iHeard.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCZKsVGXHji_"
      },
      "source": [
        "**Downloading and Initializing Required Files**"
      ]
    },
    {
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRspHwwE7qgf",
        "outputId": "c2bf35eb-156e-480f-efd3-b926b34d69e9"
      },
      "source": [
        "!pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Requirement already satisfied: torch==1.7.1+cu101 in /usr/local/lib/python3.6/dist-packages (1.7.1+cu101)\n",
            "Requirement already satisfied: torchvision==0.8.2+cu101 in /usr/local/lib/python3.6/dist-packages (0.8.2+cu101)\n",
            "Requirement already satisfied: torchaudio==0.7.2 in /usr/local/lib/python3.6/dist-packages (0.7.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.7.1+cu101) (1.19.4)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from torch==1.7.1+cu101) (0.8)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch==1.7.1+cu101) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.8.2+cu101) (7.0.0)\n"
          ],
          "name": "stdout"
        }
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      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "vAGCMEhrw57C",
        "outputId": "74c89171-c369-4598-fc03-6bb54c4b29bf"
      },
      "source": [
        "!pip install soundfile\n",
        "!pip install librosa\n",
        "!pip install tqdm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: soundfile in /usr/local/lib/python3.6/dist-packages (0.10.3.post1)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile) (1.14.4)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile) (2.20)\n",
            "Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.3)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.15.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.22.2.post1)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.9)\n",
            "Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.2)\n",
            "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.48.0)\n",
            "Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.19.4)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (51.0.0)\n",
            "Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.41.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6pbLiRP8w-w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8fd6323-4092-4b96-b8db-682d1f7a38f2"
      },
      "source": [
        "# imports\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision.datasets.utils import download_url as download_url\n",
        "from torch.utils.data import random_split\n",
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data import Dataset\n",
        "import torchaudio\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tarfile\n",
        "import csv\n",
        "import soundfile\n",
        "import librosa\n",
        "from tqdm import tqdm\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchaudio/backend/utils.py:54: UserWarning: \"sox\" backend is being deprecated. The default backend will be changed to \"sox_io\" backend in 0.8.0 and \"sox\" backend will be removed in 0.9.0. Please migrate to \"sox_io\" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.\n",
            "  '\"sox\" backend is being deprecated. '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KeUmcrc-2f1",
        "outputId": "3b23776f-0f05-4fbf-9689-8ac796895799"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
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      "source": [
        "# Downloads dataset to google drive, will have to manually rename the file to UrbanSound8K.tar.gz\n",
        "dataset_url = 'https://goo.gl/8hY5ER'\n",
        "download_url(dataset_url, \"/tmp/gdrive/My Drive/urban_data/\")"
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      "execution_count": null,
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          "text": [
            "Downloading https://goo.gl/8hY5ER to /tmp/gdrive/My Drive/urban_data/8hY5ER\n"
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-d86da4c0ed74>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Downloads dataset to google drive, will have to manually rename the file to UrbanSound8K.tar.gz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdataset_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'https://goo.gl/8hY5ER'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdownload_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"/tmp/gdrive/My Drive/urban_data/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/datasets/utils.py\u001b[0m in \u001b[0;36mdownload_url\u001b[0;34m(url, root, filename, md5)\u001b[0m\n\u001b[1;32m     70\u001b[0m             urllib.request.urlretrieve(\n\u001b[1;32m     71\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m                 \u001b[0mreporthook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgen_bar_updater\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m             )\n\u001b[1;32m     74\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mURLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m                 \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    461\u001b[0m             \u001b[0;31m# Amount is given, implement using readinto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m             \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0;31m# connection, and the user is reading more bytes than will be provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m         \u001b[0;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m             \u001b[0;31m# Ideally, we would raise IncompleteRead if the content-length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1010\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1012\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Read on closed or unwrapped SSL socket.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \"\"\"\n\u001b[1;32m    630\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRSx34Xi_cWK"
      },
      "source": [
        "with tarfile.open(\"/tmp/gdrive/MyDrive/urban_data/UrbanSound8K.tar.gz\", mode = 'r:gz') as tar:\n",
        "    tar.extractall(path='/tmp/gdrive/MyDrive/urban_data/data')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQbaBYhsHrok"
      },
      "source": [
        "**Formatting and Cleaning Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xgZEjcUaGAD"
      },
      "source": [
        "with open(\"/content/gdrive/MyDrive/urban_data/data/UrbanSound8K/metadata/UrbanSound8K.csv\") as file:\n",
        "    csv_reader = csv.reader(file)\n",
        "    for row in csv_reader:\n",
        "        print(row)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEJIPq-bFc0B",
        "outputId": "d0c73cdc-b973-43bf-fedf-5e326017f412"
      },
      "source": [
        "# explains the different categories of data available\n",
        "data_dir = \"/content/gdrive/MyDrive/urban_data/data/UrbanSound8K\"\n",
        "print(os.listdir(data_dir))\n",
        "print(os.listdir(data_dir+\"/metadata\"))\n",
        "print(os.listdir(data_dir+\"/audio\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.DS_Store', 'audio', 'FREESOUNDCREDITS.txt', 'metadata', 'UrbanSound8K_README.txt']\n",
            "['.DS_Store', 'UrbanSound8K.csv']\n",
            "['.DS_Store', 'fold1', 'fold10', 'fold2', 'fold3', 'fold4', 'fold5', 'fold6', 'fold7', 'fold8', 'fold9']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNqYgK6zFx5j",
        "outputId": "5e57beab-431f-4ec5-d55c-8ab62d1b6cba"
      },
      "source": [
        "# viewing the information about each song from the dataset using pandas\n",
        "csvData = pd.read_csv(data_dir+\"/metadata/UrbanSound8K.csv\")\n",
        "print(len(list(csvData.iloc))) # total number of songs we are dealing with\n",
