study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/3) Feature - Compare experiments - compare experiments within same backend.ipynb

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    "<a href=\"https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/3)%20Feature%20-%20Compare%20experiments%20-%20compare%20experiments%20within%20same%20backend.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    "# Goals\n",
    "\n",
    "### 1. Understand how you can compare experiments on same data\n",
    "\n",
    "### 2. Understand where to look for comparison data - logs, charts, graphs, etc when comparing experiments\n",
    "\n",
    "\n",
    "### 3. Steps\n",
    "    - You will use gluoncv mxnet backend\n",
    "    - Train three classifiers by changing parameter - base mode\n",
    "    - No need to focus on how to change hyper-parameters, will be dealt later\n",
    "    - Compare the three experiments"
   ]
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    "### Why compare experiments\n",
    "\n",
    "    - One usually runs mulitple experiments on the same data by changing hyper-params, base model, etc\n",
    "    \n",
    "    - Hence this compare exxperiments feature helps to see which combination og params works the best for that data"
   ]
  },
  {
   "cell_type": "code",
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    "# Table of Contents\n",
    "\n",
    "\n",
    "## [Install](#0)\n",
    "\n",
    "\n",
    "## [Train a classifier using Resnet18](#1)\n",
    "\n",
    "\n",
    "## [Train a classifier using Densenet121 ](#2)\n",
    "\n",
    "\n",
    "## [Train a classifier using Mobilenet V3](#3)\n",
    "\n",
    "\n",
    "## [Compare experiments](#4)\n",
    "\n",
    "\n",
    "## [Visualize and study comparison metrics](#5)"
   ]
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  {
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   "source": [
    "<a id='0'></a>\n",
    "# Install Monk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using pip (Recommended)\n",
    "\n",
    "  - colab (gpu) \n",
    "      - All bakcends: `pip install -U monk-colab`\n",
    "      \n",
    "\n",
    "  - kaggle (gpu) \n",
    "      - All backends: `pip install -U monk-kaggle`\n",
    "      \n",
    "\n",
    "  - cuda 10.2\t\n",
    "      - All backends: `pip install -U monk-cuda102`\n",
    "      - Gluon bakcned: `pip install -U monk-gluon-cuda102`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda102`\n",
    "      - Keras backend: `pip install -U monk-keras-cuda102`\n",
    "      \n",
    "\n",
    "  - cuda 10.1\t\n",
    "      - All backend: `pip install -U monk-cuda101`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda101`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda101`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda101`\n",
    "      \n",
    "\n",
    "  - cuda 10.0\t\n",
    "      - All backend: `pip install -U monk-cuda100`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda100`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda100`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda100`\n",
    "      \n",
    "\n",
    "  - cuda 9.2\t\n",
    "      - All backend: `pip install -U monk-cuda92`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda92`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda92`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda92`\n",
    "      \n",
    "\n",
    "  - cuda 9.0\t\n",
    "      - All backend: `pip install -U monk-cuda90`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda90`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda90`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda90`\n",
    "      \n",
    "\n",
    "  - cpu \t\t\n",
    "      - All backend: `pip install -U monk-cpu`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cpu`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cpu`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cpu`"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "## Install Monk Manually (Not recommended)\n",
    " \n",
    "### Step 1: Clone the library\n",
    " - git clone https://github.com/Tessellate-Imaging/monk_v1.git\n",
    " \n",
    " \n",
    " \n",
    " \n",
    "### Step 2: Install requirements \n",
    " - Linux\n",
    "     - Cuda 9.0\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu90.txt`\n",
    "     - Cuda 9.2\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu92.txt`\n",
    "     - Cuda 10.0\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu100.txt`\n",
    "     - Cuda 10.1\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu101.txt`\n",
    "     - Cuda 10.2\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu102.txt`\n",
    "     - CPU (Non gpu system)\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cpu.txt`\n",
    " \n",
    " \n",
    "  - Windows\n",
    "     - Cuda 9.0 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu90.txt`\n",
    "     - Cuda 9.2 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu92.txt`\n",
    "     - Cuda 10.0 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu100.txt`\n",
    "     - Cuda 10.1 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu101.txt`\n",
