1DCNN_IRIS_Pytorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "1DCNN_IRIS_Pytorch.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYOOWvihbSH2",
        "outputId": "f1e8263e-73b8-4c16-ee15-7dfa28dc1072"
      },
      "source": [
        "import torch\n",
        "print(torch.__version__)\n",
        "import numpy as np\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, TensorDataset, DataLoader\n",
        "from torch.utils.data import random_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from  sklearn import  datasets\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "%matplotlib inline\n",
        "\n",
        "iris = load_iris()\n",
        "X = iris['data']\n",
        "y = iris['target']\n",
        "names = iris['target_names']\n",
        "feature_names = iris['feature_names']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.9.0+cu102\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "km0_eFF-ftNa",
        "outputId": "bc967868-d124-4af6-cd7a-7af3228e3561"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "plt.style.use('ggplot')\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
        "for target, target_name in enumerate(names):\n",
        "    X_plot = X[y == target]\n",
        "    ax1.plot(X_plot[:, 0], X_plot[:, 1], \n",
        "             linestyle='none', \n",
        "             marker='o', \n",
        "             label=target_name)\n",
        "ax1.set_xlabel(feature_names[0])\n",
        "ax1.set_ylabel(feature_names[1])\n",
        "ax1.axis('equal')\n",
        "ax1.legend();\n",
        "\n",
        "for target, target_name in enumerate(names):\n",
        "    X_plot = X[y == target]\n",
        "    ax2.plot(X_plot[:, 2], X_plot[:, 3], \n",
        "             linestyle='none', \n",
        "             marker='o', \n",
        "             label=target_name)\n",
        "ax2.set_xlabel(feature_names[2])\n",
        "ax2.set_ylabel(feature_names[3])\n",
        "ax2.axis('equal')\n",
        "ax2.legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMx5gBsHftQg",
        "outputId": "6538c570-433e-460d-87a8-bff341443578"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data set into training and testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=True)\n",
        "\n",
        "X_train = X_train.reshape(X_train.shape[0], 4, 1).astype('float32')\n",
        "X_test = X_test.reshape(X_test.shape[0], 4, 1).astype('float32') \n",
        "#y_train = np.array(y_train).reshape(X_train.shape[0], 1)#.astype('float32') \n",
        "#y_test = np.array(y_test).reshape(X_test.shape[0], 1)#.astype('float32') \n",
        "\n",
        "print(\"Training Shape\", X_train.shape, y_train.shape)\n",
        "print(\"Testing Shape\", X_test.shape, y_test.shape) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Shape (105, 4, 1) (105,)\n",
            "Testing Shape (45, 4, 1) (45,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02MHItyYftTq",
        "outputId": "c369e8c7-12d7-4648-ad4c-ad32f04ce2d6"
      },
      "source": [
        "from torch.utils.data import Dataset, TensorDataset, DataLoader\n",
        "\n",
        "class MyDataset(Dataset):\n",
        "    def __init__(self, data, targets):   #, transform=None\n",
        "        self.data = data\n",
        "        self.labels = torch.LongTensor(targets)\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        x = self.data[index]\n",
        "        y = self.labels[index]\n",
        "        \n",
        "        x = torch.FloatTensor(x)\n",
        "        y = torch.from_numpy(np.array(y))\n",