        "print(csvData.iloc[0]) # exmaple of a an object in the csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8732\n",
            "slice_file_name    100032-3-0-0.wav\n",
            "fsID                         100032\n",
            "start                             0\n",
            "end                        0.317551\n",
            "salience                          1\n",
            "fold                              5\n",
            "classID                           3\n",
            "class                      dog_bark\n",
            "Name: 0, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDRRop4wQg6U"
      },
      "source": [
        "**Defining the Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "z4Aie6jPQlwM",
        "outputId": "947340f8-c059-4d06-b5b2-5f0e58271088"
      },
      "source": [
        "torchaudio.set_audio_backend(\"sox_io\")\n",
        "torchaudio.get_audio_backend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'sox_io'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlkS5tNgxNMX",
        "outputId": "ed5c325e-d1e1-4dc8-bbed-03acd7547e88"
      },
      "source": [
        "soundfile.available_subtypes('FLAC')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'PCM_16': 'Signed 16 bit PCM',\n",
              " 'PCM_24': 'Signed 24 bit PCM',\n",
              " 'PCM_S8': 'Signed 8 bit PCM'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLu7lzrwi5gj"
      },
      "source": [
        "class UrbanSounds(Dataset):\n",
        "    def __init__(self, csv_file_path, folders_file_path, seed=10, sample_size = 176400):\n",
        "        \"\"\"\n",
        "        :param csv_file_path: Path to the csv file with metadata about sounds\n",
        "        :param folders_file_path: Path to directory housing folders with\n",
        "        :param seed: Specifies which folder number to be used to test data-performance\n",
        "        :param sample_size: Specifies how big the audio samples should be (bigger is better, but requires more time and data)\n",
        "        \"\"\"\n",
        "        self.urban_sounds_frame = pd.read_csv(csv_file_path)\n",
        "        self.folders_file_path = folders_file_path\n",
        "\n",
        "        self.sound_id_name = {\n",
        "            0: \"air_conditioner\",\n",
        "            1: \"car_horn\",\n",
        "            2: \"children_playing\",\n",
        "            3: \"dog_bark\",\n",
        "            4: \"drilling\",\n",
        "            5: \"engine_idling\",\n",
        "            6: \"gun_shot\",\n",
        "            7: \"jackhammer\",\n",
        "            8: \"siren\",\n",
        "            9: \"street_music\"\n",
        "        }\n",
        "\n",
        "        self.sample_size = sample_size\n",
        "        self.seed = seed\n",
        "\n",
        "        self.train_ds = []\n",
        "        self.test_ds = []\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        # Setup iloc\n",
        "        slicer = self.urban_sounds_frame.iloc\n",
        "        wav_unit = slicer[index]\n",
        "\n",
        "        # Find the folder number, name, and classifcation associated with the index\n",
        "        wav_name = wav_unit[\"slice_file_name\"]\n",
        "        wav_fold = wav_unit[\"fold\"]\n",
        "        wav_classid = wav_unit[\"classID\"]\n",
        "        wav_classname = self.sound_id_name[wav_classid]\n",
        "\n",
        "        # Get the wav file using the path\n",
        "        wav_file = self.folders_file_path + \"/fold\"+str(wav_fold)+\"/\"+wav_name\n",
        "\n",
        "        # Convert bit depth to 32 bit\n",
        "        data, samplerate = librosa.load(wav_file, 44100, mono=True)\n",
        "        sound = torch.from_numpy(data)\n",
        "\n",
        "        # Convert the wav file to a tensor\n",
        "        # tensors, default_sample_rate = torchaudio.load(wav_file)\n",
        "        # print(samplerate, tensors.shape)\n",
        "\n",
        "        # OPTIONAL MIXING (Uncomment for the mixing to work, otherwise expect 2 channel audio)\n",
        "        # sound = torch.mean(tensors, dim=0, keepdim=True)\n",
        "\n",
        "        # Fix all wav files to 176400 samples\n",
        "        padded_data = torch.zeros(176400) #tempData accounts for audio clips that are too short\n",
        "\n",
        "        if sound.numel() < 176400:\n",
        "            padded_data[:sound.numel()] = sound[:]\n",
        "        else:\n",
        "            padded_data[:] = sound[:176400]\n",
        "        \n",
        "\n",
        "        final_data = torch.zeros(self.sample_size)\n",
        "        every_n = 176400 // self.sample_size\n",
        "\n",
        "        count = 0\n",
        "        for i in range(self.sample_size):\n",
        "            final_data[i] = padded_data[count]\n",
        "            count += every_n\n",
        "        \n",
        "        return (final_data, wav_fold, wav_classid)\n",
        "    \n",
        "    def set_train_test(self):\n",
        "        for i in tqdm(range(8372)):\n",
        "            (tensor, folder, classID) = self[i]\n",
        "            if int(folder) == self.seed:\n",
        "                self.test_ds.append((tensor, classID))\n",
        "            else:\n",
        "                self.train_ds.append((tensor, classID))\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.urban_sounds_frame)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEIoYaVogSW_",
        "outputId": "ef84910c-0844-4a27-e9fe-f29beea216a6"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EC2A6ZhUjm1i"
      },
      "source": [
        "dataset = UrbanSounds(csv_file_path=\"/tmp/gdrive/MyDrive/urban_data/data/UrbanSound8K/metadata/UrbanSound8K.csv\", folders_file_path=\"/tmp/gdrive/MyDrive/urban_data/data/UrbanSound8K/audio\", sample_size=32000)\n",
        "# dataset.set_train_test()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmYgHGGTxGVI"
      },
      "source": [
        "import pickle\n",
        "with open('train_ds.pkl', 'wb') as f:\n",
        "    pickle.dump(dataset.train_ds, f)\n",
        "with open('test_ds.pkl', 'wb') as f:\n",
        "    pickle.dump(dataset.test_ds, f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4QiWgJxzkwZ"
      },
      "source": [
        "import pickle\n",
        "dataset.train_ds = None\n",
        "with open('train_ds.pkl', 'rb') as f:\n",
        "    dataset.train_ds = pickle.load(f)\n",
        "\n",
        "dataset.test_set = None\n",
        "with open('test_ds.pkl', 'rb') as f:\n",
        "    dataset.test_ds = pickle.load(f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5O2nzTLu9xRA",
        "outputId": "c131716e-74a0-4432-bee7-e4657e923fc6"
      },
      "source": [
        "print(len(dataset.train_ds))\n",
        "print(len(dataset.test_ds))\n",
        "\n",
        "batch_size = 128\n",
        "train_dl = DataLoader(dataset.train_ds, batch_size, shuffle=True)\n",
        "test_dl = DataLoader(dataset.test_ds, batch_size*2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7583\n",
            "789\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuxkD-7JWO50"
      },
      "source": [
        "class Permute(nn.Module):\n",
        "    def forward(self, x):\n",
        "        return x.permute(0, 2, 1)\n",
        "        \n",
        "class Unsqueeze(nn.Module):\n",
        "    def forward(self, x):\n",
        "        return x.unsqueeze(1)\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.network = nn.Sequential(\n",
        "            Unsqueeze(),\n",
        "\n",
        "            nn.Conv1d(1, 64, 80, 5),\n",
        "            nn.BatchNorm1d(64),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool1d(4),\n",
        "\n",
