FineTuning.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/luciainnocenti/IncrementalLearning/blob/master/FineTuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCO7owW4O0jb",
        "colab_type": "text"
      },
      "source": [
        "# Import GitHub repository"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01VjtslGOvI3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import logging\n",
        "import sys"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSC6q-24O8Yr",
        "colab_type": "code",
        "colab": {
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          "height": 146
        },
        "outputId": "b9309abf-beb4-4609-ad94-1e5277dc8ad2"
      },
      "source": [
        "if not os.path.isdir('./DatasetCIFAR'):\n",
        "  !git clone https://github.com/luciainnocenti/IncrementalLearning.git\n",
        "  !mv 'IncrementalLearning' 'DatasetCIFAR'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'IncrementalLearning'...\n",
            "remote: Enumerating objects: 28, done.\u001b[K\n",
            "remote: Counting objects: 100% (28/28), done.\u001b[K\n",
            "remote: Compressing objects: 100% (28/28), done.\u001b[K\n",
            "remote: Total 1527 (delta 15), reused 0 (delta 0), pack-reused 1499\u001b[K\n",
            "Receiving objects: 100% (1527/1527), 1010.40 KiB | 3.47 MiB/s, done.\n",
            "Resolving deltas: 100% (979/979), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_q5sdRysO9-F",
        "colab_type": "text"
      },
      "source": [
        "# Import packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3AwEK1IPEDf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "805335c7-b992-4b00-d941-aa750bbafa6e"
      },
      "source": [
        "from DatasetCIFAR.data_set import Dataset \n",
        "from DatasetCIFAR.data_set import Subset\n",
        "from DatasetCIFAR import ResNet\n",
        "from DatasetCIFAR import utils\n",
        "from DatasetCIFAR import params\n",
        "from torchvision import models\n",
        "import torch.nn as nn\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "import numpy as np\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.nn import functional as F\n",
        "import random\n",
        "random.seed(params.SEED)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYYOJdxSKLP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9430a5e1-c7ab-4be9-871d-c2b7eed30c8f"
      },
      "source": [
        "print(params.SEED)\n",
        "print(params.NUM_WORKERS)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "653\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xU8_JQV3O_c5",
        "colab_type": "text"
      },
      "source": [
        "# Define Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SN6IDz3PJUE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "resNet = ResNet.resnet32(num_classes=100)\n",
        "resNet = resNet.to(params.DEVICE)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXGgft0iPLVD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "train_transformer = transforms.Compose([transforms.RandomCrop(size = 32, padding=4),\n",
        "                                         transforms.RandomHorizontalFlip(),\n",
        "                                         transforms.ToTensor(),\n",
        "                                         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])\n",
        "\n",
        "test_transformer = transforms.Compose([transforms.ToTensor(),\n",
        "                                         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ldJmjNkPNiG",
        "colab_type": "text"
      },
      "source": [
        "# Define DataSets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czGrTi8VPRoB",
        "colab_type": "code",
        "colab": {
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          "height": 104,
          "referenced_widgets": [
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        },
        "outputId": "0503c37c-2c11-4210-90a7-41447cab9f46"
      },
      "source": [
        "trainDS = Dataset(train=True)\n",
        "testDS = Dataset(train=False)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to data/cifar-100-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting data/cifar-100-python.tar.gz to data\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn8XrSWqPUiN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "1f7f154d-103a-4ffd-cb0b-e212fdadbf75"
      },
      "source": [
        "train_splits = trainDS.splits\n",
        "test_splits = testDS.splits\n",
        "print(train_splits)\n",
        "print(test_splits)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[94.0, 63.0, 74.0, 21.0, 35.0, 56.0, 91.0, 96.0, 87.0, 48.0], [68.0, 80.0, 22.0, 37.0, 60.0, 97.0, 51.0, 62.0, 92.0, 76.0], [75.0, 89.0, 23.0, 99.0, 39.0, 66.0, 54.0, 69.0, 84.0, 61.0], [85.0, 24.0, 98.0, 41.0, 73.0, 58.0, 78.0, 77.0, 70.0, 49.0], [65.0, 88.0, 36.0, 93.0, 45.0, 10.0, 90.0, 17.0, 32.0, 59.0], [83.0, 43.0, 53.0, 11.0, 86.0, 19.0, 38.0, 30.0, 40.0, 50.0], [57.0, 81.0, 12.0, 95.0, 25.0, 47.0, 34.0, 52.0, 44.0, 72.0], [46.0, 79.0, 20.0, 28.0, 5.0, 71.0, 8.0, 18.0, 33.0, 15.0], [55.0, 29.0, 64.0, 31.0, 67.0, 7.0, 13.0, 14.0, 42.0, 6.0], [82.0, 2.0, 27.0, 16.0, 26.0, 3.0, 4.0, 1.0, 9.0, 0.0]]\n",
            "[[94.0, 63.0, 74.0, 21.0, 35.0, 56.0, 91.0, 96.0, 87.0, 48.0], [68.0, 80.0, 22.0, 37.0, 60.0, 97.0, 51.0, 62.0, 92.0, 76.0], [75.0, 89.0, 23.0, 99.0, 39.0, 66.0, 54.0, 69.0, 84.0, 61.0], [85.0, 24.0, 98.0, 41.0, 73.0, 58.0, 78.0, 77.0, 70.0, 49.0], [65.0, 88.0, 36.0, 93.0, 45.0, 10.0, 90.0, 17.0, 32.0, 59.0], [83.0, 43.0, 53.0, 11.0, 86.0, 19.0, 38.0, 30.0, 40.0, 50.0], [57.0, 81.0, 12.0, 95.0, 25.0, 47.0, 34.0, 52.0, 44.0, 72.0], [46.0, 79.0, 20.0, 28.0, 5.0, 71.0, 8.0, 18.0, 33.0, 15.0], [55.0, 29.0, 64.0, 31.0, 67.0, 7.0, 13.0, 14.0, 42.0, 6.0], [82.0, 2.0, 27.0, 16.0, 26.0, 3.0, 4.0, 1.0, 9.0, 0.0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nc8WMLe3PQvI",
        "colab_type": "text"
      },
      "source": [
        "# Useful plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQBZdyyrPW_a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plotTask(pars_tasks):\n",
        "\n",
        "  x_tasks =  np.linspace(10, 100, 10)\n",
        "\n",
        "  plt.plot(x_tasks, pars_tasks, label=['Accuracy', 'Loss'])\n",
        "  plt.xlabel(\"Epoch\")\n",
        "  plt.title('Accuracy over tasks')\n",
        "  plt.legend(['Accuracy', 'Loss'])\n",
        "  plt.grid(True)\n",
        "  plt.show()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RzunPCcSfip",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mapFunction(labels, splits):\n",
        "\tm_l = []\n",
        "\tl_splits = list(splits)\n",
        "\tfor el in labels:\n",
        "\t\tm_l.append( l_splits.index(el) )\n",
        "\treturn torch.LongTensor(m_l).to(params.DEVICE)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPWcz2ISRN6E",
        "colab_type": "text"
      },
      "source": [
        "# Train and evaluation phase"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fs6I_lmjRLYo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fb0095c9-6d8d-4279-d634-e63aac6f3c0c"
      },
      "source": [
        "pars_tasks = []\n",
        "test_indexes = []\n",
        "\n",
        "for task in range(0, 100, params.TASK_SIZE):\n",
        "  pars_tasks.insert(task, 0)\n",
        "\n",
        "\n",
        "optimizer = torch.optim.SGD(resNet.parameters(), lr=params.LR, momentum=params.MOMENTUM, weight_decay=params.WEIGHT_DECAY)\n",
        "scheduler = optim.lr_scheduler.MultiStepLR(optimizer, params.STEP_SIZE, gamma=params.GAMMA) #allow to change the LR at predefined epochs\n",
        "criterion = torch.nn.BCEWithLogitsLoss()\n",
        "\n",
        "current_step = 0\n",
        "\n",
        "\n",
        "for task in range(0, 100, params.TASK_SIZE):\n",
        "\n",
        "  train_indexes = trainDS.__getIndexesGroups__(task)\n",
        "  test_indexes = test_indexes + testDS.__getIndexesGroups__(task)\n",
        "\n",
        "  train_dataset = Subset(trainDS, train_indexes, train_transformer)\n",
        "  test_dataset = Subset(testDS, test_indexes, test_transformer)\n",
        "\n",
        "  train_loader = DataLoader( train_dataset, num_workers=params.NUM_WORKERS, batch_size=params.BATCH_SIZE, shuffle=True)\n",
        "  test_loader = DataLoader( test_dataset, num_workers=params.NUM_WORKERS, batch_size=params.BATCH_SIZE, shuffle=True )\n",
        "  \n",
        "  col = np.array(train_splits[int(task/10)]).astype(int)\n",
        "  \n",
        "  for epoch in range (params.NUM_EPOCHS):\n",
        "    lenght = 0\n",
        "    scheduler.step() #update the learning rate\n",
        "    running_corrects = 0    \n",
        "    for images, labels, _ in train_loader:\n",
        "      images = images.float().to(params.DEVICE)\n",
        "      labels = labels.to(params.DEVICE)\n",
        "      mappedLabels = mapFunction(labels, col)   \n",
        "      onehot_labels = torch.eye(100)[labels].to(params.DEVICE)#it creates the one-hot-encoding list for the labels; needed for BCELoss    \n",
        "      optimizer.zero_grad() # Zero-ing the gradients\n",
        "      outputs = resNet(images)\n",
        "      loss = criterion(outputs,onehot_labels)   \n",
        "      cut_outputs = np.take_along_axis(outputs.to(params.DEVICE), col[None, :], axis = 1).to(params.DEVICE)\n",
        "      _, preds = torch.max(cut_outputs.data, 1)\n",
        "      running_corrects += torch.sum(preds == mappedLabels.data).data.item()   \n",
        "      loss.backward()   \n",
        "      optimizer.step()    \n",
        "      current_step += 1\n",
        "      lenght += len(images)   \n",
        "    accuracy = running_corrects / float(lenght)\n",
        "    print(\"At step \", str(task), \" and at epoch = \", epoch, \" the loss is = \", loss.item(), \" and accuracy is = \", accuracy)\n",
        "  \n",
        "  resNet = resNet.eval()\n",
        "\n",
        "  t_l = 0\n",
        "  running_corrects = 0\n",
        "  col = []\n",
        "  for i,x in enumerate( test_splits[ :int(task/10) + 1]):\n",
        "  \tv = np.array(x)\n",
        "  \tcol = np.concatenate( (col,v), axis = None)\n",
        "  col = col.astype(int)\n",
        "\n",
        "  tot_preds = []\n",
        "  tot_lab = []\n",
        "  for images, labels, _ in test_loader:\n",
        "    images = images.float().to(params.DEVICE)\n",
        "    labels = labels.to(params.DEVICE)   \n",
        "    mappedLabels = mapFunction(labels, col)\n",
        "\n",
        "    onehot_labels = torch.eye(100)[labels].to(params.DEVICE)\n",
        "\n",
        "    outputs = resNet(images).to(params.DEVICE)\n",
        "    cut_outputs = np.take_along_axis(outputs, col[None, :], axis = 1)\n",
        "    cut_outputs = cut_outputs.to(params.DEVICE)\n",
        "    _, preds = torch.max(cut_outputs.data, 1)\n",
        "    tot_preds = np.concatenate( ( tot_preds, preds.data.cpu().numpy() ) )\n",
        "    tot_lab = np.concatenate( (tot_lab, mappedLabels.data.cpu().numpy()  ) )\n",
        "\t\t# Update Corrects\n",
        "    running_corrects += torch.sum(preds == mappedLabels.data).data.item()\n",
        "    print(len(images))\n",
        "    t_l += len(images)\n",
        "  # Calculate Accuracy\n",
        "  accuracy = running_corrects / float(t_l)\n",
        "  loss = criterion(outputs,onehot_labels)\n",
        "  print('Validation Loss: {} Validation Accuracy : {}'.format(loss.item(),accuracy) )\n",
