Bottleneck_Transformer.ipynb

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      "gpuType": "V100",
      "machine_shape": "hm",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Bottleneck Transformers for CIFAR-10 Image Classification"
      ],
      "metadata": {
        "id": "OvFgEv7lZPi-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision.datasets import CIFAR10\n",
        "import torchvision.transforms as transforms\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "zInB-QI8y5uD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64\n",
        "num_workers = 4\n",
        "lr = 0.1\n",
        "weight_decay = 1e-4\n",
        "momentum = 0.9\n",
        "print_intervals = 100\n",
        "gradient_clip = 2.0"
      ],
      "metadata": {
        "id": "tEL6H8uOtAeR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the data\n",
        "\n",
        "def load_data():\n",
        "    train_transform = transforms.Compose([\n",
        "        transforms.RandomCrop(32, padding=4),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "    ])\n",
        "    train_dataset = CIFAR10('./data', train=True, transform=train_transform, download=True)\n",
        "\n",
        "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
        "\n",
        "    test_transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "    ])\n",
        "    test_dataset = CIFAR10('./data', train=False, transform=test_transform, download=True)\n",
        "\n",
        "    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
        "\n",
        "    return train_loader, test_loader"
      ],
      "metadata": {
        "id": "pRPHvrB3tMu9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model definition\n",
        "#Inspired from https://github.com/leaderj1001/BottleneckTransformers\n",
        "\n",
        "class MHSA(nn.Module):\n",
        "    def __init__(self, n_dims, width=14, height=14, heads=4):\n",
        "        super(MHSA, self).__init__()\n",
        "        self.heads = heads\n",
        "\n",
        "        self.query = nn.Conv2d(n_dims, n_dims, kernel_size=1)\n",
        "        self.key = nn.Conv2d(n_dims, n_dims, kernel_size=1)\n",
        "        self.value = nn.Conv2d(n_dims, n_dims, kernel_size=1)\n",
        "\n",
        "        self.rel_h = nn.Parameter(torch.randn([1, heads, n_dims // heads, 1, height]), requires_grad=True)\n",
        "        self.rel_w = nn.Parameter(torch.randn([1, heads, n_dims // heads, width, 1]), requires_grad=True)\n",
        "\n",
        "        self.softmax = nn.Softmax(dim=-1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        n_batch, C, width, height = x.size()\n",
        "        q = self.query(x).view(n_batch, self.heads, C // self.heads, -1)\n",
        "        k = self.key(x).view(n_batch, self.heads, C // self.heads, -1)\n",
        "        v = self.value(x).view(n_batch, self.heads, C // self.heads, -1)\n",
        "\n",
        "        content_content = torch.matmul(q.permute(0, 1, 3, 2), k)\n",
        "\n",
        "        content_position = (self.rel_h + self.rel_w).view(1, self.heads, C // self.heads, -1).permute(0, 1, 3, 2)\n",
        "        content_position = torch.matmul(content_position, q)\n",
        "\n",
        "        energy = content_content + content_position\n",
        "        attention = self.softmax(energy)\n",
        "\n",
        "        out = torch.matmul(v, attention.permute(0, 1, 3, 2))\n",
        "        out = out.view(n_batch, C, width, height)\n",
        "\n",
        "        return out\n",
        "\n",
        "\n",
        "class Bottleneck(nn.Module):\n",
        "    expansion = 4\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1, heads=4, mhsa=False, resolution=None):\n",
        "        super(Bottleneck, self).__init__()\n",
        "\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        if not mhsa:\n",
        "            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, stride=stride, bias=False)\n",
        "        else:\n",
        "            self.conv2 = nn.ModuleList()\n",
        "            self.conv2.append(MHSA(planes, width=int(resolution[0]), height=int(resolution[1]), heads=heads))\n",
        "            if stride == 2:\n",
        "                self.conv2.append(nn.AvgPool2d(2, 2))\n",
        "            self.conv2 = nn.Sequential(*self.conv2)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(self.expansion * planes)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion*planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride),\n",
        "                nn.BatchNorm2d(self.expansion*planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = F.relu(self.bn2(self.conv2(out)))\n",
        "        out = self.bn3(self.conv3(out))\n",
        "        out += self.shortcut(x)\n",
        "        out = F.relu(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "# reference\n",
        "# https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, num_blocks, num_classes=1000, resolution=(224, 224), heads=4):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_planes = 64\n",
        "        self.resolution = list(resolution)\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
        "        if self.conv1.stride[0] == 2:\n",
        "            self.resolution[0] /= 2\n",
        "        if self.conv1.stride[1] == 2:\n",
        "            self.resolution[1] /= 2\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # for ImageNet\n",
        "        #if self.maxpool.stride == 2:\n",
        "            #self.resolution[0] /= 2\n",
        "            #self.resolution[1] /= 2\n",
        "\n",
        "        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n",
        "        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, heads=heads, mhsa=True)\n",
        "\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Dropout(0.3), # All architecture deeper than ResNet-200 dropout_rate: 0.2\n",
        "            nn.Linear(512 * block.expansion, num_classes)\n",
        "        )\n",
        "\n",
        "    def _make_layer(self, block, planes, num_blocks, stride=1, heads=4, mhsa=False):\n",
        "        strides = [stride] + [1]*(num_blocks-1)\n",
        "        layers = []\n",
        "        for idx, stride in enumerate(strides):\n",
        "            layers.append(block(self.in_planes, planes, stride, heads, mhsa, self.resolution))\n",
        "            if stride == 2:\n",
        "                self.resolution[0] /= 2\n",
        "                self.resolution[1] /= 2\n",
        "            self.in_planes = planes * block.expansion\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.relu(self.bn1(self.conv1(x)))\n",
        "        # out = self.maxpool(out) # for ImageNet\n",
        "\n",
        "        out = self.layer1(out)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "        out = self.layer4(out)\n",
        "\n",
        "        out = self.avgpool(out)\n",
        "        out = torch.flatten(out, 1)\n",
        "        out = self.fc(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "def ResNet50(num_classes=10, resolution=(32, 32), heads=4):\n",
        "    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, resolution=resolution, heads=heads)"
      ],
      "metadata": {
        "id": "loDE62oVu2bW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cuda_available = torch.cuda.is_available()\n",
        "device = torch.device('cuda') if cuda_available else torch.device('cpu')\n",
        "# define model and add to gpu\n",
        "model = ResNet50().to(device)\n",
        "# load data\n",
        "train_loader, test_loader = load_data()\n",
        "optimizer = optim.SGD(model.parameters(), lr=lr, weight_decay=weight_decay, momentum=momentum)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "num_epoch = 50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZqgyqTrv21j",
        "outputId": "0e278198-d447-49a2-8c6c-1fa49ab92a47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:05<00:00, 29672104.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see some of the images we'll be working with."