        "            nn.Conv1d(64, 64, 3),\n",
        "            nn.BatchNorm1d(64),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv1d(64, 64, 3),\n",
        "            nn.BatchNorm1d(64),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool1d(4),\n",
        "\n",
        "            nn.Conv1d(64, 128, 3),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv1d(128, 128, 3),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool1d(4),\n",
        "\n",
        "            nn.Conv1d(128, 256, 3),\n",
        "            nn.BatchNorm1d(256),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv1d(256, 256, 3),\n",
        "            nn.BatchNorm1d(256),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv1d(256, 256, 3),\n",
        "            nn.BatchNorm1d(256),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool1d(4),\n",
        "\n",
        "            nn.Conv1d(256, 512, 3),\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv1d(512, 512, 3),\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.ReLU(),\n",
        "            \n",
        "            nn.AvgPool1d(19),\n",
        "            Permute(),\n",
        "            nn.Linear(512, 10),\n",
        "            nn.LogSoftmax(dim=2)\n",
        "            )\n",
        "    def forward(self, xb):\n",
        "        return self.network(xb)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Xs5yvIolimQ",
        "outputId": "1ac9ebd0-0715-46ed-f9fd-13faeee8b660"
      },
      "source": [
        "model = Net()\n",
        "model.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Net(\n",
              "  (network): Sequential(\n",
              "    (0): Unsqueeze()\n",
              "    (1): Conv1d(1, 64, kernel_size=(80,), stride=(5,))\n",
              "    (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "    (5): Conv1d(64, 64, kernel_size=(3,), stride=(1,))\n",
              "    (6): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (7): ReLU()\n",
              "    (8): Conv1d(64, 64, kernel_size=(3,), stride=(1,))\n",
              "    (9): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (10): ReLU()\n",
              "    (11): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "    (12): Conv1d(64, 128, kernel_size=(3,), stride=(1,))\n",
              "    (13): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (14): ReLU()\n",
              "    (15): Conv1d(128, 128, kernel_size=(3,), stride=(1,))\n",
              "    (16): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (17): ReLU()\n",
              "    (18): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "    (19): Conv1d(128, 256, kernel_size=(3,), stride=(1,))\n",
              "    (20): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (21): ReLU()\n",
              "    (22): Conv1d(256, 256, kernel_size=(3,), stride=(1,))\n",
              "    (23): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (24): ReLU()\n",
              "    (25): Conv1d(256, 256, kernel_size=(3,), stride=(1,))\n",
              "    (26): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (27): ReLU()\n",
              "    (28): MaxPool1d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)\n",
              "    (29): Conv1d(256, 512, kernel_size=(3,), stride=(1,))\n",
              "    (30): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (31): ReLU()\n",
              "    (32): Conv1d(512, 512, kernel_size=(3,), stride=(1,))\n",
              "    (33): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (34): ReLU()\n",
              "    (35): AvgPool1d(kernel_size=(19,), stride=(19,), padding=(0,))\n",
              "    (36): Permute()\n",
              "    (37): Linear(in_features=512, out_features=10, bias=True)\n",
              "    (38): LogSoftmax(dim=2)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iqjb_LwhP1R-"
      },
      "source": [
        "optimizer = optim.Adam(model.parameters(), lr = 0.01, weight_decay = 0.0001)\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size = 100, gamma = 0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFA6ORH3P4MA"
      },
      "source": [
        "def train(model, epoch):\n",
        "    model.train() # change the mode of the model\n",
        "    for batch_idx, (data, target) in enumerate(train_dl):\n",
        "        optimizer.zero_grad()\n",
        "        data = data.to(device)\n",
        "        target = target.to(device)\n",
        "        data = data.requires_grad_() #set requires_grad to True for training\n",
        "        output = model(data)\n",
        "        output = output.permute(1, 0, 2) #original output dimensions are batchSizex1x10 \n",
        "        loss = F.nll_loss(output[0], target) #the loss functions expects a batchSizex10 input\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "        epoch, batch_idx * len(data), len(train_dl.dataset),\n",
        "        100. * batch_idx / len(train_dl), loss))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Yrr5pi9QS3i"
      },
      "source": [
        "def test(model, epoch):\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    for data, target in test_dl:\n",
        "        data = data.to(device)\n",
        "        target = target.to(device)\n",
        "        output = model(data)\n",
        "        output = output.permute(1, 0, 2)\n",
        "        pred = output.max(2)[1] # get the index of the max log-probability\n",
        "        correct += pred.eq(target).cpu().sum().item()\n",
        "    print('\\nTest set: Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        correct, len(test_dl.dataset),\n",
        "        100. * correct / len(test_dl.dataset)))\n",
        "    return (1. * correct / len(test_dl.dataset))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Twl6g4Sq-Z_d",
        "outputId": "294d9650-b738-41a0-f3ef-066cbf151069"
      },
      "source": [
        "log_interval = 20\n",
        "counter = 0\n",
        "history = []\n",
        "for epoch in range(300):\n",
        "    if epoch > 99 and epoch % 100 == 0:\n",
        "        print((epoch//100), \"round of training complete. Dividing learning rate by 10\")\n",
        "    scheduler.step()\n",
        "    train(model, epoch)\n",
        "    acc = test(model, epoch)\n",
        "\n",
        "    history.append((epoch, acc * 100))\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 0 [1829/7583 (98%)]\tLoss: 0.396829\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 1 [1829/7583 (98%)]\tLoss: 0.837211\n",
            "\n",
            "Test set: Accuracy: 457/789 (58%)\n",
            "\n",
            "Train Epoch: 2 [1829/7583 (98%)]\tLoss: 0.583222\n",
            "\n",
            "Test set: Accuracy: 454/789 (58%)\n",
            "\n",
            "Train Epoch: 3 [1829/7583 (98%)]\tLoss: 0.533040\n",
            "\n",
            "Test set: Accuracy: 473/789 (60%)\n",
            "\n",
            "Train Epoch: 4 [1829/7583 (98%)]\tLoss: 0.932074\n",
            "\n",
            "Test set: Accuracy: 384/789 (49%)\n",
            "\n",
            "Train Epoch: 5 [1829/7583 (98%)]\tLoss: 0.627020\n",
            "\n",
            "Test set: Accuracy: 476/789 (60%)\n",
            "\n",
            "Train Epoch: 6 [1829/7583 (98%)]\tLoss: 0.512309\n",
            "\n",
            "Test set: Accuracy: 478/789 (61%)\n",
            "\n",
            "Train Epoch: 7 [1829/7583 (98%)]\tLoss: 0.699526\n",
            "\n",
            "Test set: Accuracy: 435/789 (55%)\n",
            "\n",
            "Train Epoch: 8 [1829/7583 (98%)]\tLoss: 0.735817\n",
            "\n",
            "Test set: Accuracy: 458/789 (58%)\n",
            "\n",
            "Train Epoch: 9 [1829/7583 (98%)]\tLoss: 0.626046\n",
            "\n",
            "Test set: Accuracy: 445/789 (56%)\n",