        "  cf = confusion_matrix(tot_lab, tot_preds)\n",
        "  df_cm = pd.DataFrame(cf, range(task + params.TASK_SIZE), range(task + params.TASK_SIZE))\n",
        "  sn.set(font_scale=1.4) # for label size\n",
        "  sn.heatmap(df_cm, annot=False)\n",
        "  plt.show()\n",
        "  pars_tasks[int(task/10)] = (accuracy, loss.item())\n",
        "  resNet = resNet.train(True)\n",
        "  \n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "At step  0  and at epoch =  0  the loss is =  0.02419709786772728  and accuracy is =  0.1876\n",
            "At step  0  and at epoch =  1  the loss is =  0.025315560400485992  and accuracy is =  0.4014\n",
            "At step  0  and at epoch =  2  the loss is =  0.017259353771805763  and accuracy is =  0.4832\n",
            "At step  0  and at epoch =  3  the loss is =  0.026945650577545166  and accuracy is =  0.5234\n",
            "At step  0  and at epoch =  4  the loss is =  0.028079917654395103  and accuracy is =  0.5368\n",
            "At step  0  and at epoch =  5  the loss is =  0.016922106966376305  and accuracy is =  0.6038\n",
            "At step  0  and at epoch =  6  the loss is =  0.014250035397708416  and accuracy is =  0.634\n",
            "At step  0  and at epoch =  7  the loss is =  0.0223175548017025  and accuracy is =  0.6772\n",
            "At step  0  and at epoch =  8  the loss is =  0.025830082595348358  and accuracy is =  0.6864\n",
            "At step  0  and at epoch =  9  the loss is =  0.007626925595104694  and accuracy is =  0.7036\n",
            "At step  0  and at epoch =  10  the loss is =  0.010720483027398586  and accuracy is =  0.7364\n",
            "At step  0  and at epoch =  11  the loss is =  0.02606678009033203  and accuracy is =  0.7382\n",
            "At step  0  and at epoch =  12  the loss is =  0.017161021009087563  and accuracy is =  0.7028\n",
            "At step  0  and at epoch =  13  the loss is =  0.044555142521858215  and accuracy is =  0.7486\n",
            "At step  0  and at epoch =  14  the loss is =  0.01491256058216095  and accuracy is =  0.747\n",
            "At step  0  and at epoch =  15  the loss is =  0.032698582857847214  and accuracy is =  0.7734\n",
            "At step  0  and at epoch =  16  the loss is =  0.020614733919501305  and accuracy is =  0.761\n",
            "At step  0  and at epoch =  17  the loss is =  0.024525294080376625  and accuracy is =  0.79\n",
            "At step  0  and at epoch =  18  the loss is =  0.014736180193722248  and accuracy is =  0.7872\n",
            "At step  0  and at epoch =  19  the loss is =  0.030622076243162155  and accuracy is =  0.8118\n",
            "At step  0  and at epoch =  20  the loss is =  0.008850702084600925  and accuracy is =  0.8104\n",
            "At step  0  and at epoch =  21  the loss is =  0.011380921117961407  and accuracy is =  0.8214\n",
            "At step  0  and at epoch =  22  the loss is =  0.012183153070509434  and accuracy is =  0.8232\n",
            "At step  0  and at epoch =  23  the loss is =  0.007981574162840843  and accuracy is =  0.8366\n",
            "At step  0  and at epoch =  24  the loss is =  0.01723659038543701  and accuracy is =  0.845\n",
            "At step  0  and at epoch =  25  the loss is =  0.025707239285111427  and accuracy is =  0.854\n",
            "At step  0  and at epoch =  26  the loss is =  0.004112273920327425  and accuracy is =  0.8354\n",
            "At step  0  and at epoch =  27  the loss is =  0.013419975526630878  and accuracy is =  0.8522\n",
            "At step  0  and at epoch =  28  the loss is =  0.007675605826079845  and accuracy is =  0.8464\n",
            "At step  0  and at epoch =  29  the loss is =  0.014085892587900162  and accuracy is =  0.8732\n",
            "At step  0  and at epoch =  30  the loss is =  0.004496901761740446  and accuracy is =  0.8572\n",
            "At step  0  and at epoch =  31  the loss is =  0.011737517081201077  and accuracy is =  0.87\n",
            "At step  0  and at epoch =  32  the loss is =  0.004532295744866133  and accuracy is =  0.8898\n",
            "At step  0  and at epoch =  33  the loss is =  0.0186549611389637  and accuracy is =  0.8796\n",
            "At step  0  and at epoch =  34  the loss is =  0.009580197744071484  and accuracy is =  0.8662\n",
            "At step  0  and at epoch =  35  the loss is =  0.01449004840105772  and accuracy is =  0.8788\n",
            "At step  0  and at epoch =  36  the loss is =  0.028071526437997818  and accuracy is =  0.8664\n",
            "At step  0  and at epoch =  37  the loss is =  0.007446847856044769  and accuracy is =  0.8332\n",
            "At step  0  and at epoch =  38  the loss is =  0.012590007856488228  and accuracy is =  0.8688\n",
            "At step  0  and at epoch =  39  the loss is =  0.005357357207685709  and accuracy is =  0.8712\n",
            "At step  0  and at epoch =  40  the loss is =  0.020262431353330612  and accuracy is =  0.8942\n",
            "At step  0  and at epoch =  41  the loss is =  0.026008082553744316  and accuracy is =  0.8626\n",
            "At step  0  and at epoch =  42  the loss is =  0.013562092557549477  and accuracy is =  0.8642\n",
            "At step  0  and at epoch =  43  the loss is =  0.007328039035201073  and accuracy is =  0.8802\n",
            "At step  0  and at epoch =  44  the loss is =  0.010950705967843533  and accuracy is =  0.8794\n",
            "At step  0  and at epoch =  45  the loss is =  0.005042563192546368  and accuracy is =  0.9046\n",
            "At step  0  and at epoch =  46  the loss is =  0.0063362750224769115  and accuracy is =  0.9136\n",
            "At step  0  and at epoch =  47  the loss is =  0.010769858956336975  and accuracy is =  0.8978\n",
            "At step  0  and at epoch =  48  the loss is =  0.01744729094207287  and accuracy is =  0.9272\n",
            "At step  0  and at epoch =  49  the loss is =  0.019051505252718925  and accuracy is =  0.9352\n",
            "At step  0  and at epoch =  50  the loss is =  0.01216939464211464  and accuracy is =  0.9422\n",
            "At step  0  and at epoch =  51  the loss is =  0.010090580210089684  and accuracy is =  0.9482\n",
            "At step  0  and at epoch =  52  the loss is =  0.02482517622411251  and accuracy is =  0.954\n",
            "At step  0  and at epoch =  53  the loss is =  0.01458988431841135  and accuracy is =  0.9416\n",
            "At step  0  and at epoch =  54  the loss is =  0.0010805283673107624  and accuracy is =  0.951\n",
            "At step  0  and at epoch =  55  the loss is =  0.006139087490737438  and accuracy is =  0.9628\n",
            "At step  0  and at epoch =  56  the loss is =  0.012942610308527946  and accuracy is =  0.9624\n",
            "At step  0  and at epoch =  57  the loss is =  0.005754147656261921  and accuracy is =  0.9524\n",
            "At step  0  and at epoch =  58  the loss is =  0.003449186682701111  and accuracy is =  0.9616\n",
            "At step  0  and at epoch =  59  the loss is =  0.022732259705662727  and accuracy is =  0.9576\n",
            "At step  0  and at epoch =  60  the loss is =  0.002006803173571825  and accuracy is =  0.944\n",
            "At step  0  and at epoch =  61  the loss is =  0.002869808115065098  and accuracy is =  0.9628\n",
            "At step  0  and at epoch =  62  the loss is =  0.002233895706012845  and accuracy is =  0.9672\n",
            "At step  0  and at epoch =  63  the loss is =  0.010017580352723598  and accuracy is =  0.9684\n",
            "At step  0  and at epoch =  64  the loss is =  0.0015395727241411805  and accuracy is =  0.969\n",
            "At step  0  and at epoch =  65  the loss is =  0.007118108216673136  and accuracy is =  0.9722\n",
            "At step  0  and at epoch =  66  the loss is =  0.010974366217851639  and accuracy is =  0.9716\n",
            "At step  0  and at epoch =  67  the loss is =  0.006473900750279427  and accuracy is =  0.9744\n",
            "At step  0  and at epoch =  68  the loss is =  0.003550643799826503  and accuracy is =  0.9736\n",
            "At step  0  and at epoch =  69  the loss is =  0.0051970104686915874  and accuracy is =  0.9718\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "104\n",
            "Validation Loss: 0.005908407270908356 Validation Accuracy : 0.877\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  10  and at epoch =  0  the loss is =  0.09140466153621674  and accuracy is =  0.193\n",
            "At step  10  and at epoch =  1  the loss is =  0.058071307837963104  and accuracy is =  0.2468\n",
            "At step  10  and at epoch =  2  the loss is =  0.03242584317922592  and accuracy is =  0.3098\n",
            "At step  10  and at epoch =  3  the loss is =  0.031171534210443497  and accuracy is =  0.3716\n",
            "At step  10  and at epoch =  4  the loss is =  0.023018421605229378  and accuracy is =  0.4184\n",
            "At step  10  and at epoch =  5  the loss is =  0.026273565366864204  and accuracy is =  0.4524\n",
            "At step  10  and at epoch =  6  the loss is =  0.022153712809085846  and accuracy is =  0.4892\n",
            "At step  10  and at epoch =  7  the loss is =  0.020154790952801704  and accuracy is =  0.5056\n",
            "At step  10  and at epoch =  8  the loss is =  0.019806398078799248  and accuracy is =  0.5318\n",
            "At step  10  and at epoch =  9  the loss is =  0.026033081114292145  and accuracy is =  0.561\n",
            "At step  10  and at epoch =  10  the loss is =  0.022365540266036987  and accuracy is =  0.5806\n",
            "At step  10  and at epoch =  11  the loss is =  0.025327667593955994  and accuracy is =  0.5936\n",
            "At step  10  and at epoch =  12  the loss is =  0.024388086050748825  and accuracy is =  0.6212\n",
            "At step  10  and at epoch =  13  the loss is =  0.015813784673810005  and accuracy is =  0.6206\n",
            "At step  10  and at epoch =  14  the loss is =  0.027425343170762062  and accuracy is =  0.6338\n",
            "At step  10  and at epoch =  15  the loss is =  0.016325272619724274  and accuracy is =  0.6426\n",
            "At step  10  and at epoch =  16  the loss is =  0.022796621546149254  and accuracy is =  0.6504\n",
            "At step  10  and at epoch =  17  the loss is =  0.025181693956255913  and accuracy is =  0.6594\n",
            "At step  10  and at epoch =  18  the loss is =  0.025674473494291306  and accuracy is =  0.6676\n",
            "At step  10  and at epoch =  19  the loss is =  0.02509916201233864  and accuracy is =  0.676\n",
            "At step  10  and at epoch =  20  the loss is =  0.012180912308394909  and accuracy is =  0.6818\n",
            "At step  10  and at epoch =  21  the loss is =  0.017205629497766495  and accuracy is =  0.6892\n",
            "At step  10  and at epoch =  22  the loss is =  0.013970479369163513  and accuracy is =  0.6978\n",
            "At step  10  and at epoch =  23  the loss is =  0.012078432366251945  and accuracy is =  0.6946\n",
            "At step  10  and at epoch =  24  the loss is =  0.02665374055504799  and accuracy is =  0.6972\n",
            "At step  10  and at epoch =  25  the loss is =  0.011772220022976398  and accuracy is =  0.7042\n",
            "At step  10  and at epoch =  26  the loss is =  0.02548041194677353  and accuracy is =  0.7156\n",
            "At step  10  and at epoch =  27  the loss is =  0.008869204670190811  and accuracy is =  0.7142\n",
            "At step  10  and at epoch =  28  the loss is =  0.025263674557209015  and accuracy is =  0.7254\n",