      ],
      "metadata": {
        "id": "aanosaocY7Lx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Get a batch of training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = next(dataiter)\n",
        "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "# Function to show an image\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5  # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "# Show images in a grid\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(16):\n",
        "    plt.subplot(4, 4, i + 1)\n",
        "    imshow(images[i])\n",
        "    plt.title(classes[labels[i]])\n",
        "    plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qDHcdYUuOcp5",
        "outputId": "c5a8eaf0-7b48-41a9-a75d-912acebb223e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's define a `train` function as well as a `test` function:"
      ],
      "metadata": {
        "id": "UzCEQd9RZAkT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(train_loader, epoch):\n",
        "    model.train()\n",
        "    losses = 0.\n",
        "    acc = 0.\n",
        "    total = 0.\n",
        "    for idx, (data, target) in enumerate(train_loader):\n",
        "\n",
        "        data, target = data.to(device), target.to(device)\n",
        "\n",
        "        output = model(data)\n",
        "        _, pred = F.softmax(output, dim=-1).max(1)\n",
        "        acc += pred.eq(target).sum().item()\n",
        "        total += target.size(0)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(output, target)\n",
        "        losses += loss\n",
        "        loss.backward()\n",
        "\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 2)\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "        if idx % print_intervals == 0 and idx != 0:\n",
        "            print('[Epoch: {0:4d}], Loss: {1:.3f}, Acc: {2:.3f}, Correct {3} / Total {4}'.format(epoch,\n",
        "                                                                                                 losses / (idx + 1),\n",
        "                                                                                                 acc / total * 100.,\n",
        "                                                                                                 acc, total))"
      ],
      "metadata": {
        "id": "OUo6pfOOvVom"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test(test_loader, epoch):\n",
        "    model.eval()\n",
        "\n",
        "    acc = 0.\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            _, pred = F.softmax(output, dim=-1).max(1)\n",
        "\n",
        "            acc += pred.eq(target).sum().item()\n",
        "        print('[Epoch: {0:4d}], Acc: {1:.3f}'.format(epoch, acc / len(test_loader.dataset) * 100.))\n",
        "\n",
        "    return acc / len(test_loader.dataset) * 100."
      ],
      "metadata": {
        "id": "eLvQ5nqqxCmw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can execute the training loop."
      ],
      "metadata": {
        "id": "2bfarqvpYwW1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cosine annealing refers to reducing the learning rate following a cosine curve.\n",
        "# help the model to converge to better solutions.\n",
        "lr_scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2, eta_min=0.0001)\n",
        "\n",
        "#TRAINING\n",
        "global_acc = 0.\n",
        "for epoch in range(1, num_epoch + 1):\n",
        "    train(train_loader, epoch)\n",
        "    best_acc = test(test_loader, epoch)\n",
        "\n",
        "    lr_scheduler.step()\n",
        "    print('Current Learning Rate: {}'.format(lr_scheduler.get_last_lr()))"
      ],
      "metadata": {
        "id": "ApAfU0jPxbFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66d84e91-7571-4188-db9d-56978d51d319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch:    1], Loss: 3.955, Acc: 14.140, Correct 914.0 / Total 6464.0\n",
            "[Epoch:    1], Loss: 4.481, Acc: 15.617, Correct 2009.0 / Total 12864.0\n",
            "[Epoch:    1], Loss: 4.847, Acc: 15.371, Correct 2961.0 / Total 19264.0\n",
            "[Epoch:    1], Loss: 4.813, Acc: 16.303, Correct 4184.0 / Total 25664.0\n",
            "[Epoch:    1], Loss: 4.952, Acc: 17.153, Correct 5500.0 / Total 32064.0\n",
            "[Epoch:    1], Loss: 5.010, Acc: 17.869, Correct 6873.0 / Total 38464.0\n",
            "[Epoch:    1], Loss: 5.026, Acc: 18.540, Correct 8318.0 / Total 44864.0\n",
            "[Epoch:    1], Acc: 28.890\n",
            "Current Learning Rate: [0.09755527298894293]\n",
            "[Epoch:    2], Loss: 3.849, Acc: 26.439, Correct 1709.0 / Total 6464.0\n",
            "[Epoch:    2], Loss: 3.780, Acc: 27.332, Correct 3516.0 / Total 12864.0\n",
            "[Epoch:    2], Loss: 3.896, Acc: 28.011, Correct 5396.0 / Total 19264.0\n",
            "[Epoch:    2], Loss: 3.696, Acc: 28.815, Correct 7395.0 / Total 25664.0\n",
            "[Epoch:    2], Loss: 3.502, Acc: 29.276, Correct 9387.0 / Total 32064.0\n",
            "[Epoch:    2], Loss: 3.331, Acc: 29.422, Correct 11317.0 / Total 38464.0\n",
            "[Epoch:    2], Loss: 3.160, Acc: 29.598, Correct 13279.0 / Total 44864.0\n",
            "[Epoch:    2], Acc: 35.140\n",
            "Current Learning Rate: [0.09046039886902862]\n",
            "[Epoch:    3], Loss: 1.869, Acc: 33.648, Correct 2175.0 / Total 6464.0\n",
            "[Epoch:    3], Loss: 1.829, Acc: 34.010, Correct 4375.0 / Total 12864.0\n",
            "[Epoch:    3], Loss: 1.799, Acc: 34.551, Correct 6656.0 / Total 19264.0\n",
            "[Epoch:    3], Loss: 1.781, Acc: 35.147, Correct 9020.0 / Total 25664.0\n",
            "[Epoch:    3], Loss: 1.762, Acc: 35.825, Correct 11487.0 / Total 32064.0\n",
            "[Epoch:    3], Loss: 1.750, Acc: 36.234, Correct 13937.0 / Total 38464.0\n",
            "[Epoch:    3], Loss: 1.740, Acc: 36.640, Correct 16438.0 / Total 44864.0\n",
            "[Epoch:    3], Acc: 44.870\n",
            "Current Learning Rate: [0.07940987335200904]\n",
