main_CNN_LSTM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Si2Uk1xlJmqZ"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MaroonBlue/Speech_Commands_with_RNNs/blob/main/main_CNN_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Environment setup"
      ],
      "metadata": {
        "id": "Si2Uk1xlJmqZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cloning repository"
      ],
      "metadata": {
        "id": "U3yOj5hKKM2r"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FaE53h2rG-9Y",
        "outputId": "1a59a18e-e905-4e5f-ebe3-00955226c3bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "rm: cannot remove './Speech_Commands_with_RNNs/': No such file or directory\n",
            "Cloning into 'Speech_Commands_with_RNNs'...\n",
            "remote: Enumerating objects: 293, done.\u001b[K\n",
            "remote: Counting objects: 100% (80/80), done.\u001b[K\n",
            "remote: Compressing objects: 100% (51/51), done.\u001b[K\n",
            "remote: Total 293 (delta 44), reused 58 (delta 29), pack-reused 213\u001b[K\n",
            "Receiving objects: 100% (293/293), 1.39 MiB | 9.15 MiB/s, done.\n",
            "Resolving deltas: 100% (165/165), done.\n",
            "/content/Speech_Commands_with_RNNs\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "!rm -r ./Speech_Commands_with_RNNs/\n",
        "!git clone https://github.com/MaroonBlue/Speech_Commands_with_RNNs.git\n",
        "%cd Speech_Commands_with_RNNs/"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installing packages"
      ],
      "metadata": {
        "id": "SC7iDRhkKQ0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt\n",
        "!pip install -U -q tensorflow tensorflow_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5dENBAmHRNl",
        "outputId": "53572e0b-ddb4-4991-cef7-64ee5f8b90ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: patool, pathtools, entrypoint2, EasyProcess, smmap, setproctitle, sentry-sdk, pyunpack, einops, docker-pycreds, colorama, gitdb, GitPython, array-record, wandb, kapre\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Downloading dataset"
      ],
      "metadata": {
        "id": "1wkIdcudKXIa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download form URL"
      ],
      "metadata": {
        "id": "-YZwwC0Bdv_Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python utils/download_dataset.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rew8dJE4HwHs",
        "outputId": "52b67d21-9ad9-452b-c9b6-8bf96b95a0f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-04-24 18:16:19.973780: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-04-24 18:16:21.428617: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Downloading data from https://storage.googleapis.com/kagglesdsdata/competitions/7634/46676/train.7z?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1682509177&Signature=jTadfpVkkZ2N5%2F8ExD7l5lB3sMxRPoteX6WbArpcnr%2BLyqb%2BW9ls4TvgszlpwgKS3KFVe3SuO6EC7jv5W%2FTPB935Vj6FRVZbg97NJlJwEusK2eggn%2FdVZNYEwEVO%2BcfoSZB4HQ3wGuo3oHO5OVVpWSCwgdxOZ%2B8fDjHf9r0oUUvc3EoF4B4pyw7b9jCpjxUG4UoKFNOoWH1ckXwilqK9lMS5paJYfF1Wja0HL2tcRPiHW2m9ooGrzGEL5pYdMYjucuohLTnt%2BJ5qV71CETkbamDsQsZ4GSqlUsK4yeGu1iY2CsJ8AswA%2FL8Zi2qHyi480%2BBzJn1XQJNslKeSA1MDmQ%3D%3D&response-content-disposition=attachment%3B+filename%3Dtrain.7z\n",
            "1121103842/1121103842 [==============================] - 24s 0us/step\n",
            "Moved 6798 files to the validation directory\n",
            "Moved 6835 files to the test directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate and save training and validation data"
      ],
      "metadata": {
        "id": "MQH9lVpL4uUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python ./utils/dataset.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WpOD62u4w_8",
        "outputId": "e6687985-3cdf-4ee2-af1b-442b7c9f9467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-04-24 18:19:06.305697: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-04-24 18:19:08.494141: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Training dataset: 100% 22536/22536 [06:35<00:00, 56.99it/s]\n",
            "Validation dataset: 100% 6798/6798 [01:36<00:00, 70.46it/s]\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Copy .npy files to drive"
      ],
      "metadata": {
