Loss: 136.1133| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:96/100] Training Loss: 134.3901| Testing Loss: 136.0902| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:97/100] Training Loss: 134.4025| Testing Loss: 136.0598| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:98/100] Training Loss: 134.3301| Testing Loss: 136.0406| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:99/100] Training Loss: 134.3332| Testing Loss: 136.0023| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:100/100] Training Loss: 134.3297| Testing Loss: 135.9831| Training ACC: 0.0000| Testing ACC: 0.0000|\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vV-3DPYHWGqu",
        "colab_type": "code",
        "outputId": "78d8109c-5acd-4936-c267-8d4dfd2058af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.plot([i for i in train_loss] , label='train')\n",
        "plt.plot([i for i in test_loss ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing loss: 1000, 100')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'trianing loss: 1000, 100')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SafZKEZH9DcvFWDN7F9Dq7qvz1bv7He4+193nVlVVHfJ+YroYKyKyn0IS/WZgRtb69LAs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\nkYhTohcRiTglehGRiFOiFxGJuP8PNn8+YI59g0UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xaulnRR5CAT",
        "colab_type": "code",
        "outputId": "84610e5c-ce65-4016-b284-9eb9ec370312",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# load saved encoder\n",
        "encoder.load_state_dict(torch.load('/content/drive/My Drive/ut/NNDL/encoder.pth'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7OGAtuC6304",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net2 = MLP0().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWLG1dd37xjH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_ds = FMNIST_DS(fmnist_train)\n",
        "test_ds  = FMNIST_DS(fmnist_test)\n",
        "batch_size = 256\n",
        "train_dl = DataLoader(train_ds , batch_size=batch_size , shuffle= True , num_workers = 10)\n",
        "test_dl = DataLoader(test_ds , batch_size=batch_size , shuffle= False , num_workers = 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DdYmLWr7-Hua",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss().to(device)\n",
        "optimizer = optim.SGD(net2.parameters(),lr=0.5)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cv6VF5Kv-PmI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_step(model, train_dl, criterion, optimizer, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    \n",
        "    # for i, (train_inputs, train_labels) in tqdm_notebook(enumerate(train_dl), \n",
        "    #                                                      desc='Training', \n",
        "    #                                                      total=len(train_dl)):\n",
        "    for i, (train_inputs, train_labels) in enumerate(train_dl):\n",
        "        # move to device\n",
        "        train_inputs , train_labels = train_inputs.to(device) , train_labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        train_inputs = encoder.encode(train_inputs)\n",
        "        output = model(train_inputs)\n",
        "        loss = criterion(output, train_labels)\n",
        "        loss.backward()     \n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        with torch.no_grad():\n",
        "            mx , argmx = torch.max(output, dim=1)\n",
        "            total_correct += torch.sum(argmx == train_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(train_dl), total_correct/len(train_dl)\n",
        "\n",
        "\n",
        "def validate_step(model, valid_dl, criterion, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    with torch.no_grad():\n",
        "        # for i, (test_inputs, test_labels) in tqdm_notebook(enumerate(valid_dl), \n",
        "        #                                                 desc='Validation', \n",
        "        #                                                 total=len(valid_dl)):\n",
        "        for i, (test_inputs, test_labels) in enumerate(valid_dl):\n",
        "            # move to device\n",
        "            test_inputs , test_labels = test_inputs.to(device) , test_labels.to(device)\n",
        "            test_inputs = encoder.encode(test_inputs)\n",
        "            output = model(test_inputs)\n",
        "            loss = criterion(output, test_labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            mx , argmx = torch.max(output, dim=1)\n",
        "            total_correct += torch.sum(argmx == test_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(test_dl), total_correct/len(test_dl)\n",
        "\n",
        "\n",
        "def train(model, train_dl, valid_dl, criterion, optimizer, scheduler, encoder, num_epochs, train_loss, test_loss, train_acc, test_acc):\n",
        "    \n",
        "    best_wts = None\n",
        "    best_loss = float('inf')\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        ## perform one epoch of training and validation\n",
        "        trn_loss, trn_acc = train_step(model, train_dl, criterion, optimizer, encoder)\n",
        "        val_loss, val_acc = validate_step(model, valid_dl, criterion, encoder)\n",
        "        scheduler.step()\n",
        "\n",
        "        train_loss += [trn_loss]\n",
        "        test_loss  += [val_loss]\n",
        "        train_acc  += [trn_acc ]\n",
        "        test_acc   += [val_acc ]\n",
        "\n",
        "        # save weights\n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_wts = model.state_dict().copy()\n",
        "            \n",
        "# #             torch.save(best_wts, 'models/lstm-{}-{}-{}-{}-{}-{}-{:.5f}.pth'.format(\n",
        "# #                 epoch, max_len, min_count, model.embed_size, model.hidden_size, model.num_layers, best_acc))\n",
        "\n",
        "        print(f'[Epoch:{epoch+1}/{num_epochs}] Training Loss: %.4f| Testing Loss: %.4f| Training ACC: %.4f| Testing ACC: %.4f|'\n",
        "              % (trn_loss* 10000, val_loss* 10000 , trn_acc*100, val_acc*100))\n",
        "\n",
        "    model.load_state_dict(best_wts)\n",
        "    return train_loss, test_loss, train_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "z3k2nCKC_Emn",
        "colab": {}
      },
      "source": [
        "train_loss , test_loss, train_acc, test_acc = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "50c47a07-64a2-416c-ed50-5774683cc54b",
        "id": "twj3RM_0_Ems",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_loss, test_loss, train_acc, test_acc = train(net2, train_dl, test_dl, criterion, optimizer, scheduler, encoder, 100, train_loss, test_loss, train_acc, test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch:1/100] Training Loss: 13745.4736| Testing Loss: 12322.1743| Training ACC: 77.6546| Testing ACC: 78.9648|\n",
            "[Epoch:2/100] Training Loss: 11942.4892| Testing Loss: 11820.7537| Training ACC: 80.0704| Testing ACC: 79.4043|\n",
            "[Epoch:3/100] Training Loss: 11607.4508| Testing Loss: 11606.5584| Training ACC: 80.4250| Testing ACC: 79.8145|\n",
            "[Epoch:4/100] Training Loss: 11438.2992| Testing Loss: 11481.5323| Training ACC: 80.6305| Testing ACC: 79.9609|\n",
            "[Epoch:5/100] Training Loss: 11330.6338| Testing Loss: 11395.1001| Training ACC: 80.8317| Testing ACC: 80.0781|\n",
            "[Epoch:6/100] Training Loss: 11252.7577| Testing Loss: 11332.3115| Training ACC: 80.9680| Testing ACC: 80.2637|\n",
            "[Epoch:7/100] Training Loss: 11195.2411| Testing Loss: 11286.3999| Training ACC: 81.0976| Testing ACC: 80.2441|\n",
            "[Epoch:8/100] Training Loss: 11148.7177| Testing Loss: 11246.8485| Training ACC: 81.2057| Testing ACC: 80.2539|\n",
            "[Epoch:9/100] Training Loss: 11113.1499| Testing Loss: 11218.9467| Training ACC: 81.3098| Testing ACC: 80.4980|\n",
            "[Epoch:10/100] Training Loss: 11080.4152| Testing Loss: 11188.4095| Training ACC: 81.3785| Testing ACC: 80.5273|\n",
            "[Epoch:11/100] Training Loss: 11053.0250| Testing Loss: 11162.9875| Training ACC: 81.4295| Testing ACC: 80.6250|\n",
            "[Epoch:12/100] Training Loss: 11028.2970| Testing Loss: 11145.0886| Training ACC: 81.5143| Testing ACC: 80.7227|\n",
            "[Epoch:13/100] Training Loss: 11010.5603| Testing Loss: 11128.0454| Training ACC: 81.5354| Testing ACC: 80.6836|\n",
            "[Epoch:14/100] Training Loss: 10991.8374| Testing Loss: 11113.1862| Training ACC: 81.5680| Testing ACC: 80.8691|\n",
            "[Epoch:15/100] Training Loss: 10975.9775| Testing Loss: 11097.1266| Training ACC: 81.5941| Testing ACC: 80.8398|\n",
            "[Epoch:16/100] Training Loss: 10959.0250| Testing Loss: 11086.6292| Training ACC: 81.6545| Testing ACC: 80.9277|\n",
            "[Epoch:17/100] Training Loss: 10946.9233| Testing Loss: 11073.5514| Training ACC: 81.6766| Testing ACC: 80.9766|\n",
            "[Epoch:18/100] Training Loss: 10933.8454| Testing Loss: 11064.2848| Training ACC: 81.7138| Testing ACC: 81.1133|\n",
            "[Epoch:19/100] Training Loss: 10924.0052| Testing Loss: 11055.6100| Training ACC: 81.7658| Testing ACC: 80.9375|\n",
            "[Epoch:20/100] Training Loss: 10912.9660| Testing Loss: 11045.5518| Training ACC: 81.7559| Testing ACC: 81.0742|\n",
            "[Epoch:21/100] Training Loss: 10902.6703| Testing Loss: 11037.6004| Training ACC: 81.8113| Testing ACC: 81.1230|\n",
            "[Epoch:22/100] Training Loss: 10893.0100| Testing Loss: 11030.5196| Training ACC: 81.7747| Testing ACC: 81.0547|\n",
            "[Epoch:23/100] Training Loss: 10885.7843| Testing Loss: 11024.1345| Training ACC: 81.8312| Testing ACC: 81.1230|\n",
            "[Epoch:24/100] Training Loss: 10875.8221| Testing Loss: 11017.1642| Training ACC: 81.8368| Testing ACC: 81.1230|\n",
            "[Epoch:25/100] Training Loss: 10870.5003| Testing Loss: 11010.5941| Training ACC: 81.8617| Testing ACC: 81.0938|\n",
            "[Epoch:26/100] Training Loss: 10862.7834| Testing Loss: 11003.7835| Training ACC: 81.9077| Testing ACC: 81.1133|\n",
            "[Epoch:27/100] Training Loss: 10856.4967| Testing Loss: 10999.3687| Training ACC: 81.8999| Testing ACC: 81.1426|\n",
            "[Epoch:28/100] Training Loss: 10850.1909| Testing Loss: 10994.2018| Training ACC: 81.9282| Testing ACC: 81.1426|\n",
            "[Epoch:29/100] Training Loss: 10845.8530| Testing Loss: 10990.1326| Training ACC: 81.9232| Testing ACC: 81.0645|\n",
            "[Epoch:30/100] Training Loss: 10839.8442| Testing Loss: 10986.5564| Training ACC: 81.9238| Testing ACC: 81.2012|\n",
            "[Epoch:31/100] Training Loss: 10839.0897| Testing Loss: 10980.5103| Training ACC: 81.9564| Testing ACC: 81.1816|\n",
            "[Epoch:32/100] Training Loss: 10829.8524| Testing Loss: 10974.9863| Training ACC: 82.0290| Testing ACC: 81.1328|\n",
            "[Epoch:33/100] Training Loss: 10822.8224| Testing Loss: 10973.2630| Training ACC: 81.9836| Testing ACC: 81.1523|\n",
            "[Epoch:34/100] Training Loss: 10817.3571| Testing Loss: 10968.7532| Training ACC: 82.0290| Testing ACC: 81.2207|\n",
            "[Epoch:35/100] Training Loss: 10815.2248| Testing Loss: 10964.0397| Training ACC: 81.9941| Testing ACC: 81.1816|\n",
            "[Epoch:36/100] Training Loss: 10812.4032| Testing Loss: 10960.6144| Training ACC: 82.0628| Testing ACC: 81.2207|\n",
            "[Epoch:37/100] Training Loss: 10809.0958| Testing Loss: 10958.4139| Training ACC: 82.0157| Testing ACC: 81.1621|\n",
            "[Epoch:38/100] Training Loss: 10803.2679| Testing Loss: 10955.3112| Training ACC: 82.1232| Testing ACC: 81.2891|\n",
            "[Epoch:39/100] Training Loss: 10801.0928| Testing Loss: 10953.6999| Training ACC: 82.0878| Testing ACC: 81.2695|\n",
            "[Epoch:40/100] Training Loss: 10797.4500| Testing Loss: 10949.8614| Training ACC: 82.1188| Testing ACC: 81.2891|\n",
            "[Epoch:41/100] Training Loss: 10794.3769| Testing Loss: 10946.7560| Training ACC: 82.0756| Testing ACC: 81.2305|\n",
            "[Epoch:42/100] Training Loss: 10786.9168| Testing Loss: 10943.3090| Training ACC: 82.1171| Testing ACC: 81.1914|\n",
            "[Epoch:43/100] Training Loss: 10785.9188| Testing Loss: 10941.8069| Training ACC: 82.1404| Testing ACC: 81.2402|\n",
            "[Epoch:44/100] Training Loss: 10783.8823| Testing Loss: 10937.2292| Training ACC: 82.1482| Testing ACC: 81.2793|\n",
            "[Epoch:45/100] Training Loss: 10782.4332| Testing Loss: 10936.4218| Training ACC: 82.1072| Testing ACC: 81.1816|\n",
            "[Epoch:46/100] Training Loss: 10775.9083| Testing Loss: 10934.1188| Training ACC: 82.1520| Testing ACC: 81.2891|\n",