            "[Epoch:    4], Loss: 1.578, Acc: 42.899, Correct 2773.0 / Total 6464.0\n",
            "[Epoch:    4], Loss: 1.582, Acc: 42.568, Correct 5476.0 / Total 12864.0\n",
            "[Epoch:    4], Loss: 1.581, Acc: 42.520, Correct 8191.0 / Total 19264.0\n",
            "[Epoch:    4], Loss: 1.575, Acc: 42.690, Correct 10956.0 / Total 25664.0\n",
            "[Epoch:    4], Loss: 1.564, Acc: 43.092, Correct 13817.0 / Total 32064.0\n",
            "[Epoch:    4], Loss: 1.559, Acc: 43.045, Correct 16557.0 / Total 38464.0\n",
            "[Epoch:    4], Loss: 1.554, Acc: 43.224, Correct 19392.0 / Total 44864.0\n",
            "[Epoch:    4], Acc: 48.400\n",
            "Current Learning Rate: [0.06548539886902863]\n",
            "[Epoch:    5], Loss: 1.451, Acc: 47.076, Correct 3043.0 / Total 6464.0\n",
            "[Epoch:    5], Loss: 1.453, Acc: 47.450, Correct 6104.0 / Total 12864.0\n",
            "[Epoch:    5], Loss: 1.442, Acc: 47.680, Correct 9185.0 / Total 19264.0\n",
            "[Epoch:    5], Loss: 1.436, Acc: 47.904, Correct 12294.0 / Total 25664.0\n",
            "[Epoch:    5], Loss: 1.434, Acc: 48.032, Correct 15401.0 / Total 32064.0\n",
            "[Epoch:    5], Loss: 1.429, Acc: 48.287, Correct 18573.0 / Total 38464.0\n",
            "[Epoch:    5], Loss: 1.427, Acc: 48.522, Correct 21769.0 / Total 44864.0\n",
            "[Epoch:    5], Acc: 53.540\n",
            "Current Learning Rate: [0.050050000000000004]\n",
            "[Epoch:    6], Loss: 1.318, Acc: 52.862, Correct 3417.0 / Total 6464.0\n",
            "[Epoch:    6], Loss: 1.325, Acc: 52.293, Correct 6727.0 / Total 12864.0\n",
            "[Epoch:    6], Loss: 1.318, Acc: 52.435, Correct 10101.0 / Total 19264.0\n",
            "[Epoch:    6], Loss: 1.317, Acc: 52.392, Correct 13446.0 / Total 25664.0\n",
            "[Epoch:    6], Loss: 1.320, Acc: 52.286, Correct 16765.0 / Total 32064.0\n",
            "[Epoch:    6], Loss: 1.316, Acc: 52.571, Correct 20221.0 / Total 38464.0\n",
            "[Epoch:    6], Loss: 1.310, Acc: 52.837, Correct 23705.0 / Total 44864.0\n",
            "[Epoch:    6], Acc: 57.490\n",
            "Current Learning Rate: [0.03461460113097138]\n",
            "[Epoch:    7], Loss: 1.227, Acc: 55.972, Correct 3618.0 / Total 6464.0\n",
            "[Epoch:    7], Loss: 1.222, Acc: 56.250, Correct 7236.0 / Total 12864.0\n",
            "[Epoch:    7], Loss: 1.221, Acc: 56.338, Correct 10853.0 / Total 19264.0\n",
            "[Epoch:    7], Loss: 1.210, Acc: 56.760, Correct 14567.0 / Total 25664.0\n",
            "[Epoch:    7], Loss: 1.203, Acc: 57.061, Correct 18296.0 / Total 32064.0\n",
            "[Epoch:    7], Loss: 1.199, Acc: 57.225, Correct 22011.0 / Total 38464.0\n",
            "[Epoch:    7], Loss: 1.195, Acc: 57.349, Correct 25729.0 / Total 44864.0\n",
            "[Epoch:    7], Acc: 58.910\n",
            "Current Learning Rate: [0.020690126647990973]\n",
            "[Epoch:    8], Loss: 1.094, Acc: 60.752, Correct 3927.0 / Total 6464.0\n",
            "[Epoch:    8], Loss: 1.097, Acc: 60.930, Correct 7838.0 / Total 12864.0\n",
            "[Epoch:    8], Loss: 1.095, Acc: 60.787, Correct 11710.0 / Total 19264.0\n",
            "[Epoch:    8], Loss: 1.094, Acc: 60.926, Correct 15636.0 / Total 25664.0\n",
            "[Epoch:    8], Loss: 1.095, Acc: 60.891, Correct 19524.0 / Total 32064.0\n",
            "[Epoch:    8], Loss: 1.092, Acc: 61.135, Correct 23515.0 / Total 38464.0\n",
            "[Epoch:    8], Loss: 1.085, Acc: 61.392, Correct 27543.0 / Total 44864.0\n",
            "[Epoch:    8], Acc: 63.410\n",
            "Current Learning Rate: [0.00963960113097138]\n",
            "[Epoch:    9], Loss: 1.001, Acc: 64.635, Correct 4178.0 / Total 6464.0\n",
            "[Epoch:    9], Loss: 1.011, Acc: 63.845, Correct 8213.0 / Total 12864.0\n",
            "[Epoch:    9], Loss: 1.006, Acc: 64.182, Correct 12364.0 / Total 19264.0\n",
            "[Epoch:    9], Loss: 1.002, Acc: 64.296, Correct 16501.0 / Total 25664.0\n",
            "[Epoch:    9], Loss: 1.002, Acc: 64.374, Correct 20641.0 / Total 32064.0\n",
            "[Epoch:    9], Loss: 1.000, Acc: 64.452, Correct 24791.0 / Total 38464.0\n",
            "[Epoch:    9], Loss: 0.995, Acc: 64.646, Correct 29003.0 / Total 44864.0\n",
            "[Epoch:    9], Acc: 66.540\n",
            "Current Learning Rate: [0.002544727011057081]\n",
            "[Epoch:   10], Loss: 0.949, Acc: 66.352, Correct 4289.0 / Total 6464.0\n",
            "[Epoch:   10], Loss: 0.933, Acc: 67.071, Correct 8628.0 / Total 12864.0\n",
            "[Epoch:   10], Loss: 0.925, Acc: 67.271, Correct 12959.0 / Total 19264.0\n",
            "[Epoch:   10], Loss: 0.925, Acc: 67.316, Correct 17276.0 / Total 25664.0\n",
            "[Epoch:   10], Loss: 0.927, Acc: 67.228, Correct 21556.0 / Total 32064.0\n",
            "[Epoch:   10], Loss: 0.924, Acc: 67.250, Correct 25867.0 / Total 38464.0\n",
            "[Epoch:   10], Loss: 0.924, Acc: 67.303, Correct 30195.0 / Total 44864.0\n",
            "[Epoch:   10], Acc: 68.020\n",
            "Current Learning Rate: [0.1]\n",
            "[Epoch:   11], Loss: 1.281, Acc: 54.688, Correct 3535.0 / Total 6464.0\n",
            "[Epoch:   11], Loss: 1.275, Acc: 55.053, Correct 7082.0 / Total 12864.0\n",
            "[Epoch:   11], Loss: 1.295, Acc: 54.293, Correct 10459.0 / Total 19264.0\n",
            "[Epoch:   11], Loss: 1.280, Acc: 54.758, Correct 14053.0 / Total 25664.0\n",
            "[Epoch:   11], Loss: 1.269, Acc: 55.143, Correct 17681.0 / Total 32064.0\n",
            "[Epoch:   11], Loss: 1.258, Acc: 55.462, Correct 21333.0 / Total 38464.0\n",
            "[Epoch:   11], Loss: 1.247, Acc: 55.769, Correct 25020.0 / Total 44864.0\n",
            "[Epoch:   11], Acc: 56.050\n",
            "Current Learning Rate: [0.09938503261272713]\n",
            "[Epoch:   12], Loss: 1.149, Acc: 59.731, Correct 3861.0 / Total 6464.0\n",
            "[Epoch:   12], Loss: 1.154, Acc: 59.352, Correct 7635.0 / Total 12864.0\n",
            "[Epoch:   12], Loss: 1.148, Acc: 59.458, Correct 11454.0 / Total 19264.0\n",