        "        return x, y\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "def get_default_device():\n",
        "    \"\"\"Pick GPU if available, else CPU\"\"\"\n",
        "    if torch.cuda.is_available():\n",
        "        return torch.device('cuda')\n",
        "    else:\n",
        "        return torch.device('cpu')\n",
        "\n",
        "def to_device(data, device):\n",
        "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
        "    if isinstance(data, (list,tuple)):\n",
        "        return [to_device(x, device) for x in data]\n",
        "    return data.to(device, non_blocking=True)\n",
        "\n",
        "class DeviceDataLoader():\n",
        "    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
        "    def __init__(self, dl, device):\n",
        "        self.dl = dl\n",
        "        self.device = device\n",
        "        \n",
        "    def __iter__(self):\n",
        "        \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
        "        for b in self.dl: \n",
        "            yield to_device(b, self.device)\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"Number of batches\"\"\"\n",
        "        return len(self.dl)\n",
        "\n",
        "device = get_default_device()\n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeOKJ4xfftWj"
      },
      "source": [
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "import torch.nn as nn\n",
        "\n",
        "train_dataset = MyDataset(X_train, y_train)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=5)\n",
        "test_dataset = MyDataset(X_test, y_test)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CsBMtZsftZJ"
      },
      "source": [
        "from torch.autograd import Variable\n",
        "\n",
        "class OneDCNN(nn.Module):\n",
        "  def __init__(self, num_classes, in_channels, out_channels, kernel_size=3, stride=1):\n",
        "    super(OneDCNN,self).__init__()\n",
        "    self.in_channels = in_channels\n",
        "    self.out_channels = out_channels\n",
        "    self.kernel_size = kernel_size\n",
        "    \n",
        "    self.conv1d = nn.Conv1d(in_channels, out_channels, kernel_size)\n",
        "    self.relu = nn.ReLU(inplace=True)\n",
        "    self.fc1 = nn.Linear(out_channels,50)\n",
        "    self.fc2 = nn.Linear(50,num_classes)\n",
        "        \n",
        "  def forward(self,x):\n",
        "    x = self.conv1d(x)\n",
        "    x = self.relu(x)\n",
        "    x = x.view(x.size(0), -1)\n",
        "    x = self.fc1(x)\n",
        "    x = self.relu(x)\n",
        "    x = self.fc2(x)\n",
        "        \n",
        "    return x\n",
        "\n",
        "  def training_step(self, batch):\n",
        "    data, labels = batch \n",
        "    out = self(data)                  # Generate predictions\n",
        "    loss = F.cross_entropy(out, labels) # Calculate loss\n",
        "    acc = accuracy(out, labels)           # Calculate accuracy\n",
        "    return acc, loss\n",
        "    \n",
        "  def validation_step(self, batch):\n",
        "    data, labels = batch \n",
        "    out = self(data)                    # Generate predictions\n",
        "    loss = F.cross_entropy(out, labels)   # Calculate loss\n",
        "    acc = accuracy(out, labels)           # Calculate accuracy\n",
        "    return {'val_loss': loss, 'val_acc': acc}\n",
        "        \n",
        "  def validation_epoch_end(self, outputs):\n",
        "    batch_losses = [x['val_loss'] for x in outputs]\n",
        "    epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