            "[Epoch:47/100] Training Loss: 10774.1685| Testing Loss: 10931.7409| Training ACC: 82.1459| Testing ACC: 81.2695|\n",
            "[Epoch:48/100] Training Loss: 10771.1826| Testing Loss: 10928.9550| Training ACC: 82.1736| Testing ACC: 81.2598|\n",
            "[Epoch:49/100] Training Loss: 10769.8436| Testing Loss: 10926.7936| Training ACC: 82.1520| Testing ACC: 81.3086|\n",
            "[Epoch:50/100] Training Loss: 10764.4416| Testing Loss: 10926.6018| Training ACC: 82.1892| Testing ACC: 81.2891|\n",
            "[Epoch:51/100] Training Loss: 10764.3632| Testing Loss: 10924.1842| Training ACC: 82.2014| Testing ACC: 81.3770|\n",
            "[Epoch:52/100] Training Loss: 10762.1002| Testing Loss: 10921.6321| Training ACC: 82.2002| Testing ACC: 81.3379|\n",
            "[Epoch:53/100] Training Loss: 10758.9658| Testing Loss: 10920.5839| Training ACC: 82.1864| Testing ACC: 81.3086|\n",
            "[Epoch:54/100] Training Loss: 10756.3628| Testing Loss: 10916.7996| Training ACC: 82.1975| Testing ACC: 81.3477|\n",
            "[Epoch:55/100] Training Loss: 10755.7325| Testing Loss: 10917.0087| Training ACC: 82.2152| Testing ACC: 81.2500|\n",
            "[Epoch:56/100] Training Loss: 10752.8343| Testing Loss: 10914.7579| Training ACC: 82.2246| Testing ACC: 81.3672|\n",
            "[Epoch:57/100] Training Loss: 10750.9665| Testing Loss: 10914.0724| Training ACC: 82.2135| Testing ACC: 81.3281|\n",
            "[Epoch:58/100] Training Loss: 10747.7018| Testing Loss: 10911.7790| Training ACC: 82.2490| Testing ACC: 81.3770|\n",
            "[Epoch:59/100] Training Loss: 10743.8922| Testing Loss: 10909.5341| Training ACC: 82.2252| Testing ACC: 81.4062|\n",
            "[Epoch:60/100] Training Loss: 10744.4321| Testing Loss: 10909.2321| Training ACC: 82.2357| Testing ACC: 81.3770|\n",
            "[Epoch:61/100] Training Loss: 10741.9317| Testing Loss: 10906.4950| Training ACC: 82.2745| Testing ACC: 81.4062|\n",
            "[Epoch:62/100] Training Loss: 10739.8234| Testing Loss: 10905.2029| Training ACC: 82.2678| Testing ACC: 81.3770|\n",
            "[Epoch:63/100] Training Loss: 10739.7737| Testing Loss: 10903.6287| Training ACC: 82.2446| Testing ACC: 81.4258|\n",
            "[Epoch:64/100] Training Loss: 10737.7729| Testing Loss: 10902.2685| Training ACC: 82.2446| Testing ACC: 81.4258|\n",
            "[Epoch:65/100] Training Loss: 10734.2841| Testing Loss: 10901.2662| Training ACC: 82.2479| Testing ACC: 81.4062|\n",
            "[Epoch:66/100] Training Loss: 10735.8011| Testing Loss: 10900.1460| Training ACC: 82.2728| Testing ACC: 81.3867|\n",
            "[Epoch:67/100] Training Loss: 10731.8611| Testing Loss: 10899.6362| Training ACC: 82.2850| Testing ACC: 81.4648|\n",
            "[Epoch:68/100] Training Loss: 10730.5898| Testing Loss: 10897.9637| Training ACC: 82.2806| Testing ACC: 81.4062|\n",
            "[Epoch:69/100] Training Loss: 10730.8306| Testing Loss: 10895.6872| Training ACC: 82.2978| Testing ACC: 81.4355|\n",
            "[Epoch:70/100] Training Loss: 10728.1320| Testing Loss: 10894.8609| Training ACC: 82.3077| Testing ACC: 81.3965|\n",
            "[Epoch:71/100] Training Loss: 10727.3945| Testing Loss: 10894.2886| Training ACC: 82.2490| Testing ACC: 81.4160|\n",
            "[Epoch:72/100] Training Loss: 10723.9934| Testing Loss: 10892.2756| Training ACC: 82.2878| Testing ACC: 81.4844|\n",
            "[Epoch:73/100] Training Loss: 10721.9608| Testing Loss: 10891.7191| Training ACC: 82.2906| Testing ACC: 81.4160|\n",
            "[Epoch:74/100] Training Loss: 10721.6232| Testing Loss: 10890.7161| Training ACC: 82.2967| Testing ACC: 81.4258|\n",
            "[Epoch:75/100] Training Loss: 10720.0684| Testing Loss: 10889.5938| Training ACC: 82.3033| Testing ACC: 81.4062|\n",
            "[Epoch:76/100] Training Loss: 10717.2977| Testing Loss: 10889.2137| Training ACC: 82.3061| Testing ACC: 81.4160|\n",
            "[Epoch:77/100] Training Loss: 10715.8272| Testing Loss: 10888.5684| Training ACC: 82.3404| Testing ACC: 81.4844|\n",
            "[Epoch:78/100] Training Loss: 10717.4201| Testing Loss: 10886.9499| Training ACC: 82.3000| Testing ACC: 81.4062|\n",
            "[Epoch:79/100] Training Loss: 10716.0240| Testing Loss: 10886.1249| Training ACC: 82.3022| Testing ACC: 81.4258|\n",
            "[Epoch:80/100] Training Loss: 10714.7590| Testing Loss: 10885.4096| Training ACC: 82.2900| Testing ACC: 81.4453|\n",
            "[Epoch:81/100] Training Loss: 10711.1656| Testing Loss: 10884.6416| Training ACC: 82.3360| Testing ACC: 81.4355|\n",
            "[Epoch:82/100] Training Loss: 10711.5088| Testing Loss: 10883.5811| Training ACC: 82.3393| Testing ACC: 81.4551|\n",
            "[Epoch:83/100] Training Loss: 10713.1468| Testing Loss: 10882.4046| Training ACC: 82.2961| Testing ACC: 81.4062|\n",
            "[Epoch:84/100] Training Loss: 10710.7491| Testing Loss: 10882.6879| Training ACC: 82.3238| Testing ACC: 81.4062|\n",
            "[Epoch:85/100] Training Loss: 10710.5790| Testing Loss: 10880.7780| Training ACC: 82.3227| Testing ACC: 81.3770|\n",
            "[Epoch:86/100] Training Loss: 10708.6681| Testing Loss: 10880.4039| Training ACC: 82.3493| Testing ACC: 81.4844|\n",
            "[Epoch:87/100] Training Loss: 10706.8400| Testing Loss: 10879.6416| Training ACC: 82.3282| Testing ACC: 81.4941|\n",
            "[Epoch:88/100] Training Loss: 10704.5994| Testing Loss: 10878.4913| Training ACC: 82.3221| Testing ACC: 81.4355|\n",
            "[Epoch:89/100] Training Loss: 10705.3686| Testing Loss: 10877.6348| Training ACC: 82.3365| Testing ACC: 81.4746|\n",
            "[Epoch:90/100] Training Loss: 10703.9746| Testing Loss: 10876.7974| Training ACC: 82.3454| Testing ACC: 81.4551|\n",
            "[Epoch:91/100] Training Loss: 10700.4920| Testing Loss: 10875.9540| Training ACC: 82.3631| Testing ACC: 81.4941|\n",
            "[Epoch:92/100] Training Loss: 10700.8016| Testing Loss: 10875.5123| Training ACC: 82.3820| Testing ACC: 81.4648|\n",
            "[Epoch:93/100] Training Loss: 10700.5494| Testing Loss: 10875.5138| Training ACC: 82.3266| Testing ACC: 81.4648|\n",
            "[Epoch:94/100] Training Loss: 10699.8406| Testing Loss: 10874.2804| Training ACC: 82.3238| Testing ACC: 81.4746|\n",
            "[Epoch:95/100] Training Loss: 10699.1737| Testing Loss: 10873.3788| Training ACC: 82.3598| Testing ACC: 81.4746|\n",
            "[Epoch:96/100] Training Loss: 10696.8293| Testing Loss: 10872.9247| Training ACC: 82.3537| Testing ACC: 81.5039|\n",
            "[Epoch:97/100] Training Loss: 10696.1637| Testing Loss: 10871.9052| Training ACC: 82.3487| Testing ACC: 81.4160|\n",
            "[Epoch:98/100] Training Loss: 10696.2983| Testing Loss: 10871.3651| Training ACC: 82.3449| Testing ACC: 81.4941|\n",
            "[Epoch:99/100] Training Loss: 10694.8702| Testing Loss: 10870.7813| Training ACC: 82.3454| Testing ACC: 81.4453|\n",
            "[Epoch:100/100] Training Loss: 10695.6797| Testing Loss: 10870.1085| Training ACC: 82.3382| Testing ACC: 81.4844|\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "488d93bd-99a1-438f-cac1-9952f0d3541d",
        "id": "9NUxaERP_kZG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.plot([i for i in train_loss] , label='train')\n",
        "plt.plot([i for i in test_loss ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing loss: 1000, 100')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'trianing loss: 1000, 100')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A2pLV2sJlo0Zn9CIiuypb0Lv7A+5+LHAp8KV93d7MFobj+0sO5Md9g+mV+f3eXkQkaso+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\nRCTiFPQiIhH3/wEF9xKlZ6G4owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-lfX9ulnLq_",
        "colab_type": "code",
        "outputId": "41535054-14bd-4dde-bd1e-9c69667e82cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.plot([i for i in train_acc] , label='train')\n",
        "plt.plot([i for i in test_acc ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing acc: auto encoder')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'trianing acc: auto encoder')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZB7uW2VTPle9VpyG2zIW3roDK0rr7EZ0Cib2hIAuKdtlpp/3OBjQRm6d+5XwI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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FBkI9dsfOPN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_dl = DataLoader(test_ds , batch_size=len(test_ds) , shuffle= False , num_workers = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYofoChRlikF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fs , ts = next(iter(test_dl))\n",
        "fs , ts = fs.to(device) , ts.to(device)\n",
        "\n",
        "output = net2(encoder.encode(fs))\n",
        "mx , argmx = torch.max(output, dim=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SD9Kf69itOt7",
        "colab_type": "code",
        "outputId": "7d474af9-6095-442a-abc2-f9e704ee702a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "plt_conf_mat(ts.to('cpu'), argmx.to('cpu'), fmnist_test.classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f6fb29c5588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YtiXHA4HpjwB5fgHNe/828HY+04tsxIiIiEj8lImGjIiIiERB4nfCHLWyONhX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\nrGu5IWNmZmZdyw0ZMzMz61r/B/O8fP7cxP6VAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzFh0i5i2vH7",
        "colab_type": "code",
        "outputId": "f640d7f2-2103-4e86-d75c-47f7c1eed95d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "accuracy_score(ts.to('cpu'), argmx.to('cpu'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8134"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZibI98OjzDnk",
        "colab_type": "text"
      },
      "source": [
        "# PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccy6LW-vEDnX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import PCA"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfrPEuwizIEp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pca = PCA(n_components=200 , whiten=True)\n",
        "train_dl = DataLoader(train_ds , batch_size=len(train_ds) , shuffle= False , num_workers = 1)\n",
        "fs , ts = next(iter(train_dl))\n",
        "pca.fit(fs)\n",
        "pca"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "M6tqvfZa1OXR",
        "colab": {}
      },
      "source": [
        "net3 = MLP0().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "D_5Ig1IA1OXY",
        "colab": {}
      },
      "source": [
        "train_ds = FMNIST_DS(fmnist_train)\n",
        "test_ds  = FMNIST_DS(fmnist_test)\n",
        "batch_size = 256\n",
        "train_dl = DataLoader(train_ds , batch_size=batch_size , shuffle= True , num_workers = 10)\n",
        "test_dl = DataLoader(test_ds , batch_size=batch_size , shuffle= False , num_workers = 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QBcEDem21OXc",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss().to(device)\n",
        "optimizer = optim.SGD(net3.parameters(),lr=0.5)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "f4WXj6ls1OXh",
        "colab": {}
      },
      "source": [
        "def train_step(model, train_dl, criterion, optimizer, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    \n",
        "    # for i, (train_inputs, train_labels) in tqdm_notebook(enumerate(train_dl), \n",
        "    #                                                      desc='Training', \n",
        "    #                                                      total=len(train_dl)):\n",
        "    for i, (train_inputs, train_labels) in enumerate(train_dl):\n",
        "        # move to device\n",
        "        train_inputs = encoder.transform(train_inputs)\n",
        "        train_inputs = torch.tensor(train_inputs, dtype=torch.float)\n",
        "        train_inputs , train_labels = train_inputs.to(device) , train_labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output = model(train_inputs)\n",
        "        loss = criterion(output, train_labels)\n",
        "        loss.backward()     \n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        with torch.no_grad():\n",
        "            mx , argmx = torch.max(output, dim=1)\n",
        "            total_correct += torch.sum(argmx == train_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(train_dl), total_correct/len(train_dl)\n",
        "\n",
        "\n",
        "def validate_step(model, valid_dl, criterion, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    with torch.no_grad():\n",
        "        # for i, (test_inputs, test_labels) in tqdm_notebook(enumerate(valid_dl), \n",
        "        #                                                 desc='Validation', \n",
        "        #                                                 total=len(valid_dl)):\n",
        "        for i, (test_inputs, test_labels) in enumerate(valid_dl):\n",