            "[Epoch:   12], Loss: 1.144, Acc: 59.492, Correct 15268.0 / Total 25664.0\n",
            "[Epoch:   12], Loss: 1.146, Acc: 59.475, Correct 19070.0 / Total 32064.0\n",
            "[Epoch:   12], Loss: 1.135, Acc: 59.856, Correct 23023.0 / Total 38464.0\n",
            "[Epoch:   12], Loss: 1.128, Acc: 60.001, Correct 26919.0 / Total 44864.0\n",
            "[Epoch:   12], Acc: 61.200\n",
            "Current Learning Rate: [0.09755527298894293]\n",
            "[Epoch:   13], Loss: 1.044, Acc: 63.227, Correct 4087.0 / Total 6464.0\n",
            "[Epoch:   13], Loss: 1.047, Acc: 63.207, Correct 8131.0 / Total 12864.0\n",
            "[Epoch:   13], Loss: 1.047, Acc: 63.455, Correct 12224.0 / Total 19264.0\n",
            "[Epoch:   13], Loss: 1.042, Acc: 63.494, Correct 16295.0 / Total 25664.0\n",
            "[Epoch:   13], Loss: 1.034, Acc: 63.744, Correct 20439.0 / Total 32064.0\n",
            "[Epoch:   13], Loss: 1.036, Acc: 63.615, Correct 24469.0 / Total 38464.0\n",
            "[Epoch:   13], Loss: 1.034, Acc: 63.690, Correct 28574.0 / Total 44864.0\n",
            "[Epoch:   13], Acc: 65.750\n",
            "Current Learning Rate: [0.09455577588320897]\n",
            "[Epoch:   14], Loss: 0.979, Acc: 65.579, Correct 4239.0 / Total 6464.0\n",
            "[Epoch:   14], Loss: 1.007, Acc: 64.436, Correct 8289.0 / Total 12864.0\n",
            "[Epoch:   14], Loss: 1.001, Acc: 64.659, Correct 12456.0 / Total 19264.0\n",
            "[Epoch:   14], Loss: 0.997, Acc: 64.795, Correct 16629.0 / Total 25664.0\n",
            "[Epoch:   14], Loss: 0.988, Acc: 65.216, Correct 20911.0 / Total 32064.0\n",
            "[Epoch:   14], Loss: 0.989, Acc: 65.066, Correct 25027.0 / Total 38464.0\n",
            "[Epoch:   14], Loss: 0.991, Acc: 65.057, Correct 29187.0 / Total 44864.0\n",
            "[Epoch:   14], Acc: 67.750\n",
            "Current Learning Rate: [0.09046039886902862]\n",
            "[Epoch:   15], Loss: 0.924, Acc: 67.791, Correct 4382.0 / Total 6464.0\n",
            "[Epoch:   15], Loss: 0.919, Acc: 67.545, Correct 8689.0 / Total 12864.0\n",
            "[Epoch:   15], Loss: 0.906, Acc: 68.091, Correct 13117.0 / Total 19264.0\n",
            "[Epoch:   15], Loss: 0.909, Acc: 68.068, Correct 17469.0 / Total 25664.0\n",
            "[Epoch:   15], Loss: 0.906, Acc: 68.132, Correct 21846.0 / Total 32064.0\n",
            "[Epoch:   15], Loss: 0.903, Acc: 68.259, Correct 26255.0 / Total 38464.0\n",
            "[Epoch:   15], Loss: 0.901, Acc: 68.342, Correct 30661.0 / Total 44864.0\n",
            "[Epoch:   15], Acc: 65.900\n",
            "Current Learning Rate: [0.08536998372026805]\n",
            "[Epoch:   16], Loss: 0.886, Acc: 68.379, Correct 4420.0 / Total 6464.0\n",
            "[Epoch:   16], Loss: 0.885, Acc: 68.696, Correct 8837.0 / Total 12864.0\n",
            "[Epoch:   16], Loss: 0.879, Acc: 69.051, Correct 13302.0 / Total 19264.0\n",
            "[Epoch:   16], Loss: 0.873, Acc: 69.112, Correct 17737.0 / Total 25664.0\n",
            "[Epoch:   16], Loss: 0.870, Acc: 69.421, Correct 22259.0 / Total 32064.0\n",
            "[Epoch:   16], Loss: 0.863, Acc: 69.720, Correct 26817.0 / Total 38464.0\n",
            "[Epoch:   16], Loss: 0.861, Acc: 69.873, Correct 31348.0 / Total 44864.0\n",
            "[Epoch:   16], Acc: 67.320\n",
            "Current Learning Rate: [0.07940987335200904]\n",
            "[Epoch:   17], Loss: 0.811, Acc: 71.689, Correct 4634.0 / Total 6464.0\n",
            "[Epoch:   17], Loss: 0.806, Acc: 71.533, Correct 9202.0 / Total 12864.0\n",
            "[Epoch:   17], Loss: 0.812, Acc: 71.444, Correct 13763.0 / Total 19264.0\n",
            "[Epoch:   17], Loss: 0.818, Acc: 71.427, Correct 18331.0 / Total 25664.0\n",
            "[Epoch:   17], Loss: 0.821, Acc: 71.345, Correct 22876.0 / Total 32064.0\n",
            "[Epoch:   17], Loss: 0.814, Acc: 71.664, Correct 27565.0 / Total 38464.0\n",
            "[Epoch:   17], Loss: 0.808, Acc: 71.895, Correct 32255.0 / Total 44864.0\n",
            "[Epoch:   17], Acc: 69.890\n",
            "Current Learning Rate: [0.07272682546199037]\n",
            "[Epoch:   18], Loss: 0.760, Acc: 73.236, Correct 4734.0 / Total 6464.0\n",
            "[Epoch:   18], Loss: 0.746, Acc: 73.927, Correct 9510.0 / Total 12864.0\n",
            "[Epoch:   18], Loss: 0.748, Acc: 73.889, Correct 14234.0 / Total 19264.0\n",
            "[Epoch:   18], Loss: 0.756, Acc: 73.570, Correct 18881.0 / Total 25664.0\n",
            "[Epoch:   18], Loss: 0.752, Acc: 73.699, Correct 23631.0 / Total 32064.0\n",
            "[Epoch:   18], Loss: 0.748, Acc: 73.721, Correct 28356.0 / Total 38464.0\n",
            "[Epoch:   18], Loss: 0.746, Acc: 73.859, Correct 33136.0 / Total 44864.0\n",
            "[Epoch:   18], Acc: 73.810\n",
            "Current Learning Rate: [0.06548539886902863]\n",
            "[Epoch:   19], Loss: 0.668, Acc: 76.748, Correct 4961.0 / Total 6464.0\n",
            "[Epoch:   19], Loss: 0.692, Acc: 75.832, Correct 9755.0 / Total 12864.0\n",
            "[Epoch:   19], Loss: 0.699, Acc: 75.711, Correct 14585.0 / Total 19264.0\n",
            "[Epoch:   19], Loss: 0.696, Acc: 75.935, Correct 19488.0 / Total 25664.0\n",
            "[Epoch:   19], Loss: 0.695, Acc: 75.867, Correct 24326.0 / Total 32064.0\n",
            "[Epoch:   19], Loss: 0.692, Acc: 75.913, Correct 29199.0 / Total 38464.0\n",
            "[Epoch:   19], Loss: 0.690, Acc: 76.016, Correct 34104.0 / Total 44864.0\n",
            "[Epoch:   19], Acc: 74.360\n",
            "Current Learning Rate: [0.05786390152875954]\n",
            "[Epoch:   20], Loss: 0.648, Acc: 77.305, Correct 4997.0 / Total 6464.0\n",
            "[Epoch:   20], Loss: 0.655, Acc: 77.076, Correct 9915.0 / Total 12864.0\n",
            "[Epoch:   20], Loss: 0.646, Acc: 77.559, Correct 14941.0 / Total 19264.0\n",
            "[Epoch:   20], Loss: 0.640, Acc: 77.712, Correct 19944.0 / Total 25664.0\n",
            "[Epoch:   20], Loss: 0.638, Acc: 77.798, Correct 24945.0 / Total 32064.0\n",
            "[Epoch:   20], Loss: 0.638, Acc: 77.732, Correct 29899.0 / Total 38464.0\n",