            "At step  10  and at epoch =  29  the loss is =  0.022307880222797394  and accuracy is =  0.7316\n",
            "At step  10  and at epoch =  30  the loss is =  0.03124408610165119  and accuracy is =  0.7292\n",
            "At step  10  and at epoch =  31  the loss is =  0.018353721126914024  and accuracy is =  0.731\n",
            "At step  10  and at epoch =  32  the loss is =  0.014735590666532516  and accuracy is =  0.744\n",
            "At step  10  and at epoch =  33  the loss is =  0.017841793596744537  and accuracy is =  0.7402\n",
            "At step  10  and at epoch =  34  the loss is =  0.020789597183465958  and accuracy is =  0.7498\n",
            "At step  10  and at epoch =  35  the loss is =  0.00918799638748169  and accuracy is =  0.75\n",
            "At step  10  and at epoch =  36  the loss is =  0.01782076247036457  and accuracy is =  0.755\n",
            "At step  10  and at epoch =  37  the loss is =  0.014808069914579391  and accuracy is =  0.7594\n",
            "At step  10  and at epoch =  38  the loss is =  0.014791847206652164  and accuracy is =  0.7632\n",
            "At step  10  and at epoch =  39  the loss is =  0.01692250184714794  and accuracy is =  0.7688\n",
            "At step  10  and at epoch =  40  the loss is =  0.018672263249754906  and accuracy is =  0.763\n",
            "At step  10  and at epoch =  41  the loss is =  0.014283031225204468  and accuracy is =  0.7684\n",
            "At step  10  and at epoch =  42  the loss is =  0.020322179421782494  and accuracy is =  0.7702\n",
            "At step  10  and at epoch =  43  the loss is =  0.011567175388336182  and accuracy is =  0.7778\n",
            "At step  10  and at epoch =  44  the loss is =  0.012001187540590763  and accuracy is =  0.7776\n",
            "At step  10  and at epoch =  45  the loss is =  0.009528032504022121  and accuracy is =  0.7736\n",
            "At step  10  and at epoch =  46  the loss is =  0.009911025874316692  and accuracy is =  0.782\n",
            "At step  10  and at epoch =  47  the loss is =  0.022656230255961418  and accuracy is =  0.7936\n",
            "At step  10  and at epoch =  48  the loss is =  0.015977634117007256  and accuracy is =  0.7872\n",
            "At step  10  and at epoch =  49  the loss is =  0.00846568588167429  and accuracy is =  0.7892\n",
            "At step  10  and at epoch =  50  the loss is =  0.014537902548909187  and accuracy is =  0.7926\n",
            "At step  10  and at epoch =  51  the loss is =  0.022702030837535858  and accuracy is =  0.7922\n",
            "At step  10  and at epoch =  52  the loss is =  0.01476729940623045  and accuracy is =  0.7952\n",
            "At step  10  and at epoch =  53  the loss is =  0.009576310403645039  and accuracy is =  0.7982\n",
            "At step  10  and at epoch =  54  the loss is =  0.012924475595355034  and accuracy is =  0.7972\n",
            "At step  10  and at epoch =  55  the loss is =  0.0208736564964056  and accuracy is =  0.8082\n",
            "At step  10  and at epoch =  56  the loss is =  0.016315193846821785  and accuracy is =  0.8062\n",
            "At step  10  and at epoch =  57  the loss is =  0.007128405384719372  and accuracy is =  0.8114\n",
            "At step  10  and at epoch =  58  the loss is =  0.01717592217028141  and accuracy is =  0.8128\n",
            "At step  10  and at epoch =  59  the loss is =  0.02083684876561165  and accuracy is =  0.813\n",
            "At step  10  and at epoch =  60  the loss is =  0.01061970554292202  and accuracy is =  0.8142\n",
            "At step  10  and at epoch =  61  the loss is =  0.019714230671525  and accuracy is =  0.813\n",
            "At step  10  and at epoch =  62  the loss is =  0.009271075949072838  and accuracy is =  0.8112\n",
            "At step  10  and at epoch =  63  the loss is =  0.013407347723841667  and accuracy is =  0.8168\n",
            "At step  10  and at epoch =  64  the loss is =  0.009247539564967155  and accuracy is =  0.8238\n",
            "At step  10  and at epoch =  65  the loss is =  0.011184284463524818  and accuracy is =  0.8212\n",
            "At step  10  and at epoch =  66  the loss is =  0.029836373403668404  and accuracy is =  0.8228\n",
            "At step  10  and at epoch =  67  the loss is =  0.010134756565093994  and accuracy is =  0.8274\n",
            "At step  10  and at epoch =  68  the loss is =  0.013610851019620895  and accuracy is =  0.829\n",
            "At step  10  and at epoch =  69  the loss is =  0.04425770789384842  and accuracy is =  0.827\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "80\n",
            "Validation Loss: 0.05210661515593529 Validation Accuracy : 0.3965\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  20  and at epoch =  0  the loss is =  0.09079738706350327  and accuracy is =  0.151\n",
            "At step  20  and at epoch =  1  the loss is =  0.057316284626722336  and accuracy is =  0.2224\n",
            "At step  20  and at epoch =  2  the loss is =  0.03267459571361542  and accuracy is =  0.295\n",
            "At step  20  and at epoch =  3  the loss is =  0.03536802530288696  and accuracy is =  0.3264\n",
            "At step  20  and at epoch =  4  the loss is =  0.02277151308953762  and accuracy is =  0.3926\n",
            "At step  20  and at epoch =  5  the loss is =  0.02341737225651741  and accuracy is =  0.5012\n",
            "At step  20  and at epoch =  6  the loss is =  0.020933175459504128  and accuracy is =  0.5402\n",
            "At step  20  and at epoch =  7  the loss is =  0.02811519056558609  and accuracy is =  0.5686\n",
            "At step  20  and at epoch =  8  the loss is =  0.020986570045351982  and accuracy is =  0.5908\n",
            "At step  20  and at epoch =  9  the loss is =  0.019077396020293236  and accuracy is =  0.6156\n",
            "At step  20  and at epoch =  10  the loss is =  0.031055675819516182  and accuracy is =  0.633\n",
            "At step  20  and at epoch =  11  the loss is =  0.019708583131432533  and accuracy is =  0.6432\n",
            "At step  20  and at epoch =  12  the loss is =  0.02083517238497734  and accuracy is =  0.6608\n",
            "At step  20  and at epoch =  13  the loss is =  0.016985278576612473  and accuracy is =  0.6778\n",
            "At step  20  and at epoch =  14  the loss is =  0.018624527379870415  and accuracy is =  0.6928\n",
            "At step  20  and at epoch =  15  the loss is =  0.020776292309165  and accuracy is =  0.7012\n",
            "At step  20  and at epoch =  16  the loss is =  0.026451684534549713  and accuracy is =  0.7152\n",
            "At step  20  and at epoch =  17  the loss is =  0.02355792559683323  and accuracy is =  0.7212\n",
            "At step  20  and at epoch =  18  the loss is =  0.017662275582551956  and accuracy is =  0.7314\n",
            "At step  20  and at epoch =  19  the loss is =  0.019775765016674995  and accuracy is =  0.7386\n",
            "At step  20  and at epoch =  20  the loss is =  0.012211180292069912  and accuracy is =  0.7456\n",
            "At step  20  and at epoch =  21  the loss is =  0.022088246420025826  and accuracy is =  0.7534\n",
            "At step  20  and at epoch =  22  the loss is =  0.024232562631368637  and accuracy is =  0.7656\n",
            "At step  20  and at epoch =  23  the loss is =  0.010443957522511482  and accuracy is =  0.7626\n",
            "At step  20  and at epoch =  24  the loss is =  0.01305914856493473  and accuracy is =  0.7692\n",
            "At step  20  and at epoch =  25  the loss is =  0.02585548348724842  and accuracy is =  0.7716\n",
            "At step  20  and at epoch =  26  the loss is =  0.015706131234765053  and accuracy is =  0.7724\n",
            "At step  20  and at epoch =  27  the loss is =  0.019864017143845558  and accuracy is =  0.7784\n",
            "At step  20  and at epoch =  28  the loss is =  0.016151679679751396  and accuracy is =  0.7852\n",
            "At step  20  and at epoch =  29  the loss is =  0.021043043583631516  and accuracy is =  0.7848\n",
            "At step  20  and at epoch =  30  the loss is =  0.019999727606773376  and accuracy is =  0.7922\n",
            "At step  20  and at epoch =  31  the loss is =  0.01308500487357378  and accuracy is =  0.7968\n",
            "At step  20  and at epoch =  32  the loss is =  0.011827418580651283  and accuracy is =  0.8046\n",
            "At step  20  and at epoch =  33  the loss is =  0.014426629059016705  and accuracy is =  0.8056\n",
            "At step  20  and at epoch =  34  the loss is =  0.01882362738251686  and accuracy is =  0.812\n",
            "At step  20  and at epoch =  35  the loss is =  0.007498820777982473  and accuracy is =  0.8128\n",
            "At step  20  and at epoch =  36  the loss is =  0.010915030725300312  and accuracy is =  0.8162\n",
            "At step  20  and at epoch =  37  the loss is =  0.011679325252771378  and accuracy is =  0.8182\n",
            "At step  20  and at epoch =  38  the loss is =  0.01404612511396408  and accuracy is =  0.8292\n",
            "At step  20  and at epoch =  39  the loss is =  0.01340798381716013  and accuracy is =  0.8292\n",
            "At step  20  and at epoch =  40  the loss is =  0.00805263128131628  and accuracy is =  0.833\n",
            "At step  20  and at epoch =  41  the loss is =  0.015619833953678608  and accuracy is =  0.8346\n",
            "At step  20  and at epoch =  42  the loss is =  0.01462562009692192  and accuracy is =  0.8408\n",
            "At step  20  and at epoch =  43  the loss is =  0.03210708871483803  and accuracy is =  0.8378\n",
            "At step  20  and at epoch =  44  the loss is =  0.016395606100559235  and accuracy is =  0.8452\n",
            "At step  20  and at epoch =  45  the loss is =  0.02133946865797043  and accuracy is =  0.8488\n",
            "At step  20  and at epoch =  46  the loss is =  0.010026284493505955  and accuracy is =  0.8508\n",
            "At step  20  and at epoch =  47  the loss is =  0.010653283447027206  and accuracy is =  0.8532\n",
            "At step  20  and at epoch =  48  the loss is =  0.020117975771427155  and accuracy is =  0.858\n",
            "At step  20  and at epoch =  49  the loss is =  0.01070404052734375  and accuracy is =  0.8562\n",
            "At step  20  and at epoch =  50  the loss is =  0.0091719264164567  and accuracy is =  0.857\n",
            "At step  20  and at epoch =  51  the loss is =  0.007360121235251427  and accuracy is =  0.8672\n",
            "At step  20  and at epoch =  52  the loss is =  0.019197408109903336  and accuracy is =  0.8614\n",
            "At step  20  and at epoch =  53  the loss is =  0.010213383473455906  and accuracy is =  0.8644\n",
            "At step  20  and at epoch =  54  the loss is =  0.01701955869793892  and accuracy is =  0.8728\n",
            "At step  20  and at epoch =  55  the loss is =  0.019051028415560722  and accuracy is =  0.8684\n",
            "At step  20  and at epoch =  56  the loss is =  0.01033010147511959  and accuracy is =  0.8704\n",
            "At step  20  and at epoch =  57  the loss is =  0.01194201409816742  and accuracy is =  0.8772\n",
            "At step  20  and at epoch =  58  the loss is =  0.014556782320141792  and accuracy is =  0.88\n",
            "At step  20  and at epoch =  59  the loss is =  0.005104866344481707  and accuracy is =  0.8772\n",
            "At step  20  and at epoch =  60  the loss is =  0.002479134127497673  and accuracy is =  0.8802\n",
            "At step  20  and at epoch =  61  the loss is =  0.007777161430567503  and accuracy is =  0.8826\n",