        "            # move to device\n",
        "            test_inputs = encoder.transform(test_inputs)\n",
        "            test_inputs = torch.tensor(test_inputs , dtype=torch.float)\n",
        "            test_inputs , test_labels = test_inputs.to(device) , test_labels.to(device)\n",
        "            output = model(test_inputs)\n",
        "            loss = criterion(output, test_labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            mx , argmx = torch.max(output, dim=1)\n",
        "            total_correct += torch.sum(argmx == test_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(test_dl), total_correct/len(test_dl)\n",
        "\n",
        "\n",
        "def train(model, train_dl, valid_dl, criterion, optimizer, scheduler, encoder, num_epochs, train_loss, test_loss, train_acc, test_acc):\n",
        "    \n",
        "    best_wts = None\n",
        "    best_loss = float('inf')\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        ## perform one epoch of training and validation\n",
        "        trn_loss, trn_acc = train_step(model, train_dl, criterion, optimizer, encoder)\n",
        "        val_loss, val_acc = validate_step(model, valid_dl, criterion, encoder)\n",
        "        scheduler.step()\n",
        "\n",
        "        train_loss += [trn_loss]\n",
        "        test_loss  += [val_loss]\n",
        "        train_acc  += [trn_acc ]\n",
        "        test_acc   += [val_acc ]\n",
        "\n",
        "        # save weights\n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_wts = model.state_dict().copy()\n",
        "            \n",
        "# #             torch.save(best_wts, 'models/lstm-{}-{}-{}-{}-{}-{}-{:.5f}.pth'.format(\n",
        "# #                 epoch, max_len, min_count, model.embed_size, model.hidden_size, model.num_layers, best_acc))\n",
        "\n",
        "        print(f'[Epoch:{epoch+1}/{num_epochs}] Training Loss: %.4f| Testing Loss: %.4f| Training ACC: %.4f| Testing ACC: %.4f|'\n",
        "              % (trn_loss* 10000, val_loss* 10000 , trn_acc*100, val_acc*100))\n",
        "\n",
        "    model.load_state_dict(best_wts)\n",
        "    return train_loss, test_loss, train_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nodCcSjT1OXl",
        "colab": {}
      },
      "source": [
        "train_loss , test_loss, train_acc, test_acc = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e8a55b97-79dc-486e-a2fe-502cc39ce23a",
        "id": "44EK-v9P1OXq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "source": [
        "train_loss, test_loss, train_acc, test_acc = train(net3, train_dl, test_dl, criterion, optimizer, scheduler, pca, 30, train_loss, test_loss, train_acc, test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch:1/30] Training Loss: 10215.1528| Testing Loss: 10551.8138| Training ACC: 84.8659| Testing ACC: 83.4100|\n",
            "[Epoch:2/30] Training Loss: 10210.9589| Testing Loss: 10550.1485| Training ACC: 84.8864| Testing ACC: 83.4400|\n",
            "[Epoch:3/30] Training Loss: 10205.7810| Testing Loss: 10550.1449| Training ACC: 84.9030| Testing ACC: 83.4700|\n",
            "[Epoch:4/30] Training Loss: 10203.3111| Testing Loss: 10549.5727| Training ACC: 84.9197| Testing ACC: 83.5200|\n",
            "[Epoch:5/30] Training Loss: 10201.9908| Testing Loss: 10548.6858| Training ACC: 84.9379| Testing ACC: 83.4600|\n",
            "[Epoch:6/30] Training Loss: 10200.6868| Testing Loss: 10549.2282| Training ACC: 84.9490| Testing ACC: 83.4200|\n",
            "[Epoch:7/30] Training Loss: 10198.7619| Testing Loss: 10548.6286| Training ACC: 84.9169| Testing ACC: 83.4300|\n",
            "[Epoch:8/30] Training Loss: 10197.3061| Testing Loss: 10548.4915| Training ACC: 84.9396| Testing ACC: 83.3900|\n",
            "[Epoch:9/30] Training Loss: 10195.9494| Testing Loss: 10548.4891| Training ACC: 84.9501| Testing ACC: 83.3800|\n",
            "[Epoch:10/30] Training Loss: 10194.3046| Testing Loss: 10548.2316| Training ACC: 84.9357| Testing ACC: 83.4200|\n",
            "[Epoch:11/30] Training Loss: 10192.1235| Testing Loss: 10547.9169| Training ACC: 84.9523| Testing ACC: 83.4200|\n",
            "[Epoch:12/30] Training Loss: 10194.6263| Testing Loss: 10548.1565| Training ACC: 84.9274| Testing ACC: 83.4400|\n",
            "[Epoch:13/30] Training Loss: 10192.0650| Testing Loss: 10548.0754| Training ACC: 84.9551| Testing ACC: 83.4200|\n",
            "[Epoch:14/30] Training Loss: 10189.6998| Testing Loss: 10547.7285| Training ACC: 84.9656| Testing ACC: 83.4300|\n",
            "[Epoch:15/30] Training Loss: 10191.5810| Testing Loss: 10547.9407| Training ACC: 84.9723| Testing ACC: 83.4200|\n",
            "[Epoch:16/30] Training Loss: 10189.8307| Testing Loss: 10547.7607| Training ACC: 84.9557| Testing ACC: 83.4200|\n",
            "[Epoch:17/30] Training Loss: 10186.5553| Testing Loss: 10547.7405| Training ACC: 84.9778| Testing ACC: 83.4000|\n",
            "[Epoch:18/30] Training Loss: 10188.1322| Testing Loss: 10547.6665| Training ACC: 84.9557| Testing ACC: 83.3900|\n",
            "[Epoch:19/30] Training Loss: 10188.9539| Testing Loss: 10547.5962| Training ACC: 84.9523| Testing ACC: 83.4000|\n",
            "[Epoch:20/30] Training Loss: 10186.1198| Testing Loss: 10547.5497| Training ACC: 84.9640| Testing ACC: 83.4000|\n",
            "[Epoch:21/30] Training Loss: 10188.0754| Testing Loss: 10547.5855| Training ACC: 84.9823| Testing ACC: 83.4100|\n",
            "[Epoch:22/30] Training Loss: 10186.0415| Testing Loss: 10547.5068| Training ACC: 84.9773| Testing ACC: 83.4100|\n",
            "[Epoch:23/30] Training Loss: 10186.8907| Testing Loss: 10547.5211| Training ACC: 84.9601| Testing ACC: 83.4000|\n",
            "[Epoch:24/30] Training Loss: 10184.3159| Testing Loss: 10547.4985| Training ACC: 84.9789| Testing ACC: 83.4100|\n",
            "[Epoch:25/30] Training Loss: 10186.2608| Testing Loss: 10547.5259| Training ACC: 84.9806| Testing ACC: 83.4200|\n",
            "[Epoch:26/30] Training Loss: 10183.2175| Testing Loss: 10547.4877| Training ACC: 84.9845| Testing ACC: 83.4200|\n",
            "[Epoch:27/30] Training Loss: 10183.3308| Testing Loss: 10547.5056| Training ACC: 84.9734| Testing ACC: 83.4200|\n",
            "[Epoch:28/30] Training Loss: 10184.7250| Testing Loss: 10547.5092| Training ACC: 84.9795| Testing ACC: 83.4200|\n",
            "[Epoch:29/30] Training Loss: 10184.4210| Testing Loss: 10547.5152| Training ACC: 84.9740| Testing ACC: 83.4100|\n",
            "[Epoch:30/30] Training Loss: 10183.0631| Testing Loss: 10547.5247| Training ACC: 84.9806| Testing ACC: 83.4100|\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "0bdec445-a3bf-4a31-e522-6fb8b1e6a30b",
        "id": "erg_S0z21OX1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.plot([i for i in train_loss] , label='train')\n",
        "plt.plot([i for i in test_loss ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing loss: PCA')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'trianing loss: PCA')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F+Dlw5kQuDyCZUI1eRKTYhAW9mVWYWdXg38CbgL1euTOeVKMXERkpsb8JzOwW4Fygwcya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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "5fc03a93-6f0e-4f3a-bf80-c979c4afa115",
        "id": "3jW0fHDC1OX5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.plot([i for i in train_acc] , label='train')\n",
        "plt.plot([i for i in test_acc ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing acc: PCA')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'trianing acc: PCA')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+auXn99gPEcDi1+HcP9pe66F6V0vfgLIdcNHz9nliNxuG2VPB+ODMeyG138Hr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\nCDVb4aB3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "om9D4UX_1OX9",
        "colab": {}
      },
      "source": [
        "test_dl = DataLoader(test_ds , batch_size=len(test_ds) , shuffle= False , num_workers = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AhPUGdm-1OYC",
        "outputId": "8a64f827-dd5a-4272-face-e09029fb3eb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "fs , ts = next(iter(test_dl))\n",
        "fs = pca.transform(fs)\n",
        "fs = torch.tensor(fs, dtype=torch.float)\n",
        "fs , ts = fs.to(device) , ts.to(device)\n",
        "\n",
        "output = net3(fs)\n",
        "mx , argmx = torch.max(output, dim=1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <bound method _MultiProcessingDataLoaderIter.__del__ of <torch.utils.data.dataloader._MultiProcessingDataLoaderIter object at 0x7f6f6b06f2e8>>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\", line 961, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\", line 941, in _shutdown_workers\n",
            "    w.join()\n",
            "  File \"/usr/lib/python3.6/multiprocessing/process.py\", line 122, in join\n",
            "    assert self._parent_pid == os.getpid(), 'can only join a child process'\n",
            "AssertionError: can only join a child process\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "343d4f3f-4ceb-43cf-99c7-6340d81bb117",
        "id": "aYvX5UyJ1OYG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "plt_conf_mat(ts.to('cpu'), argmx.to('cpu'), fmnist_test.classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f6fb1b692e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kfigjIyIikhCcxsiIiIiIxBNlZERERBKFxsiIiIiIxA9lZERERBJFOczIqCFT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\nZmbWsdyRMTMzs471fwpGnk40ZNf2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "68b3b2e8-d7c4-45d6-abd6-918e42d96264",
        "id": "r-xJ291J1OYJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "accuracy_score(ts.to('cpu'), argmx.to('cpu'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8341"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udBtEQHWM0hM",
        "colab_type": "text"
      },
      "source": [
        "# RBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VabTJLM_Gyb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_ds = ENCODE_DS(fmnist_train)\n",
        "test_ds  = ENCODE_DS(fmnist_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4ewTTji_Ht_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class RBM(nn.Module):\n",
        "    def __init__(self, input_dim = 784, hidden = 500):\n",
        "        super().__init__()\n",
        "        self.n = input_dim\n",
        "        self.hidden = hidden\n",
        "\n",
        "        # encode\n",
        "        self.fc1  = nn.Linear(self.n, self.hidden)\n",
        "        self.act1 = nn.Tanh()\n",
        "\n",
        "        #decode\n",
        "        self.fc2  = nn.Linear(self.hidden, self.n)\n",
        "        self.act2 = nn.Tanh()\n",
        "\n",
        "    def encode(self, x):\n",
        "        out = self.fc1(x)\n",
        "        out = self.act1(out)\n",
        "        return out\n",
        "\n",
        "    def decode(self, x):\n",
        "        out = self.fc2(x)\n",
        "        out = self.act2(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        #encode\n",
        "        out = self.encode(x)\n",
        "        #decode\n",
        "        out = self.decode(out)\n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxScvhbEC-2F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dl = DataLoader(train_ds, batch_size=256, shuffle=True,   num_workers = 8) \n",
        "test_dl  = DataLoader(test_ds , batch_size=256, shuffle= False, num_workers = 8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4f1gpIKQEyw_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RBM1 = RBM(784, 500).to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "H-4SZvpCowWK",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss().to(device)\n",
        "optimizer = optim.SGD(RBM1.parameters(),lr=0.5)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_QCqPea0owWO",
        "colab": {}
      },
      "source": [
        "def plt_conf_mat(y_true , y_pred, labels, normalize = 'true'  , include_values = True, \n",
        "                 xticks_rotation='horizontal',\n",
        "                 values_format=None,\n",
        "                 cmap='winter'):\n",
        "    plt.figure(figsize=[9, 9])\n",
        "    ax = plt.subplot(1, 1, 1)\n",
        "    cm = confusion_matrix(y_true, y_pred, normalize=normalize)\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "    return disp.plot(include_values=include_values,\n",
        "                     cmap=cmap, ax=ax, xticks_rotation=xticks_rotation,\n",
        "                     values_format=values_format)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-EIgzPV6owWR",
        "colab": {}
      },
      "source": [