            "[Epoch:   20], Loss: 0.639, Acc: 77.795, Correct 34902.0 / Total 44864.0\n",
            "[Epoch:   20], Acc: 76.780\n",
            "Current Learning Rate: [0.050050000000000004]\n",
            "[Epoch:   21], Loss: 0.585, Acc: 79.657, Correct 5149.0 / Total 6464.0\n",
            "[Epoch:   21], Loss: 0.584, Acc: 79.656, Correct 10247.0 / Total 12864.0\n",
            "[Epoch:   21], Loss: 0.585, Acc: 79.511, Correct 15317.0 / Total 19264.0\n",
            "[Epoch:   21], Loss: 0.587, Acc: 79.419, Correct 20382.0 / Total 25664.0\n",
            "[Epoch:   21], Loss: 0.590, Acc: 79.245, Correct 25409.0 / Total 32064.0\n",
            "[Epoch:   21], Loss: 0.583, Acc: 79.464, Correct 30565.0 / Total 38464.0\n",
            "[Epoch:   21], Loss: 0.582, Acc: 79.569, Correct 35698.0 / Total 44864.0\n",
            "[Epoch:   21], Acc: 78.830\n",
            "Current Learning Rate: [0.042236098471240484]\n",
            "[Epoch:   22], Loss: 0.546, Acc: 81.018, Correct 5237.0 / Total 6464.0\n",
            "[Epoch:   22], Loss: 0.545, Acc: 80.970, Correct 10416.0 / Total 12864.0\n",
            "[Epoch:   22], Loss: 0.540, Acc: 81.183, Correct 15639.0 / Total 19264.0\n",
            "[Epoch:   22], Loss: 0.533, Acc: 81.429, Correct 20898.0 / Total 25664.0\n",
            "[Epoch:   22], Loss: 0.532, Acc: 81.459, Correct 26119.0 / Total 32064.0\n",
            "[Epoch:   22], Loss: 0.535, Acc: 81.312, Correct 31276.0 / Total 38464.0\n",
            "[Epoch:   22], Loss: 0.535, Acc: 81.272, Correct 36462.0 / Total 44864.0\n",
            "[Epoch:   22], Acc: 80.050\n",
            "Current Learning Rate: [0.03461460113097138]\n",
            "[Epoch:   23], Loss: 0.494, Acc: 82.797, Correct 5352.0 / Total 6464.0\n",
            "[Epoch:   23], Loss: 0.484, Acc: 83.217, Correct 10705.0 / Total 12864.0\n",
            "[Epoch:   23], Loss: 0.480, Acc: 83.249, Correct 16037.0 / Total 19264.0\n",
            "[Epoch:   23], Loss: 0.484, Acc: 83.113, Correct 21330.0 / Total 25664.0\n",
            "[Epoch:   23], Loss: 0.486, Acc: 83.000, Correct 26613.0 / Total 32064.0\n",
            "[Epoch:   23], Loss: 0.486, Acc: 82.994, Correct 31923.0 / Total 38464.0\n",
            "[Epoch:   23], Loss: 0.486, Acc: 83.020, Correct 37246.0 / Total 44864.0\n",
            "[Epoch:   23], Acc: 82.510\n",
            "Current Learning Rate: [0.02737317453800964]\n",
            "[Epoch:   24], Loss: 0.434, Acc: 84.251, Correct 5446.0 / Total 6464.0\n",
            "[Epoch:   24], Loss: 0.437, Acc: 84.701, Correct 10896.0 / Total 12864.0\n",
            "[Epoch:   24], Loss: 0.439, Acc: 84.567, Correct 16291.0 / Total 19264.0\n",
            "[Epoch:   24], Loss: 0.439, Acc: 84.519, Correct 21691.0 / Total 25664.0\n",
            "[Epoch:   24], Loss: 0.438, Acc: 84.684, Correct 27153.0 / Total 32064.0\n",
            "[Epoch:   24], Loss: 0.437, Acc: 84.723, Correct 32588.0 / Total 38464.0\n",
            "[Epoch:   24], Loss: 0.436, Acc: 84.781, Correct 38036.0 / Total 44864.0\n",
            "[Epoch:   24], Acc: 82.790\n",
            "Current Learning Rate: [0.020690126647990973]\n",
            "[Epoch:   25], Loss: 0.394, Acc: 86.293, Correct 5578.0 / Total 6464.0\n",
            "[Epoch:   25], Loss: 0.387, Acc: 86.536, Correct 11132.0 / Total 12864.0\n",
            "[Epoch:   25], Loss: 0.387, Acc: 86.529, Correct 16669.0 / Total 19264.0\n",
            "[Epoch:   25], Loss: 0.392, Acc: 86.393, Correct 22172.0 / Total 25664.0\n",
            "[Epoch:   25], Loss: 0.391, Acc: 86.337, Correct 27683.0 / Total 32064.0\n",
            "[Epoch:   25], Loss: 0.392, Acc: 86.343, Correct 33211.0 / Total 38464.0\n",
            "[Epoch:   25], Loss: 0.393, Acc: 86.274, Correct 38706.0 / Total 44864.0\n",
            "[Epoch:   25], Acc: 83.210\n",
            "Current Learning Rate: [0.014730016279731955]\n",
            "[Epoch:   26], Loss: 0.346, Acc: 87.825, Correct 5677.0 / Total 6464.0\n",
            "[Epoch:   26], Loss: 0.346, Acc: 87.554, Correct 11263.0 / Total 12864.0\n",
            "[Epoch:   26], Loss: 0.347, Acc: 87.749, Correct 16904.0 / Total 19264.0\n",
            "[Epoch:   26], Loss: 0.346, Acc: 87.816, Correct 22537.0 / Total 25664.0\n",
            "[Epoch:   26], Loss: 0.347, Acc: 87.862, Correct 28172.0 / Total 32064.0\n",
            "[Epoch:   26], Loss: 0.346, Acc: 87.874, Correct 33800.0 / Total 38464.0\n",
            "[Epoch:   26], Loss: 0.348, Acc: 87.790, Correct 39386.0 / Total 44864.0\n",
            "[Epoch:   26], Acc: 84.760\n",
            "Current Learning Rate: [0.00963960113097138]\n",
            "[Epoch:   27], Loss: 0.314, Acc: 89.093, Correct 5759.0 / Total 6464.0\n",
            "[Epoch:   27], Loss: 0.305, Acc: 89.195, Correct 11474.0 / Total 12864.0\n",
            "[Epoch:   27], Loss: 0.312, Acc: 88.938, Correct 17133.0 / Total 19264.0\n",
            "[Epoch:   27], Loss: 0.312, Acc: 88.907, Correct 22817.0 / Total 25664.0\n",
            "[Epoch:   27], Loss: 0.315, Acc: 88.741, Correct 28454.0 / Total 32064.0\n",
            "[Epoch:   27], Loss: 0.318, Acc: 88.701, Correct 34118.0 / Total 38464.0\n",
            "[Epoch:   27], Loss: 0.317, Acc: 88.746, Correct 39815.0 / Total 44864.0\n",
            "[Epoch:   27], Acc: 85.530\n",
            "Current Learning Rate: [0.0055442241167910295]\n",
            "[Epoch:   28], Loss: 0.279, Acc: 90.347, Correct 5840.0 / Total 6464.0\n",
            "[Epoch:   28], Loss: 0.287, Acc: 90.096, Correct 11590.0 / Total 12864.0\n",
            "[Epoch:   28], Loss: 0.287, Acc: 90.085, Correct 17354.0 / Total 19264.0\n",
            "[Epoch:   28], Loss: 0.284, Acc: 90.076, Correct 23117.0 / Total 25664.0\n",
            "[Epoch:   28], Loss: 0.285, Acc: 90.045, Correct 28872.0 / Total 32064.0\n",
            "[Epoch:   28], Loss: 0.282, Acc: 90.141, Correct 34672.0 / Total 38464.0\n",
            "[Epoch:   28], Loss: 0.285, Acc: 90.061, Correct 40405.0 / Total 44864.0\n",
            "[Epoch:   28], Acc: 85.960\n",
            "Current Learning Rate: [0.002544727011057081]\n",