            "At step  20  and at epoch =  62  the loss is =  0.027073979377746582  and accuracy is =  0.8874\n",
            "At step  20  and at epoch =  63  the loss is =  0.015165738761425018  and accuracy is =  0.893\n",
            "At step  20  and at epoch =  64  the loss is =  0.006747855804860592  and accuracy is =  0.8882\n",
            "At step  20  and at epoch =  65  the loss is =  0.008703384548425674  and accuracy is =  0.8858\n",
            "At step  20  and at epoch =  66  the loss is =  0.007820975966751575  and accuracy is =  0.8932\n",
            "At step  20  and at epoch =  67  the loss is =  0.01649652235209942  and accuracy is =  0.8936\n",
            "At step  20  and at epoch =  68  the loss is =  0.004747095983475447  and accuracy is =  0.9038\n",
            "At step  20  and at epoch =  69  the loss is =  0.011843370273709297  and accuracy is =  0.8982\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "56\n",
            "Validation Loss: 0.06791970133781433 Validation Accuracy : 0.2816666666666667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  30  and at epoch =  0  the loss is =  0.079085573554039  and accuracy is =  0.1104\n",
            "At step  30  and at epoch =  1  the loss is =  0.055673740804195404  and accuracy is =  0.2222\n",
            "At step  30  and at epoch =  2  the loss is =  0.03766603022813797  and accuracy is =  0.2846\n",
            "At step  30  and at epoch =  3  the loss is =  0.02666720375418663  and accuracy is =  0.4136\n",
            "At step  30  and at epoch =  4  the loss is =  0.02476496249437332  and accuracy is =  0.552\n",
            "At step  30  and at epoch =  5  the loss is =  0.025930117815732956  and accuracy is =  0.6238\n",
            "At step  30  and at epoch =  6  the loss is =  0.022806363180279732  and accuracy is =  0.6414\n",
            "At step  30  and at epoch =  7  the loss is =  0.021443912759423256  and accuracy is =  0.6648\n",
            "At step  30  and at epoch =  8  the loss is =  0.015915796160697937  and accuracy is =  0.6748\n",
            "At step  30  and at epoch =  9  the loss is =  0.017926296219229698  and accuracy is =  0.6994\n",
            "At step  30  and at epoch =  10  the loss is =  0.02058960311114788  and accuracy is =  0.7062\n",
            "At step  30  and at epoch =  11  the loss is =  0.017466353252530098  and accuracy is =  0.7178\n",
            "At step  30  and at epoch =  12  the loss is =  0.02286008559167385  and accuracy is =  0.7286\n",
            "At step  30  and at epoch =  13  the loss is =  0.022238854318857193  and accuracy is =  0.7386\n",
            "At step  30  and at epoch =  14  the loss is =  0.018833141773939133  and accuracy is =  0.7456\n",
            "At step  30  and at epoch =  15  the loss is =  0.023092571645975113  and accuracy is =  0.7464\n",
            "At step  30  and at epoch =  16  the loss is =  0.01614793762564659  and accuracy is =  0.7664\n",
            "At step  30  and at epoch =  17  the loss is =  0.014401772059500217  and accuracy is =  0.7662\n",
            "At step  30  and at epoch =  18  the loss is =  0.02006414532661438  and accuracy is =  0.7766\n",
            "At step  30  and at epoch =  19  the loss is =  0.0148093206807971  and accuracy is =  0.7866\n",
            "At step  30  and at epoch =  20  the loss is =  0.009495854377746582  and accuracy is =  0.7826\n",
            "At step  30  and at epoch =  21  the loss is =  0.016003064811229706  and accuracy is =  0.7844\n",
            "At step  30  and at epoch =  22  the loss is =  0.011882650665938854  and accuracy is =  0.7952\n",
            "At step  30  and at epoch =  23  the loss is =  0.016117434948682785  and accuracy is =  0.8004\n",
            "At step  30  and at epoch =  24  the loss is =  0.042706336826086044  and accuracy is =  0.7992\n",
            "At step  30  and at epoch =  25  the loss is =  0.030176596716046333  and accuracy is =  0.8016\n",
            "At step  30  and at epoch =  26  the loss is =  0.008482502773404121  and accuracy is =  0.807\n",
            "At step  30  and at epoch =  27  the loss is =  0.028753060847520828  and accuracy is =  0.8146\n",
            "At step  30  and at epoch =  28  the loss is =  0.0072591593489050865  and accuracy is =  0.824\n",
            "At step  30  and at epoch =  29  the loss is =  0.012937590479850769  and accuracy is =  0.8296\n",
            "At step  30  and at epoch =  30  the loss is =  0.008487301878631115  and accuracy is =  0.8258\n",
            "At step  30  and at epoch =  31  the loss is =  0.010664699599146843  and accuracy is =  0.8328\n",
            "At step  30  and at epoch =  32  the loss is =  0.007435972336679697  and accuracy is =  0.8344\n",
            "At step  30  and at epoch =  33  the loss is =  0.01664827950298786  and accuracy is =  0.8448\n",
            "At step  30  and at epoch =  34  the loss is =  0.013884770683944225  and accuracy is =  0.8382\n",
            "At step  30  and at epoch =  35  the loss is =  0.010773719288408756  and accuracy is =  0.8376\n",
            "At step  30  and at epoch =  36  the loss is =  0.01772323250770569  and accuracy is =  0.8408\n",
            "At step  30  and at epoch =  37  the loss is =  0.020920779556035995  and accuracy is =  0.8518\n",
            "At step  30  and at epoch =  38  the loss is =  0.01314079761505127  and accuracy is =  0.8472\n",
            "At step  30  and at epoch =  39  the loss is =  0.007961002178490162  and accuracy is =  0.8574\n",
            "At step  30  and at epoch =  40  the loss is =  0.021590733900666237  and accuracy is =  0.8572\n",
            "At step  30  and at epoch =  41  the loss is =  0.008319137617945671  and accuracy is =  0.8654\n",
            "At step  30  and at epoch =  42  the loss is =  0.012837948277592659  and accuracy is =  0.8648\n",
            "At step  30  and at epoch =  43  the loss is =  0.005472662393003702  and accuracy is =  0.8656\n",
            "At step  30  and at epoch =  44  the loss is =  0.016212470829486847  and accuracy is =  0.8622\n",
            "At step  30  and at epoch =  45  the loss is =  0.010214805603027344  and accuracy is =  0.8656\n",
            "At step  30  and at epoch =  46  the loss is =  0.014331836253404617  and accuracy is =  0.879\n",
            "At step  30  and at epoch =  47  the loss is =  0.01588430069386959  and accuracy is =  0.8726\n",
            "At step  30  and at epoch =  48  the loss is =  0.021772148087620735  and accuracy is =  0.8848\n",
            "At step  30  and at epoch =  49  the loss is =  0.014494330622255802  and accuracy is =  0.8762\n",
            "At step  30  and at epoch =  50  the loss is =  0.014930387027561665  and accuracy is =  0.892\n",
            "At step  30  and at epoch =  51  the loss is =  0.008411365561187267  and accuracy is =  0.8834\n",
            "At step  30  and at epoch =  52  the loss is =  0.012538803741335869  and accuracy is =  0.8858\n",
            "At step  30  and at epoch =  53  the loss is =  0.011441031470894814  and accuracy is =  0.8892\n",
            "At step  30  and at epoch =  54  the loss is =  0.01852789893746376  and accuracy is =  0.8906\n",
            "At step  30  and at epoch =  55  the loss is =  0.012795346789062023  and accuracy is =  0.8932\n",
            "At step  30  and at epoch =  56  the loss is =  0.01523171178996563  and accuracy is =  0.894\n",
            "At step  30  and at epoch =  57  the loss is =  0.020353736355900764  and accuracy is =  0.8948\n",
            "At step  30  and at epoch =  58  the loss is =  0.006563063710927963  and accuracy is =  0.89\n",
            "At step  30  and at epoch =  59  the loss is =  0.014672140590846539  and accuracy is =  0.893\n",
            "At step  30  and at epoch =  60  the loss is =  0.013984501361846924  and accuracy is =  0.8952\n",
            "At step  30  and at epoch =  61  the loss is =  0.002083522966131568  and accuracy is =  0.8954\n",
            "At step  30  and at epoch =  62  the loss is =  0.005020392592996359  and accuracy is =  0.9042\n",
            "At step  30  and at epoch =  63  the loss is =  0.010007672011852264  and accuracy is =  0.9072\n",
            "At step  30  and at epoch =  64  the loss is =  0.004625065252184868  and accuracy is =  0.9116\n",
            "At step  30  and at epoch =  65  the loss is =  0.01582455448806286  and accuracy is =  0.91\n",
            "At step  30  and at epoch =  66  the loss is =  0.007814177311956882  and accuracy is =  0.9094\n",
            "At step  30  and at epoch =  67  the loss is =  0.005676926113665104  and accuracy is =  0.9092\n",
            "At step  30  and at epoch =  68  the loss is =  0.01885412633419037  and accuracy is =  0.9108\n",
            "At step  30  and at epoch =  69  the loss is =  0.003944220021367073  and accuracy is =  0.9178\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "32\n",
            "Validation Loss: 0.07929892838001251 Validation Accuracy : 0.20925\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  40  and at epoch =  0  the loss is =  0.08150667697191238  and accuracy is =  0.1274\n",
            "At step  40  and at epoch =  1  the loss is =  0.050561387091875076  and accuracy is =  0.2\n",
            "At step  40  and at epoch =  2  the loss is =  0.03555247187614441  and accuracy is =  0.3202\n",
            "At step  40  and at epoch =  3  the loss is =  0.032062310725450516  and accuracy is =  0.3498\n",
            "At step  40  and at epoch =  4  the loss is =  0.035075582563877106  and accuracy is =  0.4064\n",
            "At step  40  and at epoch =  5  the loss is =  0.031074199825525284  and accuracy is =  0.4812\n",
            "At step  40  and at epoch =  6  the loss is =  0.029395481571555138  and accuracy is =  0.5216\n",
            "At step  40  and at epoch =  7  the loss is =  0.026939991861581802  and accuracy is =  0.5584\n",
            "At step  40  and at epoch =  8  the loss is =  0.023202652111649513  and accuracy is =  0.5756\n",
            "At step  40  and at epoch =  9  the loss is =  0.024337271228432655  and accuracy is =  0.5898\n",
            "At step  40  and at epoch =  10  the loss is =  0.023331139236688614  and accuracy is =  0.6082\n",
            "At step  40  and at epoch =  11  the loss is =  0.029273197054862976  and accuracy is =  0.6132\n",
            "At step  40  and at epoch =  12  the loss is =  0.025315411388874054  and accuracy is =  0.6238\n",
            "At step  40  and at epoch =  13  the loss is =  0.015967994928359985  and accuracy is =  0.6424\n",
            "At step  40  and at epoch =  14  the loss is =  0.012164274230599403  and accuracy is =  0.647\n",
            "At step  40  and at epoch =  15  the loss is =  0.016660671681165695  and accuracy is =  0.6538\n",
            "At step  40  and at epoch =  16  the loss is =  0.01891680806875229  and accuracy is =  0.6624\n",
            "At step  40  and at epoch =  17  the loss is =  0.024809304624795914  and accuracy is =  0.6742\n",
            "At step  40  and at epoch =  18  the loss is =  0.01691807433962822  and accuracy is =  0.6756\n",
            "At step  40  and at epoch =  19  the loss is =  0.011204601265490055  and accuracy is =  0.6832\n",
            "At step  40  and at epoch =  20  the loss is =  0.01972394809126854  and accuracy is =  0.6958\n",
            "At step  40  and at epoch =  21  the loss is =  0.022355416789650917  and accuracy is =  0.7016\n",