    "     - Cuda 10.2 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu102.txt`\n",
    "     - CPU (Non gpu system)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cpu.txt`\n",
    "         \n",
    "         \n",
    "  - Mac\n",
    "     - CPU (Non gpu system)\n",
    "         - `cd monk_v1/installation/Mac && pip install -r requirements_cpu.txt`\n",
    "         \n",
    "         \n",
    "  - Misc\n",
    "      - Colab (GPU)\n",
    "          - `cd monk_v1/installation/Misc && pip install -r requirements_colab.txt`\n",
    "      - Kaggle (GPU)\n",
    "          - `cd monk_v1/installation/Misc && pip install -r requirements_kaggle.txt`\n",
    " \n",
    " \n",
    " \n",
    "### Step 3: Add to system path (Required for every terminal or kernel run)\n",
    " - `import sys`\n",
    " - `sys.path.append(\"monk_v1/\");`"
   ]
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  {
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   "metadata": {},
   "source": [
    "## Dataset - Malarial cell images\n",
    "    - Credits: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1mMEtGIK8UZNCrErXRJR-kutNTaN1zxjC' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1mMEtGIK8UZNCrErXRJR-kutNTaN1zxjC\" -O malaria_cell.zip && rm -rf /tmp/cookies.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "! unzip -qq malaria_cell.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports \n",
    "\n",
    "    - Using single mxnet-gluoncv backend for this tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using mxnet-gluon backend \n",
    "\n",
    "# When installed using pip\n",
    "from monk.gluon_prototype import prototype\n",
    "\n",
    "\n",
    "# When installed manually (Uncomment the following)\n",
    "#import os\n",
    "#import sys\n",
    "#sys.path.append(\"monk_v1/\");\n",
    "#sys.path.append(\"monk_v1/monk/\");\n",
    "#from monk.gluon_prototype import prototype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a>\n",
    "# Train a classifier using Resnet18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing experiments\n",
    "        - Provide project name\n",
    "        - Provide experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mxnet Version: 1.5.1\n",
      "\n",
      "Experiment Details\n",
      "    Project: Malaria-Cell\n",
      "    Experiment: exp-resnet18\n",
      "    Dir: /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-resnet18/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf = prototype(verbose=1);\n",
    "gtf.Prototype(\"Malaria-Cell\", \"exp-resnet18\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------Malaria-Cell\n",
    "                        |\n",
    "                        |\n",
    "                        |-----exp-resnet18\n",
    "                                    |\n",
    "                                    |-----experiment-state.json\n",
    "                                    |\n",
    "                                    |-----output\n",
    "                                            |\n",
    "                                            |------logs (All training logs and graphs saved here)\n",
    "                                            |\n",
    "                                            |------models (all trained models saved here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Details\n",
      "    Train path:     malaria_cell\n",
      "    Val path:       None\n",
      "    CSV train path: None\n",
      "    CSV val path:   None\n",
      "    Label Type:     single\n",
      "\n",
      "Dataset Params\n",
      "    Input Size:   224\n",
      "    Batch Size:   4\n",
      "    Data Shuffle: True\n",
      "    Processors:   8\n",
      "    Train-val split:   0.7\n",
      "\n",
      "Pre-Composed Train Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Pre-Composed Val Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Dataset Numbers\n",
      "    Num train images: 1411\n",
      "    Num val images:   605\n",
      "    Num classes:      2\n",
      "\n",
      "Model Params\n",
      "    Model name:           resnet18_v2\n",
      "    Use Gpu:              True\n",
      "    Use pretrained:       True\n",
      "    Freeze base network:  True\n",
      "\n",
      "Model Details\n",
      "    Loading pretrained model\n",
      "    Model Loaded on device\n",
      "        Model name:                           resnet18_v2\n",
      "        Num of potentially trainable layers:  40\n",
      "        Num of actual trainable layers:       1\n",
      "\n",
      "Optimizer\n",
      "    Name:          sgd\n",
      "    Learning rate: 0.01\n",
      "    Params:        {'lr': 0.01, 'momentum': 0, 'weight_decay': 0, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n",
      "\n",
      "\n",
      "\n",
      "Learning rate scheduler\n",
      "    Name:   steplr\n",
      "    Params: {'step_size': 1, 'gamma': 0.98, 'last_epoch': -1}\n",
      "\n",
      "Loss\n",
      "    Name:          softmaxcrossentropy\n",
      "    Params:        {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n",
      "\n",
      "Training params\n",
      "    Num Epochs: 5\n",
      "\n",
      "Display params\n",
      "    Display progress:          True\n",