        "    batch_accs = [x['val_acc'] for x in outputs]\n",
        "    epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
        "    return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
        "    \n",
        "  def epoch_end(self, epoch, result):\n",
        "    print(\"Epoch [{}]: train_acc: {:.4f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
        "        epoch, result['train_acc'], result['train_loss'], result['val_loss'], result['val_acc']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_C0ZqrFftcG"
      },
      "source": [
        "train_dl = DeviceDataLoader(train_dataloader, device)\n",
        "val_dl = DeviceDataLoader(test_dataloader, device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4Irnm1XftfI",
        "outputId": "ca3661e7-9e26-48e6-b459-7f14c0107183"
      },
      "source": [
        "num_epochs = 100\n",
        "learning_rate = 0.001\n",
        "\n",
        "in_channels = 4\n",
        "out_channels = 4\n",
        "kernel_size = 1\n",
        "\n",
        "num_classes = 3\n",
        "\n",
        "model = to_device(OneDCNN(num_classes, in_channels, out_channels, kernel_size), device)\n",
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneDCNN(\n",
              "  (conv1d): Conv1d(4, 4, kernel_size=(1,), stride=(1,))\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (fc1): Linear(in_features=4, out_features=50, bias=True)\n",
              "  (fc2): Linear(in_features=50, out_features=3, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZNi3O-Wf9I4",
        "outputId": "755392d2-bac0-44c8-9b9a-f4b2565e6f7a"
      },
      "source": [
        "def accuracy(outputs, labels):\n",
        "  _, preds = torch.max(outputs, dim=1)\n",
        "  return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
        "\n",
        "@torch.no_grad()\n",
        "def evaluate(model, val_loader):\n",
        "  outputs = [model.validation_step(batch) for batch in val_loader]\n",
        "  return model.validation_epoch_end(outputs)\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "#optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate)\n",
        "\n",
        "best_acc = -np.inf\n",
        "history = []\n",
        "# Train the model\n",
        "for epoch in range(num_epochs):\n",
        "  # Training Phase \n",
        "  # model.train()\n",
        "  train_acc = []\n",
        "  train_losses = []\n",
        "  for batch in train_dl:\n",
        "    acc, loss = model.training_step(batch)\n",
        "    train_acc.append(acc)\n",
        "    train_losses.append(loss)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "  # Validation phase\n",
        "  result = evaluate(model, val_dl)\n",
        "  result['train_acc'] = torch.stack(train_acc).mean().item()\n",
        "  result['train_loss'] = torch.stack(train_losses).mean().item()\n",
        "    \n",
        "  if result['val_acc']>best_acc:\n",
        "    best_acc = result['val_acc']\n",
        "    torch.save(model.state_dict(), '1dcnn-iris.pth')\n",
        "\n",
        "  model.epoch_end(epoch, result)\n",
        "  history.append(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0]: train_acc: 0.3524, train_loss: 1.1158, val_loss: 1.0493, val_acc: 0.5778\n",
            "Epoch [1]: train_acc: 0.6762, train_loss: 1.0409, val_loss: 0.9959, val_acc: 0.7556\n",
            "Epoch [2]: train_acc: 0.7810, train_loss: 0.9783, val_loss: 0.9355, val_acc: 0.7556\n",
            "Epoch [3]: train_acc: 0.8190, train_loss: 0.9111, val_loss: 0.8634, val_acc: 0.7556\n",