        "id": "eSh7yQLu5Cxp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "!cp -R saved_data ../drive/MyDrive/project_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gk23eYax5Gwz",
        "outputId": "0cc6b8fc-72de-4893-d664-169dad6a401c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Copy from drive"
      ],
      "metadata": {
        "id": "aBzzrerY5ffd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/project_data')\n",
        "!cp -R ../content/drive/project_data saved_data"
      ],
      "metadata": {
        "id": "SrhvSdU95hMx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "0f53c1a7-ca6c-4cf4-cd08-6b3458edbeff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-aa0a49a9b693>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/project_data'\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      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cp -R ../content/drive/project_data saved_data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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    102\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    104\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    277\u001b[0m             \u001b[0;34m'https://research.google.com/colaboratory/faq.html#drive-timeout'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         )\n\u001b[0;32m--> 279\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mount failed'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mextra_reason\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    280\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m       \u001b[0;31m# Terminate the DriveFS binary before killing bash.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: mount failed"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Copy from local"
      ],
      "metadata": {
        "id": "RIIqyrd5Y5iq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -R data ../data"
      ],
      "metadata": {
        "id": "DCMQ5JhhY7fM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -R ../data data"
      ],
      "metadata": {
        "id": "jmo99bwjBPDg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp ./saved_data/X_t.npy ../X_t.npy\n",
        "!cp ./saved_data/y_t.npy ../y_t.npy\n",
        "!cp ./saved_data/X_v.npy ../X_v.npy\n",
        "!cp ./saved_data/y_v.npy ../y_v.npy"
      ],
      "metadata": {
        "id": "XMD7PnuBlI5Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp ../X_t.npy ./saved_data/X_t.npy\n",
        "!cp ../y_t.npy ./saved_data/y_t.npy\n",
        "!cp ../X_v.npy ./saved_data/X_v.npy\n",
        "!cp ../y_v.npy ./saved_data/y_v.npy"
      ],
      "metadata": {
        "id": "ZoRf8SnBmdSW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Experiments"
      ],
      "metadata": {
        "id": "hqJHBPmzOLRp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup and imports"
      ],
      "metadata": {
        "id": "FbUY1QNAOOF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd Speech_Commands_with_RNNs/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mo-qe8XvoBte",
        "outputId": "f41d7175-47ef-4b7d-dfce-4638077ab065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Speech_Commands_with_RNNs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import wandb\n",
        "import tensorflow as tf\n",
        "import sys\n",
        "import numpy as np\n",
        "\n",
        "from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler\n",
        "from numpy import load\n",
        "\n",
        "sys.path.append(\"./\")\n",
        "from utils.utils import set_seeds, make_configs, step_decay\n",
        "from models.test_model import get_test_model\n",
        "from models.CNN_LSTM import CNN_LSTM1\n",
        "from models.GRU import GRU1, GRU2, GRU3\n",
        "from models.LSTM import LSTM1, LSTM2, LSTM3\n",
        "from models.simpleRNN import simpleRNN1, simpleRNN2, simpleRNN3\n",
        "\n",
        "import absl.logging\n",
        "absl.logging.set_verbosity(absl.logging.ERROR)"
      ],
      "metadata": {
        "id": "t7bALYwDOI_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configs and data"
      ],
      "metadata": {
        "id": "s2DSa_UlOTYl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ENTITY = 'wo-team'\n",
        "PROJECT = 'dl-rnn-audio'\n",
        "GROUP = 'simple-lstm-gru'\n",
        "NAME = '_'\n",
        "SAVE_PATH = 'weights/'\n",
        "\n",
        "models = {\n",
        "    'CNN_LSTM': CNN_LSTM1,\n",
