Kannada Character Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Import required libraries</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from fastai.vision import *\n",
    "from fastai.metrics import error_rate\n",
    "from sklearn.metrics import f1_score, classification_report\n",
    "from fastai.basic_data import DatasetType\n",
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check whether GPU is available to perform processing.<br>\n",
    "If the below line outputs <b>False</b>, all processing will happen on the CPU, which is significantly slower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set <b>image size</b> and <b>batch size</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sz = 64\n",
    "bs = 64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Load the images using ImageDataBunch</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ImageDataBunch.from_csv('.', folder='data/', size=sz, bs=bs, \n",
    "                               ds_tfms=get_transforms(do_flip=False, max_zoom=1.)).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ImageDataBunch;\n",
       "\n",
       "Train: LabelList\n",
       "y: CategoryList (13140 items)\n",
       "[Category 338, Category 0, Category 0, Category 0, Category 0]...\n",
       "Path: .\n",
       "x: ImageItemList (13140 items)\n",
       "[Image (3, 900, 1200), Image (3, 900, 1200), Image (3, 900, 1200), Image (3, 900, 1200), Image (3, 900, 1200)]...\n",
       "Path: .;\n",
       "\n",
       "Valid: LabelList\n",
       "y: CategoryList (3285 items)\n",
       "[Category 534, Category 430, Category 444, Category 423, Category 554]...\n",
       "Path: .\n",
       "x: ImageItemList (3285 items)\n",
       "[Image (3, 900, 1200), Image (3, 900, 1200), Image (3, 900, 1200), Image (3, 900, 1200), Image (3, 900, 1200)]...\n",
       "Path: .;\n",
       "\n",
       "Test: None"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Check whether the images have loaded properly</h3> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "Image (3, 900, 1200)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.train_ds.x[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display a batch of images with the applied transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Create a model to begin training.</h1>\n",
    "<ul>\n",
    "    <li>I have used a relatively simple pretrained ResNet18 network.</li>\n",
    "    <li>And set the error metric as '<b>error_rate</b>' which is basically 1 - '<b>accuracy</b>'.</li>\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = create_cnn(data, models.resnet18, metrics=[error_rate]).load('4_resnet18_0.86')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace)\n",
       "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (4): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (7): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): Lambda()\n",
       "    (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25)\n",
       "    (4): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5)\n",
       "    (8): Linear(in_features=512, out_features=657, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using <b>lr_find</b> function to find the optimum learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model with the optimum learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 07:41 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>error_rate</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>4.930855</th>\n",
       "    <th>3.985311</th>\n",
       "    <th>0.846271</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>3.347235</th>\n",
       "    <th>2.914221</th>\n",
       "    <th>0.735464</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit(2, 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model with the optimum learning rate and with <b>cosine annhealing</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 14:27 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>error_rate</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>2.504883</th>\n",
       "    <th>2.843563</th>\n",
       "    <th>0.713546</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.397729</th>\n",
       "    <th>2.096642</th>\n",
       "    <th>0.571385</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>1.795351</th>\n",
       "    <th>1.213723</th>\n",
       "    <th>0.363775</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.413342</th>\n",
       "    <th>1.052895</th>\n",
       "    <th>0.317504</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(4, 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model just in case the <b>error_rate</b> get worse with further training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('1_resnet18_0.65')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfreeze the top layers in order to train them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 07:09 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>error_rate</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>1.423073</th>\n",
       "    <th>0.967676</th>\n",
       "    <th>0.276408</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.982740</th>\n",
       "    <th>0.642702</th>\n",
       "    <th>0.184779</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = 0.001\n",
    "learn.fit_one_cycle(2, [lr/9, lr/3, lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('2_resnet18_0.80')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 07:07 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>error_rate</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>1.126152</th>\n",