        "def train_step(model, train_dl, criterion, optimizer):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    \n",
        "    # for i, (train_inputs, train_labels) in tqdm_notebook(enumerate(train_dl), \n",
        "    #                                                      desc='Training', \n",
        "    #                                                      total=len(train_dl)):\n",
        "    for i, (train_inputs, train_labels) in enumerate(train_dl):\n",
        "        # move to device\n",
        "        train_inputs , train_labels = train_inputs.to(device) , train_labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output = model(train_inputs)\n",
        "        loss = criterion(output, train_labels)\n",
        "        loss.backward()     \n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        # with torch.no_grad():\n",
        "        #     mx , argmx = torch.max(output, dim=1)\n",
        "        #     total_correct += torch.sum(argmx == train_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(train_dl), total_correct/len(train_dl)\n",
        "\n",
        "\n",
        "def validate_step(model, valid_dl, criterion):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    with torch.no_grad():\n",
        "        # for i, (test_inputs, test_labels) in tqdm_notebook(enumerate(valid_dl), \n",
        "        #                                                 desc='Validation', \n",
        "        #                                                 total=len(valid_dl)):\n",
        "        for i, (test_inputs, test_labels) in enumerate(valid_dl):\n",
        "            # move to device\n",
        "            test_inputs , test_labels = test_inputs.to(device) , test_labels.to(device)\n",
        "\n",
        "            output = model(test_inputs)\n",
        "            loss = criterion(output, test_labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            # mx , argmx = torch.max(output, dim=1)\n",
        "            # total_correct += torch.sum(argmx == test_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(test_dl), total_correct/len(test_dl)\n",
        "\n",
        "\n",
        "def train(model, train_dl, valid_dl, criterion, optimizer, scheduler, num_epochs, train_loss, test_loss, train_acc, test_acc):\n",
        "    \n",
        "    best_wts = None\n",
        "    best_loss = float('inf')\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        ## perform one epoch of training and validation\n",
        "        trn_loss, trn_acc = train_step(model, train_dl, criterion, optimizer)\n",
        "        val_loss, val_acc = validate_step(model, valid_dl, criterion)\n",
        "        scheduler.step()\n",
        "\n",
        "        train_loss += [trn_loss]\n",
        "        test_loss  += [val_loss]\n",
        "        train_acc  += [trn_acc ]\n",
        "        test_acc   += [val_acc ]\n",
        "\n",
        "        # save weights\n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_wts = model.state_dict().copy()\n",
        "            \n",
        "# #             torch.save(best_wts, 'models/lstm-{}-{}-{}-{}-{}-{}-{:.5f}.pth'.format(\n",
        "# #                 epoch, max_len, min_count, model.embed_size, model.hidden_size, model.num_layers, best_acc))\n",
        "\n",
        "        print(f'[Epoch:{epoch+1}/{num_epochs}] Training Loss: %.4f| Testing Loss: %.4f| Training ACC: %.4f| Testing ACC: %.4f|'\n",
        "              % (trn_loss* 10000, val_loss* 10000 , trn_acc*100, val_acc*100))\n",
        "\n",
        "    model.load_state_dict(best_wts)\n",
        "    return train_loss, test_loss, train_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XKfripKkowWV",
        "colab": {}
      },
      "source": [
        "train_loss , test_loss, train_acc, test_acc = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "89cbb018-501c-4b26-d7eb-03ab781ce00f",
        "id": "c_m-HPzGowWZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "train_loss, test_loss, train_acc, test_acc = train(RBM1, train_dl, test_dl, criterion, optimizer, scheduler, 50, train_loss, test_loss, train_acc, test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch:1/50] Training Loss: 132.5824| Testing Loss: 133.4821| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:2/50] Training Loss: 131.7721| Testing Loss: 132.6987| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:3/50] Training Loss: 130.9768| Testing Loss: 131.9229| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:4/50] Training Loss: 130.2654| Testing Loss: 131.1832| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:5/50] Training Loss: 129.4928| Testing Loss: 130.4636| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:6/50] Training Loss: 128.7513| Testing Loss: 129.7600| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:7/50] Training Loss: 128.0375| Testing Loss: 129.0665| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:8/50] Training Loss: 127.4121| Testing Loss: 128.4030| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:9/50] Training Loss: 126.7352| Testing Loss: 127.7705| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:10/50] Training Loss: 126.0985| Testing Loss: 127.1270| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:11/50] Training Loss: 125.4670| Testing Loss: 126.5153| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:12/50] Training Loss: 124.8669| Testing Loss: 125.9154| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:13/50] Training Loss: 124.2946| Testing Loss: 125.3315| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:14/50] Training Loss: 123.7227| Testing Loss: 124.7627| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:15/50] Training Loss: 123.1535| Testing Loss: 124.2234| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:16/50] Training Loss: 122.6150| Testing Loss: 123.6791| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:17/50] Training Loss: 122.0862| Testing Loss: 123.1380| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:18/50] Training Loss: 121.5524| Testing Loss: 122.6215| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:19/50] Training Loss: 121.0571| Testing Loss: 122.1211| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:20/50] Training Loss: 120.5431| Testing Loss: 121.6349| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:21/50] Training Loss: 120.0769| Testing Loss: 121.1458| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:22/50] Training Loss: 119.6150| Testing Loss: 120.6870| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:23/50] Training Loss: 119.0850| Testing Loss: 120.2165| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:24/50] Training Loss: 118.6971| Testing Loss: 119.7677| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:25/50] Training Loss: 118.2219| Testing Loss: 119.3373| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:26/50] Training Loss: 117.7798| Testing Loss: 118.9011| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:27/50] Training Loss: 117.3403| Testing Loss: 118.4786| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:28/50] Training Loss: 116.9462| Testing Loss: 118.0736| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:29/50] Training Loss: 116.5484| Testing Loss: 117.6728| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:30/50] Training Loss: 116.1240| Testing Loss: 117.2806| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:31/50] Training Loss: 115.7530| Testing Loss: 116.8837| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:32/50] Training Loss: 115.3436| Testing Loss: 116.5135| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:33/50] Training Loss: 114.9962| Testing Loss: 116.1382| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:34/50] Training Loss: 114.6096| Testing Loss: 115.7754| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:35/50] Training Loss: 114.2366| Testing Loss: 115.4186| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:36/50] Training Loss: 113.9095| Testing Loss: 115.0723| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:37/50] Training Loss: 113.5601| Testing Loss: 114.7315| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:38/50] Training Loss: 113.2491| Testing Loss: 114.4017| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:39/50] Training Loss: 112.8699| Testing Loss: 114.0647| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:40/50] Training Loss: 112.5751| Testing Loss: 113.7432| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:41/50] Training Loss: 112.2341| Testing Loss: 113.4323| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:42/50] Training Loss: 111.9164| Testing Loss: 113.1160| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:43/50] Training Loss: 111.6337| Testing Loss: 112.8143| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:44/50] Training Loss: 111.3419| Testing Loss: 112.5189| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:45/50] Training Loss: 111.0265| Testing Loss: 112.2179| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:46/50] Training Loss: 110.7503| Testing Loss: 111.9312| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:47/50] Training Loss: 110.4717| Testing Loss: 111.6526| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:48/50] Training Loss: 110.1584| Testing Loss: 111.3772| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:49/50] Training Loss: 109.8802| Testing Loss: 111.1084| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:50/50] Training Loss: 109.6366| Testing Loss: 110.8316| Training ACC: 0.0000| Testing ACC: 0.0000|\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_ifnXk6uowWd",
        "outputId": "7e72d08d-f76e-4d07-b0e4-69081da2dc02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([i for i in train_loss] , label='train')\n",
        "plt.plot([i for i in test_loss ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing loss RMB1: 784, 500')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('MSE loss')\n",
        "plt.grid()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XraOzU3eZE5GhTQHRXyMOg0ln8IHmP1NT18Dzq3WPCBEZ2hQQe+LEz5No2crH\ncl/kty+syXRpRETSSgGxJyafARWHclXBX/jzP9exYlNDpkskIpI2Cog9YQYnXs7Iptc4JbaCHz25\nItMlEhFJGwXEnnrXBZA3jG+X/pm5r67XKa8iMmQpIPZUPB9O+zrj6xcwO76QHz+hVoSIDE0KiL1x\n7CUwYhrfStzL/MVrWLK+LtMlEhEZcAqIvRHNgbNvpKRlPVcm5nLDo8tx13URIjK0KCD21sT3wNRz\nuTwyh1UrlvPQQk3iJyJDiwJiX7z/v4lGjB+X/Jbr/7SELQ0tmS6RiMiAUUDsi9KDsDOu4biW5zi3\n4zGum7Mk0yUSERkwCoh9dcL/g8nv45rYb1ix+AUeW6y7zonI0KCA2FeRCJx7K9G8Em7Lu4XrHnyR\nddubM10qEZF9poAYCIUjsI/8jAmda/hy5+18/u4FtLR3ZLpUIiL7RAExUCafAad8ifNsPjM23sv1\nf1qa6RKJiOwTBcRAOv0aOPzDfCP2W7a8+CC/X7A20yUSEdlrCoiBFInAR2+DMcfyf7k/5bd/+AN/\nW7E106USEdkrCoiBFsvDPnkvseJKfh37X75398O8snZ7pkslIrLHFBDpUDiCyKf/QFF+gjsi/831\nv36IlZsbM10qEZE9ooBIl/JJRC95hOL8XH7e+S2+8YsHWbVFISEiBw4FRDpVHELOJY9QmhfjlrZr\n+ObP7mHZBt0/QkQODAqIdBs+hZxLH6W4qIjbOr7JzT//GYs0JiEiBwAFxGCoOITYvz1BTsUk/o8b\nuP8XN/Dksk2ZLpWISJ8UEIOleBTxzz5G50Hv5juRW1n9my/y66dX6D4SIrLfUkAMpkQx8Ysfpm3G\nv/HZnLkc+pdP890Hn6G1vTPTJRMReQcFxGCLxoid8306Z9/KcTmruPjVz3DtT37J2m1NmS6ZiMgu\nFBAZEjn6QmL/No/SwkL+p/a/eOTmK/nLq9WZLpaIyE4KiEwaPZ2CL/yd5sPP53IeYNjvz+UH9z3C\njpb2TJdMREQBkXG5RRRe8Avazv0FU2Mb+fdlF3P39/+Dl1ZvyXTJRCTLpTUgzGyWmb1mZivN7Ooe\n1uea2e/C9c+b2YRw+Zlm9pKZvRr+Pj2d5dwfxKZ/nLwvLqBp/Glc3nYX8V+fyW33PUCjWhMikiFp\nCwgziwK3AGcDU4FPmtnUbptdCtS6+2TgJuDGcPlW4EPufiRwMXB3usq5XykaybBL7qf53F8xIV7H\nZ5d9lnk3fpInFizV6bAiMujS2YI4Hljp7m+4eytwHzC72zazgTvDxw8AZ5iZufvL7r4+XL4EyDOz\n3DSWdf9hRt708yj68iK2HiLO+I8AABQtSURBVHEJszuf4Ng/ncndP/oay9dp6nARGTyWrm+mZnYe\nMMvdPxs+/xRwgrtfkbLN4nCb6vD5qnCbrd32c7m7v6+HY1wGXAZQWVl57H333bfX5W1sbKSwsHCv\nX58uiYbVDF/ySyYlF7O6s5K5pf/CuKnvoTix79m+v9Y53bKx3qpz9tjTep922mkvufuMHle6e1p+\ngPOAX6Y8/xTwk27bLAbGpjxfBVSkPJ8WLpu0u+Mde+yxvi+eeuqpfXp9WnV2ev0//+ybvnuU+zeL\n/dVrp/vDv7vdG5pb92m3+3Wd0ygb6606Z489rTewwHv5XE1nF9M6YFzK87Hhsh63MbMcoASoCZ+P\nBR4GPu3uq9JYzv2fGUVHfpARX1nA5tN/yMhYE+cu/SJv3PBuHnv4Lpo1kC0iaZDOgHgROMTMJppZ\nHLgAmNNtmzkEg9AQtDjmu7ubWSnwCHC1u/89jWU8sERzGHHqpVRc/SprT/4Oo6O1zHrlStZ891ie\n+P2tNCVbMl1CERlC0hYQ7t4OXAE8DiwD7nf3JWZ2vZl9ONzsV0C5ma0EvgR0nQp7BTAZuNbMFoU/\nI9JV1gNOTpxxZ/47FV9byuqTv0dRTjvvW3I1NTccyfw7r6dmW02mSygiQ0BOOnfu7nOBud2WXZvy\nOAmc38Prvg18O51lGxJy4kw883NwxmdZ9czv8H/8hNNX/4D6H9/KMxXnMOrMK5h82FGZLqWIHKDS\nGhAySCJRJr33QnjvhVS/+jQ1T/yIE7c+SOy++1kUP5a2oz/DUad/gnhudpwpLCIDQwExxIw98lTG\nHnkqdZvXsOLRWxi/+n6GP38lW5+/hhWjzmHc6Zcx9hC1KkRk9xQQQ1TJiIOYcfGNdLR/m1effpD2\nBXdy3PrfkvObe3g9dih1h5xHa+GhmS6miOzHFBBDXDQnxpGnXwCnX8DmdW+xcv6vGbn6IY5b+j+0\neZRXlxxP5F3nM+XUjxPLK8p0cUVkP6KAyCIjxoxnxKeuw/2bvPbKP1g971aO3vF3Kp/9Es3Pfo3l\nJScRP/IjTDr5o+TkFWe6uCKSYQqILGRmHDr9ZDZsb6Ps5DtY8Pe5NC96kMNrn6Lib1Uk//YVXi86\njsjUczj45POJFw/PdJFFJAMUEFkuHsthxswPw8wPk2xp5fm/P0rTq3/k0G1/ZfTzf6fj+a+zKm8a\nLRPfz0EnfYTCsUeCWaaLLSKDQAEhOyVy45