            "Epoch [4]: train_acc: 0.8286, train_loss: 0.8338, val_loss: 0.7834, val_acc: 0.7556\n",
            "Epoch [5]: train_acc: 0.8286, train_loss: 0.7502, val_loss: 0.7042, val_acc: 0.7556\n",
            "Epoch [6]: train_acc: 0.8286, train_loss: 0.6668, val_loss: 0.6326, val_acc: 0.7556\n",
            "Epoch [7]: train_acc: 0.8476, train_loss: 0.5915, val_loss: 0.5729, val_acc: 0.7778\n",
            "Epoch [8]: train_acc: 0.8476, train_loss: 0.5296, val_loss: 0.5243, val_acc: 0.7778\n",
            "Epoch [9]: train_acc: 0.8476, train_loss: 0.4813, val_loss: 0.4858, val_acc: 0.7778\n",
            "Epoch [10]: train_acc: 0.8476, train_loss: 0.4443, val_loss: 0.4551, val_acc: 0.8000\n",
            "Epoch [11]: train_acc: 0.8476, train_loss: 0.4141, val_loss: 0.4305, val_acc: 0.8000\n",
            "Epoch [12]: train_acc: 0.8667, train_loss: 0.3899, val_loss: 0.4105, val_acc: 0.8000\n",
            "Epoch [13]: train_acc: 0.8667, train_loss: 0.3699, val_loss: 0.3928, val_acc: 0.8000\n",
            "Epoch [14]: train_acc: 0.8762, train_loss: 0.3521, val_loss: 0.3773, val_acc: 0.8222\n",
            "Epoch [15]: train_acc: 0.8762, train_loss: 0.3365, val_loss: 0.3632, val_acc: 0.8222\n",
            "Epoch [16]: train_acc: 0.8762, train_loss: 0.3229, val_loss: 0.3508, val_acc: 0.8222\n",
            "Epoch [17]: train_acc: 0.8762, train_loss: 0.3107, val_loss: 0.3395, val_acc: 0.8222\n",
            "Epoch [18]: train_acc: 0.8857, train_loss: 0.2994, val_loss: 0.3286, val_acc: 0.8222\n",
            "Epoch [19]: train_acc: 0.8857, train_loss: 0.2890, val_loss: 0.3184, val_acc: 0.8222\n",
            "Epoch [20]: train_acc: 0.8857, train_loss: 0.2793, val_loss: 0.3088, val_acc: 0.8444\n",
            "Epoch [21]: train_acc: 0.8952, train_loss: 0.2700, val_loss: 0.2995, val_acc: 0.8444\n",
            "Epoch [22]: train_acc: 0.8952, train_loss: 0.2610, val_loss: 0.2904, val_acc: 0.8444\n",
            "Epoch [23]: train_acc: 0.9048, train_loss: 0.2521, val_loss: 0.2809, val_acc: 0.8444\n",
            "Epoch [24]: train_acc: 0.9143, train_loss: 0.2432, val_loss: 0.2714, val_acc: 0.8667\n",
            "Epoch [25]: train_acc: 0.9238, train_loss: 0.2343, val_loss: 0.2620, val_acc: 0.8889\n",
            "Epoch [26]: train_acc: 0.9333, train_loss: 0.2253, val_loss: 0.2523, val_acc: 0.9111\n",
            "Epoch [27]: train_acc: 0.9333, train_loss: 0.2161, val_loss: 0.2424, val_acc: 0.9111\n",
            "Epoch [28]: train_acc: 0.9333, train_loss: 0.2069, val_loss: 0.2318, val_acc: 0.9111\n",
            "Epoch [29]: train_acc: 0.9429, train_loss: 0.1979, val_loss: 0.2211, val_acc: 0.9111\n",
            "Epoch [30]: train_acc: 0.9429, train_loss: 0.1889, val_loss: 0.2103, val_acc: 0.9333\n",
            "Epoch [31]: train_acc: 0.9429, train_loss: 0.1801, val_loss: 0.1995, val_acc: 0.9333\n",
            "Epoch [32]: train_acc: 0.9524, train_loss: 0.1713, val_loss: 0.1887, val_acc: 0.9556\n",
            "Epoch [33]: train_acc: 0.9524, train_loss: 0.1632, val_loss: 0.1791, val_acc: 0.9556\n",
            "Epoch [34]: train_acc: 0.9429, train_loss: 0.1555, val_loss: 0.1700, val_acc: 0.9556\n",
            "Epoch [35]: train_acc: 0.9524, train_loss: 0.1485, val_loss: 0.1615, val_acc: 0.9556\n",
            "Epoch [36]: train_acc: 0.9619, train_loss: 0.1420, val_loss: 0.1537, val_acc: 0.9556\n",
            "Epoch [37]: train_acc: 0.9619, train_loss: 0.1361, val_loss: 0.1466, val_acc: 0.9556\n",
            "Epoch [38]: train_acc: 0.9619, train_loss: 0.1307, val_loss: 0.1400, val_acc: 0.9556\n",
            "Epoch [39]: train_acc: 0.9619, train_loss: 0.1258, val_loss: 0.1340, val_acc: 0.9556\n",