            "\n",
            "Train Epoch: 10 [1829/7583 (98%)]\tLoss: 0.520502\n",
            "\n",
            "Test set: Accuracy: 408/789 (52%)\n",
            "\n",
            "Train Epoch: 11 [1829/7583 (98%)]\tLoss: 0.268406\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 12 [1829/7583 (98%)]\tLoss: 0.452541\n",
            "\n",
            "Test set: Accuracy: 415/789 (53%)\n",
            "\n",
            "Train Epoch: 13 [1829/7583 (98%)]\tLoss: 0.415976\n",
            "\n",
            "Test set: Accuracy: 546/789 (69%)\n",
            "\n",
            "Train Epoch: 14 [1829/7583 (98%)]\tLoss: 0.560108\n",
            "\n",
            "Test set: Accuracy: 438/789 (56%)\n",
            "\n",
            "Train Epoch: 15 [1829/7583 (98%)]\tLoss: 0.888896\n",
            "\n",
            "Test set: Accuracy: 510/789 (65%)\n",
            "\n",
            "Train Epoch: 16 [1829/7583 (98%)]\tLoss: 0.528589\n",
            "\n",
            "Test set: Accuracy: 474/789 (60%)\n",
            "\n",
            "Train Epoch: 17 [1829/7583 (98%)]\tLoss: 0.754546\n",
            "\n",
            "Test set: Accuracy: 386/789 (49%)\n",
            "\n",
            "Train Epoch: 18 [1829/7583 (98%)]\tLoss: 1.202896\n",
            "\n",
            "Test set: Accuracy: 444/789 (56%)\n",
            "\n",
            "Train Epoch: 19 [1829/7583 (98%)]\tLoss: 0.341446\n",
            "\n",
            "Test set: Accuracy: 494/789 (63%)\n",
            "\n",
            "Train Epoch: 20 [1829/7583 (98%)]\tLoss: 0.198552\n",
            "\n",
            "Test set: Accuracy: 358/789 (45%)\n",
            "\n",
            "Train Epoch: 21 [1829/7583 (98%)]\tLoss: 0.600576\n",
            "\n",
            "Test set: Accuracy: 392/789 (50%)\n",
            "\n",
            "Train Epoch: 22 [1829/7583 (98%)]\tLoss: 0.547598\n",
            "\n",
            "Test set: Accuracy: 455/789 (58%)\n",
            "\n",
            "Train Epoch: 23 [1829/7583 (98%)]\tLoss: 0.295342\n",
            "\n",
            "Test set: Accuracy: 404/789 (51%)\n",
            "\n",
            "Train Epoch: 24 [1829/7583 (98%)]\tLoss: 0.387947\n",
            "\n",
            "Test set: Accuracy: 496/789 (63%)\n",
            "\n",
            "Train Epoch: 25 [1829/7583 (98%)]\tLoss: 0.973921\n",
            "\n",
            "Test set: Accuracy: 394/789 (50%)\n",
            "\n",
            "Train Epoch: 26 [1829/7583 (98%)]\tLoss: 0.740477\n",
            "\n",
            "Test set: Accuracy: 483/789 (61%)\n",
            "\n",
            "Train Epoch: 27 [1829/7583 (98%)]\tLoss: 0.753338\n",
            "\n",
            "Test set: Accuracy: 398/789 (50%)\n",
            "\n",
            "Train Epoch: 28 [1829/7583 (98%)]\tLoss: 0.289161\n",
            "\n",
            "Test set: Accuracy: 431/789 (55%)\n",
            "\n",
            "Train Epoch: 29 [1829/7583 (98%)]\tLoss: 0.277224\n",
            "\n",
            "Test set: Accuracy: 438/789 (56%)\n",
            "\n",
            "Train Epoch: 30 [1829/7583 (98%)]\tLoss: 0.262064\n",
            "\n",
            "Test set: Accuracy: 424/789 (54%)\n",
            "\n",
            "Train Epoch: 31 [1829/7583 (98%)]\tLoss: 0.615408\n",
            "\n",
            "Test set: Accuracy: 469/789 (59%)\n",
            "\n",
            "Train Epoch: 32 [1829/7583 (98%)]\tLoss: 0.533768\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 33 [1829/7583 (98%)]\tLoss: 0.246464\n",
            "\n",
            "Test set: Accuracy: 446/789 (57%)\n",
            "\n",
            "Train Epoch: 34 [1829/7583 (98%)]\tLoss: 0.416156\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 35 [1829/7583 (98%)]\tLoss: 0.610081\n",
            "\n",
            "Test set: Accuracy: 476/789 (60%)\n",
            "\n",
            "Train Epoch: 36 [1829/7583 (98%)]\tLoss: 0.515315\n",
            "\n",
            "Test set: Accuracy: 448/789 (57%)\n",
            "\n",
            "Train Epoch: 37 [1829/7583 (98%)]\tLoss: 0.440939\n",
            "\n",
            "Test set: Accuracy: 421/789 (53%)\n",
            "\n",
            "Train Epoch: 38 [1829/7583 (98%)]\tLoss: 0.905167\n",
            "\n",
            "Test set: Accuracy: 451/789 (57%)\n",
            "\n",
            "Train Epoch: 39 [1829/7583 (98%)]\tLoss: 0.315864\n",
            "\n",
            "Test set: Accuracy: 481/789 (61%)\n",
            "\n",
            "Train Epoch: 40 [1829/7583 (98%)]\tLoss: 0.372669\n",
            "\n",
            "Test set: Accuracy: 470/789 (60%)\n",
            "\n",
            "Train Epoch: 41 [1829/7583 (98%)]\tLoss: 0.783551\n",
            "\n",
            "Test set: Accuracy: 407/789 (52%)\n",
            "\n",
            "Train Epoch: 42 [1829/7583 (98%)]\tLoss: 0.858255\n",
            "\n",
            "Test set: Accuracy: 469/789 (59%)\n",
            "\n",
            "Train Epoch: 43 [1829/7583 (98%)]\tLoss: 0.316459\n",
            "\n",
            "Test set: Accuracy: 469/789 (59%)\n",
            "\n",
            "Train Epoch: 44 [1829/7583 (98%)]\tLoss: 0.517457\n",
            "\n",
            "Test set: Accuracy: 434/789 (55%)\n",
            "\n",
            "Train Epoch: 45 [1829/7583 (98%)]\tLoss: 0.188958\n",
            "\n",
            "Test set: Accuracy: 492/789 (62%)\n",
            "\n",
            "Train Epoch: 46 [1829/7583 (98%)]\tLoss: 0.081474\n",
            "\n",
            "Test set: Accuracy: 455/789 (58%)\n",
            "\n",
            "Train Epoch: 47 [1829/7583 (98%)]\tLoss: 0.349524\n",
            "\n",
            "Test set: Accuracy: 417/789 (53%)\n",
            "\n",
            "Train Epoch: 48 [1829/7583 (98%)]\tLoss: 0.631781\n",
            "\n",
            "Test set: Accuracy: 413/789 (52%)\n",
            "\n",
            "Train Epoch: 49 [1829/7583 (98%)]\tLoss: 0.363003\n",
            "\n",
            "Test set: Accuracy: 511/789 (65%)\n",
            "\n",
            "Train Epoch: 50 [1829/7583 (98%)]\tLoss: 0.274457\n",
            "\n",
            "Test set: Accuracy: 492/789 (62%)\n",
            "\n",
            "Train Epoch: 51 [1829/7583 (98%)]\tLoss: 0.063976\n",
            "\n",
            "Test set: Accuracy: 458/789 (58%)\n",
            "\n",
            "Train Epoch: 52 [1829/7583 (98%)]\tLoss: 0.527553\n",
            "\n",
            "Test set: Accuracy: 454/789 (58%)\n",
            "\n",
            "Train Epoch: 53 [1829/7583 (98%)]\tLoss: 0.576464\n",
            "\n",
            "Test set: Accuracy: 410/789 (52%)\n",
            "\n",
            "Train Epoch: 54 [1829/7583 (98%)]\tLoss: 0.286296\n",
            "\n",
            "Test set: Accuracy: 492/789 (62%)\n",
            "\n",
            "Train Epoch: 55 [1829/7583 (98%)]\tLoss: 0.718017\n",
            "\n",
            "Test set: Accuracy: 434/789 (55%)\n",
            "\n",
            "Train Epoch: 56 [1829/7583 (98%)]\tLoss: 0.634227\n",
            "\n",
            "Test set: Accuracy: 382/789 (48%)\n",
            "\n",
            "Train Epoch: 57 [1829/7583 (98%)]\tLoss: 0.358487\n",
            "\n",
            "Test set: Accuracy: 417/789 (53%)\n",
            "\n",
            "Train Epoch: 58 [1829/7583 (98%)]\tLoss: 0.455198\n",
            "\n",
            "Test set: Accuracy: 381/789 (48%)\n",
            "\n",
            "Train Epoch: 59 [1829/7583 (98%)]\tLoss: 0.218060\n",
            "\n",
            "Test set: Accuracy: 410/789 (52%)\n",
            "\n",
            "Train Epoch: 60 [1829/7583 (98%)]\tLoss: 0.381367\n",
            "\n",
            "Test set: Accuracy: 478/789 (61%)\n",
            "\n",
            "Train Epoch: 61 [1829/7583 (98%)]\tLoss: 0.406755\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 62 [1829/7583 (98%)]\tLoss: 0.123452\n",
            "\n",