       "    <th>0.763488</th>\n",
       "    <th>0.232572</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.761815</th>\n",
       "    <th>0.495910</th>\n",
       "    <th>0.139117</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(2, [lr/9, lr/3, lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('3_resnet18_0.85')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 07:09 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>error_rate</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.667587</th>\n",
       "    <th>0.474281</th>\n",
       "    <th>0.134855</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.638861</th>\n",
       "    <th>0.461255</th>\n",
       "    <th>0.129072</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = 0.0001\n",
    "learn.fit_one_cycle(2, [lr/9, lr/3, lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use <b>cosine annhealing</b> and train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 17:53 <p><table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>error_rate</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.613025</th>\n",
       "    <th>0.460637</th>\n",
       "    <th>0.129072</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.623763</th>\n",
       "    <th>0.448039</th>\n",
       "    <th>0.125419</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.580721</th>\n",
       "    <th>0.431217</th>\n",
       "    <th>0.122374</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.565162</th>\n",
       "    <th>0.413852</th>\n",
       "    <th>0.117199</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.524925</th>\n",
       "    <th>0.418823</th>\n",
       "    <th>0.119939</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, [lr/9, lr/3, lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('4_resnet18_0.86')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Additional Steps</h1>\n",
    "<br>\n",
    "We can also calculate the F1 Score on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds, y = learn.get_preds(ds_type=DatasetType.Valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = preds.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_preds.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8800608828006088"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, y_pred, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         5\n",
      "           1       0.80      0.80      0.80         5\n",
      "           2       1.00      1.00      1.00         4\n",
      "           3       1.00      0.88      0.93         8\n",
      "           4       1.00      1.00      1.00         3\n",
      "           5       1.00      1.00      1.00         4\n",
      "           6       1.00      1.00      1.00         5\n",
      "           7       1.00      1.00      1.00         6\n",
      "           8       0.83      1.00      0.91         5\n",
      "           9       1.00      0.57      0.73         7\n",
      "          10       1.00      0.67      0.80         3\n",
      "          11       0.83      1.00      0.91         5\n",
      "          12       1.00      0.75      0.86         4\n",
      "          13       1.00      1.00      1.00         2\n",
      "          14       1.00      1.00      1.00         1\n",
      "          15       1.00      1.00      1.00         6\n",
      "          16       1.00      0.83      0.91         6\n",
      "          17       0.80      1.00      0.89         4\n",
      "          18       1.00      0.67      0.80         3\n",
      "          19       1.00      1.00      1.00         5\n",
      "          20       0.88      0.78      0.82         9\n",
      "          21       1.00      1.00      1.00         3\n",
      "          22       1.00      1.00      1.00         6\n",
      "          23       1.00      1.00      1.00         3\n",
      "          24       1.00      1.00      1.00         4\n",
      "          25       1.00      1.00      1.00         7\n",
      "          26       0.75      0.75      0.75         4\n",
      "          27       1.00      0.83      0.91         6\n",
      "          28       0.67      0.67      0.67         3\n",
      "          29       0.86      0.75      0.80         8\n",
      "          30       0.80      0.80      0.80         5\n",
      "          31       1.00      1.00      1.00         5\n",
      "          32       1.00      1.00      1.00         4\n",
      "          33       1.00      1.00      1.00         7\n",
      "          34       1.00      1.00      1.00         6\n",
      "          35       0.89      1.00      0.94         8\n",
      "          36       1.00      1.00      1.00         5\n",
      "          37       1.00      0.67      0.80         6\n",
      "          38       1.00      1.00      1.00         4\n",
      "          39       1.00      1.00      1.00         4\n",
      "          40       0.71      1.00      0.83         5\n",
      "          41       1.00      0.75      0.86         8\n",
      "          42       1.00      1.00      1.00         5\n",
      "          43       1.00      1.00      1.00         6\n",
      "          44       1.00      1.00      1.00         5\n",
      "          45       0.80      0.67      0.73         6\n",
      "          46       0.60      0.75      0.67         4\n",
      "          47       1.00      1.00      1.00         4\n",
      "          48       1.00      1.00      1.00         4\n",
      "          49       0.80      1.00      0.89         4\n",