            "At step  40  and at epoch =  22  the loss is =  0.025381144136190414  and accuracy is =  0.7016\n",
            "At step  40  and at epoch =  23  the loss is =  0.03326122835278511  and accuracy is =  0.7122\n",
            "At step  40  and at epoch =  24  the loss is =  0.016232667490839958  and accuracy is =  0.708\n",
            "At step  40  and at epoch =  25  the loss is =  0.022077010944485664  and accuracy is =  0.714\n",
            "At step  40  and at epoch =  26  the loss is =  0.020026471465826035  and accuracy is =  0.7214\n",
            "At step  40  and at epoch =  27  the loss is =  0.009559264406561852  and accuracy is =  0.718\n",
            "At step  40  and at epoch =  28  the loss is =  0.020257022231817245  and accuracy is =  0.7378\n",
            "At step  40  and at epoch =  29  the loss is =  0.03147926554083824  and accuracy is =  0.7426\n",
            "At step  40  and at epoch =  30  the loss is =  0.02120104432106018  and accuracy is =  0.7332\n",
            "At step  40  and at epoch =  31  the loss is =  0.02070908062160015  and accuracy is =  0.7462\n",
            "At step  40  and at epoch =  32  the loss is =  0.009723475202918053  and accuracy is =  0.752\n",
            "At step  40  and at epoch =  33  the loss is =  0.01319908257573843  and accuracy is =  0.747\n",
            "At step  40  and at epoch =  34  the loss is =  0.01691180281341076  and accuracy is =  0.7586\n",
            "At step  40  and at epoch =  35  the loss is =  0.023859329521656036  and accuracy is =  0.756\n",
            "At step  40  and at epoch =  36  the loss is =  0.013555130921304226  and accuracy is =  0.7616\n",
            "At step  40  and at epoch =  37  the loss is =  0.038880713284015656  and accuracy is =  0.7646\n",
            "At step  40  and at epoch =  38  the loss is =  0.023081017658114433  and accuracy is =  0.7664\n",
            "At step  40  and at epoch =  39  the loss is =  0.023431969806551933  and accuracy is =  0.7776\n",
            "At step  40  and at epoch =  40  the loss is =  0.010097965598106384  and accuracy is =  0.775\n",
            "At step  40  and at epoch =  41  the loss is =  0.023975202813744545  and accuracy is =  0.7834\n",
            "At step  40  and at epoch =  42  the loss is =  0.01214178092777729  and accuracy is =  0.787\n",
            "At step  40  and at epoch =  43  the loss is =  0.01819075085222721  and accuracy is =  0.79\n",
            "At step  40  and at epoch =  44  the loss is =  0.009426329284906387  and accuracy is =  0.797\n",
            "At step  40  and at epoch =  45  the loss is =  0.018469586968421936  and accuracy is =  0.7956\n",
            "At step  40  and at epoch =  46  the loss is =  0.015469357371330261  and accuracy is =  0.7978\n",
            "At step  40  and at epoch =  47  the loss is =  0.015888642519712448  and accuracy is =  0.8004\n",
            "At step  40  and at epoch =  48  the loss is =  0.03099381923675537  and accuracy is =  0.8014\n",
            "At step  40  and at epoch =  49  the loss is =  0.012121234089136124  and accuracy is =  0.8032\n",
            "At step  40  and at epoch =  50  the loss is =  0.007151068188250065  and accuracy is =  0.8058\n",
            "At step  40  and at epoch =  51  the loss is =  0.02309029921889305  and accuracy is =  0.8068\n",
            "At step  40  and at epoch =  52  the loss is =  0.01407183613628149  and accuracy is =  0.8018\n",
            "At step  40  and at epoch =  53  the loss is =  0.008523812517523766  and accuracy is =  0.8174\n",
            "At step  40  and at epoch =  54  the loss is =  0.027392536401748657  and accuracy is =  0.8116\n",
            "At step  40  and at epoch =  55  the loss is =  0.016460442915558815  and accuracy is =  0.8154\n",
            "At step  40  and at epoch =  56  the loss is =  0.010373217985033989  and accuracy is =  0.8188\n",
            "At step  40  and at epoch =  57  the loss is =  0.01602768339216709  and accuracy is =  0.8226\n",
            "At step  40  and at epoch =  58  the loss is =  0.0183803029358387  and accuracy is =  0.8264\n",
            "At step  40  and at epoch =  59  the loss is =  0.014369020238518715  and accuracy is =  0.8262\n",
            "At step  40  and at epoch =  60  the loss is =  0.016209227964282036  and accuracy is =  0.8274\n",
            "At step  40  and at epoch =  61  the loss is =  0.010279962792992592  and accuracy is =  0.8346\n",
            "At step  40  and at epoch =  62  the loss is =  0.012972526252269745  and accuracy is =  0.8336\n",
            "At step  40  and at epoch =  63  the loss is =  0.02672569267451763  and accuracy is =  0.827\n",
            "At step  40  and at epoch =  64  the loss is =  0.02069261483848095  and accuracy is =  0.8356\n",
            "At step  40  and at epoch =  65  the loss is =  0.010490219108760357  and accuracy is =  0.838\n",
            "At step  40  and at epoch =  66  the loss is =  0.015395247377455235  and accuracy is =  0.8436\n",
            "At step  40  and at epoch =  67  the loss is =  0.013959093019366264  and accuracy is =  0.846\n",
            "At step  40  and at epoch =  68  the loss is =  0.011037273332476616  and accuracy is =  0.8484\n",
            "At step  40  and at epoch =  69  the loss is =  0.010132662951946259  and accuracy is =  0.8422\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "8\n",
            "Validation Loss: 0.10358760505914688 Validation Accuracy : 0.1552\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  50  and at epoch =  0  the loss is =  0.08232478052377701  and accuracy is =  0.1328\n",
            "At step  50  and at epoch =  1  the loss is =  0.049917299300432205  and accuracy is =  0.2432\n",
            "At step  50  and at epoch =  2  the loss is =  0.03229658678174019  and accuracy is =  0.2946\n",
            "At step  50  and at epoch =  3  the loss is =  0.02591278962790966  and accuracy is =  0.3764\n",
            "At step  50  and at epoch =  4  the loss is =  0.02687804214656353  and accuracy is =  0.4712\n",
            "At step  50  and at epoch =  5  the loss is =  0.02169707790017128  and accuracy is =  0.5308\n",
            "At step  50  and at epoch =  6  the loss is =  0.024108324199914932  and accuracy is =  0.5594\n",
            "At step  50  and at epoch =  7  the loss is =  0.019715525209903717  and accuracy is =  0.5812\n",
            "At step  50  and at epoch =  8  the loss is =  0.015811167657375336  and accuracy is =  0.6044\n",
            "At step  50  and at epoch =  9  the loss is =  0.020476598292589188  and accuracy is =  0.6226\n",
            "At step  50  and at epoch =  10  the loss is =  0.01578328385949135  and accuracy is =  0.6378\n",
            "At step  50  and at epoch =  11  the loss is =  0.023335052654147148  and accuracy is =  0.653\n",
            "At step  50  and at epoch =  12  the loss is =  0.020513342693448067  and accuracy is =  0.6698\n",
            "At step  50  and at epoch =  13  the loss is =  0.02131567895412445  and accuracy is =  0.6784\n",
            "At step  50  and at epoch =  14  the loss is =  0.022892680019140244  and accuracy is =  0.6844\n",
            "At step  50  and at epoch =  15  the loss is =  0.017401281744241714  and accuracy is =  0.691\n",
            "At step  50  and at epoch =  16  the loss is =  0.03103097341954708  and accuracy is =  0.7064\n",
            "At step  50  and at epoch =  17  the loss is =  0.02614905685186386  and accuracy is =  0.7122\n",
            "At step  50  and at epoch =  18  the loss is =  0.01809445396065712  and accuracy is =  0.7114\n",
            "At step  50  and at epoch =  19  the loss is =  0.022894036024808884  and accuracy is =  0.7268\n",
            "At step  50  and at epoch =  20  the loss is =  0.015475384891033173  and accuracy is =  0.7266\n",
            "At step  50  and at epoch =  21  the loss is =  0.02056947723031044  and accuracy is =  0.7378\n",
            "At step  50  and at epoch =  22  the loss is =  0.008661705069243908  and accuracy is =  0.7408\n",
            "At step  50  and at epoch =  23  the loss is =  0.030455579981207848  and accuracy is =  0.7424\n",
            "At step  50  and at epoch =  24  the loss is =  0.013006915338337421  and accuracy is =  0.743\n",
            "At step  50  and at epoch =  25  the loss is =  0.012346298433840275  and accuracy is =  0.7584\n",
            "At step  50  and at epoch =  26  the loss is =  0.01508715283125639  and accuracy is =  0.7558\n",
            "At step  50  and at epoch =  27  the loss is =  0.0193210206925869  and accuracy is =  0.7618\n",
            "At step  50  and at epoch =  28  the loss is =  0.026080550625920296  and accuracy is =  0.7646\n",
            "At step  50  and at epoch =  29  the loss is =  0.015965914353728294  and accuracy is =  0.7674\n",
            "At step  50  and at epoch =  30  the loss is =  0.010159353725612164  and accuracy is =  0.7776\n",
            "At step  50  and at epoch =  31  the loss is =  0.014808687381446362  and accuracy is =  0.7806\n",
            "At step  50  and at epoch =  32  the loss is =  0.016769761219620705  and accuracy is =  0.784\n",
            "At step  50  and at epoch =  33  the loss is =  0.014692933298647404  and accuracy is =  0.7846\n",
            "At step  50  and at epoch =  34  the loss is =  0.016600487753748894  and accuracy is =  0.7878\n",
            "At step  50  and at epoch =  35  the loss is =  0.01649957336485386  and accuracy is =  0.7968\n",
            "At step  50  and at epoch =  36  the loss is =  0.006977437064051628  and accuracy is =  0.8026\n",
            "At step  50  and at epoch =  37  the loss is =  0.022803688421845436  and accuracy is =  0.7962\n",
            "At step  50  and at epoch =  38  the loss is =  0.02627469412982464  and accuracy is =  0.8034\n",
            "At step  50  and at epoch =  39  the loss is =  0.010682675987482071  and accuracy is =  0.8\n",
            "At step  50  and at epoch =  40  the loss is =  0.011411381885409355  and accuracy is =  0.8078\n",
            "At step  50  and at epoch =  41  the loss is =  0.015228038653731346  and accuracy is =  0.8136\n",
            "At step  50  and at epoch =  42  the loss is =  0.008588574826717377  and accuracy is =  0.8146\n",
            "At step  50  and at epoch =  43  the loss is =  0.006090076640248299  and accuracy is =  0.8212\n",
            "At step  50  and at epoch =  44  the loss is =  0.010792267508804798  and accuracy is =  0.8224\n",
            "At step  50  and at epoch =  45  the loss is =  0.01416204683482647  and accuracy is =  0.8174\n",
            "At step  50  and at epoch =  46  the loss is =  0.01547721866518259  and accuracy is =  0.8316\n",
            "At step  50  and at epoch =  47  the loss is =  0.014725016430020332  and accuracy is =  0.8334\n",
            "At step  50  and at epoch =  48  the loss is =  0.008552693761885166  and accuracy is =  0.829\n",
            "At step  50  and at epoch =  49  the loss is =  0.014535431750118732  and accuracy is =  0.8392\n",
            "At step  50  and at epoch =  50  the loss is =  0.013457213528454304  and accuracy is =  0.8382\n",
            "At step  50  and at epoch =  51  the loss is =  0.017663655802607536  and accuracy is =  0.8424\n",
            "At step  50  and at epoch =  52  the loss is =  0.015258672647178173  and accuracy is =  0.8454\n",
            "At step  50  and at epoch =  53  the loss is =  0.02166583761572838  and accuracy is =  0.8424\n",
            "At step  50  and at epoch =  54  the loss is =  0.02332242764532566  and accuracy is =  0.8474\n",