            "[Epoch:   29], Loss: 0.255, Acc: 91.136, Correct 5891.0 / Total 6464.0\n",
            "[Epoch:   29], Loss: 0.248, Acc: 91.433, Correct 11762.0 / Total 12864.0\n",
            "[Epoch:   29], Loss: 0.252, Acc: 91.165, Correct 17562.0 / Total 19264.0\n",
            "[Epoch:   29], Loss: 0.252, Acc: 91.159, Correct 23395.0 / Total 25664.0\n",
            "[Epoch:   29], Loss: 0.256, Acc: 90.990, Correct 29175.0 / Total 32064.0\n",
            "[Epoch:   29], Loss: 0.257, Acc: 90.968, Correct 34990.0 / Total 38464.0\n",
            "[Epoch:   29], Loss: 0.259, Acc: 90.915, Correct 40788.0 / Total 44864.0\n",
            "[Epoch:   29], Acc: 86.240\n",
            "Current Learning Rate: [0.000714967387272874]\n",
            "[Epoch:   30], Loss: 0.243, Acc: 91.507, Correct 5915.0 / Total 6464.0\n",
            "[Epoch:   30], Loss: 0.247, Acc: 91.426, Correct 11761.0 / Total 12864.0\n",
            "[Epoch:   30], Loss: 0.249, Acc: 91.321, Correct 17592.0 / Total 19264.0\n",
            "[Epoch:   30], Loss: 0.249, Acc: 91.334, Correct 23440.0 / Total 25664.0\n",
            "[Epoch:   30], Loss: 0.249, Acc: 91.311, Correct 29278.0 / Total 32064.0\n",
            "[Epoch:   30], Loss: 0.249, Acc: 91.330, Correct 35129.0 / Total 38464.0\n",
            "[Epoch:   30], Loss: 0.248, Acc: 91.369, Correct 40992.0 / Total 44864.0\n",
            "[Epoch:   30], Acc: 86.400\n",
            "Current Learning Rate: [0.1]\n",
            "[Epoch:   31], Loss: 0.598, Acc: 78.976, Correct 5105.0 / Total 6464.0\n",
            "[Epoch:   31], Loss: 0.652, Acc: 77.301, Correct 9944.0 / Total 12864.0\n",
            "[Epoch:   31], Loss: 0.663, Acc: 77.004, Correct 14834.0 / Total 19264.0\n",
            "[Epoch:   31], Loss: 0.666, Acc: 77.034, Correct 19770.0 / Total 25664.0\n",
            "[Epoch:   31], Loss: 0.666, Acc: 76.968, Correct 24679.0 / Total 32064.0\n",
            "[Epoch:   31], Loss: 0.668, Acc: 76.898, Correct 29578.0 / Total 38464.0\n",
            "[Epoch:   31], Loss: 0.668, Acc: 76.912, Correct 34506.0 / Total 44864.0\n",
            "[Epoch:   31], Acc: 75.100\n",
            "Current Learning Rate: [0.09984602081996975]\n",
            "[Epoch:   32], Loss: 0.607, Acc: 78.666, Correct 5085.0 / Total 6464.0\n",
            "[Epoch:   32], Loss: 0.616, Acc: 78.312, Correct 10074.0 / Total 12864.0\n",
            "[Epoch:   32], Loss: 0.617, Acc: 78.338, Correct 15091.0 / Total 19264.0\n",
            "[Epoch:   32], Loss: 0.619, Acc: 78.491, Correct 20144.0 / Total 25664.0\n",
            "[Epoch:   32], Loss: 0.615, Acc: 78.736, Correct 25246.0 / Total 32064.0\n",
            "[Epoch:   32], Loss: 0.617, Acc: 78.705, Correct 30273.0 / Total 38464.0\n",
            "[Epoch:   32], Loss: 0.619, Acc: 78.655, Correct 35288.0 / Total 44864.0\n",
            "[Epoch:   32], Acc: 77.180\n",
            "Current Learning Rate: [0.09938503261272713]\n",
            "[Epoch:   33], Loss: 0.570, Acc: 80.012, Correct 5172.0 / Total 6464.0\n",
            "[Epoch:   33], Loss: 0.572, Acc: 80.216, Correct 10319.0 / Total 12864.0\n",
            "[Epoch:   33], Loss: 0.564, Acc: 80.534, Correct 15514.0 / Total 19264.0\n",
            "[Epoch:   33], Loss: 0.573, Acc: 80.241, Correct 20593.0 / Total 25664.0\n",
            "[Epoch:   33], Loss: 0.574, Acc: 80.264, Correct 25736.0 / Total 32064.0\n",
            "[Epoch:   33], Loss: 0.573, Acc: 80.317, Correct 30893.0 / Total 38464.0\n",
            "[Epoch:   33], Loss: 0.571, Acc: 80.412, Correct 36076.0 / Total 44864.0\n",
            "[Epoch:   33], Acc: 79.570\n",
            "Current Learning Rate: [0.09861987752386395]\n",
            "[Epoch:   34], Loss: 0.557, Acc: 80.786, Correct 5222.0 / Total 6464.0\n",
            "[Epoch:   34], Loss: 0.545, Acc: 81.289, Correct 10457.0 / Total 12864.0\n",
            "[Epoch:   34], Loss: 0.557, Acc: 80.746, Correct 15555.0 / Total 19264.0\n",
            "[Epoch:   34], Loss: 0.551, Acc: 80.868, Correct 20754.0 / Total 25664.0\n",
            "[Epoch:   34], Loss: 0.549, Acc: 80.813, Correct 25912.0 / Total 32064.0\n",
            "[Epoch:   34], Loss: 0.548, Acc: 80.907, Correct 31120.0 / Total 38464.0\n",
            "[Epoch:   34], Loss: 0.546, Acc: 81.000, Correct 36340.0 / Total 44864.0\n",
            "[Epoch:   34], Acc: 79.220\n",
            "Current Learning Rate: [0.09755527298894293]\n",
            "[Epoch:   35], Loss: 0.499, Acc: 83.014, Correct 5366.0 / Total 6464.0\n",
            "[Epoch:   35], Loss: 0.501, Acc: 82.789, Correct 10650.0 / Total 12864.0\n",
            "[Epoch:   35], Loss: 0.508, Acc: 82.553, Correct 15903.0 / Total 19264.0\n",
            "[Epoch:   35], Loss: 0.510, Acc: 82.489, Correct 21170.0 / Total 25664.0\n",
            "[Epoch:   35], Loss: 0.516, Acc: 82.232, Correct 26367.0 / Total 32064.0\n",
            "[Epoch:   35], Loss: 0.518, Acc: 82.066, Correct 31566.0 / Total 38464.0\n",
            "[Epoch:   35], Loss: 0.518, Acc: 82.086, Correct 36827.0 / Total 44864.0\n",
            "[Epoch:   35], Acc: 81.430\n",
            "Current Learning Rate: [0.09619778264893877]\n",
            "[Epoch:   36], Loss: 0.500, Acc: 82.689, Correct 5345.0 / Total 6464.0\n",
            "[Epoch:   36], Loss: 0.510, Acc: 82.432, Correct 10604.0 / Total 12864.0\n",
            "[Epoch:   36], Loss: 0.507, Acc: 82.485, Correct 15890.0 / Total 19264.0\n",
            "[Epoch:   36], Loss: 0.506, Acc: 82.493, Correct 21171.0 / Total 25664.0\n",
            "[Epoch:   36], Loss: 0.503, Acc: 82.641, Correct 26498.0 / Total 32064.0\n",
            "[Epoch:   36], Loss: 0.501, Acc: 82.654, Correct 31792.0 / Total 38464.0\n",
            "[Epoch:   36], Loss: 0.506, Acc: 82.405, Correct 36970.0 / Total 44864.0\n",
            "[Epoch:   36], Acc: 81.100\n",
            "Current Learning Rate: [0.09455577588320897]\n",
            "[Epoch:   37], Loss: 0.475, Acc: 83.679, Correct 5409.0 / Total 6464.0\n",
            "[Epoch:   37], Loss: 0.484, Acc: 83.248, Correct 10709.0 / Total 12864.0\n",
            "[Epoch:   37], Loss: 0.481, Acc: 83.311, Correct 16049.0 / Total 19264.0\n",