      "          50       0.86      0.86      0.86         7\n",
      "          51       1.00      0.78      0.88         9\n",
      "          52       1.00      1.00      1.00         6\n",
      "          53       1.00      1.00      1.00         7\n",
      "          54       1.00      1.00      1.00         7\n",
      "          55       1.00      1.00      1.00         5\n",
      "          56       1.00      0.90      0.95        10\n",
      "          57       1.00      1.00      1.00         6\n",
      "          58       1.00      1.00      1.00         4\n",
      "          59       0.89      1.00      0.94         8\n",
      "          60       1.00      0.86      0.92         7\n",
      "          61       1.00      1.00      1.00         4\n",
      "          62       0.60      1.00      0.75         3\n",
      "          63       1.00      0.67      0.80         6\n",
      "          64       0.83      1.00      0.91         5\n",
      "          65       0.80      1.00      0.89         4\n",
      "          66       0.67      1.00      0.80         4\n",
      "          67       0.57      1.00      0.73         4\n",
      "          68       1.00      0.75      0.86         8\n",
      "          69       0.86      0.86      0.86         7\n",
      "          70       1.00      1.00      1.00         5\n",
      "          71       0.75      0.86      0.80         7\n",
      "          72       1.00      1.00      1.00         4\n",
      "          73       1.00      1.00      1.00         4\n",
      "          74       1.00      1.00      1.00         1\n",
      "          75       1.00      1.00      1.00         2\n",
      "          76       1.00      0.60      0.75         5\n",
      "          77       1.00      1.00      1.00         3\n",
      "          78       1.00      1.00      1.00         3\n",
      "          79       1.00      0.60      0.75         5\n",
      "          80       0.80      0.67      0.73         6\n",
      "          81       0.33      0.50      0.40         2\n",
      "          82       1.00      1.00      1.00         4\n",
      "          83       1.00      0.83      0.91         6\n",
      "          84       1.00      1.00      1.00         3\n",
      "          85       0.86      1.00      0.92         6\n",
      "          86       1.00      0.88      0.93         8\n",
      "          87       0.33      0.25      0.29         4\n",
      "          88       0.50      0.75      0.60         4\n",
      "          89       1.00      1.00      1.00        11\n",
      "          90       1.00      1.00      1.00         6\n",
      "          91       1.00      0.67      0.80         3\n",
      "          92       0.88      1.00      0.93         7\n",
      "          93       0.50      0.50      0.50         6\n",
      "          94       1.00      0.50      0.67         6\n",
      "          95       0.88      1.00      0.93         7\n",
      "          96       1.00      0.75      0.86         4\n",
      "          97       0.67      1.00      0.80         6\n",
      "          98       1.00      1.00      1.00         6\n",
      "          99       0.83      1.00      0.91         5\n",
      "         100       1.00      0.83      0.91         6\n",
      "         101       0.80      0.67      0.73         6\n",
      "         102       0.50      0.80      0.62         5\n",
      "         103       0.86      1.00      0.92         6\n",
      "         104       1.00      1.00      1.00         4\n",
      "         105       1.00      0.86      0.92         7\n",
      "         106       1.00      1.00      1.00         6\n",
      "         107       1.00      0.75      0.86         4\n",
      "         108       1.00      1.00      1.00         6\n",
      "         109       1.00      1.00      1.00         2\n",
      "         110       0.75      0.75      0.75         4\n",
      "         111       0.80      1.00      0.89         4\n",
      "         112       0.88      1.00      0.93         7\n",
      "         113       0.71      1.00      0.83         5\n",
      "         114       1.00      0.50      0.67         4\n",
      "         115       1.00      1.00      1.00         3\n",
      "         116       1.00      1.00      1.00         4\n",
      "         117       1.00      1.00      1.00        11\n",
      "         118       1.00      1.00      1.00         2\n",
      "         119       0.83      0.83      0.83         6\n",
      "         120       1.00      0.83      0.91         6\n",
      "         121       1.00      1.00      1.00         3\n",
      "         122       1.00      1.00      1.00         7\n",
      "         123       1.00      1.00      1.00         1\n",
      "         124       1.00      1.00      1.00         8\n",
      "         125       1.00      0.83      0.91         6\n",
      "         126       0.75      1.00      0.86         3\n",
      "         127       1.00      1.00      1.00         3\n",
      "         128       1.00      1.00      1.00         7\n",
      "         129       1.00      1.00      1.00         2\n",
      "         130       1.00      0.71      0.83         7\n",
      "         131       0.83      0.71      0.77         7\n",
      "         132       0.88      1.00      0.93         7\n",
      "         133       1.00      1.00      1.00         6\n",
      "         134       1.00      1.00      1.00         5\n",
      "         135       1.00      1.00      1.00         4\n",
      "         136       1.00      1.00      1.00         3\n",