            "Epoch [40]: train_acc: 0.9619, train_loss: 0.1214, val_loss: 0.1287, val_acc: 0.9556\n",
            "Epoch [41]: train_acc: 0.9619, train_loss: 0.1174, val_loss: 0.1239, val_acc: 0.9556\n",
            "Epoch [42]: train_acc: 0.9619, train_loss: 0.1138, val_loss: 0.1195, val_acc: 0.9556\n",
            "Epoch [43]: train_acc: 0.9619, train_loss: 0.1105, val_loss: 0.1156, val_acc: 0.9556\n",
            "Epoch [44]: train_acc: 0.9619, train_loss: 0.1074, val_loss: 0.1121, val_acc: 0.9556\n",
            "Epoch [45]: train_acc: 0.9619, train_loss: 0.1045, val_loss: 0.1088, val_acc: 0.9556\n",
            "Epoch [46]: train_acc: 0.9619, train_loss: 0.1019, val_loss: 0.1057, val_acc: 0.9556\n",
            "Epoch [47]: train_acc: 0.9619, train_loss: 0.0995, val_loss: 0.1029, val_acc: 0.9556\n",
            "Epoch [48]: train_acc: 0.9619, train_loss: 0.0972, val_loss: 0.1003, val_acc: 0.9556\n",
            "Epoch [49]: train_acc: 0.9619, train_loss: 0.0951, val_loss: 0.0980, val_acc: 0.9556\n",
            "Epoch [50]: train_acc: 0.9619, train_loss: 0.0932, val_loss: 0.0958, val_acc: 0.9556\n",
            "Epoch [51]: train_acc: 0.9619, train_loss: 0.0913, val_loss: 0.0938, val_acc: 0.9556\n",
            "Epoch [52]: train_acc: 0.9619, train_loss: 0.0896, val_loss: 0.0920, val_acc: 0.9556\n",
            "Epoch [53]: train_acc: 0.9619, train_loss: 0.0879, val_loss: 0.0903, val_acc: 0.9556\n",
            "Epoch [54]: train_acc: 0.9619, train_loss: 0.0864, val_loss: 0.0888, val_acc: 0.9556\n",
            "Epoch [55]: train_acc: 0.9619, train_loss: 0.0849, val_loss: 0.0874, val_acc: 0.9556\n",
            "Epoch [56]: train_acc: 0.9619, train_loss: 0.0835, val_loss: 0.0861, val_acc: 0.9556\n",
            "Epoch [57]: train_acc: 0.9619, train_loss: 0.0822, val_loss: 0.0848, val_acc: 0.9556\n",
            "Epoch [58]: train_acc: 0.9619, train_loss: 0.0809, val_loss: 0.0837, val_acc: 0.9556\n",
            "Epoch [59]: train_acc: 0.9619, train_loss: 0.0797, val_loss: 0.0827, val_acc: 0.9556\n",
            "Epoch [60]: train_acc: 0.9619, train_loss: 0.0786, val_loss: 0.0817, val_acc: 0.9556\n",
            "Epoch [61]: train_acc: 0.9619, train_loss: 0.0775, val_loss: 0.0807, val_acc: 0.9556\n",
            "Epoch [62]: train_acc: 0.9619, train_loss: 0.0765, val_loss: 0.0798, val_acc: 0.9556\n",
            "Epoch [63]: train_acc: 0.9619, train_loss: 0.0755, val_loss: 0.0791, val_acc: 0.9556\n",
            "Epoch [64]: train_acc: 0.9619, train_loss: 0.0745, val_loss: 0.0783, val_acc: 0.9556\n",
            "Epoch [65]: train_acc: 0.9619, train_loss: 0.0736, val_loss: 0.0777, val_acc: 0.9556\n",
            "Epoch [66]: train_acc: 0.9619, train_loss: 0.0727, val_loss: 0.0771, val_acc: 0.9556\n",
            "Epoch [67]: train_acc: 0.9619, train_loss: 0.0718, val_loss: 0.0765, val_acc: 0.9556\n",
            "Epoch [68]: train_acc: 0.9619, train_loss: 0.0710, val_loss: 0.0760, val_acc: 0.9556\n",
            "Epoch [69]: train_acc: 0.9714, train_loss: 0.0701, val_loss: 0.0756, val_acc: 0.9556\n",
            "Epoch [70]: train_acc: 0.9714, train_loss: 0.0693, val_loss: 0.0751, val_acc: 0.9556\n",
            "Epoch [71]: train_acc: 0.9714, train_loss: 0.0685, val_loss: 0.0746, val_acc: 0.9556\n",
            "Epoch [72]: train_acc: 0.9714, train_loss: 0.0678, val_loss: 0.0740, val_acc: 0.9556\n",
            "Epoch [73]: train_acc: 0.9714, train_loss: 0.0670, val_loss: 0.0736, val_acc: 0.9556\n",
            "Epoch [74]: train_acc: 0.9714, train_loss: 0.0663, val_loss: 0.0731, val_acc: 0.9556\n",
            "Epoch [75]: train_acc: 0.9714, train_loss: 0.0656, val_loss: 0.0727, val_acc: 0.9556\n",