            "At step  50  and at epoch =  55  the loss is =  0.011013597249984741  and accuracy is =  0.8392\n",
            "At step  50  and at epoch =  56  the loss is =  0.014506635256111622  and accuracy is =  0.8578\n",
            "At step  50  and at epoch =  57  the loss is =  0.007316882256418467  and accuracy is =  0.8562\n",
            "At step  50  and at epoch =  58  the loss is =  0.01789271831512451  and accuracy is =  0.8582\n",
            "At step  50  and at epoch =  59  the loss is =  0.02733645774424076  and accuracy is =  0.8526\n",
            "At step  50  and at epoch =  60  the loss is =  0.009967334568500519  and accuracy is =  0.8576\n",
            "At step  50  and at epoch =  61  the loss is =  0.0192649457603693  and accuracy is =  0.8652\n",
            "At step  50  and at epoch =  62  the loss is =  0.004319238476455212  and accuracy is =  0.866\n",
            "At step  50  and at epoch =  63  the loss is =  0.01206320058554411  and accuracy is =  0.8684\n",
            "At step  50  and at epoch =  64  the loss is =  0.019811129197478294  and accuracy is =  0.8696\n",
            "At step  50  and at epoch =  65  the loss is =  0.01670517958700657  and accuracy is =  0.8678\n",
            "At step  50  and at epoch =  66  the loss is =  0.016393929719924927  and accuracy is =  0.874\n",
            "At step  50  and at epoch =  67  the loss is =  0.026868117973208427  and accuracy is =  0.874\n",
            "At step  50  and at epoch =  68  the loss is =  0.010052354075014591  and accuracy is =  0.8696\n",
            "At step  50  and at epoch =  69  the loss is =  0.004516710061579943  and accuracy is =  0.8768\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
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            "128\n",
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            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
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            "128\n",
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            "128\n",
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            "128\n",
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            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "112\n",
            "Validation Loss: 0.09974606335163116 Validation Accuracy : 0.13333333333333333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  60  and at epoch =  0  the loss is =  0.07533807307481766  and accuracy is =  0.1316\n",
            "At step  60  and at epoch =  1  the loss is =  0.04757527634501457  and accuracy is =  0.2528\n",
            "At step  60  and at epoch =  2  the loss is =  0.031206635758280754  and accuracy is =  0.3632\n",
            "At step  60  and at epoch =  3  the loss is =  0.029963776469230652  and accuracy is =  0.4396\n",
            "At step  60  and at epoch =  4  the loss is =  0.022486915811896324  and accuracy is =  0.5368\n",
            "At step  60  and at epoch =  5  the loss is =  0.02506822533905506  and accuracy is =  0.5752\n",
            "At step  60  and at epoch =  6  the loss is =  0.02048979327082634  and accuracy is =  0.6046\n",
            "At step  60  and at epoch =  7  the loss is =  0.026613272726535797  and accuracy is =  0.6326\n",
            "At step  60  and at epoch =  8  the loss is =  0.02110525593161583  and accuracy is =  0.6452\n",
            "At step  60  and at epoch =  9  the loss is =  0.020817359909415245  and accuracy is =  0.6624\n",
            "At step  60  and at epoch =  10  the loss is =  0.018821991980075836  and accuracy is =  0.676\n",
            "At step  60  and at epoch =  11  the loss is =  0.023185161873698235  and accuracy is =  0.6904\n",
            "At step  60  and at epoch =  12  the loss is =  0.017049124464392662  and accuracy is =  0.6942\n",
            "At step  60  and at epoch =  13  the loss is =  0.016324467957019806  and accuracy is =  0.714\n",
            "At step  60  and at epoch =  14  the loss is =  0.015122849494218826  and accuracy is =  0.7174\n",
            "At step  60  and at epoch =  15  the loss is =  0.013884413056075573  and accuracy is =  0.7206\n",
            "At step  60  and at epoch =  16  the loss is =  0.020470580086112022  and accuracy is =  0.7316\n",
            "At step  60  and at epoch =  17  the loss is =  0.015019851736724377  and accuracy is =  0.7358\n",
            "At step  60  and at epoch =  18  the loss is =  0.017070695757865906  and accuracy is =  0.7424\n",
            "At step  60  and at epoch =  19  the loss is =  0.014962619170546532  and accuracy is =  0.7492\n",
            "At step  60  and at epoch =  20  the loss is =  0.025655319914221764  and accuracy is =  0.7554\n",
            "At step  60  and at epoch =  21  the loss is =  0.03170112147927284  and accuracy is =  0.7618\n",
            "At step  60  and at epoch =  22  the loss is =  0.02174925059080124  and accuracy is =  0.7624\n",
            "At step  60  and at epoch =  23  the loss is =  0.010979307815432549  and accuracy is =  0.769\n",
            "At step  60  and at epoch =  24  the loss is =  0.020584944635629654  and accuracy is =  0.7704\n",
            "At step  60  and at epoch =  25  the loss is =  0.023874131962656975  and accuracy is =  0.773\n",
            "At step  60  and at epoch =  26  the loss is =  0.018491283059120178  and accuracy is =  0.7784\n",
            "At step  60  and at epoch =  27  the loss is =  0.019785039126873016  and accuracy is =  0.7838\n",
            "At step  60  and at epoch =  28  the loss is =  0.01382152084261179  and accuracy is =  0.7908\n",
            "At step  60  and at epoch =  29  the loss is =  0.016999036073684692  and accuracy is =  0.792\n",
            "At step  60  and at epoch =  30  the loss is =  0.0179353728890419  and accuracy is =  0.7964\n",
            "At step  60  and at epoch =  31  the loss is =  0.016505660489201546  and accuracy is =  0.8006\n",
            "At step  60  and at epoch =  32  the loss is =  0.006822516676038504  and accuracy is =  0.7966\n",
            "At step  60  and at epoch =  33  the loss is =  0.019011305645108223  and accuracy is =  0.7984\n",
            "At step  60  and at epoch =  34  the loss is =  0.008499321527779102  and accuracy is =  0.8102\n",
            "At step  60  and at epoch =  35  the loss is =  0.01563737913966179  and accuracy is =  0.811\n",
            "At step  60  and at epoch =  36  the loss is =  0.008634810335934162  and accuracy is =  0.8142\n",
            "At step  60  and at epoch =  37  the loss is =  0.024158945307135582  and accuracy is =  0.815\n",
            "At step  60  and at epoch =  38  the loss is =  0.029072238132357597  and accuracy is =  0.816\n",
            "At step  60  and at epoch =  39  the loss is =  0.027340689674019814  and accuracy is =  0.8178\n",
            "At step  60  and at epoch =  40  the loss is =  0.016677817329764366  and accuracy is =  0.8176\n",
            "At step  60  and at epoch =  41  the loss is =  0.02052328735589981  and accuracy is =  0.8248\n",
            "At step  60  and at epoch =  42  the loss is =  0.010456448420882225  and accuracy is =  0.8228\n",
            "At step  60  and at epoch =  43  the loss is =  0.02088145725429058  and accuracy is =  0.8204\n",
            "At step  60  and at epoch =  44  the loss is =  0.014631303958594799  and accuracy is =  0.8396\n",
            "At step  60  and at epoch =  45  the loss is =  0.012633547186851501  and accuracy is =  0.8334\n",
            "At step  60  and at epoch =  46  the loss is =  0.01316344365477562  and accuracy is =  0.8348\n",
            "At step  60  and at epoch =  47  the loss is =  0.016165193170309067  and accuracy is =  0.8386\n",
            "At step  60  and at epoch =  48  the loss is =  0.020086580887436867  and accuracy is =  0.8456\n",
            "At step  60  and at epoch =  49  the loss is =  0.017791543155908585  and accuracy is =  0.8456\n",
            "At step  60  and at epoch =  50  the loss is =  0.02003958821296692  and accuracy is =  0.846\n",
            "At step  60  and at epoch =  51  the loss is =  0.013865198940038681  and accuracy is =  0.847\n",
            "At step  60  and at epoch =  52  the loss is =  0.012348034419119358  and accuracy is =  0.8508\n",
            "At step  60  and at epoch =  53  the loss is =  0.021780962124466896  and accuracy is =  0.8438\n",
            "At step  60  and at epoch =  54  the loss is =  0.015342310070991516  and accuracy is =  0.8554\n",
            "At step  60  and at epoch =  55  the loss is =  0.01842387206852436  and accuracy is =  0.8602\n",
            "At step  60  and at epoch =  56  the loss is =  0.0059039476327598095  and accuracy is =  0.8598\n",
            "At step  60  and at epoch =  57  the loss is =  0.01299749780446291  and accuracy is =  0.867\n",
            "At step  60  and at epoch =  58  the loss is =  0.01304576639086008  and accuracy is =  0.8626\n",
            "At step  60  and at epoch =  59  the loss is =  0.013921065255999565  and accuracy is =  0.8608\n",
            "At step  60  and at epoch =  60  the loss is =  0.01693894900381565  and accuracy is =  0.8674\n",
            "At step  60  and at epoch =  61  the loss is =  0.0037078838795423508  and accuracy is =  0.862\n",
            "At step  60  and at epoch =  62  the loss is =  0.019698865711688995  and accuracy is =  0.8774\n",
            "At step  60  and at epoch =  63  the loss is =  0.010113891214132309  and accuracy is =  0.8716\n",
            "At step  60  and at epoch =  64  the loss is =  0.011088593862950802  and accuracy is =  0.8742\n",
            "At step  60  and at epoch =  65  the loss is =  0.005677474197000265  and accuracy is =  0.8802\n",
            "At step  60  and at epoch =  66  the loss is =  0.019261891022324562  and accuracy is =  0.882\n",
            "At step  60  and at epoch =  67  the loss is =  0.015157858841121197  and accuracy is =  0.871\n",
            "At step  60  and at epoch =  68  the loss is =  0.015219084918498993  and accuracy is =  0.877\n",
            "At step  60  and at epoch =  69  the loss is =  0.009992629289627075  and accuracy is =  0.8838\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
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            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "88\n",
            "Validation Loss: 0.10506127029657364 Validation Accuracy : 0.11028571428571429\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  70  and at epoch =  0  the loss is =  0.07971280813217163  and accuracy is =  0.1896\n",
            "At step  70  and at epoch =  1  the loss is =  0.044970910996198654  and accuracy is =  0.335\n",
            "At step  70  and at epoch =  2  the loss is =  0.03129711002111435  and accuracy is =  0.3828\n",
            "At step  70  and at epoch =  3  the loss is =  0.022420959547162056  and accuracy is =  0.4602\n",
            "At step  70  and at epoch =  4  the loss is =  0.025200650095939636  and accuracy is =  0.5632\n",
            "At step  70  and at epoch =  5  the loss is =  0.027037834748625755  and accuracy is =  0.6068\n",
            "At step  70  and at epoch =  6  the loss is =  0.020328249782323837  and accuracy is =  0.6366\n",
            "At step  70  and at epoch =  7  the loss is =  0.016934223473072052  and accuracy is =  0.6558\n",
            "At step  70  and at epoch =  8  the loss is =  0.01881583221256733  and accuracy is =  0.6804\n",
            "At step  70  and at epoch =  9  the loss is =  0.027683112770318985  and accuracy is =  0.6966\n",