            "[Epoch:   37], Loss: 0.480, Acc: 83.358, Correct 21393.0 / Total 25664.0\n",
            "[Epoch:   37], Loss: 0.481, Acc: 83.271, Correct 26700.0 / Total 32064.0\n",
            "[Epoch:   37], Loss: 0.482, Acc: 83.351, Correct 32060.0 / Total 38464.0\n",
            "[Epoch:   37], Loss: 0.484, Acc: 83.252, Correct 37350.0 / Total 44864.0\n",
            "[Epoch:   37], Acc: 80.110\n",
            "Current Learning Rate: [0.09263937620948691]\n",
            "[Epoch:   38], Loss: 0.467, Acc: 83.772, Correct 5415.0 / Total 6464.0\n",
            "[Epoch:   38], Loss: 0.465, Acc: 83.909, Correct 10794.0 / Total 12864.0\n",
            "[Epoch:   38], Loss: 0.465, Acc: 83.778, Correct 16139.0 / Total 19264.0\n",
            "[Epoch:   38], Loss: 0.469, Acc: 83.584, Correct 21451.0 / Total 25664.0\n",
            "[Epoch:   38], Loss: 0.467, Acc: 83.689, Correct 26834.0 / Total 32064.0\n",
            "[Epoch:   38], Loss: 0.469, Acc: 83.590, Correct 32152.0 / Total 38464.0\n",
            "[Epoch:   38], Loss: 0.472, Acc: 83.566, Correct 37491.0 / Total 44864.0\n",
            "[Epoch:   38], Acc: 81.230\n",
            "Current Learning Rate: [0.09046039886902862]\n",
            "[Epoch:   39], Loss: 0.450, Acc: 85.164, Correct 5505.0 / Total 6464.0\n",
            "[Epoch:   39], Loss: 0.449, Acc: 84.756, Correct 10903.0 / Total 12864.0\n",
            "[Epoch:   39], Loss: 0.446, Acc: 84.619, Correct 16301.0 / Total 19264.0\n",
            "[Epoch:   39], Loss: 0.451, Acc: 84.496, Correct 21685.0 / Total 25664.0\n",
            "[Epoch:   39], Loss: 0.454, Acc: 84.397, Correct 27061.0 / Total 32064.0\n",
            "[Epoch:   39], Loss: 0.454, Acc: 84.344, Correct 32442.0 / Total 38464.0\n",
            "[Epoch:   39], Loss: 0.454, Acc: 84.366, Correct 37850.0 / Total 44864.0\n",
            "[Epoch:   39], Acc: 82.870\n",
            "Current Learning Rate: [0.08803227798172156]\n",
            "[Epoch:   40], Loss: 0.410, Acc: 85.705, Correct 5540.0 / Total 6464.0\n",
            "[Epoch:   40], Loss: 0.420, Acc: 85.308, Correct 10974.0 / Total 12864.0\n",
            "[Epoch:   40], Loss: 0.437, Acc: 84.712, Correct 16319.0 / Total 19264.0\n",
            "[Epoch:   40], Loss: 0.439, Acc: 84.687, Correct 21734.0 / Total 25664.0\n",
            "[Epoch:   40], Loss: 0.443, Acc: 84.699, Correct 27158.0 / Total 32064.0\n",
            "[Epoch:   40], Loss: 0.444, Acc: 84.609, Correct 32544.0 / Total 38464.0\n",
            "[Epoch:   40], Loss: 0.445, Acc: 84.527, Correct 37922.0 / Total 44864.0\n",
            "[Epoch:   40], Acc: 84.110\n",
            "Current Learning Rate: [0.08536998372026805]\n",
            "[Epoch:   41], Loss: 0.439, Acc: 84.715, Correct 5476.0 / Total 6464.0\n",
            "[Epoch:   41], Loss: 0.428, Acc: 85.145, Correct 10953.0 / Total 12864.0\n",
            "[Epoch:   41], Loss: 0.432, Acc: 84.956, Correct 16366.0 / Total 19264.0\n",
            "[Epoch:   41], Loss: 0.431, Acc: 84.940, Correct 21799.0 / Total 25664.0\n",
            "[Epoch:   41], Loss: 0.433, Acc: 84.924, Correct 27230.0 / Total 32064.0\n",
            "[Epoch:   41], Loss: 0.430, Acc: 85.087, Correct 32728.0 / Total 38464.0\n",
            "[Epoch:   41], Loss: 0.430, Acc: 85.111, Correct 38184.0 / Total 44864.0\n",
            "[Epoch:   41], Acc: 82.670\n",
            "Current Learning Rate: [0.08248993001409269]\n",
            "[Epoch:   42], Loss: 0.398, Acc: 85.814, Correct 5547.0 / Total 6464.0\n",
            "[Epoch:   42], Loss: 0.404, Acc: 85.868, Correct 11046.0 / Total 12864.0\n",
            "[Epoch:   42], Loss: 0.404, Acc: 86.057, Correct 16578.0 / Total 19264.0\n",
            "[Epoch:   42], Loss: 0.409, Acc: 85.883, Correct 22041.0 / Total 25664.0\n",
            "[Epoch:   42], Loss: 0.410, Acc: 85.785, Correct 27506.0 / Total 32064.0\n",
            "[Epoch:   42], Loss: 0.414, Acc: 85.704, Correct 32965.0 / Total 38464.0\n",
            "[Epoch:   42], Loss: 0.415, Acc: 85.654, Correct 38428.0 / Total 44864.0\n",
            "[Epoch:   42], Acc: 83.300\n",
            "Current Learning Rate: [0.07940987335200904]\n",
            "[Epoch:   43], Loss: 0.375, Acc: 86.742, Correct 5607.0 / Total 6464.0\n",
            "[Epoch:   43], Loss: 0.386, Acc: 86.521, Correct 11130.0 / Total 12864.0\n",
            "[Epoch:   43], Loss: 0.392, Acc: 86.405, Correct 16645.0 / Total 19264.0\n",
            "[Epoch:   43], Loss: 0.400, Acc: 86.035, Correct 22080.0 / Total 25664.0\n",
            "[Epoch:   43], Loss: 0.400, Acc: 86.084, Correct 27602.0 / Total 32064.0\n",
            "[Epoch:   43], Loss: 0.398, Acc: 86.125, Correct 33127.0 / Total 38464.0\n",
            "[Epoch:   43], Loss: 0.402, Acc: 86.013, Correct 38589.0 / Total 44864.0\n",
            "[Epoch:   43], Acc: 83.520\n",
            "Current Learning Rate: [0.07614880330756164]\n",
            "[Epoch:   44], Loss: 0.383, Acc: 86.866, Correct 5615.0 / Total 6464.0\n",
            "[Epoch:   44], Loss: 0.382, Acc: 87.158, Correct 11212.0 / Total 12864.0\n",
            "[Epoch:   44], Loss: 0.379, Acc: 87.116, Correct 16782.0 / Total 19264.0\n",
            "[Epoch:   44], Loss: 0.382, Acc: 86.908, Correct 22304.0 / Total 25664.0\n",
            "[Epoch:   44], Loss: 0.385, Acc: 86.761, Correct 27819.0 / Total 32064.0\n",
            "[Epoch:   44], Loss: 0.387, Acc: 86.699, Correct 33348.0 / Total 38464.0\n",
            "[Epoch:   44], Loss: 0.387, Acc: 86.691, Correct 38893.0 / Total 44864.0\n",
            "[Epoch:   44], Acc: 84.180\n",
            "Current Learning Rate: [0.07272682546199037]\n",
            "[Epoch:   45], Loss: 0.360, Acc: 87.732, Correct 5671.0 / Total 6464.0\n",
            "[Epoch:   45], Loss: 0.362, Acc: 87.368, Correct 11239.0 / Total 12864.0\n",
            "[Epoch:   45], Loss: 0.362, Acc: 87.375, Correct 16832.0 / Total 19264.0\n",
            "[Epoch:   45], Loss: 0.367, Acc: 87.196, Correct 22378.0 / Total 25664.0\n",
            "[Epoch:   45], Loss: 0.368, Acc: 87.201, Correct 27960.0 / Total 32064.0\n",
            "[Epoch:   45], Loss: 0.370, Acc: 87.196, Correct 33539.0 / Total 38464.0\n",