            "Epoch [76]: train_acc: 0.9714, train_loss: 0.0649, val_loss: 0.0723, val_acc: 0.9556\n",
            "Epoch [77]: train_acc: 0.9714, train_loss: 0.0642, val_loss: 0.0720, val_acc: 0.9556\n",
            "Epoch [78]: train_acc: 0.9714, train_loss: 0.0635, val_loss: 0.0717, val_acc: 0.9556\n",
            "Epoch [79]: train_acc: 0.9714, train_loss: 0.0629, val_loss: 0.0715, val_acc: 0.9556\n",
            "Epoch [80]: train_acc: 0.9714, train_loss: 0.0622, val_loss: 0.0714, val_acc: 0.9556\n",
            "Epoch [81]: train_acc: 0.9714, train_loss: 0.0615, val_loss: 0.0713, val_acc: 0.9556\n",
            "Epoch [82]: train_acc: 0.9714, train_loss: 0.0609, val_loss: 0.0712, val_acc: 0.9556\n",
            "Epoch [83]: train_acc: 0.9714, train_loss: 0.0603, val_loss: 0.0711, val_acc: 0.9556\n",
            "Epoch [84]: train_acc: 0.9714, train_loss: 0.0597, val_loss: 0.0711, val_acc: 0.9556\n",
            "Epoch [85]: train_acc: 0.9810, train_loss: 0.0591, val_loss: 0.0711, val_acc: 0.9556\n",
            "Epoch [86]: train_acc: 0.9810, train_loss: 0.0585, val_loss: 0.0711, val_acc: 0.9556\n",
            "Epoch [87]: train_acc: 0.9810, train_loss: 0.0579, val_loss: 0.0710, val_acc: 0.9556\n",
            "Epoch [88]: train_acc: 0.9810, train_loss: 0.0573, val_loss: 0.0702, val_acc: 0.9556\n",
            "Epoch [89]: train_acc: 0.9810, train_loss: 0.0568, val_loss: 0.0702, val_acc: 0.9556\n",
            "Epoch [90]: train_acc: 0.9810, train_loss: 0.0563, val_loss: 0.0695, val_acc: 0.9556\n",
            "Epoch [91]: train_acc: 0.9810, train_loss: 0.0559, val_loss: 0.0696, val_acc: 0.9556\n",
            "Epoch [92]: train_acc: 0.9810, train_loss: 0.0553, val_loss: 0.0690, val_acc: 0.9556\n",
            "Epoch [93]: train_acc: 0.9905, train_loss: 0.0549, val_loss: 0.0685, val_acc: 0.9556\n",
            "Epoch [94]: train_acc: 0.9905, train_loss: 0.0545, val_loss: 0.0688, val_acc: 0.9556\n",
            "Epoch [95]: train_acc: 0.9905, train_loss: 0.0540, val_loss: 0.0684, val_acc: 0.9556\n",
            "Epoch [96]: train_acc: 0.9905, train_loss: 0.0536, val_loss: 0.0679, val_acc: 0.9556\n",
            "Epoch [97]: train_acc: 0.9905, train_loss: 0.0532, val_loss: 0.0674, val_acc: 0.9556\n",
            "Epoch [98]: train_acc: 0.9905, train_loss: 0.0528, val_loss: 0.0670, val_acc: 0.9556\n",
            "Epoch [99]: train_acc: 0.9905, train_loss: 0.0524, val_loss: 0.0666, val_acc: 0.9556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Q-zowlZZf9L1",
        "outputId": "c4d92459-7ddb-4e6d-945a-61956e50f28f"
      },
      "source": [
        "def plot_losses(history):\n",
        "    train_losses = [x.get('train_loss') for x in history]\n",
        "    val_losses = [x['val_loss'] for x in history]\n",
        "    plt.plot(train_losses, '-bx')\n",
        "    plt.plot(val_losses, '-rx')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('loss')\n",
        "    plt.legend(['Training', 'Validation'])\n",
        "    plt.title('Loss vs. No. of epochs');\n",
        "    \n",
        "plot_losses(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "oTz8KMQEf9Oe",
        "outputId": "a70f2882-53db-4180-bca3-dd3dda069de6"
      },
      "source": [
        "accuracies = [x['val_acc'] for x in history]\n",
        "plt.plot(accuracies, '-x')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.title('Accuracy vs. No. of epochs');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJe5Gz5vgFLT",
        "outputId": "50f3424e-c0a5-4823-d043-4075d20a95c6"
      },
      "source": [
