lab4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Train a Logistic Regression Classifier on the Alzheimer4 MRI slices dataset.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m \n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m \n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import os\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import training Dataset (modify the path of the data)\n",
    "\n",
    "path1 = []\n",
    "path2 = []\n",
    "path3 = []\n",
    "path4 = []\n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/train/NonDemented'):\n",
    "    for filename in filenames:\n",
    "        path1.append(os.path.join(dirname, filename))\n",
    "        \n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/train/VeryMildDemented'):\n",
    "    for filename in filenames:\n",
    "        path2.append(os.path.join(dirname, filename))\n",
    "        \n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/train/MildDemented'):\n",
    "    for filename in filenames:\n",
    "        path3.append(os.path.join(dirname, filename))\n",
    "        \n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/train/ModerateDemented'):\n",
    "    for filename in filenames:\n",
    "        path4.append(os.path.join(dirname, filename))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "\n",
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       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
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       "  background-color: var(--sklearn-color-background);\n",
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       "#sk-container-id-1 div.sk-parallel-item {\n",
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       "\n",
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       "  align-self: flex-start;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
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       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
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       "#sk-container-id-1 div.sk-toggleable {\n",
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       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "  display: block;\n",
       "  width: 100%;\n",
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       "\n",
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       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
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       "  text-align: left;\n",
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       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
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       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
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       "#sk-container-id-1 div.sk-label label {\n",
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       "  font-weight: bold;\n",
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       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
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       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "OneHotEncoder()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One Hot Encoding\n",
    "encoder = OneHotEncoder()\n",
    "encoder.fit([[0],[1],[2],[3]])\n",
    "\n",
    "# 0 --> Non Demented\n",
    "# 1 --> Very Mild Dementia\n",
    "# 2 --> Mild Dementia\n",
    "# 3 --> Moderate Dementia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = []\n",
    "result = []\n",
    "for path in path1:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        train_data.append(np.array(img))\n",
    "        result.append(encoder.transform([[0]]).toarray())\n",
    "        \n",
    "for path in path2:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        train_data.append(np.array(img))\n",
    "        result.append(encoder.transform([[1]]).toarray()) \n",
    "        \n",
    "for path in path3:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        train_data.append(np.array(img))\n",
    "        result.append(encoder.transform([[2]]).toarray())\n",
    "        \n",
    "for path in path4:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        train_data.append(np.array(img))\n",
    "        result.append(encoder.transform([[3]]).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5121, 128, 128)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = np.array(train_data)\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5121, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label = np.array(result)\n",
    "train_label = train_label.reshape((5121,4))\n",
    "train_label.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import test Dataset (you can use the above code, or write better codes)\n",
    "\n",
    "testpath1 = []\n",
    "testpath2 = []\n",
    "testpath3 = []\n",
    "testpath4 = []\n",
    " \n",
    "\n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/test/NonDemented'):\n",
    "    for filename in filenames:\n",
    "        testpath1.append(os.path.join(dirname, filename))\n",
    "        \n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/test/VeryMildDemented'):\n",
    "    for filename in filenames:\n",
    "        testpath2.append(os.path.join(dirname, filename))\n",
    "        \n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/test/MildDemented'):\n",
    "    for filename in filenames:\n",
    "        testpath3.append(os.path.join(dirname, filename))\n",
    "        \n",
    "for dirname, _, filenames in os.walk('../lab2/Alzheimer_s Dataset/test/ModerateDemented'):\n",
    "    for filename in filenames:\n",
    "        testpath4.append(os.path.join(dirname, filename))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = []\n",
    "test_result = []\n",
    "\n",
    "for path in testpath1:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        test_data.append(np.array(img))\n",
    "        test_result.append(encoder.transform([[0]]).toarray())\n",
    "        \n",
    "for path in testpath2:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        test_data.append(np.array(img))\n",
    "        test_result.append(encoder.transform([[1]]).toarray())\n",
    "        \n",
    "for path in testpath3:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        test_data.append(np.array(img))\n",
    "        test_result.append(encoder.transform([[2]]).toarray())\n",
    "        \n",
    "for path in testpath4:\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((128,128))\n",
    "    img = np.array(img)\n",
    "    if(img.shape == (128,128)):\n",
    "        test_data.append(np.array(img))\n",
    "        test_result.append(encoder.transform([[3]]).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1279, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label = np.array(test_result)\n",
    "test_label = test_label.reshape((1279,4))\n",
    "test_label.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show one image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 176)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "a=matplotlib.image.imread(path1[1])\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#If you inspect the first image in the training set, \n",
    "#you will see that the pixel values fall in the range of 0 to 255:\n",
    "plt.figure()\n",