            "[Epoch:   45], Loss: 0.372, Acc: 87.128, Correct 39089.0 / Total 44864.0\n",
            "[Epoch:   45], Acc: 85.250\n",
            "Current Learning Rate: [0.06916503744663624]\n",
            "[Epoch:   46], Loss: 0.333, Acc: 88.088, Correct 5694.0 / Total 6464.0\n",
            "[Epoch:   46], Loss: 0.335, Acc: 88.052, Correct 11327.0 / Total 12864.0\n",
            "[Epoch:   46], Loss: 0.340, Acc: 87.988, Correct 16950.0 / Total 19264.0\n",
            "[Epoch:   46], Loss: 0.343, Acc: 87.816, Correct 22537.0 / Total 25664.0\n",
            "[Epoch:   46], Loss: 0.346, Acc: 87.612, Correct 28092.0 / Total 32064.0\n",
            "[Epoch:   46], Loss: 0.352, Acc: 87.529, Correct 33667.0 / Total 38464.0\n",
            "[Epoch:   46], Loss: 0.352, Acc: 87.507, Correct 39259.0 / Total 44864.0\n",
            "[Epoch:   46], Acc: 84.710\n",
            "Current Learning Rate: [0.06548539886902863]\n",
            "[Epoch:   47], Loss: 0.337, Acc: 88.304, Correct 5708.0 / Total 6464.0\n",
            "[Epoch:   47], Loss: 0.332, Acc: 88.433, Correct 11376.0 / Total 12864.0\n",
            "[Epoch:   47], Loss: 0.330, Acc: 88.497, Correct 17048.0 / Total 19264.0\n",
            "[Epoch:   47], Loss: 0.333, Acc: 88.381, Correct 22682.0 / Total 25664.0\n",
            "[Epoch:   47], Loss: 0.336, Acc: 88.326, Correct 28321.0 / Total 32064.0\n",
            "[Epoch:   47], Loss: 0.338, Acc: 88.142, Correct 33903.0 / Total 38464.0\n",
            "[Epoch:   47], Loss: 0.339, Acc: 88.111, Correct 39530.0 / Total 44864.0\n",
            "[Epoch:   47], Acc: 85.680\n",
            "Current Learning Rate: [0.061710595924602485]\n",
            "[Epoch:   48], Loss: 0.311, Acc: 89.248, Correct 5769.0 / Total 6464.0\n",
            "[Epoch:   48], Loss: 0.314, Acc: 89.179, Correct 11472.0 / Total 12864.0\n",
            "[Epoch:   48], Loss: 0.312, Acc: 89.104, Correct 17165.0 / Total 19264.0\n",
            "[Epoch:   48], Loss: 0.316, Acc: 88.922, Correct 22821.0 / Total 25664.0\n",
            "[Epoch:   48], Loss: 0.320, Acc: 88.713, Correct 28445.0 / Total 32064.0\n",
            "[Epoch:   48], Loss: 0.324, Acc: 88.556, Correct 34062.0 / Total 38464.0\n",
            "[Epoch:   48], Loss: 0.324, Acc: 88.592, Correct 39746.0 / Total 44864.0\n",
            "[Epoch:   48], Acc: 85.550\n",
            "Current Learning Rate: [0.05786390152875954]\n",
            "[Epoch:   49], Loss: 0.301, Acc: 89.295, Correct 5772.0 / Total 6464.0\n",
            "[Epoch:   49], Loss: 0.295, Acc: 89.521, Correct 11516.0 / Total 12864.0\n",
            "[Epoch:   49], Loss: 0.298, Acc: 89.519, Correct 17245.0 / Total 19264.0\n",
            "[Epoch:   49], Loss: 0.294, Acc: 89.624, Correct 23001.0 / Total 25664.0\n",
            "[Epoch:   49], Loss: 0.298, Acc: 89.543, Correct 28711.0 / Total 32064.0\n",
            "[Epoch:   49], Loss: 0.302, Acc: 89.351, Correct 34368.0 / Total 38464.0\n",
            "[Epoch:   49], Loss: 0.304, Acc: 89.366, Correct 40093.0 / Total 44864.0\n",
            "[Epoch:   49], Acc: 84.660\n",
            "Current Learning Rate: [0.05396903183160586]\n",
            "[Epoch:   50], Loss: 0.299, Acc: 89.573, Correct 5790.0 / Total 6464.0\n",
            "[Epoch:   50], Loss: 0.285, Acc: 89.863, Correct 11560.0 / Total 12864.0\n",
            "[Epoch:   50], Loss: 0.284, Acc: 89.909, Correct 17320.0 / Total 19264.0\n",
            "[Epoch:   50], Loss: 0.285, Acc: 89.951, Correct 23085.0 / Total 25664.0\n",
            "[Epoch:   50], Loss: 0.289, Acc: 89.864, Correct 28814.0 / Total 32064.0\n",
            "[Epoch:   50], Loss: 0.290, Acc: 89.803, Correct 34542.0 / Total 38464.0\n",
            "[Epoch:   50], Loss: 0.289, Acc: 89.814, Correct 40294.0 / Total 44864.0\n",
            "[Epoch:   50], Acc: 85.060\n",
            "Current Learning Rate: [0.050050000000000004]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After training the model, we test on the test dataloader."
      ],
      "metadata": {
        "id": "GWgSMil6Y1mW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#EVAL\n",
        "test(test_loader, 1)"
      ],
      "metadata": {
        "id": "LHITcZk9xndM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d4d79cc-2029-4772-ab17-4f2d38966ac5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch:    1], Acc: 85.060\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85.06"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can display some of the test images with the actual and predicted labels."
      ],
      "metadata": {
        "id": "oLi1r1WoYmKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'images', 'labels' are from your dataloader and 'model' is your trained model\n",
        "\n",
        "\n",
        "model.eval()\n",
        "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch_idx, (data, target) in enumerate(test_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        output = model(data)\n",
        "        _, pred = F.softmax(output, dim=-1).max(1)\n",
        "        images = data.cpu()\n",
        "        target = target.cpu()\n",
        "        pred = pred.cpu()\n",
        "\n",
        "        # Display images and compare actual and predicted labels\n",
        "        plt.figure(figsize=(20, 20))  # Adjust the figure size\n",
        "        for i in range(len(images)):\n",
        "            plt.subplot(8, 8, i + 1)  # Configure for a grid of 8x8\n",
        "            plt.imshow(images[i].permute(1, 2, 0))  # Adjust image tensor format for display\n",
        "            plt.title(f'Actual: {target[i]}, Predicted: {pred[i]}')\n",
        "            plt.axis('off')\n",
        "        plt.show()\n",
        "\n",
        "        break  # Break after the first batch\n"
      ],
      "metadata": {
        "id": "mjzxgMLTYBll",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "35c9a71e-9790-473b-cca4-3b45093cfb00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 64 Axes>"
            ],
            "image/png": 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