      "         137       1.00      1.00      1.00         3\n",
      "         138       1.00      1.00      1.00         5\n",
      "         139       0.67      0.80      0.73         5\n",
      "         140       1.00      1.00      1.00         4\n",
      "         141       1.00      1.00      1.00         7\n",
      "         142       0.71      0.83      0.77         6\n",
      "         143       0.80      0.80      0.80         5\n",
      "         144       0.75      1.00      0.86         3\n",
      "         145       1.00      0.80      0.89         5\n",
      "         146       1.00      0.67      0.80         3\n",
      "         147       0.80      1.00      0.89         4\n",
      "         148       1.00      0.60      0.75         5\n",
      "         149       1.00      1.00      1.00         6\n",
      "         150       0.67      1.00      0.80         2\n",
      "         151       1.00      1.00      1.00         3\n",
      "         152       1.00      1.00      1.00         4\n",
      "         153       0.67      1.00      0.80         2\n",
      "         154       1.00      1.00      1.00         5\n",
      "         155       1.00      1.00      1.00         1\n",
      "         156       1.00      1.00      1.00         4\n",
      "         157       1.00      1.00      1.00         5\n",
      "         158       1.00      1.00      1.00         6\n",
      "         159       1.00      0.86      0.92         7\n",
      "         160       1.00      0.83      0.91         6\n",
      "         161       1.00      1.00      1.00         3\n",
      "         162       1.00      1.00      1.00         6\n",
      "         163       1.00      1.00      1.00         3\n",
      "         164       0.40      0.29      0.33         7\n",
      "         165       0.29      0.40      0.33         5\n",
      "         166       1.00      1.00      1.00         7\n",
      "         167       1.00      1.00      1.00         9\n",
      "         168       1.00      1.00      1.00         4\n",
      "         169       1.00      0.75      0.86         4\n",
      "         170       0.71      1.00      0.83         5\n",
      "         171       1.00      0.83      0.91         6\n",
      "         172       0.50      0.75      0.60         4\n",
      "         173       0.17      1.00      0.29         1\n",
      "         174       0.00      0.00      0.00         1\n",
      "         175       1.00      0.75      0.86         4\n",
      "         176       0.67      0.50      0.57         4\n",
      "         177       1.00      1.00      1.00         2\n",
      "         178       1.00      0.25      0.40         4\n",
      "         179       0.67      0.29      0.40         7\n",
      "         180       1.00      1.00      1.00         7\n",
      "         181       0.20      1.00      0.33         1\n",
      "         182       1.00      0.56      0.71         9\n",
      "         183       1.00      0.67      0.80         3\n",
      "         184       1.00      1.00      1.00         5\n",
      "         185       1.00      1.00      1.00         6\n",
      "         186       1.00      0.75      0.86         8\n",
      "         187       0.83      1.00      0.91         5\n",
      "         188       1.00      1.00      1.00         4\n",
      "         189       0.80      0.67      0.73         6\n",
      "         190       1.00      0.83      0.91         6\n",
      "         191       1.00      0.75      0.86         4\n",
      "         192       0.75      1.00      0.86         3\n",
      "         193       1.00      1.00      1.00         9\n",
      "         194       1.00      0.86      0.92         7\n",
      "         195       1.00      0.75      0.86         4\n",
      "         196       0.75      1.00      0.86         3\n",
      "         197       1.00      0.80      0.89         5\n",
      "         198       1.00      0.25      0.40         8\n",
      "         199       0.33      1.00      0.50         3\n",
      "         200       1.00      1.00      1.00         4\n",
      "         201       0.80      0.80      0.80         5\n",
      "         202       1.00      1.00      1.00         5\n",
      "         203       1.00      1.00      1.00         6\n",
      "         204       1.00      1.00      1.00         6\n",
      "         205       1.00      0.83      0.91         6\n",
      "         206       1.00      1.00      1.00         4\n",
      "         207       1.00      0.83      0.91         6\n",
      "         208       1.00      1.00      1.00         5\n",
      "         209       1.00      1.00      1.00         3\n",
      "         210       1.00      1.00      1.00         7\n",
      "         211       0.67      1.00      0.80         2\n",
      "         212       1.00      1.00      1.00         5\n",
      "         213       1.00      1.00      1.00         3\n",
      "         214       0.75      1.00      0.86         3\n",
      "         215       1.00      0.71      0.83         7\n",
      "         216       0.83      1.00      0.91         5\n",
      "         217       1.00      1.00      1.00         7\n",
      "         218       1.00      0.86      0.92         7\n",
      "         219       1.00      1.00      1.00         5\n",
      "         220       1.00      1.00      1.00         4\n",
      "         221       0.80      1.00      0.89         4\n",
      "         222       0.67      1.00      0.80         2\n",
      "         223       1.00      1.00      1.00         7\n",