            "Test set: Accuracy: 450/789 (57%)\n",
            "\n",
            "Train Epoch: 63 [1829/7583 (98%)]\tLoss: 0.168679\n",
            "\n",
            "Test set: Accuracy: 466/789 (59%)\n",
            "\n",
            "Train Epoch: 64 [1829/7583 (98%)]\tLoss: 0.357236\n",
            "\n",
            "Test set: Accuracy: 454/789 (58%)\n",
            "\n",
            "Train Epoch: 65 [1829/7583 (98%)]\tLoss: 0.279545\n",
            "\n",
            "Test set: Accuracy: 472/789 (60%)\n",
            "\n",
            "Train Epoch: 66 [1829/7583 (98%)]\tLoss: 0.732948\n",
            "\n",
            "Test set: Accuracy: 476/789 (60%)\n",
            "\n",
            "Train Epoch: 67 [1829/7583 (98%)]\tLoss: 0.132802\n",
            "\n",
            "Test set: Accuracy: 462/789 (59%)\n",
            "\n",
            "Train Epoch: 68 [1829/7583 (98%)]\tLoss: 0.899048\n",
            "\n",
            "Test set: Accuracy: 420/789 (53%)\n",
            "\n",
            "Train Epoch: 69 [1829/7583 (98%)]\tLoss: 0.401323\n",
            "\n",
            "Test set: Accuracy: 415/789 (53%)\n",
            "\n",
            "Train Epoch: 70 [1829/7583 (98%)]\tLoss: 0.708711\n",
            "\n",
            "Test set: Accuracy: 489/789 (62%)\n",
            "\n",
            "Train Epoch: 71 [1829/7583 (98%)]\tLoss: 0.479021\n",
            "\n",
            "Test set: Accuracy: 470/789 (60%)\n",
            "\n",
            "Train Epoch: 72 [1829/7583 (98%)]\tLoss: 0.342845\n",
            "\n",
            "Test set: Accuracy: 484/789 (61%)\n",
            "\n",
            "Train Epoch: 73 [1829/7583 (98%)]\tLoss: 0.408706\n",
            "\n",
            "Test set: Accuracy: 422/789 (53%)\n",
            "\n",
            "Train Epoch: 74 [1829/7583 (98%)]\tLoss: 0.191577\n",
            "\n",
            "Test set: Accuracy: 431/789 (55%)\n",
            "\n",
            "Train Epoch: 75 [1829/7583 (98%)]\tLoss: 0.357016\n",
            "\n",
            "Test set: Accuracy: 437/789 (55%)\n",
            "\n",
            "Train Epoch: 76 [1829/7583 (98%)]\tLoss: 0.378996\n",
            "\n",
            "Test set: Accuracy: 399/789 (51%)\n",
            "\n",
            "Train Epoch: 77 [1829/7583 (98%)]\tLoss: 0.568071\n",
            "\n",
            "Test set: Accuracy: 428/789 (54%)\n",
            "\n",
            "Train Epoch: 78 [1829/7583 (98%)]\tLoss: 0.647622\n",
            "\n",
            "Test set: Accuracy: 482/789 (61%)\n",
            "\n",
            "Train Epoch: 79 [1829/7583 (98%)]\tLoss: 0.266470\n",
            "\n",
            "Test set: Accuracy: 481/789 (61%)\n",
            "\n",
            "Train Epoch: 80 [1829/7583 (98%)]\tLoss: 0.354425\n",
            "\n",
            "Test set: Accuracy: 532/789 (67%)\n",
            "\n",
            "Train Epoch: 81 [1829/7583 (98%)]\tLoss: 0.307789\n",
            "\n",
            "Test set: Accuracy: 494/789 (63%)\n",
            "\n",
            "Train Epoch: 82 [1829/7583 (98%)]\tLoss: 0.220565\n",
            "\n",
            "Test set: Accuracy: 471/789 (60%)\n",
            "\n",
            "Train Epoch: 83 [1829/7583 (98%)]\tLoss: 0.364337\n",
            "\n",
            "Test set: Accuracy: 452/789 (57%)\n",
            "\n",
            "Train Epoch: 84 [1829/7583 (98%)]\tLoss: 0.593809\n",
            "\n",
            "Test set: Accuracy: 452/789 (57%)\n",
            "\n",
            "Train Epoch: 85 [1829/7583 (98%)]\tLoss: 0.499492\n",
            "\n",
            "Test set: Accuracy: 396/789 (50%)\n",
            "\n",
            "Train Epoch: 86 [1829/7583 (98%)]\tLoss: 0.468949\n",
            "\n",
            "Test set: Accuracy: 475/789 (60%)\n",
            "\n",
            "Train Epoch: 87 [1829/7583 (98%)]\tLoss: 0.358892\n",
            "\n",
            "Test set: Accuracy: 502/789 (64%)\n",
            "\n",
            "Train Epoch: 88 [1829/7583 (98%)]\tLoss: 0.413035\n",
            "\n",
            "Test set: Accuracy: 447/789 (57%)\n",
            "\n",
            "Train Epoch: 89 [1829/7583 (98%)]\tLoss: 0.084403\n",
            "\n",
            "Test set: Accuracy: 402/789 (51%)\n",
            "\n",
            "Train Epoch: 90 [1829/7583 (98%)]\tLoss: 0.501249\n",
            "\n",
            "Test set: Accuracy: 503/789 (64%)\n",
            "\n",
            "Train Epoch: 91 [1829/7583 (98%)]\tLoss: 0.336660\n",
            "\n",
            "Test set: Accuracy: 522/789 (66%)\n",
            "\n",
            "Train Epoch: 92 [1829/7583 (98%)]\tLoss: 0.105165\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 93 [1829/7583 (98%)]\tLoss: 0.606994\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 94 [1829/7583 (98%)]\tLoss: 0.310082\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 95 [1829/7583 (98%)]\tLoss: 0.083241\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 96 [1829/7583 (98%)]\tLoss: 0.052277\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 97 [1829/7583 (98%)]\tLoss: 0.259859\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "Train Epoch: 98 [1829/7583 (98%)]\tLoss: 0.330584\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 99 [1829/7583 (98%)]\tLoss: 0.034591\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "1 round of training complete. Dividing learning rate by 10\n",
            "Train Epoch: 100 [1829/7583 (98%)]\tLoss: 0.077754\n",
            "\n",
            "Test set: Accuracy: 506/789 (64%)\n",
            "\n",
            "Train Epoch: 101 [1829/7583 (98%)]\tLoss: 0.079078\n",
            "\n",
            "Test set: Accuracy: 511/789 (65%)\n",
            "\n",
            "Train Epoch: 102 [1829/7583 (98%)]\tLoss: 0.174537\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 103 [1829/7583 (98%)]\tLoss: 0.022308\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 104 [1829/7583 (98%)]\tLoss: 0.089414\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 105 [1829/7583 (98%)]\tLoss: 0.012431\n",
            "\n",
            "Test set: Accuracy: 509/789 (65%)\n",
            "\n",
            "Train Epoch: 106 [1829/7583 (98%)]\tLoss: 0.105991\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 107 [1829/7583 (98%)]\tLoss: 0.124287\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 108 [1829/7583 (98%)]\tLoss: 0.105252\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 109 [1829/7583 (98%)]\tLoss: 0.430987\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 110 [1829/7583 (98%)]\tLoss: 0.096370\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 111 [1829/7583 (98%)]\tLoss: 0.195198\n",
            "\n",
            "Test set: Accuracy: 534/789 (68%)\n",
            "\n",
            "Train Epoch: 112 [1829/7583 (98%)]\tLoss: 0.020644\n",
            "\n",
            "Test set: Accuracy: 529/789 (67%)\n",
            "\n",
            "Train Epoch: 113 [1829/7583 (98%)]\tLoss: 0.247940\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 114 [1829/7583 (98%)]\tLoss: 0.071044\n",
            "\n",
            "Test set: Accuracy: 511/789 (65%)\n",
            "\n",
            "Train Epoch: 115 [1829/7583 (98%)]\tLoss: 0.102481\n",
            "\n",
            "Test set: Accuracy: 528/789 (67%)\n",
            "\n",
            "Train Epoch: 116 [1829/7583 (98%)]\tLoss: 0.026421\n",
            "\n",
            "Test set: Accuracy: 527/789 (67%)\n",
            "\n",
            "Train Epoch: 117 [1829/7583 (98%)]\tLoss: 0.431285\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 118 [1829/7583 (98%)]\tLoss: 0.299169\n",
            "\n",
            "Test set: Accuracy: 538/789 (68%)\n",
            "\n",
            "Train Epoch: 119 [1829/7583 (98%)]\tLoss: 0.056582\n",
            "\n",
            "Test set: Accuracy: 531/789 (67%)\n",
            "\n",
            "Train Epoch: 120 [1829/7583 (98%)]\tLoss: 0.173989\n",