xw+mw4fTbJ1nZeePFp6hb9kTFbnmbq0h/C0h+yNTKc\nzZWnUHzk2Yw5+v1YXlmmiy0iaaKAkB4l4jkcf/LpcPLpdHQ6ryxbxroFf6K4+inetf4xijc8TMe8\nCNV5h9Ny0KlUTj+LkkPeDTm61kJkqFBAyG5FI8ZR06Zy1LSpwFfZuK2BBS/8hZbX5zNm23NMW/4z\noq/dSpI464uOonPCKYw66kwKJhwHOfFMF19E9pICQvbYyGFFjJz1UZj1Udo7Oln8xhqqFz1J9K1n\nmFi/gENfvQlevYkkuWwsPhIfdwIjpp1GwaQTIVen0oocKBQQsk9yohGOOmQCRx1yKXApybYOXnh9\nFetfmU/O2r8zse6fHFb3U6JLbqGDCJsSB7NjxLEUTT6JEYe/h0jFJA16i+ynFBAyoBKxKMdPmwLT\npgCX09zawUur1rJ+ydPY2ucZUfdPjnhrDkVrfgfzoTFSRE3JEUTGHEP5oSeRP+E4KBqZ6WqICAoI\nSbO8eJTjD58Ah08APo27s3pzPX9f8hKNK58lb8tCDq55jSnbniO6+BYA6nIqqC+bRs7Yoxk2+Xhy\nx06H4tFqaYgMMgWEDCoz4+DKEg6uPB1OPx2AuqY2nn1zPRuWv0B79UJKahczZdMKxmx+msjLwT3T\nG6MlbC8+FK88gpIJR1M0/ihs+GE6a0okjRQQknEl+TFOmToepo4HzsfdWV+XZP5b69myciGd6xdR\nVLuM8TWrOWzb3eQuvx2ADiLUJMaTLJtC7ugjKJvwLuKjpkHZRIjqn7bIvtL/ItnvmBljSvMYUzoJ\njppE1z2ltje1snBdLevfWEyy+lViW5dS3rSSyU0vU7nhcXgpeH0bMbbnj6e1bDKxysMoHX8E8RGH\nQvlkiOdnrmIiBxgFhBwwSvPjnHRIJRxSCZwBQEens2ZbE/PWbmDL6ldp37iMRO3rDG98k0mNCxlV\n/TiRhb5zH3XxkYyLjmDThndRMPowCkYegpVPhtKDdM2GSDcKCDmgRSPGxIoCJlZMhqMnAx8BoKW9\ngze3NvH4hq3UvLWElk2vEatdRWnTmxzEBoYvf4jC15p27qeDCA25o0gWj8fKJ5E/cjKFlZOxYQdD\n2XiIF2SohiKZo4CQISk3J8qhI4s4dGQRHD0ROAcIWhwPPfYU2w+exvr11TRvXEFHzSoS9aspa6rm\noOaNTNy8iKLlTbvsrzGnjKaCcXjJQcQqJlBUeTCx8glBy6NkLMTyBr+SImmmgJCsEo0Yw/MjzDys\nEg6rBI7dua6to5Pq2mZe2tLIxs0badq4go6aN4jVr6WgaR2jWzYyrvYFhr31KDHr2GW/O2JlJPNG\n01E8hljZOPKGjydRPh5KxgWn6BaOgEh0kGsrsm8UECKhWDQSdlcVwOGVwNv37nZ3tjS2sHZbM4u2\nNbBt/Vu0bF1N5/Y1xBrXUZTcyKjkVkZvX8KYtX8lYS277LuDKDtyh9OaV0ln8WhipWPIKx9HYtjY\nIECKRgUXCKolIvsRBYRIP5gZI4oSjChKcOz4Mjj6IOA9O9d3dDqbG5JU1zaztLaJ2prNJLe+RUft\nWiKNG0g0b6SkaTOVTbWM3LaIUTb/HSEC0BwtJpmooD1/BJGikcRKR5M/bBQ5JaOhsDL4KaqE3GJd\nOChpp4AQGQDRiDGqJI9RJXkwYRgwFjhml21a2zvZVJ9kY32SxbVN1G7bSlPNWtpr1xHZsYl48yby\nW7ZQ3lpLZcNWRmxeSSHbybH2dxyv3eI055bTlqjAC4YTLRpBvLiSRGklkaJKKBj+9k/+sMH5I8iQ\no4AQGSTxnAjjhuUzblh+SohM32Ubd6euuY2N9UlW1CX5R12S7ds201y7jvb6TVjjZmJNm8hr3UZZ\n+3aG76ijfNsqKmwhhdQTsc53HNcxZkQKqXuxgo68cqyggkhBOfHiEeSWjCBSUBGESN6wt3/nFqmF\nIgoIkf2JmVGaH6c0P85hI4vDpQcBM3bZzt1pbGlna2MrNY0tLGxoYUtDM421W2ip30xH/SZoqiGn\neSu5LdsobN3OsLZ6yhsaGMYGyqyBQhqImL+jDAAdlkNLrIS2eCmdiTLIH0akYBixwgpyi8qJFpRD\nXhnklQa/E+HveIGCZQhRQIgcgMyMokSMokQsGFTf6eAet5/35FMcOeNEahpb2bijlWVNrWxrTNJU\nt5XWhq10NNbgTVuJNG8np6WW3LbtFLc1UNbcSFl9I6VsoswaSNBItIcury4dFqU1p5i2eAkduSWQ\nKMXyyojmlxIrKCVeOIxIXrCcRMmuP7nFulhxP6OAEMkC8WjKGMkuJvW4fVcLZXtTG9ub2tjc1Mrr\nzW1s39FCQ0MDLQ1b6WjcSmfzdmjeTrRlO7G2euJt9RS3NVKS3EExOyi1NRSxnGJrIkETkW6nB3fX\nHsmlNaeQ9lgxnblFkFuMJYqJ5pWSk19CLL+EaF5x0AUWrg9+iiC3MPgdL9QpxQNEASEi75DaQhm3\nB2PcXcFSn2ynvrktGE9pbqM+2U5dUytNO+ppadhG645aaN5OpKWOaGs9Oa0NRNsaibc2Uty6g2Jr\npogmimwjRaymyJoooqnHM7960hrN5xgSNCwooTNWAPEgQCKJIqKJInLyisnJKyLSFSzxgiBY4qmP\nC97+ydLAUUCIyIBJDZYxpXt+TUd7RycNyXYaku3UJ9uoT7axJQybhmQ7TckWWpvqaWuqozNZT2ey\nAVrqoKWRaGsj0fZGYu07KGgPAqawNUkBzRTYNgpZTwHNFFozBSSJWFu/y9UWyaUjmk97Tj6dsXw8\nlg/xQixeQCS3gEi8gGiigFiikEhX4MTyg8khYwXB9S3x8HcsP/zJ2+/DRwEhIvuNnGiEsoI4ZQV7\nPxbh7rS0dzJv/tMccezxYeC0sb61gx2t7exo6WBHSzs7mpO0NtXT3txAR0sD3tKAtzZhbTvCsGki\n2t5ErGMHebSQTwsFliSfFvJJkm9bKGAteeHyPFqI0QK9DPz3pt3idEQTdOTk0Rn+eE4exPKwWB4W\nzycazyeaG/7E87CdIZMIfpdNhHHH7fXfrDcKCBEZUsyMRCxKca4xoWLfJ1nsCpym1g6aWttpau2g\nsaWdHS3tbG1pp7mtg+bWzmBdSzvJliY6mhvpSO6gs7URb23CW5uItDdhbU1EOpqJtCfJaW8m2hkE\nTl5bkjxaybMW8mghQQsJaySfFnJpJWGt4fI2ErRi3UJoybAzmfaFB/a5rt0pIERE+tAVOIlYlGH7\n0LLpSWr4JNs6wrAJHifbOtnW2r5z2Y6u5a3ttLY009HahLc2423NTBlTwbQBLVkgrQFhZrOAHwNR\n4JfufkO39bnAXQQzptUAn3D3N82sHHgAOA64w92vSGc5RUQyITV89keRdO3YzKLALcDZwFTgk2Y2\ntdtmlwK17j4ZuAm4MVyeBK4Bvpyu8omISN/SFhDA8cBKd3/D3VuB+4DZ3baZDdwZPn4AOMPMzN13\nuPvfCIJCREQyIJ1dTGOAtSnPq4ETetvG3dvNrA4oB7b25wBmdhlwGUBlZSVVVVV7XdjGxsZ9ev2B\nKBvrDNlZb9U5ewxkvQ/oQWp3vw24DWDGjBk+c+bMvd5XVVUV+/L6A1E21hmys96qc/YYyHqns4tp\nHTAu5fnYcFmP25hZDlBCMFgtIiIZls6AeBE4xMwmmlkcuACY022bOcDF4ePzgPnuvmdXmYiISFqk\nrYspHFO4Anic4DTX2919iZldDyxw9znAr4C7zWwlsI0gRAAwszeBYiBuZucC73f3pekqr4iI7Cqt\nYxDuPheY223ZtSmPk8D5vbx2QjrLJiIifbOh0qNjZluAt/ZhFxX08+ypISQb6wzZWW/VOXvsab3H\nu/vwnlYMmYDYV2a2wN1n7H7LoSMb6wzZWW/VOXsMZL3TOUgtIiIHMAWEiIj0SAHxttsyXYAMyMY6\nQ3bWW3XOHgNWb41BiIhIj9SCEBGRHikgRESkR1kfEGY2y8xeM7OVZnZ1psuTDmY2zsyeMrOlZrbE\nzK4Klw8zs7+Y2Yrwd1mmy5oOZhY1s5fN7M/h84lm9nz4nv8unApmyDCzUjN7wMyWm9kyMzspG95r\nM/uP8N/3YjO718wSQ/G9NrPbzWyzmS1OWdbj+2uBm8P6/9PMjtmTY2V1QPTzpkZDQTvwn+4+FTgR\n+PewnlcDT7r7IcCT4fOh6CpgWcrzG4GbwhtV1RLcuGoo+THwmLsfBhxFUPch/V6b2RjgC8AMdz+C\nYHqfCxia7/UdwKxuy3p7f88GDgl/LgNu3ZMDZXVA0L+bGh3w3H2Duy8MHzcQfGCMYdcbNt0JnJuZ\nEqaPmY0FPgj8MnxuwOkEN6iCIVZvMysBTiWY5wx3b3X37WTBe00wdVBeODN0PrCBIfheu/vTBHPX\npert/Z0N3OWB54BSMxvV32Nle0D0dFOjMRkqy6AwswnA0cDzQKW7bwhXbQQqM1SsdPoR8F9AZ/i8\nHNju7u3h86H2nk8EtgC/DrvVfmlmBQzx99rd1wHfB9YQBEMd8BJD+71O1dv7u0+fcdkeEFnFzAqB\nB4Evunt96rpwmvUhdc6zmZ0DbHb3lzJdlkGUAxwD3OruRwM76NadNETf6zKCb8sTgdFAAe/shskK\nA/n+ZntA9OemRkOCmcUIwuE37v5QuHhTV3Mz/L05U+VLk5OBD4dTx99H0N3wY4JmdtdMxkPtPa8G\nqt39+fD5AwSBMdTf6/cBq919i7u3AQ8RvP9D+b1O1dv7u0+fcdkeEP25qdEBL+x3/xWwzN1/mLIq\n9YZNFwN/HOyypZO7f83dx4ZTx19AcEOqfwGeIrhBFQyxerv7RmCtmR0aLjoDWMoQf68JupZONLP8\n8N97V72H7HvdTW/v7xzg0+HZTCcCdSldUbuV9VdSm9kHCPqpu25q9D8ZLtKAM7NTgGeAV3m7L/7r\nBOMQ9wMHEUyV/nF37z74NSSY2Uzgy+5+jpkdTNCiGAa8DFzk7i2ZLN9AMrPpBIPyceAN4BKCL4ND\n+r02s28BnyA4a+9l4LME/e1D6r02s3uBmQTTem8Cvgn8gR7e3zAsf0LQ3dYEXOLuC/p9rGwPCBER\n6Vm2dzGJiEgvFBAiItIjBYSIiPRIASEiIj1SQIiISI8UECL7ATOb2TXbrMj+QgEhIiI9UkCI7AEz\nu8jMXjCzRWb28/BeE41mdlN4L4InzWx4uO10M3sunIf/4ZQ5+ieb2RNm9oqZLTSzSeHuC1Pu4/Cb\n8CInkYxRQIj0k5kdTnCl7snuPh3oAP6FYGK4Be4+DfgrwZWtAHcBX3X3dxFcxd61/DfALe5+FPBu\ngtlHIZhl94sE9yY5mGAuIZGMydn9JiISOgM4Fngx/HKfRzApWifwu3Cbe4CHwvsylLr7X8PldwK/\nN7MiYIy7Pwzg7kmAcH8vuHt1+HwRMAH4W/qrJdIzBYRI/xlwp7t/bZeFZtd0225v569JnSOoA/3/\nlAxTF5NI/z0JnGdmI2DnfYDHE/w/6pox9ELgb+5eB9Sa2XvC5Z8C/hre0a/azM4N95FrZvmDWguR\nftI3FJF+cvelZvYNYJ6ZRYA24N8JbspzfLhuM8E4BQTTLv8sDICuWVUhCIufm9n14T7OH8RqiPSb\nZnMV2Udm1ujuhZkuh8hAUxeTiIj0SC0IERHpkVoQIiLSIwWEiIj0SAEhIiI9UkCIiEiPFBAiItKj\n/x/KWXrmgT4pFQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fOFdQJ3qVq1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " torch.save(RBM1.state_dict().copy(), '/content/drive/My Drive/ut/NNDL/RBM1.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlgZm_FnDKzY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class stackedRBM():\n",
        "    def __init__(self, RBMs):\n",
        "        self.RBMS = RBMs\n",
        "        \n",
        "    def encode(self, x):\n",
        "        out = x\n",
        "        for rbm in self.RBMS:\n",
        "            out = rbm.encode(out)\n",
        "        return out\n",
        "\n",
        "    def decode(self, x):\n",
        "        out = x\n",
        "        for rbm in self.RBMS:\n",
        "            out = rbm.decode(out)\n",
        "        return out\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.decode(self.encode(x))\n",
        "\n",
        "    def __call__(self, x):\n",
        "        return self.forward(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FtqiPFLEQNH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_step(model, train_dl, criterion, optimizer, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    \n",
        "    # for i, (train_inputs, train_labels) in tqdm_notebook(enumerate(train_dl), \n",
        "    #                                                      desc='Training', \n",
        "    #                                                      total=len(train_dl)):\n",
        "    for i, (train_inputs, train_labels) in enumerate(train_dl):\n",
        "        # move to device\n",
        "        train_inputs , train_labels = train_inputs.to(device) , train_labels.to(device)\n",
        "        train_inputs = encoder.encode(train_inputs)\n",
        "        train_labels = encoder.encode(train_labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output = model(train_inputs)\n",
        "        loss = criterion(output, train_labels)\n",
        "        loss.backward()     \n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        # with torch.no_grad():\n",
        "        #     mx , argmx = torch.max(output, dim=1)\n",
        "        #     total_correct += torch.sum(argmx == train_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(train_dl), total_correct/len(train_dl)\n",
        "\n",
        "\n",
        "def validate_step(model, valid_dl, criterion, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    with torch.no_grad():\n",
        "        # for i, (test_inputs, test_labels) in tqdm_notebook(enumerate(valid_dl), \n",
        "        #                                                 desc='Validation', \n",
        "        #                                                 total=len(valid_dl)):\n",
        "        for i, (test_inputs, test_labels) in enumerate(valid_dl):\n",
        "            # move to device\n",
        "            test_inputs , test_labels = test_inputs.to(device) , test_labels.to(device)\n",
        "            test_inputs = encoder.encode(test_inputs)\n",
        "            test_labels = encoder.encode(test_labels)\n",
        "\n",
        "            output = model(test_inputs)\n",
        "            loss = criterion(output, test_labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            # mx , argmx = torch.max(output, dim=1)\n",
        "            # total_correct += torch.sum(argmx == test_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(test_dl), total_correct/len(test_dl)\n",
        "\n",
        "\n",
        "def train(model, encoder, train_dl, valid_dl, criterion, optimizer, scheduler, num_epochs, train_loss, test_loss, train_acc, test_acc):\n",
        "    \n",
        "    best_wts = None\n",
        "    best_loss = float('inf')\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        ## perform one epoch of training and validation\n",
        "        trn_loss, trn_acc = train_step(model, train_dl, criterion, optimizer, encoder)\n",
        "        val_loss, val_acc = validate_step(model, valid_dl, criterion, encoder)\n",
        "        scheduler.step()\n",
        "\n",
        "        train_loss += [trn_loss]\n",
        "        test_loss  += [val_loss]\n",
        "        train_acc  += [trn_acc ]\n",
        "        test_acc   += [val_acc ]\n",
        "\n",
        "        # save weights\n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_wts = model.state_dict().copy()\n",
        "            \n",
        "# #             torch.save(best_wts, 'models/lstm-{}-{}-{}-{}-{}-{}-{:.5f}.pth'.format(\n",
        "# #                 epoch, max_len, min_count, model.embed_size, model.hidden_size, model.num_layers, best_acc))\n",
        "\n",
        "        print(f'[Epoch:{epoch+1}/{num_epochs}] Training Loss: %.4f| Testing Loss: %.4f| Training ACC: %.4f| Testing ACC: %.4f|'\n",
        "              % (trn_loss* 10000, val_loss* 10000 , trn_acc*100, val_acc*100))\n",
        "\n",
        "    model.load_state_dict(best_wts)\n",
        "    return train_loss, test_loss, train_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3y1gEfOHCmvP",
        "colab": {}
      },
      "source": [
        "encoder = stackedRBM([RBM1])\n",
        "RBM2 = RBM(500, 300).to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zw6n9ufvCmvX",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss().to(device)\n",
        "optimizer = optim.SGD(RBM2.parameters(),lr=0.5)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GECHMwaEFOlE",
        "colab": {}
      },
      "source": [
        "train_loss , test_loss, train_acc, test_acc = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "ab92a14d-2124-486b-f248-f40cbcae890b",