            "At step  70  and at epoch =  10  the loss is =  0.02293216623365879  and accuracy is =  0.7194\n",
            "At step  70  and at epoch =  11  the loss is =  0.019822895526885986  and accuracy is =  0.7364\n",
            "At step  70  and at epoch =  12  the loss is =  0.010642585344612598  and accuracy is =  0.7436\n",
            "At step  70  and at epoch =  13  the loss is =  0.015061070211231709  and accuracy is =  0.7628\n",
            "At step  70  and at epoch =  14  the loss is =  0.015868449583649635  and accuracy is =  0.7706\n",
            "At step  70  and at epoch =  15  the loss is =  0.01800353080034256  and accuracy is =  0.7888\n",
            "At step  70  and at epoch =  16  the loss is =  0.023298075422644615  and accuracy is =  0.7946\n",
            "At step  70  and at epoch =  17  the loss is =  0.02078455314040184  and accuracy is =  0.8046\n",
            "At step  70  and at epoch =  18  the loss is =  0.021665386855602264  and accuracy is =  0.8084\n",
            "At step  70  and at epoch =  19  the loss is =  0.025193138048052788  and accuracy is =  0.8204\n",
            "At step  70  and at epoch =  20  the loss is =  0.012420892715454102  and accuracy is =  0.812\n",
            "At step  70  and at epoch =  21  the loss is =  0.02093711867928505  and accuracy is =  0.8284\n",
            "At step  70  and at epoch =  22  the loss is =  0.01570015586912632  and accuracy is =  0.8246\n",
            "At step  70  and at epoch =  23  the loss is =  0.01350259780883789  and accuracy is =  0.8362\n",
            "At step  70  and at epoch =  24  the loss is =  0.007440367713570595  and accuracy is =  0.8452\n",
            "At step  70  and at epoch =  25  the loss is =  0.0076936050318181515  and accuracy is =  0.8504\n",
            "At step  70  and at epoch =  26  the loss is =  0.009598413482308388  and accuracy is =  0.8496\n",
            "At step  70  and at epoch =  27  the loss is =  0.006069117691367865  and accuracy is =  0.857\n",
            "At step  70  and at epoch =  28  the loss is =  0.028676697984337807  and accuracy is =  0.8584\n",
            "At step  70  and at epoch =  29  the loss is =  0.01978372596204281  and accuracy is =  0.8608\n",
            "At step  70  and at epoch =  30  the loss is =  0.009449519217014313  and accuracy is =  0.8618\n",
            "At step  70  and at epoch =  31  the loss is =  0.0026032787282019854  and accuracy is =  0.8706\n",
            "At step  70  and at epoch =  32  the loss is =  0.02559160254895687  and accuracy is =  0.8736\n",
            "At step  70  and at epoch =  33  the loss is =  0.01190862711519003  and accuracy is =  0.87\n",
            "At step  70  and at epoch =  34  the loss is =  0.009702703915536404  and accuracy is =  0.873\n",
            "At step  70  and at epoch =  35  the loss is =  0.016028568148612976  and accuracy is =  0.8824\n",
            "At step  70  and at epoch =  36  the loss is =  0.006857361178845167  and accuracy is =  0.882\n",
            "At step  70  and at epoch =  37  the loss is =  0.00951288640499115  and accuracy is =  0.8896\n",
            "At step  70  and at epoch =  38  the loss is =  0.008792715147137642  and accuracy is =  0.8896\n",
            "At step  70  and at epoch =  39  the loss is =  0.010057603009045124  and accuracy is =  0.8838\n",
            "At step  70  and at epoch =  40  the loss is =  0.022632993757724762  and accuracy is =  0.8874\n",
            "At step  70  and at epoch =  41  the loss is =  0.013359091244637966  and accuracy is =  0.8888\n",
            "At step  70  and at epoch =  42  the loss is =  0.014352398924529552  and accuracy is =  0.8912\n",
            "At step  70  and at epoch =  43  the loss is =  0.011002259328961372  and accuracy is =  0.8974\n",
            "At step  70  and at epoch =  44  the loss is =  0.003735356731340289  and accuracy is =  0.8986\n",
            "At step  70  and at epoch =  45  the loss is =  0.01292833685874939  and accuracy is =  0.9032\n",
            "At step  70  and at epoch =  46  the loss is =  0.01736200414597988  and accuracy is =  0.9042\n",
            "At step  70  and at epoch =  47  the loss is =  0.0034096629824489355  and accuracy is =  0.904\n",
            "At step  70  and at epoch =  48  the loss is =  0.010013860650360584  and accuracy is =  0.9104\n",
            "At step  70  and at epoch =  49  the loss is =  0.0069738999009132385  and accuracy is =  0.9148\n",
            "At step  70  and at epoch =  50  the loss is =  0.0040764519944787025  and accuracy is =  0.9142\n",
            "At step  70  and at epoch =  51  the loss is =  0.010125958360731602  and accuracy is =  0.9146\n",
            "At step  70  and at epoch =  52  the loss is =  0.022266415879130363  and accuracy is =  0.9176\n",
            "At step  70  and at epoch =  53  the loss is =  0.01153637282550335  and accuracy is =  0.9196\n",
            "At step  70  and at epoch =  54  the loss is =  0.008803007192909718  and accuracy is =  0.9132\n",
            "At step  70  and at epoch =  55  the loss is =  0.02402971126139164  and accuracy is =  0.9206\n",
            "At step  70  and at epoch =  56  the loss is =  0.023494187742471695  and accuracy is =  0.9234\n",
            "At step  70  and at epoch =  57  the loss is =  0.006383508909493685  and accuracy is =  0.9168\n",
            "At step  70  and at epoch =  58  the loss is =  0.009105092845857143  and accuracy is =  0.9268\n",
            "At step  70  and at epoch =  59  the loss is =  0.02675049751996994  and accuracy is =  0.9274\n",
            "At step  70  and at epoch =  60  the loss is =  0.01620415970683098  and accuracy is =  0.9216\n",
            "At step  70  and at epoch =  61  the loss is =  0.013975252397358418  and accuracy is =  0.9258\n",
            "At step  70  and at epoch =  62  the loss is =  0.017427921295166016  and accuracy is =  0.9296\n",
            "At step  70  and at epoch =  63  the loss is =  0.0031845164485275745  and accuracy is =  0.9284\n",
            "At step  70  and at epoch =  64  the loss is =  0.009196968749165535  and accuracy is =  0.937\n",
            "At step  70  and at epoch =  65  the loss is =  0.011168250814080238  and accuracy is =  0.9286\n",
            "At step  70  and at epoch =  66  the loss is =  0.010126510635018349  and accuracy is =  0.9346\n",
            "At step  70  and at epoch =  67  the loss is =  0.008642315864562988  and accuracy is =  0.9342\n",
            "At step  70  and at epoch =  68  the loss is =  0.0068274675868451595  and accuracy is =  0.9436\n",
            "At step  70  and at epoch =  69  the loss is =  0.027012832462787628  and accuracy is =  0.9276\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
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            "128\n",
            "128\n",
            "128\n",
            "64\n",
            "Validation Loss: 0.10795734077692032 Validation Accuracy : 0.1045\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  80  and at epoch =  0  the loss is =  0.08586291968822479  and accuracy is =  0.1378\n",
            "At step  80  and at epoch =  1  the loss is =  0.05688546225428581  and accuracy is =  0.239\n",
            "At step  80  and at epoch =  2  the loss is =  0.02987605892121792  and accuracy is =  0.2566\n",
            "At step  80  and at epoch =  3  the loss is =  0.039675988256931305  and accuracy is =  0.3154\n",
            "At step  80  and at epoch =  4  the loss is =  0.02893221750855446  and accuracy is =  0.4062\n",
            "At step  80  and at epoch =  5  the loss is =  0.025723299011588097  and accuracy is =  0.4592\n",
            "At step  80  and at epoch =  6  the loss is =  0.02009139023721218  and accuracy is =  0.4944\n",
            "At step  80  and at epoch =  7  the loss is =  0.02319740317761898  and accuracy is =  0.5112\n",
            "At step  80  and at epoch =  8  the loss is =  0.017026370391249657  and accuracy is =  0.5402\n",
            "At step  80  and at epoch =  9  the loss is =  0.02292197197675705  and accuracy is =  0.5498\n",
            "At step  80  and at epoch =  10  the loss is =  0.022103382274508476  and accuracy is =  0.59\n",
            "At step  80  and at epoch =  11  the loss is =  0.02787170372903347  and accuracy is =  0.5872\n",
            "At step  80  and at epoch =  12  the loss is =  0.025032754987478256  and accuracy is =  0.6042\n",
            "At step  80  and at epoch =  13  the loss is =  0.022244371473789215  and accuracy is =  0.6158\n",
            "At step  80  and at epoch =  14  the loss is =  0.01760311983525753  and accuracy is =  0.6226\n",
            "At step  80  and at epoch =  15  the loss is =  0.024081189185380936  and accuracy is =  0.632\n",
            "At step  80  and at epoch =  16  the loss is =  0.014845793135464191  and accuracy is =  0.6508\n",
            "At step  80  and at epoch =  17  the loss is =  0.019841665402054787  and accuracy is =  0.655\n",
            "At step  80  and at epoch =  18  the loss is =  0.01928040012717247  and accuracy is =  0.6616\n",
            "At step  80  and at epoch =  19  the loss is =  0.012788939289748669  and accuracy is =  0.674\n",
            "At step  80  and at epoch =  20  the loss is =  0.01892663910984993  and accuracy is =  0.675\n",
            "At step  80  and at epoch =  21  the loss is =  0.016418740153312683  and accuracy is =  0.6848\n",
            "At step  80  and at epoch =  22  the loss is =  0.02236771583557129  and accuracy is =  0.6978\n",
            "At step  80  and at epoch =  23  the loss is =  0.022002877667546272  and accuracy is =  0.6956\n",
            "At step  80  and at epoch =  24  the loss is =  0.01851685345172882  and accuracy is =  0.707\n",
            "At step  80  and at epoch =  25  the loss is =  0.020268075168132782  and accuracy is =  0.7206\n",
            "At step  80  and at epoch =  26  the loss is =  0.021094411611557007  and accuracy is =  0.7228\n",
            "At step  80  and at epoch =  27  the loss is =  0.0190030075609684  and accuracy is =  0.7334\n",
            "At step  80  and at epoch =  28  the loss is =  0.014326068572700024  and accuracy is =  0.7382\n",
            "At step  80  and at epoch =  29  the loss is =  0.017645275220274925  and accuracy is =  0.7464\n",
            "At step  80  and at epoch =  30  the loss is =  0.014869959093630314  and accuracy is =  0.7482\n",
            "At step  80  and at epoch =  31  the loss is =  0.018753739073872566  and accuracy is =  0.7566\n",
            "At step  80  and at epoch =  32  the loss is =  0.007889598608016968  and accuracy is =  0.755\n",
            "At step  80  and at epoch =  33  the loss is =  0.021120278164744377  and accuracy is =  0.764\n",
            "At step  80  and at epoch =  34  the loss is =  0.011227271519601345  and accuracy is =  0.7728\n",
            "At step  80  and at epoch =  35  the loss is =  0.013575803488492966  and accuracy is =  0.7748\n",
            "At step  80  and at epoch =  36  the loss is =  0.020080866292119026  and accuracy is =  0.7806\n",
            "At step  80  and at epoch =  37  the loss is =  0.014461233280599117  and accuracy is =  0.7874\n",
            "At step  80  and at epoch =  38  the loss is =  0.01695866882801056  and accuracy is =  0.7868\n",
            "At step  80  and at epoch =  39  the loss is =  0.019376851618289948  and accuracy is =  0.7948\n",
            "At step  80  and at epoch =  40  the loss is =  0.012391014024615288  and accuracy is =  0.8002\n",