        "test_loader = DeviceDataLoader(DataLoader(test_dataset, batch_size=10), device)\n",
        "result = evaluate(model, test_loader)\n",
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'val_acc': 0.9399999380111694, 'val_loss': 0.08290161937475204}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "wNeGD1b4gFOg",
        "outputId": "4f6920f8-34cf-4b3d-e13f-0f832273765d"
      },
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "\n",
        "# One hot encoding\n",
        "enc = OneHotEncoder()\n",
        "Y_onehot = enc.fit_transform(y_test[:, np.newaxis]).toarray()\n",
        "\n",
        "filename = \"/content/1dcnn-iris.pth\" \n",
        "model.load_state_dict(torch.load(filename, map_location=device))\n",
        "print('>loaded %s' % filename)\n",
        "model.to(device)\n",
        "model.eval()\n",
        "#to_device(model, device)\n",
        "\n",
        "with torch.no_grad():\n",
        "    y_pred = model(to_device(torch.FloatTensor(X_test), device))\n",
        "    y_pred = y_pred.cpu().detach().numpy()\n",
        "    fpr, tpr, threshold = roc_curve(Y_onehot.ravel(), y_pred.ravel())\n",
        "    \n",
        "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc(fpr, tpr)))\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">loaded /content/1dcnn-iris.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqXkccUsgUls",
        "outputId": "6827e916-76e0-42c9-bf8a-aa558b98e6d2"
      },
      "source": [
        "from torch.autograd import Variable\n",
        "\n",
        "file_name = \"/content/1dcnn-iris.pth\" \n",
        "#model = LSTM(num_classes, input_size, hidden_size, num_layers)\n",
        "model.load_state_dict(torch.load(file_name))\n",
        "print('>loaded %s' % file_name)\n",
        "model.eval()\n",
        "\n",
        "# Prediction\n",
        "my_test = Variable(torch.from_numpy(X_test)).float()\n",
        "model.to(device)\n",
        "pred = model(to_device(my_test, device))\n",
        "pred = pred.cpu().detach().numpy()\n",
        "pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">loaded /content/1dcnn-iris.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6.7500696 , -4.0792084 , -8.716827  ],\n",
              "       [-0.9561133 ,  1.7890706 , -1.3765395 ],\n",
              "       [-2.5430958 ,  1.2458416 ,  0.35402447],\n",
              "       [ 5.897292  , -3.3694701 , -7.7223725 ],\n",
              "       [-5.8203344 ,  0.22115336,  2.753671  ],\n",
              "       [-2.9767642 ,  1.0404797 ,  0.723459  ],\n",
              "       [-5.548086  ,  0.37485424,  3.2137012 ],\n",
              "       [ 5.142126  , -2.769493  , -6.869877  ],\n",
              "       [ 3.860781  , -1.7582047 , -5.4378695 ],\n",
              "       [-6.047629  ,  0.22139567,  3.628973  ],\n",
              "       [-2.1982205 ,  1.8122692 , -0.10769048],\n",
              "       [ 5.6943192 , -3.2310295 , -7.5173726 ],\n",
              "       [-5.963333  ,  0.30589128,  3.5702412 ],\n",
              "       [-2.6092048 ,  1.1705227 ,  0.3988505 ],\n",
              "       [-2.9485261 ,  1.3402432 ,  0.7040927 ],\n",
              "       [ 3.9117374 , -1.7946768 , -5.4899726 ],\n",
              "       [-1.8486362 ,  1.9248018 , -0.4695928 ],\n",
              "       [-2.708215  ,  1.4507468 ,  0.45391083],\n",
              "       [ 4.388341  , -2.137412  , -5.983832  ],\n",
              "       [ 4.1976285 , -1.9997578 , -5.7838283 ],\n",
              "       [-2.2403584 ,  1.8850467 , -0.10836027],\n",
              "       [-2.6601393 ,  1.5177169 ,  0.39287972],\n",
              "       [-4.137767  ,  0.7845259 ,  1.9010108 ],\n",