        "id": "TSpFD8QzFOlJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "train_loss, test_loss, train_acc, test_acc = train(RBM2,encoder, train_dl, test_dl, criterion, optimizer, scheduler, 50, train_loss, test_loss, train_acc, test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch:1/50] Training Loss: 33.7430| Testing Loss: 34.0284| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:2/50] Training Loss: 33.5929| Testing Loss: 33.8836| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:3/50] Training Loss: 33.4536| Testing Loss: 33.7420| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:4/50] Training Loss: 33.3123| Testing Loss: 33.6034| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:5/50] Training Loss: 33.1731| Testing Loss: 33.4681| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:6/50] Training Loss: 33.0422| Testing Loss: 33.3355| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:7/50] Training Loss: 32.9048| Testing Loss: 33.2063| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:8/50] Training Loss: 32.7840| Testing Loss: 33.0799| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:9/50] Training Loss: 32.6456| Testing Loss: 32.9561| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:10/50] Training Loss: 32.5192| Testing Loss: 32.8347| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:11/50] Training Loss: 32.4157| Testing Loss: 32.7157| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:12/50] Training Loss: 32.3003| Testing Loss: 32.5997| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:13/50] Training Loss: 32.1761| Testing Loss: 32.4859| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:14/50] Training Loss: 32.0570| Testing Loss: 32.3742| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:15/50] Training Loss: 31.9420| Testing Loss: 32.2647| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:16/50] Training Loss: 31.8305| Testing Loss: 32.1575| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:17/50] Training Loss: 31.7411| Testing Loss: 32.0529| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:18/50] Training Loss: 31.6317| Testing Loss: 31.9497| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:19/50] Training Loss: 31.5331| Testing Loss: 31.8489| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:20/50] Training Loss: 31.4311| Testing Loss: 31.7500| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:21/50] Training Loss: 31.3390| Testing Loss: 31.6531| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:22/50] Training Loss: 31.2584| Testing Loss: 31.5575| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:23/50] Training Loss: 31.1502| Testing Loss: 31.4639| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:24/50] Training Loss: 31.0422| Testing Loss: 31.3723| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:25/50] Training Loss: 30.9649| Testing Loss: 31.2824| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:26/50] Training Loss: 30.8746| Testing Loss: 31.1937| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:27/50] Training Loss: 30.7857| Testing Loss: 31.1069| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:28/50] Training Loss: 30.7005| Testing Loss: 31.0218| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:29/50] Training Loss: 30.6140| Testing Loss: 30.9380| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:30/50] Training Loss: 30.5276| Testing Loss: 30.8558| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:31/50] Training Loss: 30.4466| Testing Loss: 30.7751| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:32/50] Training Loss: 30.3701| Testing Loss: 30.6960| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:33/50] Training Loss: 30.2897| Testing Loss: 30.6181| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:34/50] Training Loss: 30.2125| Testing Loss: 30.5414| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:35/50] Training Loss: 30.1440| Testing Loss: 30.4662| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:36/50] Training Loss: 30.0607| Testing Loss: 30.3923| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:37/50] Training Loss: 29.9911| Testing Loss: 30.3194| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:38/50] Training Loss: 29.9228| Testing Loss: 30.2480| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:39/50] Training Loss: 29.8535| Testing Loss: 30.1777| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:40/50] Training Loss: 29.7776| Testing Loss: 30.1085| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:41/50] Training Loss: 29.7053| Testing Loss: 30.0406| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:42/50] Training Loss: 29.6361| Testing Loss: 29.9737| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:43/50] Training Loss: 29.5801| Testing Loss: 29.9079| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:44/50] Training Loss: 29.5173| Testing Loss: 29.8432| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:45/50] Training Loss: 29.4476| Testing Loss: 29.7795| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:46/50] Training Loss: 29.3834| Testing Loss: 29.7169| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:47/50] Training Loss: 29.3210| Testing Loss: 29.6553| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:48/50] Training Loss: 29.2562| Testing Loss: 29.5946| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:49/50] Training Loss: 29.2044| Testing Loss: 29.5350| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:50/50] Training Loss: 29.1492| Testing Loss: 29.4761| Training ACC: 0.0000| Testing ACC: 0.0000|\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "f15a19f8-23ea-4651-f83a-13ef61f6defd",
        "id": "NW6JyyA8FOlN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([i for i in train_loss][50:] , label='train')\n",
        "plt.plot([i for i in test_loss ][50:] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing loss RMB2: 500, 300')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('MSE loss')\n",
        "plt.grid()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DJ7x9vyaxsPw5ebl9VfqevIfTfnlh0n1w/u/rGYIxxviU15KNqsYD/YHZODPHJqjqehF5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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lliYjBJHFOlT",
        "colab": {}
      },
      "source": [
        "#  torch.save(RBM2.state_dict().copy(), '/content/drive/My Drive/ut/NNDL/RBM2.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wNxAzT4J3X7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-yxkx7F_KCrC",
        "colab": {}
      },
      "source": [
        "encoder = stackedRBM([RBM1, RBM2])\n",
        "RBM3 = RBM(300, 200).to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kGh0cpxRKCrI",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss().to(device)\n",
        "optimizer = optim.SGD(RBM3.parameters(),lr=0.5)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fM8kVEptKCrL",
        "colab": {}
      },
      "source": [
        "train_loss , test_loss, train_acc, test_acc = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "926a243c-a321-4744-8e88-7b660b50b803",
        "id": "2gQ_wlLwKCrO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_loss, test_loss, train_acc, test_acc = train(RBM3,encoder, train_dl, test_dl, criterion, optimizer, scheduler, 100, train_loss, test_loss, train_acc, test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch:1/100] Training Loss: 534.0623| Testing Loss: 375.0516| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:2/100] Training Loss: 307.3522| Testing Loss: 257.4380| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:3/100] Training Loss: 226.1966| Testing Loss: 201.0855| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:4/100] Training Loss: 182.2878| Testing Loss: 166.7760| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:5/100] Training Loss: 153.7702| Testing Loss: 142.9997| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:6/100] Training Loss: 133.1547| Testing Loss: 125.0844| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:7/100] Training Loss: 117.2549| Testing Loss: 110.8994| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:8/100] Training Loss: 104.5281| Testing Loss: 99.3207| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:9/100] Training Loss: 94.0237| Testing Loss: 89.7179| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:10/100] Training Loss: 85.2527| Testing Loss: 81.6555| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:11/100] Training Loss: 77.8828| Testing Loss: 74.8329| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:12/100] Training Loss: 71.6260| Testing Loss: 69.0130| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:13/100] Training Loss: 66.2589| Testing Loss: 64.0238| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:14/100] Training Loss: 61.6430| Testing Loss: 59.7114| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:15/100] Training Loss: 57.6513| Testing Loss: 55.9649| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:16/100] Training Loss: 54.1582| Testing Loss: 52.6870| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:17/100] Training Loss: 51.1030| Testing Loss: 49.8001| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:18/100] Training Loss: 48.3920| Testing Loss: 47.2484| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:19/100] Training Loss: 45.9916| Testing Loss: 44.9740| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:20/100] Training Loss: 43.8421| Testing Loss: 42.9394| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:21/100] Training Loss: 41.9129| Testing Loss: 41.1089| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:22/100] Training Loss: 40.1906| Testing Loss: 39.4562| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:23/100] Training Loss: 38.6164| Testing Loss: 37.9561| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:24/100] Training Loss: 37.1864| Testing Loss: 36.5885| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:25/100] Training Loss: 35.8925| Testing Loss: 35.3403| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:26/100] Training Loss: 34.6895| Testing Loss: 34.1953| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:27/100] Training Loss: 33.5989| Testing Loss: 33.1428| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:28/100] Training Loss: 32.5847| Testing Loss: 32.1710| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:29/100] Training Loss: 31.6570| Testing Loss: 31.2739| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:30/100] Training Loss: 30.7888| Testing Loss: 30.4410| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:31/100] Training Loss: 29.9848| Testing Loss: 29.6663| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:32/100] Training Loss: 29.2454| Testing Loss: 28.9453| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:33/100] Training Loss: 28.5428| Testing Loss: 28.2732| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:34/100] Training Loss: 27.8942| Testing Loss: 27.6442| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:35/100] Training Loss: 27.2833| Testing Loss: 27.0545| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:36/100] Training Loss: 26.7122| Testing Loss: 26.5008| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:37/100] Training Loss: 26.1776| Testing Loss: 25.9804| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:38/100] Training Loss: 25.6680| Testing Loss: 25.4905| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:39/100] Training Loss: 25.1930| Testing Loss: 25.0279| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:40/100] Training Loss: 24.7419| Testing Loss: 24.5907| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:41/100] Training Loss: 24.3159| Testing Loss: 24.1768| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:42/100] Training Loss: 23.9102| Testing Loss: 23.7850| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:43/100] Training Loss: 23.5283| Testing Loss: 23.4136| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:44/100] Training Loss: 23.1675| Testing Loss: 23.0604| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:45/100] Training Loss: 22.8226| Testing Loss: 22.7246| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:46/100] Training Loss: 22.4906| Testing Loss: 22.4047| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:47/100] Training Loss: 22.1781| Testing Loss: 22.0998| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:48/100] Training Loss: 21.8785| Testing Loss: 21.8093| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:49/100] Training Loss: 21.5920| Testing Loss: 21.5319| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:50/100] Training Loss: 21.3235| Testing Loss: 21.2666| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:51/100] Training Loss: 21.0583| Testing Loss: 21.0127| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:52/100] Training Loss: 20.8137| Testing Loss: 20.7697| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:53/100] Training Loss: 20.5732| Testing Loss: 20.5369| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:54/100] Training Loss: 20.3456| Testing Loss: 20.3135| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:55/100] Training Loss: 20.1206| Testing Loss: 20.0989| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:56/100] Training Loss: 19.9114| Testing Loss: 19.8932| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:57/100] Training Loss: 19.7108| Testing Loss: 19.6949| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:58/100] Training Loss: 19.5149| Testing Loss: 19.5048| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:59/100] Training Loss: 19.3328| Testing Loss: 19.3212| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:60/100] Training Loss: 19.1466| Testing Loss: 19.1447| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:61/100] Training Loss: 18.9729| Testing Loss: 18.9748| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:62/100] Training Loss: 18.8108| Testing Loss: 