      "    Display progress realtime: True\n",
      "    Save Training logs:        True\n",
      "    Save Intermediate models:  True\n",
      "    Intermediate model prefix: intermediate_model_\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk\n",
      "  warnings.warn(msg)\n",
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "gtf.Default(dataset_path=\"malaria_cell\", \n",
    "            model_name=\"resnet18_v2\", \n",
    "            num_epochs=5);\n",
    "\n",
    "#Read the summary generated once you run this cell. "
   ]
  },
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   "execution_count": 4,
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    {
     "name": "stdout",
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     "text": [
      "Training Start\n",
      "    Epoch 1/5\n",
      "    ----------\n"
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     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.01\n",
      "    [Epoch 1] Train-acc: 0.727, Train-loss: 0.669 | Val-acc: 0.829752, Val-loss: 0.443, | time: 4.0 sec\n",
      "\n",
      "    Epoch 2/5\n",
      "    ----------\n"
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     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.0098\n",
      "    [Epoch 2] Train-acc: 0.787, Train-loss: 0.561 | Val-acc: 0.857851, Val-loss: 0.349, | time: 4.1 sec\n",
      "\n",
      "    Epoch 3/5\n",
      "    ----------\n"
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     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.009604\n",
      "    [Epoch 3] Train-acc: 0.773, Train-loss: 0.584 | Val-acc: 0.844628, Val-loss: 0.405, | time: 3.9 sec\n",
      "\n",
      "    Epoch 4/5\n",
      "    ----------\n"
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     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.009411919999999999\n",
      "    [Epoch 4] Train-acc: 0.787, Train-loss: 0.580 | Val-acc: 0.856198, Val-loss: 0.349, | time: 3.8 sec\n",
      "\n",
      "    Epoch 5/5\n",
      "    ----------\n"
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2127439bb184e00bfc44ebd75d10cfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.009223681599999999\n",
      "    [Epoch 5] Train-acc: 0.784, Train-loss: 0.534 | Val-acc: 0.836364, Val-loss: 0.436, | time: 3.9 sec\n",
      "\n",
      "    Training completed in: 0m 17s\n",
      "    Best val Acc:          0.857851\n",
      "\n",
      "Training End\n",
      "\n",
      "Training Outputs\n",
      "    Model Dir:   /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-resnet18/output/models/\n",
      "    Log Dir:     /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-resnet18/output/logs/\n",
      "    Final model: final\n",
      "    Best model:  best_model\n",
      "    Log 1 - Validation accuracy history log: val_acc_history.npy\n",
      "    Log 2 - Validation loss history log:     val_loss_history.npy\n",
      "    Log 3 - Training accuracy history log:   train_acc_history.npy\n",
      "    Log 4 - Training loss history log:       train_loss_history.npy\n",
      "    Log 5 - Training curve:                  train_loss_history.npy\n",
      "    Log 6 - Validation curve:                train_loss_history.npy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Start Training\n",
    "gtf.Train();\n",
    "\n",
    "#Read the training summary generated once you run the cell and training is completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2'></a>\n",
    "# Train a classifier using Densenet121"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing experiments\n",
    "        - Provide project name\n",
    "        - Provide experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mxnet Version: 1.5.1\n",
      "\n",
      "Experiment Details\n",
      "    Project: Malaria-Cell\n",
      "    Experiment: exp-densenet121\n",
      "    Dir: /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-densenet121/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf = prototype(verbose=1);\n",
    "gtf.Prototype(\"Malaria-Cell\", \"exp-densenet121\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------Malaria-Cell\n",
    "                        |\n",
    "                        |\n",
    "                        |-----exp-densenet121\n",
    "                                    |\n",
    "                                    |-----experiment-state.json\n",
    "                                    |\n",
    "                                    |-----output\n",
    "                                            |\n",
    "                                            |------logs (All training logs and graphs saved here)\n",
    "                                            |\n",
    "                                            |------models (all trained models saved here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Details\n",
      "    Train path:     malaria_cell\n",
      "    Val path:       None\n",
      "    CSV train path: None\n",
      "    CSV val path:   None\n",
      "    Label Type:     single\n",
      "\n",
      "Dataset Params\n",
      "    Input Size:   224\n",
      "    Batch Size:   4\n",
      "    Data Shuffle: True\n",
      "    Processors:   8\n",