            "\n",
            "Test set: Accuracy: 531/789 (67%)\n",
            "\n",
            "Train Epoch: 121 [1829/7583 (98%)]\tLoss: 0.273572\n",
            "\n",
            "Test set: Accuracy: 534/789 (68%)\n",
            "\n",
            "Train Epoch: 122 [1829/7583 (98%)]\tLoss: 0.106725\n",
            "\n",
            "Test set: Accuracy: 547/789 (69%)\n",
            "\n",
            "Train Epoch: 123 [1829/7583 (98%)]\tLoss: 0.163126\n",
            "\n",
            "Test set: Accuracy: 534/789 (68%)\n",
            "\n",
            "Train Epoch: 124 [1829/7583 (98%)]\tLoss: 0.050557\n",
            "\n",
            "Test set: Accuracy: 529/789 (67%)\n",
            "\n",
            "Train Epoch: 125 [1829/7583 (98%)]\tLoss: 0.066334\n",
            "\n",
            "Test set: Accuracy: 535/789 (68%)\n",
            "\n",
            "Train Epoch: 126 [1829/7583 (98%)]\tLoss: 0.001856\n",
            "\n",
            "Test set: Accuracy: 537/789 (68%)\n",
            "\n",
            "Train Epoch: 127 [1829/7583 (98%)]\tLoss: 0.067688\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 128 [1829/7583 (98%)]\tLoss: 0.033272\n",
            "\n",
            "Test set: Accuracy: 529/789 (67%)\n",
            "\n",
            "Train Epoch: 129 [1829/7583 (98%)]\tLoss: 0.028104\n",
            "\n",
            "Test set: Accuracy: 534/789 (68%)\n",
            "\n",
            "Train Epoch: 130 [1829/7583 (98%)]\tLoss: 0.031013\n",
            "\n",
            "Test set: Accuracy: 536/789 (68%)\n",
            "\n",
            "Train Epoch: 131 [1829/7583 (98%)]\tLoss: 0.035238\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 132 [1829/7583 (98%)]\tLoss: 0.251244\n",
            "\n",
            "Test set: Accuracy: 542/789 (69%)\n",
            "\n",
            "Train Epoch: 133 [1829/7583 (98%)]\tLoss: 0.012159\n",
            "\n",
            "Test set: Accuracy: 535/789 (68%)\n",
            "\n",
            "Train Epoch: 134 [1829/7583 (98%)]\tLoss: 0.049577\n",
            "\n",
            "Test set: Accuracy: 532/789 (67%)\n",
            "\n",
            "Train Epoch: 135 [1829/7583 (98%)]\tLoss: 0.189921\n",
            "\n",
            "Test set: Accuracy: 520/789 (66%)\n",
            "\n",
            "Train Epoch: 136 [1829/7583 (98%)]\tLoss: 0.065732\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 137 [1829/7583 (98%)]\tLoss: 0.094543\n",
            "\n",
            "Test set: Accuracy: 530/789 (67%)\n",
            "\n",
            "Train Epoch: 138 [1829/7583 (98%)]\tLoss: 0.142159\n",
            "\n",
            "Test set: Accuracy: 522/789 (66%)\n",
            "\n",
            "Train Epoch: 139 [1829/7583 (98%)]\tLoss: 0.246375\n",
            "\n",
            "Test set: Accuracy: 529/789 (67%)\n",
            "\n",
            "Train Epoch: 140 [1829/7583 (98%)]\tLoss: 0.139515\n",
            "\n",
            "Test set: Accuracy: 549/789 (70%)\n",
            "\n",
            "Train Epoch: 141 [1829/7583 (98%)]\tLoss: 0.034778\n",
            "\n",
            "Test set: Accuracy: 535/789 (68%)\n",
            "\n",
            "Train Epoch: 142 [1829/7583 (98%)]\tLoss: 0.026360\n",
            "\n",
            "Test set: Accuracy: 548/789 (69%)\n",
            "\n",
            "Train Epoch: 143 [1829/7583 (98%)]\tLoss: 0.145528\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 144 [1829/7583 (98%)]\tLoss: 0.015024\n",
            "\n",
            "Test set: Accuracy: 537/789 (68%)\n",
            "\n",
            "Train Epoch: 145 [1829/7583 (98%)]\tLoss: 0.139592\n",
            "\n",
            "Test set: Accuracy: 528/789 (67%)\n",
            "\n",
            "Train Epoch: 146 [1829/7583 (98%)]\tLoss: 0.038319\n",
            "\n",
            "Test set: Accuracy: 502/789 (64%)\n",
            "\n",
            "Train Epoch: 147 [1829/7583 (98%)]\tLoss: 0.428179\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 148 [1829/7583 (98%)]\tLoss: 0.098641\n",
            "\n",
            "Test set: Accuracy: 511/789 (65%)\n",
            "\n",
            "Train Epoch: 149 [1829/7583 (98%)]\tLoss: 0.411356\n",
            "\n",
            "Test set: Accuracy: 532/789 (67%)\n",
            "\n",
            "Train Epoch: 150 [1829/7583 (98%)]\tLoss: 0.127797\n",
            "\n",
            "Test set: Accuracy: 512/789 (65%)\n",
            "\n",
            "Train Epoch: 151 [1829/7583 (98%)]\tLoss: 0.050505\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 152 [1829/7583 (98%)]\tLoss: 0.030301\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 153 [1829/7583 (98%)]\tLoss: 0.009782\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 154 [1829/7583 (98%)]\tLoss: 0.046672\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 155 [1829/7583 (98%)]\tLoss: 0.016227\n",
            "\n",
            "Test set: Accuracy: 522/789 (66%)\n",
            "\n",
            "Train Epoch: 156 [1829/7583 (98%)]\tLoss: 0.019981\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 157 [1829/7583 (98%)]\tLoss: 0.045499\n",
            "\n",
            "Test set: Accuracy: 539/789 (68%)\n",
            "\n",
            "Train Epoch: 158 [1829/7583 (98%)]\tLoss: 0.163492\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 159 [1829/7583 (98%)]\tLoss: 0.119080\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 160 [1829/7583 (98%)]\tLoss: 0.213595\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 161 [1829/7583 (98%)]\tLoss: 0.157875\n",
            "\n",
            "Test set: Accuracy: 555/789 (70%)\n",
            "\n",
            "Train Epoch: 162 [1829/7583 (98%)]\tLoss: 0.198597\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 163 [1829/7583 (98%)]\tLoss: 0.023176\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 164 [1829/7583 (98%)]\tLoss: 0.105190\n",
            "\n",
            "Test set: Accuracy: 536/789 (68%)\n",
            "\n",
            "Train Epoch: 165 [1829/7583 (98%)]\tLoss: 0.075404\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 166 [1829/7583 (98%)]\tLoss: 0.160897\n",
            "\n",
            "Test set: Accuracy: 494/789 (63%)\n",
            "\n",
            "Train Epoch: 167 [1829/7583 (98%)]\tLoss: 0.036130\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 168 [1829/7583 (98%)]\tLoss: 0.006655\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 169 [1829/7583 (98%)]\tLoss: 0.102480\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 170 [1829/7583 (98%)]\tLoss: 0.281314\n",
            "\n",
            "Test set: Accuracy: 539/789 (68%)\n",
            "\n",
            "Train Epoch: 171 [1829/7583 (98%)]\tLoss: 0.029030\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 172 [1829/7583 (98%)]\tLoss: 0.112857\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 173 [1829/7583 (98%)]\tLoss: 0.140150\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 174 [1829/7583 (98%)]\tLoss: 0.156556\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 175 [1829/7583 (98%)]\tLoss: 0.064362\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 176 [1829/7583 (98%)]\tLoss: 0.042311\n",
            "\n",
            "Test set: Accuracy: 488/789 (62%)\n",
            "\n",
            "Train Epoch: 177 [1829/7583 (98%)]\tLoss: 0.136779\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 178 [1829/7583 (98%)]\tLoss: 0.026170\n",
            "\n",
            "Test set: Accuracy: 528/789 (67%)\n",
            "\n",
            "Train Epoch: 179 [1829/7583 (98%)]\tLoss: 0.052007\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 180 [1829/7583 (98%)]\tLoss: 0.041007\n",
            "\n",
            "Test set: Accuracy: 536/789 (68%)\n",
            "\n",
            "Train Epoch: 181 [1829/7583 (98%)]\tLoss: 0.315615\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 182 [1829/7583 (98%)]\tLoss: 0.138464\n",