        "}\n",
        "\n",
        "base_config = {\n",
        "    'dataloader': {\n",
        "        'sample_rate': 8000,\n",
        "        'unknown_silence_samples': 2000,\n",
        "        'seed': 0,\n",
        "        'batch_size': 128,\n",
        "        'convert_to_image': False,\n",
        "    },\n",
        "    'training': {\n",
        "        'n_epochs': 50,\n",
        "        'dropout': 0.3,\n",
        "    },\n",
        "    'compile':{\n",
        "        'loss': 'sparse_categorical_crossentropy',\n",
        "        'optimizer': 'adam',\n",
        "        'metrics': ['accuracy', 'sparse_categorical_accuracy']\n",
        "    },\n",
        "    'model': {\n",
        "        'architecture': 'CNN_LSTM',\n",
        "        'model_init': None,\n",
        "        'id': None,\n",
        "        'save_path': None,\n",
        "    },\n",
        "    'early_stopper':{\n",
        "        'monitor': 'val_sparse_categorical_accuracy',\n",
        "        'min_delta': 0.001,\n",
        "        'patience': 4,\n",
        "        'verbose': 1,\n",
        "        'start_from_epoch': 10,\n",
        "        'restore_best_weights': True,\n",
        "    },\n",
        "    'checkpointer':{\n",
        "        'monitor': 'val_sparse_categorical_accuracy',\n",
        "        \"verbose\": 1,\n",
        "        'save_best_only': True\n",
        "    },\n",
        "    'scheduler': LearningRateScheduler(step_decay),\n",
        "    'other':{\n",
        "            'num_classes':12,\n",
        "    }\n",
        "}\n",
        "\n",
        "combinations = {\n",
        "    'seeds': {\n",
        "        'dict_path': ['dataloader', 'seed'],\n",
        "        'values': [0, 1, 2]\n",
        "    },\n",
        "    'dropout': {\n",
        "        'dict_path': ['training', 'dropout'],\n",
        "        'values': [0.3, 0.5]\n",
        "    }\n",
        "\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "configs = make_configs(base_config, combinations)\n",
        "\n",
        "LOAD_PTH = \"./saved_data/\"\n",
        "X_t = load(LOAD_PTH + \"X_t.npy\")\n",
        "y_t = load(LOAD_PTH + \"y_t.npy\")\n",
        "X_v = load(LOAD_PTH + \"X_v.npy\")\n",
        "y_v = load(LOAD_PTH + \"y_v.npy\")\n",
        "y_t = np.argmax(y_t, axis=1).transpose()\n",
        "y_v = np.argmax(y_v, axis=1).transpose()"
      ],
      "metadata": {
        "id": "rIlwhg_8vTpA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "AeiVR0QXORC2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_config = int(input(\"Provide ID of first config: \"))\n",
        "\n",
        "for i, config in enumerate(configs):\n",
        "    i = i + 54\n",
        "    if i < start_config:\n",
        "        continue\n",
        "\n",
        "    set_seeds(config['dataloader']['seed'])\n",
        "    config['model']['id'] = i\n",
        "    NAME = str(config['model']['id']) + config['model']['architecture'] + \"_seed\" + str(config['dataloader']['seed']) +\"_dropout\"+str(config['training']['dropout']).replace(\".\", \",\")\n",
        "    config['model']['model_init'] = models[config['model']['architecture']]\n",
        "\n",
        "    wandb.init(\n",
        "        project = PROJECT,\n",
        "        entity = ENTITY,\n",
        "        group = GROUP,\n",
        "        name = NAME,\n",
        "        config = config)\n",
        "    \n",
        "    l = len(configs)\n",
        "    print(f\"---------------\\nConfig {i+1}/{l}\\n---------------\\n\\n\")\n",
        "    print('Running config:', config, \"\\n\")\n",
        "\n",
        "    input_shape = X_t.shape[1:]\n",
        "\n",
        "    model = config['model']['model_init'](input_shape=input_shape, output_nodes=12, dropout=config['training']['dropout'])\n",
        "\n",
        "    model.compile(**config[\"compile\"])\n",
        "    earlystopper = EarlyStopping(**config[\"early_stopper\"])\n",
        "    checkpointer = ModelCheckpoint(NAME+'.h5', **config[\"checkpointer\"])\n",
        "    lrate = config[\"scheduler\"]\n",
        "\n",
        "    history = model.fit(\n",
        "                X_t,\n",
        "                y_t,\n",
        "                epochs=config['training']['n_epochs'],\n",
        "                validation_data=(X_v, y_v),\n",