      "    Train-val split:   0.7\n",
      "\n",
      "Pre-Composed Train Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Pre-Composed Val Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Dataset Numbers\n",
      "    Num train images: 1411\n",
      "    Num val images:   605\n",
      "    Num classes:      2\n",
      "\n",
      "Model Params\n",
      "    Model name:           densenet121\n",
      "    Use Gpu:              True\n",
      "    Use pretrained:       True\n",
      "    Freeze base network:  True\n",
      "\n",
      "Model Details\n",
      "    Loading pretrained model\n",
      "    Model Loaded on device\n",
      "        Model name:                           densenet121\n",
      "        Num of potentially trainable layers:  242\n",
      "        Num of actual trainable layers:       1\n",
      "\n",
      "Optimizer\n",
      "    Name:          sgd\n",
      "    Learning rate: 0.01\n",
      "    Params:        {'lr': 0.01, 'momentum': 0, 'weight_decay': 0.0001, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n",
      "\n",
      "\n",
      "\n",
      "Learning rate scheduler\n",
      "    Name:   steplr\n",
      "    Params: {'step_size': 1, 'gamma': 0.98, 'last_epoch': -1}\n",
      "\n",
      "Loss\n",
      "    Name:          softmaxcrossentropy\n",
      "    Params:        {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n",
      "\n",
      "Training params\n",
      "    Num Epochs: 5\n",
      "\n",
      "Display params\n",
      "    Display progress:          True\n",
      "    Display progress realtime: True\n",
      "    Save Training logs:        True\n",
      "    Save Intermediate models:  True\n",
      "    Intermediate model prefix: intermediate_model_\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk\n",
      "  warnings.warn(msg)\n",
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "gtf.Default(dataset_path=\"malaria_cell\", \n",
    "            model_name=\"densenet121\", \n",
    "            num_epochs=5);\n",
    "\n",
    "#Read the summary generated once you run this cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Start\n",
      "    Epoch 1/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed9c9ddf21a24015ae04d5e8ef308df4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30a38803ee7c431586e6ac2ef6c361ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.01\n",
      "    [Epoch 1] Train-acc: 0.731, Train-loss: 0.694 | Val-acc: 0.824793, Val-loss: 0.417, | time: 9.2 sec\n",
      "\n",
      "    Epoch 2/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b93aa50525b4b449b357b9c4cddc835",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22b41c68a9124f36bf56a831ba0db3ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.0098\n",
      "    [Epoch 2] Train-acc: 0.782, Train-loss: 0.594 | Val-acc: 0.776860, Val-loss: 0.666, | time: 8.8 sec\n",
      "\n",
      "    Epoch 3/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89bd51370b8247ddba0d929d7f215189",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b81d51cc8a34916b1c2cf9aee61731b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.009604\n",
      "    [Epoch 3] Train-acc: 0.806, Train-loss: 0.581 | Val-acc: 0.849587, Val-loss: 0.386, | time: 8.9 sec\n",
      "\n",
      "    Epoch 4/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac25f8a9f3bf4640b66296ba9686b305",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77ff74ef996f4b01ba8bb9c5486aa982",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.009411919999999999\n",
      "    [Epoch 4] Train-acc: 0.814, Train-loss: 0.535 | Val-acc: 0.804959, Val-loss: 0.540, | time: 8.7 sec\n",
      "\n",
      "    Epoch 5/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37ec5e549f6948c78324c3b3f626b06f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc280c08f6db47779acb5390fadd4aa2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.009223681599999999\n",
      "    [Epoch 5] Train-acc: 0.782, Train-loss: 0.616 | Val-acc: 0.809917, Val-loss: 0.468, | time: 8.7 sec\n",
      "\n",
      "    Training completed in: 0m 41s\n",
      "    Best val Acc:          0.849587\n",
      "\n",
      "Training End\n",
      "\n",
      "Training Outputs\n",
      "    Model Dir:   /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-densenet121/output/models/\n",
      "    Log Dir:     /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-densenet121/output/logs/\n",
      "    Final model: final\n",
      "    Best model:  best_model\n",
      "    Log 1 - Validation accuracy history log: val_acc_history.npy\n",
      "    Log 2 - Validation loss history log:     val_loss_history.npy\n",
      "    Log 3 - Training accuracy history log:   train_acc_history.npy\n",
      "    Log 4 - Training loss history log:       train_loss_history.npy\n",
      "    Log 5 - Training curve:                  train_loss_history.npy\n",
      "    Log 6 - Validation curve:                train_loss_history.npy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Start Training\n",
    "gtf.Train();\n",
    "\n",
    "#Read the training summary generated once you run the cell and training is completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3'></a>\n",