            "At step  80  and at epoch =  41  the loss is =  0.01619471237063408  and accuracy is =  0.8074\n",
            "At step  80  and at epoch =  42  the loss is =  0.017691588029265404  and accuracy is =  0.8082\n",
            "At step  80  and at epoch =  43  the loss is =  0.020260686054825783  and accuracy is =  0.8102\n",
            "At step  80  and at epoch =  44  the loss is =  0.003941784612834454  and accuracy is =  0.816\n",
            "At step  80  and at epoch =  45  the loss is =  0.010593929328024387  and accuracy is =  0.8204\n",
            "At step  80  and at epoch =  46  the loss is =  0.018609385937452316  and accuracy is =  0.8272\n",
            "At step  80  and at epoch =  47  the loss is =  0.016707079485058784  and accuracy is =  0.8198\n",
            "At step  80  and at epoch =  48  the loss is =  0.009110676124691963  and accuracy is =  0.825\n",
            "At step  80  and at epoch =  49  the loss is =  0.033384960144758224  and accuracy is =  0.8304\n",
            "At step  80  and at epoch =  50  the loss is =  0.01882627233862877  and accuracy is =  0.8352\n",
            "At step  80  and at epoch =  51  the loss is =  0.01528392918407917  and accuracy is =  0.8358\n",
            "At step  80  and at epoch =  52  the loss is =  0.012627389281988144  and accuracy is =  0.8328\n",
            "At step  80  and at epoch =  53  the loss is =  0.018507082015275955  and accuracy is =  0.8308\n",
            "At step  80  and at epoch =  54  the loss is =  0.025819430127739906  and accuracy is =  0.8426\n",
            "At step  80  and at epoch =  55  the loss is =  0.022209584712982178  and accuracy is =  0.8498\n",
            "At step  80  and at epoch =  56  the loss is =  0.02155097760260105  and accuracy is =  0.8482\n",
            "At step  80  and at epoch =  57  the loss is =  0.015673542395234108  and accuracy is =  0.8436\n",
            "At step  80  and at epoch =  58  the loss is =  0.027641186490654945  and accuracy is =  0.8436\n",
            "At step  80  and at epoch =  59  the loss is =  0.018366333097219467  and accuracy is =  0.8514\n",
            "At step  80  and at epoch =  60  the loss is =  0.017333203926682472  and accuracy is =  0.8588\n",
            "At step  80  and at epoch =  61  the loss is =  0.016262676566839218  and accuracy is =  0.8604\n",
            "At step  80  and at epoch =  62  the loss is =  0.030197186395525932  and accuracy is =  0.8542\n",
            "At step  80  and at epoch =  63  the loss is =  0.011148035526275635  and accuracy is =  0.8566\n",
            "At step  80  and at epoch =  64  the loss is =  0.010481721721589565  and accuracy is =  0.8564\n",
            "At step  80  and at epoch =  65  the loss is =  0.009993916377425194  and accuracy is =  0.8756\n",
            "At step  80  and at epoch =  66  the loss is =  0.026467405259609222  and accuracy is =  0.861\n",
            "At step  80  and at epoch =  67  the loss is =  0.00912022590637207  and accuracy is =  0.8712\n",
            "At step  80  and at epoch =  68  the loss is =  0.007397920358926058  and accuracy is =  0.8784\n",
            "At step  80  and at epoch =  69  the loss is =  0.011399809271097183  and accuracy is =  0.8702\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
            "128\n",
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            "128\n",
            "128\n",
            "40\n",
            "Validation Loss: 0.10668110847473145 Validation Accuracy : 0.08211111111111111\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "At step  90  and at epoch =  0  the loss is =  0.07598738372325897  and accuracy is =  0.1428\n",
            "At step  90  and at epoch =  1  the loss is =  0.04642574116587639  and accuracy is =  0.2702\n",
            "At step  90  and at epoch =  2  the loss is =  0.028447693213820457  and accuracy is =  0.3416\n",
            "At step  90  and at epoch =  3  the loss is =  0.03486960753798485  and accuracy is =  0.4154\n",
            "At step  90  and at epoch =  4  the loss is =  0.028613056987524033  and accuracy is =  0.522\n",
            "At step  90  and at epoch =  5  the loss is =  0.020753657445311546  and accuracy is =  0.5572\n",
            "At step  90  and at epoch =  6  the loss is =  0.01910163275897503  and accuracy is =  0.592\n",
            "At step  90  and at epoch =  7  the loss is =  0.021030038595199585  and accuracy is =  0.625\n",
            "At step  90  and at epoch =  8  the loss is =  0.017465833574533463  and accuracy is =  0.653\n",
            "At step  90  and at epoch =  9  the loss is =  0.02410450018942356  and accuracy is =  0.6634\n",
            "At step  90  and at epoch =  10  the loss is =  0.024056127294898033  and accuracy is =  0.695\n",
            "At step  90  and at epoch =  11  the loss is =  0.012215308845043182  and accuracy is =  0.7102\n",
            "At step  90  and at epoch =  12  the loss is =  0.012219649739563465  and accuracy is =  0.7264\n",
            "At step  90  and at epoch =  13  the loss is =  0.010082129389047623  and accuracy is =  0.7368\n",
            "At step  90  and at epoch =  14  the loss is =  0.0216550100594759  and accuracy is =  0.7506\n",
            "At step  90  and at epoch =  15  the loss is =  0.02301405370235443  and accuracy is =  0.76\n",
            "At step  90  and at epoch =  16  the loss is =  0.012199887074530125  and accuracy is =  0.7674\n",
            "At step  90  and at epoch =  17  the loss is =  0.01288987323641777  and accuracy is =  0.7728\n",
            "At step  90  and at epoch =  18  the loss is =  0.023396886885166168  and accuracy is =  0.7866\n",
            "At step  90  and at epoch =  19  the loss is =  0.023979391902685165  and accuracy is =  0.7882\n",
            "At step  90  and at epoch =  20  the loss is =  0.00976228341460228  and accuracy is =  0.7948\n",
            "At step  90  and at epoch =  21  the loss is =  0.016785182058811188  and accuracy is =  0.807\n",
            "At step  90  and at epoch =  22  the loss is =  0.008280719630420208  and accuracy is =  0.8106\n",
            "At step  90  and at epoch =  23  the loss is =  0.014305328018963337  and accuracy is =  0.81\n",
            "At step  90  and at epoch =  24  the loss is =  0.016194812953472137  and accuracy is =  0.8186\n",
            "At step  90  and at epoch =  25  the loss is =  0.01312253437936306  and accuracy is =  0.8238\n",
            "At step  90  and at epoch =  26  the loss is =  0.00943310372531414  and accuracy is =  0.824\n",
            "At step  90  and at epoch =  27  the loss is =  0.011012312024831772  and accuracy is =  0.833\n",
            "At step  90  and at epoch =  28  the loss is =  0.018221860751509666  and accuracy is =  0.8376\n",
            "At step  90  and at epoch =  29  the loss is =  0.021741552278399467  and accuracy is =  0.8454\n",
            "At step  90  and at epoch =  30  the loss is =  0.0156682301312685  and accuracy is =  0.8482\n",
            "At step  90  and at epoch =  31  the loss is =  0.007032424211502075  and accuracy is =  0.851\n",
            "At step  90  and at epoch =  32  the loss is =  0.012307138182222843  and accuracy is =  0.8544\n",
            "At step  90  and at epoch =  33  the loss is =  0.014717613346874714  and accuracy is =  0.8528\n",
            "At step  90  and at epoch =  34  the loss is =  0.004065005108714104  and accuracy is =  0.8534\n",
            "At step  90  and at epoch =  35  the loss is =  0.01255797129124403  and accuracy is =  0.8642\n",
            "At step  90  and at epoch =  36  the loss is =  0.01418764516711235  and accuracy is =  0.864\n",
            "At step  90  and at epoch =  37  the loss is =  0.012709174305200577  and accuracy is =  0.8666\n",
            "At step  90  and at epoch =  38  the loss is =  0.020540272817015648  and accuracy is =  0.8724\n",
            "At step  90  and at epoch =  39  the loss is =  0.018215160816907883  and accuracy is =  0.8764\n",
            "At step  90  and at epoch =  40  the loss is =  0.022513670846819878  and accuracy is =  0.8812\n",
            "At step  90  and at epoch =  41  the loss is =  0.013433484360575676  and accuracy is =  0.8806\n",
            "At step  90  and at epoch =  42  the loss is =  0.014352869242429733  and accuracy is =  0.8754\n",
            "At step  90  and at epoch =  43  the loss is =  0.012520495802164078  and accuracy is =  0.8858\n",
            "At step  90  and at epoch =  44  the loss is =  0.009007017128169537  and accuracy is =  0.8796\n",
            "At step  90  and at epoch =  45  the loss is =  0.026828913018107414  and accuracy is =  0.8936\n",
            "At step  90  and at epoch =  46  the loss is =  0.0048093656077980995  and accuracy is =  0.882\n",
            "At step  90  and at epoch =  47  the loss is =  0.022436950355768204  and accuracy is =  0.8924\n",
            "At step  90  and at epoch =  48  the loss is =  0.010698618367314339  and accuracy is =  0.8862\n",
            "At step  90  and at epoch =  49  the loss is =  0.0098904799669981  and accuracy is =  0.899\n",
            "At step  90  and at epoch =  50  the loss is =  0.015265879221260548  and accuracy is =  0.8996\n",
            "At step  90  and at epoch =  51  the loss is =  0.022492198273539543  and accuracy is =  0.9072\n",
            "At step  90  and at epoch =  52  the loss is =  0.019813429564237595  and accuracy is =  0.903\n",
            "At step  90  and at epoch =  53  the loss is =  0.012235126458108425  and accuracy is =  0.8994\n",
            "At step  90  and at epoch =  54  the loss is =  0.018360109999775887  and accuracy is =  0.908\n",
            "At step  90  and at epoch =  55  the loss is =  0.007220737636089325  and accuracy is =  0.9016\n",
            "At step  90  and at epoch =  56  the loss is =  0.008870169520378113  and accuracy is =  0.8982\n",
            "At step  90  and at epoch =  57  the loss is =  0.014106296934187412  and accuracy is =  0.913\n",
            "At step  90  and at epoch =  58  the loss is =  0.019592374563217163  and accuracy is =  0.9172\n",
            "At step  90  and at epoch =  59  the loss is =  0.0030163079500198364  and accuracy is =  0.9094\n",
            "At step  90  and at epoch =  60  the loss is =  0.021117430180311203  and accuracy is =  0.9182\n",
            "At step  90  and at epoch =  61  the loss is =  0.017317814752459526  and accuracy is =  0.909\n",
            "At step  90  and at epoch =  62  the loss is =  0.008677671663463116  and accuracy is =  0.913\n",
            "At step  90  and at epoch =  63  the loss is =  0.02575497515499592  and accuracy is =  0.9032\n",
            "At step  90  and at epoch =  64  the loss is =  0.011488225311040878  and accuracy is =  0.9078\n",
            "At step  90  and at epoch =  65  the loss is =  0.010647764429450035  and accuracy is =  0.9236\n",
            "At step  90  and at epoch =  66  the loss is =  0.008862984366714954  and accuracy is =  0.9152\n",
            "At step  90  and at epoch =  67  the loss is =  0.007502211257815361  and accuracy is =  0.916\n",
            "At step  90  and at epoch =  68  the loss is =  0.006959833204746246  and accuracy is =  0.932\n",
            "At step  90  and at epoch =  69  the loss is =  0.006683373358100653  and accuracy is =  0.9314\n",
            "128\n",
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            "16\n",
            "Validation Loss: 0.11833896487951279 Validation Accuracy : 0.0811\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}