18.8107| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:63/100] Training Loss: 18.6443| Testing Loss: 18.6526| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:64/100] Training Loss: 18.4918| Testing Loss: 18.5001| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:65/100] Training Loss: 18.3373| Testing Loss: 18.3527| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:66/100] Training Loss: 18.1935| Testing Loss: 18.2101| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:67/100] Training Loss: 18.0544| Testing Loss: 18.0723| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:68/100] Training Loss: 17.9161| Testing Loss: 17.9390| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:69/100] Training Loss: 17.7865| Testing Loss: 17.8099| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:70/100] Training Loss: 17.6556| Testing Loss: 17.6852| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:71/100] Training Loss: 17.5343| Testing Loss: 17.5642| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:72/100] Training Loss: 17.4138| Testing Loss: 17.4469| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:73/100] Training Loss: 17.3029| Testing Loss: 17.3332| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:74/100] Training Loss: 17.1909| Testing Loss: 17.2229| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:75/100] Training Loss: 17.0759| Testing Loss: 17.1161| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:76/100] Training Loss: 16.9707| Testing Loss: 17.0122| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:77/100] Training Loss: 16.8739| Testing Loss: 16.9113| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:78/100] Training Loss: 16.7679| Testing Loss: 16.8133| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:79/100] Training Loss: 16.6736| Testing Loss: 16.7179| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:80/100] Training Loss: 16.5802| Testing Loss: 16.6254| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:81/100] Training Loss: 16.4867| Testing Loss: 16.5354| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:82/100] Training Loss: 16.3984| Testing Loss: 16.4477| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:83/100] Training Loss: 16.3096| Testing Loss: 16.3624| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:84/100] Training Loss: 16.2294| Testing Loss: 16.2792| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:85/100] Training Loss: 16.1491| Testing Loss: 16.1985| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:86/100] Training Loss: 16.0662| Testing Loss: 16.1197| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:87/100] Training Loss: 15.9839| Testing Loss: 16.0429| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:88/100] Training Loss: 15.9112| Testing Loss: 15.9681| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:89/100] Training Loss: 15.8339| Testing Loss: 15.8950| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:90/100] Training Loss: 15.7649| Testing Loss: 15.8239| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:91/100] Training Loss: 15.6950| Testing Loss: 15.7544| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:92/100] Training Loss: 15.6276| Testing Loss: 15.6868| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:93/100] Training Loss: 15.5598| Testing Loss: 15.6205| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:94/100] Training Loss: 15.4951| Testing Loss: 15.5559| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:95/100] Training Loss: 15.4309| Testing Loss: 15.4929| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:96/100] Training Loss: 15.3711| Testing Loss: 15.4313| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:97/100] Training Loss: 15.3026| Testing Loss: 15.3711| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:98/100] Training Loss: 15.2450| Testing Loss: 15.3123| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:99/100] Training Loss: 15.1912| Testing Loss: 15.2548| Training ACC: 0.0000| Testing ACC: 0.0000|\n",
            "[Epoch:100/100] Training Loss: 15.1328| Testing Loss: 15.1986| Training ACC: 0.0000| Testing ACC: 0.0000|\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "1a07718d-138b-446b-f83f-36ffc9b4add4",
        "id": "thcuovOhKCrS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([i for i in train_loss] , label='train')\n",
        "plt.plot([i for i in test_loss ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing loss RMB3: 300, 200')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('MSE loss')\n",
        "plt.grid()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cUaNG9W/MIT1JTeeDcoWOQkSK3SOPPMIjjzzCMcccw7HHHsvixYt5++23OfLII/nTn/7E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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "shx88UAtKCrX",
        "colab": {}
      },
      "source": [
        "#  torch.save(RBM3.state_dict().copy(), '/content/drive/My Drive/ut/NNDL/RBM3.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrWO82W0Mj7N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder = stackedRBM([RBM1, RBM2, RBM3])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "59vQXHenM41E",
        "colab": {}
      },
      "source": [
        "net2 = MLP0().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IeEGj_NIM41N",
        "colab": {}
      },
      "source": [
        "train_ds = FMNIST_DS(fmnist_train)\n",
        "test_ds  = FMNIST_DS(fmnist_test)\n",
        "batch_size = 256\n",
        "train_dl = DataLoader(train_ds , batch_size=batch_size , shuffle= True , num_workers = 10)\n",
        "test_dl = DataLoader(test_ds , batch_size=batch_size , shuffle= False , num_workers = 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WSQaHHMtM41R",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss().to(device)\n",
        "optimizer = optim.SGD(net2.parameters(),lr=0.5)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fb2X8C-gM41U",
        "colab": {}
      },
      "source": [
        "def train_step(model, train_dl, criterion, optimizer, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    \n",
        "    # for i, (train_inputs, train_labels) in tqdm_notebook(enumerate(train_dl), \n",
        "    #                                                      desc='Training', \n",
        "    #                                                      total=len(train_dl)):\n",
        "    for i, (train_inputs, train_labels) in enumerate(train_dl):\n",
        "        # move to device\n",
        "        train_inputs , train_labels = train_inputs.to(device) , train_labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        train_inputs = encoder.encode(train_inputs)\n",
        "        output = model(train_inputs)\n",
        "        loss = criterion(output, train_labels)\n",
        "        loss.backward()     \n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        with torch.no_grad():\n",
        "            mx , argmx = torch.max(output, dim=1)\n",
        "            total_correct += torch.sum(argmx == train_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(train_dl), total_correct/len(train_dl)\n",
        "\n",
        "\n",
        "def validate_step(model, valid_dl, criterion, encoder):\n",
        "    total_loss = 0.0\n",
        "    total_correct = 0\n",
        "    with torch.no_grad():\n",
        "        # for i, (test_inputs, test_labels) in tqdm_notebook(enumerate(valid_dl), \n",
        "        #                                                 desc='Validation', \n",
        "        #                                                 total=len(valid_dl)):\n",
        "        for i, (test_inputs, test_labels) in enumerate(valid_dl):\n",
        "            # move to device\n",
        "            test_inputs , test_labels = test_inputs.to(device) , test_labels.to(device)\n",
        "            test_inputs = encoder.encode(test_inputs)\n",
        "            output = model(test_inputs)\n",
        "            loss = criterion(output, test_labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            mx , argmx = torch.max(output, dim=1)\n",
        "            total_correct += torch.sum(argmx == test_labels).item()/len(argmx)\n",
        "\n",
        "    return total_loss/len(test_dl), total_correct/len(test_dl)\n",
        "\n",
        "\n",
        "def train(model, train_dl, valid_dl, criterion, optimizer, scheduler, encoder, num_epochs, train_loss, test_loss, train_acc, test_acc):\n",
        "    \n",
        "    best_wts = None\n",
        "    best_loss = float('inf')\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        ## perform one epoch of training and validation\n",
        "        trn_loss, trn_acc = train_step(model, train_dl, criterion, optimizer, encoder)\n",
        "        val_loss, val_acc = validate_step(model, valid_dl, criterion, encoder)\n",
        "        scheduler.step()\n",
        "\n",
        "        train_loss += [trn_loss]\n",
        "        test_loss  += [val_loss]\n",
        "        train_acc  += [trn_acc ]\n",
        "        test_acc   += [val_acc ]\n",
        "\n",
        "        # save weights\n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_wts = model.state_dict().copy()\n",
        "            \n",
        "# #             torch.save(best_wts, 'models/lstm-{}-{}-{}-{}-{}-{}-{:.5f}.pth'.format(\n",
        "# #                 epoch, max_len, min_count, model.embed_size, model.hidden_size, model.num_layers, best_acc))\n",
        "\n",
        "        print(f'[Epoch:{epoch+1}/{num_epochs}] Training Loss: %.4f| Testing Loss: %.4f| Training ACC: %.4f| Testing ACC: %.4f|'\n",
        "              % (trn_loss* 10000, val_loss* 10000 , trn_acc*100, val_acc*100))\n",
        "\n",
        "    model.load_state_dict(best_wts)\n",
        "    return train_loss, test_loss, train_acc, test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vo0Z9FkcM41a",
        "colab": {}
      },
      "source": [
        "train_loss , test_loss, train_acc, test_acc = [], [], [], []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "1df5b7b7-97bc-4705-deb9-11953949d2aa",
        "id": "GfqEVIllM41d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_loss, test_loss, train_acc, test_acc = train(net2, train_dl, test_dl, criterion, optimizer, scheduler, encoder, 100, train_loss, test_loss, train_acc, test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch:1/100] Training Loss: 10568.2201| Testing Loss: 10766.7170| Training ACC: 82.6707| Testing ACC: 81.7188|\n",
            "[Epoch:2/100] Training Loss: 10570.0126| Testing Loss: 10766.2668| Training ACC: 82.6357| Testing ACC: 81.7480|\n",
            "[Epoch:3/100] Training Loss: 10566.3621| Testing Loss: 10766.0594| Training ACC: 82.6568| Testing ACC: 81.7578|\n",
            "[Epoch:4/100] Training Loss: 10565.0638| Testing Loss: 10765.8508| Training ACC: 82.6468| Testing ACC: 81.7578|\n",
            "[Epoch:5/100] Training Loss: 10567.6041| Testing Loss: 10765.7301| Training ACC: 82.6463| Testing ACC: 81.7188|\n",
            "[Epoch:6/100] Training Loss: 10565.8253| Testing Loss: 10765.6514| Training ACC: 82.6629| Testing ACC: 81.7285|\n",
            "[Epoch:7/100] Training Loss: 10565.8393| Testing Loss: 10765.5621| Training ACC: 82.6734| Testing ACC: 81.7383|\n",
            "[Epoch:8/100] Training Loss: 10567.9461| Testing Loss: 10765.4991| Training ACC: 82.6502| Testing ACC: 81.7285|\n",
            "[Epoch:9/100] Training Loss: 10563.7192| Testing Loss: 10765.4407| Training ACC: 82.6773| Testing ACC: 81.7383|\n",
            "[Epoch:10/100] Training Loss: 10567.2182| Testing Loss: 10765.3775| Training ACC: 82.6507| Testing ACC: 81.7285|\n",
            "[Epoch:11/100] Training Loss: 10562.8366| Testing Loss: 10765.3253| Training ACC: 82.6740| Testing ACC: 81.7383|\n",
            "[Epoch:12/100] Training Loss: 10566.2176| Testing Loss: 10765.2902| Training ACC: 82.6518| Testing ACC: 81.7285|\n",
            "[Epoch:13/100] Training Loss: 10565.6453| Testing Loss: 10765.2684| Training ACC: 82.6457| Testing ACC: 81.7285|\n",
            "[Epoch:14/100] Training Loss: 10566.7085| Testing Loss: 10765.2508| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:15/100] Training Loss: 10566.8468| Testing Loss: 10765.2361| Training ACC: 82.6629| Testing ACC: 81.7285|\n",
            "[Epoch:16/100] Training Loss: 10564.4943| Testing Loss: 10765.2261| Training ACC: 82.6679| Testing ACC: 81.7285|\n",
            "[Epoch:17/100] Training Loss: 10562.8173| Testing Loss: 10765.2169| Training ACC: 82.6723| Testing ACC: 81.7285|\n",
            "[Epoch:18/100] Training Loss: 10567.3498| Testing Loss: 10765.2059| Training ACC: 82.6568| Testing ACC: 81.7285|\n",