        "                batch_size=config['dataloader']['batch_size'],\n",
        "                shuffle=True,\n",
        "                callbacks=[\n",
        "                    earlystopper, \n",
        "                    checkpointer, \n",
        "                    lrate,\n",
        "                    WandbMetricsLogger(log_freq=5),\n",
        "                    WandbModelCheckpoint(\"weights/wandb\")\n",
        "                ])\n",
        "    save_path = os.path.join(SAVE_PATH, NAME)\n",
        "    model.save(save_path)\n",
        "\n",
        "    wandb.finish()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "oNkpnv67TVgX",
        "outputId": "53723e29-611a-4a2f-d84b-9f1fb835d203",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmartyna-majchrzak19\u001b[0m (\u001b[33mwo-team\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.15.0"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/content/Speech_Commands_with_RNNs/wandb/run-20230424_202418-xokdrqex</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/wo-team/dl-rnn-audio/runs/xokdrqex' target=\"_blank\">54CNN_LSTM_seed0_dropout0,3</a></strong> to <a href='https://wandb.ai/wo-team/dl-rnn-audio' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/wo-team/dl-rnn-audio' target=\"_blank\">https://wandb.ai/wo-team/dl-rnn-audio</a>"
            ],
            "text/plain": [
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          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/wo-team/dl-rnn-audio/runs/xokdrqex' target=\"_blank\">https://wandb.ai/wo-team/dl-rnn-audio/runs/xokdrqex</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        {
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          "name": "stdout",
          "text": [
            "---------------\n",
            "Config 55/6\n",
            "---------------\n",
            "\n",
            "\n",
            "Running config: {'dataloader': {'sample_rate': 8000, 'unknown_silence_samples': 2000, 'seed': 0, 'batch_size': 128, 'convert_to_image': False}, 'training': {'n_epochs': 50, 'dropout': 0.3}, 'compile': {'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam', 'metrics': ['accuracy', 'sparse_categorical_accuracy']}, 'model': {'architecture': 'CNN_LSTM', 'model_init': <function CNN_LSTM1 at 0x7fe9110074c0>, 'id': 54, 'save_path': None}, 'early_stopper': {'monitor': 'val_sparse_categorical_accuracy', 'min_delta': 0.001, 'patience': 4, 'verbose': 1, 'start_from_epoch': 10, 'restore_best_weights': True}, 'checkpointer': {'monitor': 'val_sparse_categorical_accuracy', 'verbose': 1, 'save_best_only': True}, 'scheduler': <keras.callbacks.LearningRateScheduler object at 0x7fe9940cfd00>, 'other': {'num_classes': 12}} \n",
            "\n",
            "Changing learning rate to 0.001\n",
            "Epoch 1/50\n",
            "160/160 [==============================] - ETA: 0s - loss: 1.3057 - accuracy: 0.5559 - sparse_categorical_accuracy: 0.5559\n",
            "Epoch 1: val_sparse_categorical_accuracy improved from -inf to 0.50481, saving model to 54CNN_LSTM_seed0_dropout0,3.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./weights/wandb)... Done. 0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r160/160 [==============================] - 804s 5s/step - loss: 1.3057 - accuracy: 0.5559 - sparse_categorical_accuracy: 0.5559 - val_loss: 1.3875 - val_accuracy: 0.5048 - val_sparse_categorical_accuracy: 0.5048 - lr: 0.0010\n",
            "Changing learning rate to 0.001\n",
            "Epoch 2/50\n",
            "160/160 [==============================] - ETA: 0s - loss: 0.4823 - accuracy: 0.8469 - sparse_categorical_accuracy: 0.8469\n",
            "Epoch 2: val_sparse_categorical_accuracy improved from 0.50481 to 0.70197, saving model to 54CNN_LSTM_seed0_dropout0,3.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./weights/wandb)... Done. 0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r160/160 [==============================] - 746s 5s/step - loss: 0.4823 - accuracy: 0.8469 - sparse_categorical_accuracy: 0.8469 - val_loss: 0.8564 - val_accuracy: 0.7020 - val_sparse_categorical_accuracy: 0.7020 - lr: 0.0010\n",
            "Changing learning rate to 0.001\n",
            "Epoch 3/50\n",
            " 80/160 [==============>...............] - ETA: 5:24 - loss: 0.3686 - accuracy: 0.8824 - sparse_categorical_accuracy: 0.8824"
          ]
        }
      ]
    }
  ]
}