    "# Train a classifier using Mobilenet-v3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing experiments\n",
    "        - Provide project name\n",
    "        - Provide experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mxnet Version: 1.5.1\n",
      "\n",
      "Experiment Details\n",
      "    Project: Malaria-Cell\n",
      "    Experiment: exp-movilenet-v3\n",
      "    Dir: /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-movilenet-v3/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf = prototype(verbose=1);\n",
    "gtf.Prototype(\"Malaria-Cell\", \"exp-movilenet-v3\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------Malaria-Cell\n",
    "                        |\n",
    "                        |\n",
    "                        |-----exp-mobilenet-v3\n",
    "                                    |\n",
    "                                    |-----experiment-state.json\n",
    "                                    |\n",
    "                                    |-----output\n",
    "                                            |\n",
    "                                            |------logs (All training logs and graphs saved here)\n",
    "                                            |\n",
    "                                            |------models (all trained models saved here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Details\n",
      "    Train path:     malaria_cell\n",
      "    Val path:       None\n",
      "    CSV train path: None\n",
      "    CSV val path:   None\n",
      "    Label Type:     single\n",
      "\n",
      "Dataset Params\n",
      "    Input Size:   224\n",
      "    Batch Size:   4\n",
      "    Data Shuffle: True\n",
      "    Processors:   8\n",
      "    Train-val split:   0.7\n",
      "\n",
      "Pre-Composed Train Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Pre-Composed Val Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Dataset Numbers\n",
      "    Num train images: 1411\n",
      "    Num val images:   605\n",
      "    Num classes:      2\n",
      "\n",
      "Model Params\n",
      "    Model name:           mobilenetv3_small\n",
      "    Use Gpu:              True\n",
      "    Use pretrained:       True\n",
      "    Freeze base network:  True\n",
      "\n",
      "Model Details\n",
      "    Loading pretrained model\n",
      "Downloading /home/ubuntu/.mxnet/models/mobilenetv3_small-10430433.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenetv3_small-10430433.zip...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11568/11568 [00:00<00:00, 22616.40KB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Model Loaded on device\n",
      "        Model name:                           mobilenetv3_small\n",
      "        Num of potentially trainable layers:  88\n",
      "        Num of actual trainable layers:       1\n",
      "\n",
      "Optimizer\n",
      "    Name:          sgd\n",
      "    Learning rate: 0.01\n",
      "    Params:        {'lr': 0.01, 'momentum': 0.9, 'weight_decay': 4e-05, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n",
      "\n",
      "\n",
      "\n",
      "Learning rate scheduler\n",
      "    Name:   steplr\n",
      "    Params: {'step_size': 1, 'gamma': 0.97, 'last_epoch': -1}\n",
      "\n",
      "Loss\n",
      "    Name:          softmaxcrossentropy\n",
      "    Params:        {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n",
      "\n",
      "Training params\n",
      "    Num Epochs: 5\n",
      "\n",
      "Display params\n",
      "    Display progress:          True\n",
      "    Display progress realtime: True\n",
      "    Save Training logs:        True\n",
      "    Save Intermediate models:  True\n",
      "    Intermediate model prefix: intermediate_model_\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk\n",
      "  warnings.warn(msg)\n",
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "gtf.Default(dataset_path=\"malaria_cell\", \n",
    "            model_name=\"mobilenetv3_small\", \n",
    "            num_epochs=5);\n",
    "\n",
    "#Read the summary generated once you run this cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Start\n",
      "    Epoch 1/5\n",
      "    ----------\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.01\n",
      "    [Epoch 1] Train-acc: 0.843, Train-loss: 0.383 | Val-acc: 0.904132, Val-loss: 0.321, | time: 32.4 sec\n",
      "\n",
      "    Epoch 2/5\n",
      "    ----------\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.0097\n",
      "    [Epoch 2] Train-acc: 0.894, Train-loss: 0.282 | Val-acc: 0.897521, Val-loss: 0.302, | time: 32.4 sec\n",
      "\n",
      "    Epoch 3/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
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       "model_id": "e95f9b03bb4642f5a6cf7dbcbd48d7c2",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.009409\n",
      "    [Epoch 3] Train-acc: 0.910, Train-loss: 0.298 | Val-acc: 0.880992, Val-loss: 0.381, | time: 32.3 sec\n",
      "\n",
      "    Epoch 4/5\n",
      "    ----------\n"
     ]
    },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.00912673\n",
      "    [Epoch 4] Train-acc: 0.902, Train-loss: 0.291 | Val-acc: 0.909091, Val-loss: 0.310, | time: 32.0 sec\n",