            "[Epoch:19/100] Training Loss: 10566.1977| Testing Loss: 10765.1978| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:20/100] Training Loss: 10565.4974| Testing Loss: 10765.1924| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:21/100] Training Loss: 10567.4734| Testing Loss: 10765.1883| Training ACC: 82.6424| Testing ACC: 81.7285|\n",
            "[Epoch:22/100] Training Loss: 10565.6912| Testing Loss: 10765.1855| Training ACC: 82.6612| Testing ACC: 81.7285|\n",
            "[Epoch:23/100] Training Loss: 10565.7122| Testing Loss: 10765.1833| Training ACC: 82.6612| Testing ACC: 81.7285|\n",
            "[Epoch:24/100] Training Loss: 10565.7947| Testing Loss: 10765.1799| Training ACC: 82.6452| Testing ACC: 81.7285|\n",
            "[Epoch:25/100] Training Loss: 10567.6575| Testing Loss: 10765.1787| Training ACC: 82.6452| Testing ACC: 81.7285|\n",
            "[Epoch:26/100] Training Loss: 10565.0301| Testing Loss: 10765.1763| Training ACC: 82.6562| Testing ACC: 81.7285|\n",
            "[Epoch:27/100] Training Loss: 10566.1089| Testing Loss: 10765.1758| Training ACC: 82.6524| Testing ACC: 81.7285|\n",
            "[Epoch:28/100] Training Loss: 10564.9889| Testing Loss: 10765.1747| Training ACC: 82.6490| Testing ACC: 81.7285|\n",
            "[Epoch:29/100] Training Loss: 10563.8546| Testing Loss: 10765.1740| Training ACC: 82.6745| Testing ACC: 81.7285|\n",
            "[Epoch:30/100] Training Loss: 10565.3298| Testing Loss: 10765.1734| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:31/100] Training Loss: 10566.0244| Testing Loss: 10765.1730| Training ACC: 82.6441| Testing ACC: 81.7285|\n",
            "[Epoch:32/100] Training Loss: 10567.1935| Testing Loss: 10765.1725| Training ACC: 82.6424| Testing ACC: 81.7285|\n",
            "[Epoch:33/100] Training Loss: 10565.5420| Testing Loss: 10765.1720| Training ACC: 82.6479| Testing ACC: 81.7285|\n",
            "[Epoch:34/100] Training Loss: 10565.5649| Testing Loss: 10765.1719| Training ACC: 82.6701| Testing ACC: 81.7285|\n",
            "[Epoch:35/100] Training Loss: 10564.9016| Testing Loss: 10765.1714| Training ACC: 82.6646| Testing ACC: 81.7285|\n",
            "[Epoch:36/100] Training Loss: 10565.3854| Testing Loss: 10765.1713| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:37/100] Training Loss: 10563.3213| Testing Loss: 10765.1713| Training ACC: 82.6673| Testing ACC: 81.7285|\n",
            "[Epoch:38/100] Training Loss: 10563.7228| Testing Loss: 10765.1713| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:39/100] Training Loss: 10564.9411| Testing Loss: 10765.1712| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:40/100] Training Loss: 10565.1057| Testing Loss: 10765.1713| Training ACC: 82.6646| Testing ACC: 81.7285|\n",
            "[Epoch:41/100] Training Loss: 10564.2069| Testing Loss: 10765.1710| Training ACC: 82.6729| Testing ACC: 81.7285|\n",
            "[Epoch:42/100] Training Loss: 10566.4081| Testing Loss: 10765.1710| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:43/100] Training Loss: 10564.8860| Testing Loss: 10765.1707| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:44/100] Training Loss: 10563.9926| Testing Loss: 10765.1706| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:45/100] Training Loss: 10564.5702| Testing Loss: 10765.1707| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:46/100] Training Loss: 10562.7715| Testing Loss: 10765.1703| Training ACC: 82.6812| Testing ACC: 81.7285|\n",
            "[Epoch:47/100] Training Loss: 10567.0005| Testing Loss: 10765.1705| Training ACC: 82.6424| Testing ACC: 81.7285|\n",
            "[Epoch:48/100] Training Loss: 10564.7937| Testing Loss: 10765.1702| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:49/100] Training Loss: 10564.6557| Testing Loss: 10765.1702| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:50/100] Training Loss: 10566.0904| Testing Loss: 10765.1702| Training ACC: 82.6341| Testing ACC: 81.7285|\n",
            "[Epoch:51/100] Training Loss: 10563.1316| Testing Loss: 10765.1702| Training ACC: 82.6701| Testing ACC: 81.7285|\n",
            "[Epoch:52/100] Training Loss: 10564.6943| Testing Loss: 10765.1703| Training ACC: 82.6646| Testing ACC: 81.7285|\n",
            "[Epoch:53/100] Training Loss: 10564.3738| Testing Loss: 10765.1703| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:54/100] Training Loss: 10564.7996| Testing Loss: 10765.1703| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:55/100] Training Loss: 10563.8795| Testing Loss: 10765.1703| Training ACC: 82.6646| Testing ACC: 81.7285|\n",
            "[Epoch:56/100] Training Loss: 10563.1815| Testing Loss: 10765.1704| Training ACC: 82.6701| Testing ACC: 81.7285|\n",
            "[Epoch:57/100] Training Loss: 10564.3971| Testing Loss: 10765.1704| Training ACC: 82.6562| Testing ACC: 81.7285|\n",
            "[Epoch:58/100] Training Loss: 10564.0956| Testing Loss: 10765.1704| Training ACC: 82.6673| Testing ACC: 81.7285|\n",
            "[Epoch:59/100] Training Loss: 10564.7358| Testing Loss: 10765.1704| Training ACC: 82.6729| Testing ACC: 81.7285|\n",
            "[Epoch:60/100] Training Loss: 10563.7997| Testing Loss: 10765.1704| Training ACC: 82.6646| Testing ACC: 81.7285|\n",
            "[Epoch:61/100] Training Loss: 10564.5295| Testing Loss: 10765.1704| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:62/100] Training Loss: 10565.6770| Testing Loss: 10765.1704| Training ACC: 82.6562| Testing ACC: 81.7285|\n",
            "[Epoch:63/100] Training Loss: 10564.2365| Testing Loss: 10765.1704| Training ACC: 82.6701| Testing ACC: 81.7285|\n",
            "[Epoch:64/100] Training Loss: 10566.8532| Testing Loss: 10765.1704| Training ACC: 82.6341| Testing ACC: 81.7285|\n",
            "[Epoch:65/100] Training Loss: 10566.3258| Testing Loss: 10765.1704| Training ACC: 82.6507| Testing ACC: 81.7285|\n",
            "[Epoch:66/100] Training Loss: 10565.6894| Testing Loss: 10765.1704| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:67/100] Training Loss: 10564.8971| Testing Loss: 10765.1704| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:68/100] Training Loss: 10566.3768| Testing Loss: 10765.1704| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:69/100] Training Loss: 10565.3802| Testing Loss: 10765.1704| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:70/100] Training Loss: 10566.8252| Testing Loss: 10765.1704| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:71/100] Training Loss: 10565.0834| Testing Loss: 10765.1704| Training ACC: 82.6562| Testing ACC: 81.7285|\n",
            "[Epoch:72/100] Training Loss: 10563.3575| Testing Loss: 10765.1704| Training ACC: 82.6812| Testing ACC: 81.7285|\n",
            "[Epoch:73/100] Training Loss: 10563.5328| Testing Loss: 10765.1704| Training ACC: 82.6673| Testing ACC: 81.7285|\n",
            "[Epoch:74/100] Training Loss: 10566.6591| Testing Loss: 10765.1704| Training ACC: 82.6562| Testing ACC: 81.7285|\n",
            "[Epoch:75/100] Training Loss: 10564.6939| Testing Loss: 10765.1704| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:76/100] Training Loss: 10564.4776| Testing Loss: 10765.1704| Training ACC: 82.6729| Testing ACC: 81.7285|\n",
            "[Epoch:77/100] Training Loss: 10565.1942| Testing Loss: 10765.1704| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:78/100] Training Loss: 10564.4813| Testing Loss: 10765.1704| Training ACC: 82.6701| Testing ACC: 81.7285|\n",
            "[Epoch:79/100] Training Loss: 10564.8124| Testing Loss: 10765.1704| Training ACC: 82.6562| Testing ACC: 81.7285|\n",
            "[Epoch:80/100] Training Loss: 10565.5324| Testing Loss: 10765.1704| Training ACC: 82.6479| Testing ACC: 81.7285|\n",
            "[Epoch:81/100] Training Loss: 10563.6815| Testing Loss: 10765.1704| Training ACC: 82.6673| Testing ACC: 81.7285|\n",
            "[Epoch:82/100] Training Loss: 10563.9346| Testing Loss: 10765.1704| Training ACC: 82.6673| Testing ACC: 81.7285|\n",
            "[Epoch:83/100] Training Loss: 10564.4851| Testing Loss: 10765.1704| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:84/100] Training Loss: 10564.3109| Testing Loss: 10765.1704| Training ACC: 82.6590| Testing ACC: 81.7285|\n",
            "[Epoch:85/100] Training Loss: 10567.5242| Testing Loss: 10765.1704| Training ACC: 82.6424| Testing ACC: 81.7285|\n",
            "[Epoch:86/100] Training Loss: 10564.4790| Testing Loss: 10765.1704| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:87/100] Training Loss: 10564.1987| Testing Loss: 10765.1704| Training ACC: 82.6535| Testing ACC: 81.7285|\n",
            "[Epoch:88/100] Training Loss: 10563.0309| Testing Loss: 10765.1704| Training ACC: 82.6756| Testing ACC: 81.7285|\n",
            "[Epoch:89/100] Training Loss: 10567.5773| Testing Loss: 10765.1704| Training ACC: 82.6341| Testing ACC: 81.7285|\n",
            "[Epoch:90/100] Training Loss: 10563.3085| Testing Loss: 10765.1704| Training ACC: 82.6812| Testing ACC: 81.7285|\n",
            "[Epoch:91/100] Training Loss: 10562.9965| Testing Loss: 10765.1704| Training ACC: 82.6756| Testing ACC: 81.7285|\n",
            "[Epoch:92/100] Training Loss: 10564.5308| Testing Loss: 10765.1704| Training ACC: 82.6562| Testing ACC: 81.7285|\n",
            "[Epoch:93/100] Training Loss: 10562.6309| Testing Loss: 10765.1704| Training ACC: 82.6701| Testing ACC: 81.7285|\n",
            "[Epoch:94/100] Training Loss: 10564.5849| Testing Loss: 10765.1704| Training ACC: 82.6673| Testing ACC: 81.7285|\n",
            "[Epoch:95/100] Training Loss: 10565.0298| Testing Loss: 10765.1704| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:96/100] Training Loss: 10564.1653| Testing Loss: 10765.1704| Training ACC: 82.6618| Testing ACC: 81.7285|\n",
            "[Epoch:97/100] Training Loss: 10566.0022| Testing Loss: 10765.1704| Training ACC: 82.6479| Testing ACC: 81.7285|\n",
            "[Epoch:98/100] Training Loss: 10563.7604| Testing Loss: 10765.1704| Training ACC: 82.6646| Testing ACC: 81.7285|\n",
            "[Epoch:99/100] Training Loss: 10567.0333| Testing Loss: 10765.1704| Training ACC: 82.6424| Testing ACC: 81.7285|\n",
            "[Epoch:100/100] Training Loss: 10565.7091| Testing Loss: 10765.1704| Training ACC: 82.6618| Testing ACC: 81.7285|\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d635671c-60d4-4f25-c63c-40bfa0425672",
        "id": "NfVPlr_fM41i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.plot([i for i in train_loss] , label='train')\n",
        "plt.plot([i for i in test_loss ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing loss: 1000, 100')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'trianing loss: 1000, 100')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q9/5trN9gREaTmq2gn05Xv+1oTphfynfue5lrf/U8v3h6B1//86W8cU5JoUs7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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "a450a50c-42f1-45c2-ad31-b99ba2d31c83",
        "id": "owY7GkbpM41m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.plot([i for i in train_acc] , label='train')\n",
        "plt.plot([i for i in test_acc ] , label ='test')\n",
        "plt.legend()\n",
        "plt.title('trianing acc: auto encoder')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'trianing acc: auto encoder')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EDPv6rAftxUQn/8SOiwK2pZzudEFMaOOGXMlD4OQfdzxWpVSv1SsS/Zr8coZn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\nlHK5/w83K8pYQcD9+AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cefK9WkAM41q",
        "colab": {}
      },
      "source": [
        "test_dl = DataLoader(test_ds , batch_size=len(test_ds) , shuffle= False , num_workers = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fF7XYAj3M41u",
        "colab": {}
      },
      "source": [
        "fs , ts = next(iter(test_dl))\n",
        "fs , ts = fs.to(device) , ts.to(device)\n",
        "\n",
        "output = net2(encoder.encode(fs))\n",
        "mx , argmx = torch.max(output, dim=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "94d70ecf-be2f-4241-bc7c-a8feadb9e9b5",
        "id": "56EcpBRHM41y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "plt_conf_mat(ts.to('cpu'), argmx.to('cpu'), fmnist_test.classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f53b1e3de80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7etBgN7w+HjKy4aUOcGfP4Dz9FsJ9n1rHrexUuPYS2FyzbPF1XwFPzoDkPBjZ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\nbsiYmZlZy/o/m78Qd1N+wVYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "f1ec5ce9-2944-43fe-cbfd-fe3653106d94",
        "id": "DqyltlwmM412",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "accuracy_score(ts.to('cpu'), argmx.to('cpu'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8174"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOLydsIRQN5e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}