            "\n",
            "Test set: Accuracy: 536/789 (68%)\n",
            "\n",
            "Train Epoch: 183 [1829/7583 (98%)]\tLoss: 0.015979\n",
            "\n",
            "Test set: Accuracy: 512/789 (65%)\n",
            "\n",
            "Train Epoch: 184 [1829/7583 (98%)]\tLoss: 0.072328\n",
            "\n",
            "Test set: Accuracy: 509/789 (65%)\n",
            "\n",
            "Train Epoch: 185 [1829/7583 (98%)]\tLoss: 0.077701\n",
            "\n",
            "Test set: Accuracy: 533/789 (68%)\n",
            "\n",
            "Train Epoch: 186 [1829/7583 (98%)]\tLoss: 0.099895\n",
            "\n",
            "Test set: Accuracy: 532/789 (67%)\n",
            "\n",
            "Train Epoch: 187 [1829/7583 (98%)]\tLoss: 0.052245\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 188 [1829/7583 (98%)]\tLoss: 0.075641\n",
            "\n",
            "Test set: Accuracy: 534/789 (68%)\n",
            "\n",
            "Train Epoch: 189 [1829/7583 (98%)]\tLoss: 0.435774\n",
            "\n",
            "Test set: Accuracy: 494/789 (63%)\n",
            "\n",
            "Train Epoch: 190 [1829/7583 (98%)]\tLoss: 0.285603\n",
            "\n",
            "Test set: Accuracy: 538/789 (68%)\n",
            "\n",
            "Train Epoch: 191 [1829/7583 (98%)]\tLoss: 0.036451\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 192 [1829/7583 (98%)]\tLoss: 0.224665\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 193 [1829/7583 (98%)]\tLoss: 0.010757\n",
            "\n",
            "Test set: Accuracy: 528/789 (67%)\n",
            "\n",
            "Train Epoch: 194 [1829/7583 (98%)]\tLoss: 0.410497\n",
            "\n",
            "Test set: Accuracy: 520/789 (66%)\n",
            "\n",
            "Train Epoch: 195 [1829/7583 (98%)]\tLoss: 0.065865\n",
            "\n",
            "Test set: Accuracy: 527/789 (67%)\n",
            "\n",
            "Train Epoch: 196 [1829/7583 (98%)]\tLoss: 0.157762\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 197 [1829/7583 (98%)]\tLoss: 0.024184\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 198 [1829/7583 (98%)]\tLoss: 0.050220\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 199 [1829/7583 (98%)]\tLoss: 0.062027\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "2 round of training complete. Dividing learning rate by 10\n",
            "Train Epoch: 200 [1829/7583 (98%)]\tLoss: 0.045938\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 201 [1829/7583 (98%)]\tLoss: 0.002168\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "Train Epoch: 202 [1829/7583 (98%)]\tLoss: 0.036326\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 203 [1829/7583 (98%)]\tLoss: 0.056834\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "Train Epoch: 204 [1829/7583 (98%)]\tLoss: 0.011679\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 205 [1829/7583 (98%)]\tLoss: 0.006969\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 206 [1829/7583 (98%)]\tLoss: 0.205847\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 207 [1829/7583 (98%)]\tLoss: 0.424894\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 208 [1829/7583 (98%)]\tLoss: 0.009284\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 209 [1829/7583 (98%)]\tLoss: 0.015651\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 210 [1829/7583 (98%)]\tLoss: 0.015415\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 211 [1829/7583 (98%)]\tLoss: 0.019466\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 212 [1829/7583 (98%)]\tLoss: 0.046673\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 213 [1829/7583 (98%)]\tLoss: 0.239502\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 214 [1829/7583 (98%)]\tLoss: 0.247892\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 215 [1829/7583 (98%)]\tLoss: 0.029843\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 216 [1829/7583 (98%)]\tLoss: 0.180990\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 217 [1829/7583 (98%)]\tLoss: 0.206574\n",
            "\n",
            "Test set: Accuracy: 520/789 (66%)\n",
            "\n",
            "Train Epoch: 218 [1829/7583 (98%)]\tLoss: 0.112507\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 219 [1829/7583 (98%)]\tLoss: 0.032664\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 220 [1829/7583 (98%)]\tLoss: 0.036152\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 221 [1829/7583 (98%)]\tLoss: 0.185718\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 222 [1829/7583 (98%)]\tLoss: 0.109267\n",
            "\n",
            "Test set: Accuracy: 537/789 (68%)\n",
            "\n",
            "Train Epoch: 223 [1829/7583 (98%)]\tLoss: 0.263078\n",
            "\n",
            "Test set: Accuracy: 532/789 (67%)\n",
            "\n",
            "Train Epoch: 224 [1829/7583 (98%)]\tLoss: 0.165529\n",
            "\n",
            "Test set: Accuracy: 528/789 (67%)\n",
            "\n",
            "Train Epoch: 225 [1829/7583 (98%)]\tLoss: 0.227979\n",
            "\n",
            "Test set: Accuracy: 531/789 (67%)\n",
            "\n",
            "Train Epoch: 226 [1829/7583 (98%)]\tLoss: 0.441517\n",
            "\n",
            "Test set: Accuracy: 532/789 (67%)\n",
            "\n",
            "Train Epoch: 227 [1829/7583 (98%)]\tLoss: 0.021954\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 228 [1829/7583 (98%)]\tLoss: 0.462448\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 229 [1829/7583 (98%)]\tLoss: 0.477536\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 230 [1829/7583 (98%)]\tLoss: 0.129124\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "Train Epoch: 231 [1829/7583 (98%)]\tLoss: 0.007764\n",
            "\n",
            "Test set: Accuracy: 509/789 (65%)\n",
            "\n",
            "Train Epoch: 232 [1829/7583 (98%)]\tLoss: 0.051608\n",
            "\n",
            "Test set: Accuracy: 509/789 (65%)\n",
            "\n",
            "Train Epoch: 233 [1829/7583 (98%)]\tLoss: 0.065212\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 234 [1829/7583 (98%)]\tLoss: 0.009194\n",
            "\n",
            "Test set: Accuracy: 530/789 (67%)\n",
            "\n",
            "Train Epoch: 235 [1829/7583 (98%)]\tLoss: 0.048792\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 236 [1829/7583 (98%)]\tLoss: 0.443944\n",
            "\n",
            "Test set: Accuracy: 532/789 (67%)\n",
            "\n",
            "Train Epoch: 237 [1829/7583 (98%)]\tLoss: 0.012404\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 238 [1829/7583 (98%)]\tLoss: 0.029633\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 239 [1829/7583 (98%)]\tLoss: 0.015595\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 240 [1829/7583 (98%)]\tLoss: 0.008988\n",
            "\n",
            "Test set: Accuracy: 510/789 (65%)\n",
            "\n",
            "Train Epoch: 241 [1829/7583 (98%)]\tLoss: 0.279217\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 242 [1829/7583 (98%)]\tLoss: 0.127890\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 243 [1829/7583 (98%)]\tLoss: 0.185591\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 244 [1829/7583 (98%)]\tLoss: 0.003571\n",
            "\n",
            "Test set: Accuracy: 534/789 (68%)\n",
            "\n",
            "Train Epoch: 245 [1829/7583 (98%)]\tLoss: 0.152203\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 246 [1829/7583 (98%)]\tLoss: 0.059544\n",
            "\n",
            "Test set: Accuracy: 510/789 (65%)\n",
            "\n",
            "Train Epoch: 247 [1829/7583 (98%)]\tLoss: 0.227753\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 248 [1829/7583 (98%)]\tLoss: 0.250371\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "Train Epoch: 249 [1829/7583 (98%)]\tLoss: 0.154616\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 250 [1829/7583 (98%)]\tLoss: 0.204352\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 251 [1829/7583 (98%)]\tLoss: 0.009161\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 252 [1829/7583 (98%)]\tLoss: 0.008275\n",