      "\n",
      "    Epoch 5/5\n",
      "    ----------\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.008852928099999999\n",
      "    [Epoch 5] Train-acc: 0.884, Train-loss: 0.321 | Val-acc: 0.871074, Val-loss: 0.329, | time: 32.6 sec\n",
      "\n",
      "    Training completed in: 2m 40s\n",
      "    Best val Acc:          0.909091\n",
      "\n",
      "Training End\n",
      "\n",
      "Training Outputs\n",
      "    Model Dir:   /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-movilenet-v3/output/models/\n",
      "    Log Dir:     /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-movilenet-v3/output/logs/\n",
      "    Final model: final\n",
      "    Best model:  best_model\n",
      "    Log 1 - Validation accuracy history log: val_acc_history.npy\n",
      "    Log 2 - Validation loss history log:     val_loss_history.npy\n",
      "    Log 3 - Training accuracy history log:   train_acc_history.npy\n",
      "    Log 4 - Training loss history log:       train_loss_history.npy\n",
      "    Log 5 - Training curve:                  train_loss_history.npy\n",
      "    Log 6 - Validation curve:                train_loss_history.npy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Start Training\n",
    "gtf.Train();\n",
    "\n",
    "#Read the training summary generated once you run the cell and training is completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4'></a>\n",
    "# Compare experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Invoke the comparison class\n",
    "from monk.compare_prototype import compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing comparison experiments\n",
    "        - Provide project name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison: - Sample-Comparison-1\n"
     ]
    }
   ],
   "source": [
    "# Create a project \n",
    "gtf = compare(verbose=1);\n",
    "gtf.Comparison(\"Sample-Comparison-1\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------comparison\n",
    "                        |\n",
    "                        |\n",
    "                        |-----Sample-Comparison-1\n",
    "                                    |\n",
    "                                    |------stats_best_val_acc.png\n",
    "                                    |------stats_max_gpu_usage.png\n",
    "                                    |------stats_training_time.png\n",
    "                                    |------train_accuracy.png\n",
    "                                    |------train_loss.png\n",
    "                                    |------val_accuracy.png\n",
    "                                    |------val_loss.png\n",
    "                                    \n",
    "                        |\n",
    "                        |-----comparison.csv (Contains necessary details of all experiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the experiments\n",
    "        - First argument - Project name\n",
    "        - Second argument - Experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project - Malaria-Cell, Experiment - exp-resnet18 added\n",
      "Project - Malaria-Cell, Experiment - exp-densenet121 added\n",
      "Project - Malaria-Cell, Experiment - exp-movilenet-v3 added\n"
     ]
    }
   ],
   "source": [
    "gtf.Add_Experiment(\"Malaria-Cell\", \"exp-resnet18\");\n",
    "gtf.Add_Experiment(\"Malaria-Cell\", \"exp-densenet121\");\n",
    "gtf.Add_Experiment(\"Malaria-Cell\", \"exp-movilenet-v3\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating statistics...\n",
      "Generated\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gtf.Generate_Statistics();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5'></a>\n",
    "# Visualize and study comparison metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Accuracy Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-1/train_accuracy.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loss Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-1/train_loss.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation Accuracy Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-1/val_accuracy.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation loss curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-1/val_loss.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training time curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-1/stats_training_time.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max GPU usages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-1/stats_max_gpu_usage.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Validation accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-1/stats_best_val_acc.png\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goals Completed\n",
    "\n",
    "### 1. Understand how you can compare experiments on same data\n",
    "\n",
    "### 2. Understand where to look for comparison data - logs, charts, graphs, etc when comparing experiments\n",
    "\n",
    "\n",
    "### 3. Steps learnt\n",
    "    - Train three classifiers by changing parameter - base mode\n",
    "    - Compare the three experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}