              "       [ 4.718825  , -2.3757846 , -6.331322  ],\n",
              "       [-5.3978443 ,  0.3351606 ,  2.9451118 ],\n",
              "       [-1.9513415 ,  1.79771   , -0.32909256],\n",
              "       [ 7.04957   , -4.327344  , -9.062065  ],\n",
              "       [ 5.4939623 , -3.0240176 , -7.2406135 ],\n",
              "       [-2.532588  ,  1.5124621 ,  0.28044242],\n",
              "       [-4.532692  ,  1.1550674 ,  2.1505454 ],\n",
              "       [-2.675845  ,  1.3304508 ,  0.4543867 ],\n",
              "       [-6.724515  ,  0.07391787,  4.166558  ],\n",
              "       [-1.5217967 ,  2.055558  , -0.8674203 ],\n",
              "       [-5.5143056 ,  0.68897766,  3.1102457 ],\n",
              "       [-5.3688364 ,  0.8926443 ,  2.9270914 ],\n",
              "       [ 5.4686365 , -3.0064301 , -7.214703  ],\n",
              "       [-2.0424845 ,  2.125228  , -0.4522906 ],\n",
              "       [ 4.638708  , -2.317998  , -6.247082  ],\n",
              "       [-3.3393342 ,  1.155375  ,  1.1107711 ],\n",
              "       [-5.2689013 ,  0.8827412 ,  2.8458614 ],\n",
              "       [-5.5186057 ,  0.45544755,  3.1783304 ],\n",
              "       [ 4.7024174 , -2.3639507 , -6.3140707 ],\n",
              "       [-3.5985801 ,  1.7291217 ,  1.0999953 ],\n",
              "       [-4.8977766 ,  0.7574127 ,  2.575317  ],\n",
              "       [-2.6611757 ,  1.2744709 ,  0.45700192]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQTQAXLMgFSC",
        "outputId": "0ec0a2c8-2925-4b00-babd-426301b6f803"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print (\"The accuracy is\", accuracy_score(y_test, np.argmax(pred, axis=1)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy is 0.9555555555555556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjJtTAOsgVpx",
        "outputId": "3944ebb5-0fb3-413c-d348-c49ec624b512"
      },
      "source": [
        "np.argmax(pred, axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 2, 1, 2, 0, 0, 2, 1, 0, 2, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       2, 0, 2, 1, 0, 0, 1, 2, 1, 2, 1, 2, 2, 0, 1, 0, 1, 2, 2, 0, 1, 2,\n",
              "       1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oyLwRiF4gZwe",
        "outputId": "a8b97cc4-3d31-4447-ec27-68f70a21eef1"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 2, 1, 2, 0, 0, 2, 1, 0, 2, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 0, 2, 1, 0, 0, 1, 2, 1, 2, 1, 2, 2, 0, 1, 0, 1, 2, 2, 0, 2, 2,\n",
              "       1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Gka939UgbuW",
        "outputId": "235751d7-3695-4d90-8b77-b2c502c27a63"
      },
      "source": [
        "# Checking for a single instance / predicted class label\n",
        "k = 44\n",
        "test0 = torch.reshape(Variable(torch.Tensor(X_test[k])), (1, 4, 1)).float()\n",
        "model.to(device)\n",
        "pred = model(to_device(test0, device))\n",
        "pred = pred.cpu().detach().numpy()\n",
        "pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.6611755,  1.2744709,  0.4570018]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-W-sLjufgbxK",
        "outputId": "4760997a-12a9-4961-81d3-6bfc6959c24e"
      },
      "source": [
        "pred = np.argmax(pred, axis=1)\n",
        "pred[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5FGswdxgb0B",
        "outputId": "9790226a-090a-4267-952f-0a7cbbaa784e"
      },
      "source": [
        "# Actual class label\n",
        "y_test[k]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    }
  ]
}