            "\n",
            "Test set: Accuracy: 512/789 (65%)\n",
            "\n",
            "Train Epoch: 253 [1829/7583 (98%)]\tLoss: 0.044350\n",
            "\n",
            "Test set: Accuracy: 522/789 (66%)\n",
            "\n",
            "Train Epoch: 254 [1829/7583 (98%)]\tLoss: 0.051252\n",
            "\n",
            "Test set: Accuracy: 511/789 (65%)\n",
            "\n",
            "Train Epoch: 255 [1829/7583 (98%)]\tLoss: 0.055242\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 256 [1829/7583 (98%)]\tLoss: 0.052480\n",
            "\n",
            "Test set: Accuracy: 509/789 (65%)\n",
            "\n",
            "Train Epoch: 257 [1829/7583 (98%)]\tLoss: 0.023238\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 258 [1829/7583 (98%)]\tLoss: 0.025456\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 259 [1829/7583 (98%)]\tLoss: 0.103843\n",
            "\n",
            "Test set: Accuracy: 512/789 (65%)\n",
            "\n",
            "Train Epoch: 260 [1829/7583 (98%)]\tLoss: 0.009237\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 261 [1829/7583 (98%)]\tLoss: 0.164984\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 262 [1829/7583 (98%)]\tLoss: 0.001461\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 263 [1829/7583 (98%)]\tLoss: 0.511620\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 264 [1829/7583 (98%)]\tLoss: 0.020681\n",
            "\n",
            "Test set: Accuracy: 530/789 (67%)\n",
            "\n",
            "Train Epoch: 265 [1829/7583 (98%)]\tLoss: 0.021737\n",
            "\n",
            "Test set: Accuracy: 530/789 (67%)\n",
            "\n",
            "Train Epoch: 266 [1829/7583 (98%)]\tLoss: 0.272307\n",
            "\n",
            "Test set: Accuracy: 528/789 (67%)\n",
            "\n",
            "Train Epoch: 267 [1829/7583 (98%)]\tLoss: 0.073027\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 268 [1829/7583 (98%)]\tLoss: 0.194825\n",
            "\n",
            "Test set: Accuracy: 530/789 (67%)\n",
            "\n",
            "Train Epoch: 269 [1829/7583 (98%)]\tLoss: 0.017471\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 270 [1829/7583 (98%)]\tLoss: 0.079474\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 271 [1829/7583 (98%)]\tLoss: 0.018235\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 272 [1829/7583 (98%)]\tLoss: 0.058144\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "Train Epoch: 273 [1829/7583 (98%)]\tLoss: 0.010847\n",
            "\n",
            "Test set: Accuracy: 513/789 (65%)\n",
            "\n",
            "Train Epoch: 274 [1829/7583 (98%)]\tLoss: 0.044397\n",
            "\n",
            "Test set: Accuracy: 530/789 (67%)\n",
            "\n",
            "Train Epoch: 275 [1829/7583 (98%)]\tLoss: 0.529941\n",
            "\n",
            "Test set: Accuracy: 534/789 (68%)\n",
            "\n",
            "Train Epoch: 276 [1829/7583 (98%)]\tLoss: 0.010669\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 277 [1829/7583 (98%)]\tLoss: 0.127405\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 278 [1829/7583 (98%)]\tLoss: 0.126957\n",
            "\n",
            "Test set: Accuracy: 523/789 (66%)\n",
            "\n",
            "Train Epoch: 279 [1829/7583 (98%)]\tLoss: 0.005721\n",
            "\n",
            "Test set: Accuracy: 520/789 (66%)\n",
            "\n",
            "Train Epoch: 280 [1829/7583 (98%)]\tLoss: 0.081499\n",
            "\n",
            "Test set: Accuracy: 539/789 (68%)\n",
            "\n",
            "Train Epoch: 281 [1829/7583 (98%)]\tLoss: 0.153089\n",
            "\n",
            "Test set: Accuracy: 525/789 (67%)\n",
            "\n",
            "Train Epoch: 282 [1829/7583 (98%)]\tLoss: 0.042532\n",
            "\n",
            "Test set: Accuracy: 526/789 (67%)\n",
            "\n",
            "Train Epoch: 283 [1829/7583 (98%)]\tLoss: 0.056685\n",
            "\n",
            "Test set: Accuracy: 520/789 (66%)\n",
            "\n",
            "Train Epoch: 284 [1829/7583 (98%)]\tLoss: 0.153711\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 285 [1829/7583 (98%)]\tLoss: 0.009067\n",
            "\n",
            "Test set: Accuracy: 519/789 (66%)\n",
            "\n",
            "Train Epoch: 286 [1829/7583 (98%)]\tLoss: 0.329741\n",
            "\n",
            "Test set: Accuracy: 510/789 (65%)\n",
            "\n",
            "Train Epoch: 287 [1829/7583 (98%)]\tLoss: 0.005462\n",
            "\n",
            "Test set: Accuracy: 508/789 (64%)\n",
            "\n",
            "Train Epoch: 288 [1829/7583 (98%)]\tLoss: 0.006938\n",
            "\n",
            "Test set: Accuracy: 515/789 (65%)\n",
            "\n",
            "Train Epoch: 289 [1829/7583 (98%)]\tLoss: 0.135158\n",
            "\n",
            "Test set: Accuracy: 521/789 (66%)\n",
            "\n",
            "Train Epoch: 290 [1829/7583 (98%)]\tLoss: 0.008804\n",
            "\n",
            "Test set: Accuracy: 518/789 (66%)\n",
            "\n",
            "Train Epoch: 291 [1829/7583 (98%)]\tLoss: 0.097873\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 292 [1829/7583 (98%)]\tLoss: 0.265614\n",
            "\n",
            "Test set: Accuracy: 524/789 (66%)\n",
            "\n",
            "Train Epoch: 293 [1829/7583 (98%)]\tLoss: 0.068003\n",
            "\n",
            "Test set: Accuracy: 511/789 (65%)\n",
            "\n",
            "Train Epoch: 294 [1829/7583 (98%)]\tLoss: 0.053651\n",
            "\n",
            "Test set: Accuracy: 514/789 (65%)\n",
            "\n",
            "Train Epoch: 295 [1829/7583 (98%)]\tLoss: 0.026821\n",
            "\n",
            "Test set: Accuracy: 512/789 (65%)\n",
            "\n",
            "Train Epoch: 296 [1829/7583 (98%)]\tLoss: 0.021220\n",
            "\n",
            "Test set: Accuracy: 506/789 (64%)\n",
            "\n",
            "Train Epoch: 297 [1829/7583 (98%)]\tLoss: 0.156651\n",
            "\n",
            "Test set: Accuracy: 517/789 (66%)\n",
            "\n",
            "Train Epoch: 298 [1829/7583 (98%)]\tLoss: 0.012465\n",
            "\n",
            "Test set: Accuracy: 516/789 (65%)\n",
            "\n",
            "Train Epoch: 299 [1829/7583 (98%)]\tLoss: 0.064234\n",
            "\n",
            "Test set: Accuracy: 510/789 (65%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "CGO74xJR3sCQ",
        "outputId": "808da426-c476-468e-9881-074a9505bf46"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.clf()\n",
        "print(history)\n",
        "y_val = [x[1] for x in history]\n",
        "x_val = [x[0] for x in history]\n",
        "plt.plot(y_val)\n",
        "\n",
        "plt.xlabel('Epoch Number')\n",
        "plt.ylabel('Percentage Accuracy')\n",
        "plt.ylim(0, 100)\n",
        "plt.title('Percentage Accuracy for Deep Learning Model')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
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          "name": "stdout"
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        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hRew-3Ov-w2"
      },
      "source": [
        "# torch.cuda.empty_cache()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXHXNw0L9L6X"
      },
      "source": [
        "torch.save(model.state_dict(), 'deep_net.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlfShW2d9T89"
      },
      "source": [
        "# Loading model\n",
        "# model = to_device(Net(), device)\n",
        "# model.load_state_dict(torch.load('cifar10-cnn.pth'))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}