      "         224       1.00      1.00      1.00         3\n",
      "         225       1.00      1.00      1.00         3\n",
      "         226       1.00      1.00      1.00         6\n",
      "         227       0.75      1.00      0.86         3\n",
      "         228       1.00      1.00      1.00         4\n",
      "         229       0.67      0.67      0.67         3\n",
      "         230       0.75      1.00      0.86         3\n",
      "         231       1.00      1.00      1.00         7\n",
      "         232       0.57      0.67      0.62         6\n",
      "         233       0.40      0.67      0.50         3\n",
      "         234       0.80      1.00      0.89         4\n",
      "         235       1.00      1.00      1.00         7\n",
      "         236       1.00      1.00      1.00         5\n",
      "         237       1.00      1.00      1.00         4\n",
      "         238       1.00      0.83      0.91         6\n",
      "         239       1.00      1.00      1.00         6\n",
      "         240       1.00      1.00      1.00         6\n",
      "         241       1.00      1.00      1.00         8\n",
      "         242       0.83      0.83      0.83         6\n",
      "         243       1.00      1.00      1.00         7\n",
      "         244       1.00      0.60      0.75         5\n",
      "         245       1.00      1.00      1.00         4\n",
      "         246       1.00      0.86      0.92         7\n",
      "         247       0.80      1.00      0.89         4\n",
      "         248       1.00      1.00      1.00         7\n",
      "         249       0.75      0.75      0.75         4\n",
      "         250       1.00      0.50      0.67         6\n",
      "         251       1.00      1.00      1.00         4\n",
      "         252       1.00      1.00      1.00         6\n",
      "         253       1.00      1.00      1.00         5\n",
      "         254       1.00      0.86      0.92         7\n",
      "         255       0.80      0.80      0.80         5\n",
      "         256       0.71      1.00      0.83         5\n",
      "         257       0.60      0.86      0.71         7\n",
      "         258       0.75      0.43      0.55         7\n",
      "         259       0.50      1.00      0.67         1\n",
      "         260       1.00      0.75      0.86         4\n",
      "         261       0.86      1.00      0.92         6\n",
      "         262       1.00      1.00      1.00         8\n",
      "         263       0.75      0.60      0.67         5\n",
      "         264       0.83      0.83      0.83         6\n",
      "         265       1.00      1.00      1.00         7\n",
      "         266       0.67      0.67      0.67         6\n",
      "         267       0.50      0.67      0.57         3\n",
      "         268       1.00      1.00      1.00         7\n",
      "         269       1.00      1.00      1.00         2\n",
      "         270       0.83      1.00      0.91         5\n",
      "         271       1.00      1.00      1.00         6\n",
      "         272       1.00      0.50      0.67         2\n",
      "         273       1.00      1.00      1.00         9\n",
      "         274       1.00      1.00      1.00         5\n",
      "         275       1.00      0.91      0.95        11\n",
      "         276       1.00      1.00      1.00         4\n",
      "         277       1.00      0.88      0.93         8\n",
      "         278       1.00      1.00      1.00         8\n",
      "         279       0.88      1.00      0.93         7\n",
      "         280       0.67      0.67      0.67         3\n",
      "         281       0.00      0.00      0.00         0\n",
      "         282       1.00      0.89      0.94         9\n",
      "         283       0.80      0.67      0.73         6\n",
      "         284       0.83      1.00      0.91         5\n",
      "         285       1.00      1.00      1.00         3\n",
      "         286       0.86      1.00      0.92         6\n",
      "         287       1.00      1.00      1.00         5\n",
      "         288       1.00      0.75      0.86         4\n",
      "         289       1.00      1.00      1.00         3\n",
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      "         642       1.00      1.00      1.00         4\n",
      "         643       0.75      1.00      0.86         3\n",
      "         644       1.00      1.00      1.00         5\n",
      "         645       0.00      0.00      0.00         1\n",
      "         646       0.25      0.25      0.25         4\n",
      "         647       0.80      1.00      0.89         4\n",
      "         648       1.00      1.00      1.00         6\n",
      "         649       1.00      1.00      1.00         4\n",
      "         650       0.83      0.83      0.83         6\n",
      "         651       1.00      1.00      1.00         5\n",
      "         652       0.71      1.00      0.83         5\n",
      "         653       1.00      0.88      0.93         8\n",
      "         654       0.83      1.00      0.91         5\n",
      "         655       1.00      0.80      0.89         5\n",
      "         656       1.00      1.00      1.00         6\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      3285\n",
      "   macro avg       0.88      0.88      0.87      3285\n",
      "weighted avg       0.90      0.88      0.88      3285\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, y_pred))"
   ]
  }
 ],
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