    "plt.imshow(a)\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare training and test data\n",
    "\n",
    "# Shuffle data to avoid learning patterns from order\n",
    "\n",
    "shuffle_index = np.random.permutation(len(train_data))\n",
    "X_train = train_data[shuffle_index]\n",
    "y_train = train_label[shuffle_index]\n",
    "\n",
    "X_test = np.array(test_data)\n",
    "y_test = test_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5121, 128, 128)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 2, 2, 1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert one-hot encoding to label encoding\n",
    "y_train = np.where(y_train==1)[1]\n",
    "y_test = np.where(y_test==1)[1]\n",
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1: Deep Neural Network (DNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonzhang/Documents/Classes/Spring2024/MATH4570/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import Perceptron \n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5121, 128, 128) (5121,)\n",
      "(1279, 128, 128) (1279,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pixel intensity is represented as a byte (0 to 255):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split the full training set into a validation set and a (smaller) training set. We also scale the pixel intensities down to the 0-1 range and convert them to floats, by dividing by 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4121, 128, 128) (1000, 128, 128) (1279, 128, 128)\n",
      "(4121,) (1000,) (1279,)\n"
     ]
    }
   ],
   "source": [
    "n_validation = 1000\n",
    "X_valid, X_train = X_train[:n_validation] / 255., X_train[n_validation:] / 255.\n",
    "y_valid, y_train = y_train[:n_validation], y_train[n_validation:]\n",
    "X_test = X_test / 255.\n",
    "print(X_train.shape, X_valid.shape, X_test.shape)\n",
    "print(y_train.shape, y_valid.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 2, 2, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Build the Neural Network for image classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonzhang/Documents/Classes/Spring2024/MATH4570/.venv/lib/python3.9/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[128, 128]))\n",
    "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
    "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[128, 128]),\n",
    "    keras.layers.Dense(300, activation=\"relu\"),\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Flatten name=flatten, built=True>,\n",
       " <Dense name=dense, built=True>,\n",
       " <Dense name=dense_1, built=True>,\n",
       " <Dense name=dense_2, built=True>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16384</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,915,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">30,100</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16384\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m)            │     \u001b[38;5;34m4,915,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m30,100\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,946,610</span> (18.87 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,946,610\u001b[0m (18.87 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,946,610</span> (18.87 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,946,610\u001b[0m (18.87 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dense'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hidden1 = model.layers[1]\n",
    "hidden1.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_layer(hidden1.name) is hidden1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights, biases = hidden1.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-9.1693038e-03,  3.3549033e-03, -1.8499875e-02, ...,\n",
       "         1.2365663e-02, -1.5821271e-02,  1.0913555e-02],\n",
       "       [ 7.1887374e-03,  6.9646351e-03,  1.7841304e-02, ...,\n",
       "        -1.6086189e-02,  2.2588037e-03,  1.7848222e-02],\n",
       "       [-1.7246293e-02,  1.7573705e-02, -1.5271003e-02, ...,\n",
       "         1.7493365e-02,  1.1193795e-02, -1.3285237e-02],\n",
       "       ...,\n",
       "       [ 8.7507479e-03, -6.9541112e-04,  4.9469080e-03, ...,\n",
       "         1.6834823e-02,  1.3165133e-02,  1.6077755e-02],\n",
       "       [-4.1230591e-03, -1.6507024e-02, -4.8338436e-04, ...,\n",
       "         1.5714472e-02, -9.4591891e-03,  1.1844775e-02],\n",
       "       [ 1.0396885e-02, -6.9737630e-03, -5.2974559e-04, ...,\n",
       "         4.1637570e-05, -9.1103781e-03,  8.4850658e-03]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16384, 300)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biases.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, we've constructed out model. To compile it we use `model.compile`. We will use the Adam optimizer, which automatically sets a different learning schedule for each weight. For more information about Adam, see [Jason Brownlee's excelent post](https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/) or [the original paper of Kingma and Ba](https://arxiv.org/abs/1412.6980). \n",
    "\n",
    "To train the model, use\n",
    "\n",
    "`model.fit(X_train, Y_train,\n",
    "          batch_size=, epochs=20,\n",
    "          verbose=2,\n",
    "          validation_data=(X_test, Y_test))`\n",
    "\n",
    "* `X_train` is the whole set of training data.\n",
    "* `Y_train` is the whole set of label data.\n",
    "* `batch_size` is size of each training minibatch. Remember that 1 is __stochastic gradient decent__ while 60000 (the size of the whole data set) would be __gradient decent__. \n",
    "* `verbose` sets how much information to output during fitting. 0 = silent, 1 = progress bar, 2 = one line per epoch.\n",
    "* `validation_data=()` specifies data to validate on after each training epoch. \n",
    "\n",
    "We will save the output of the training in a variable called `history` for later viewing. \n",
    "\n",
    "https://keras.io/models/model/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"sgd\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is equivalent to:\n",
    "\n",
    "```python\n",
    "model.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.SGD(),\n",
    "              metrics=[keras.metrics.sparse_categorical_accuracy])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Neural Network model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 0.4395 - loss: 1.3133 - val_accuracy: 0.4760 - val_loss: 1.0630\n",
      "Epoch 2/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5227 - loss: 1.0230 - val_accuracy: 0.5150 - val_loss: 1.0023\n",
      "Epoch 3/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5379 - loss: 0.9879 - val_accuracy: 0.4880 - val_loss: 1.0528\n",
      "Epoch 4/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5370 - loss: 0.9762 - val_accuracy: 0.4830 - val_loss: 1.0559\n",
      "Epoch 5/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 0.5509 - loss: 0.9640 - val_accuracy: 0.4840 - val_loss: 1.0817\n",
      "Epoch 6/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5539 - loss: 0.9601 - val_accuracy: 0.5230 - val_loss: 0.9725\n",
      "Epoch 7/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5624 - loss: 0.9389 - val_accuracy: 0.5010 - val_loss: 1.0643\n",
      "Epoch 8/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5613 - loss: 0.9421 - val_accuracy: 0.5370 - val_loss: 0.9454\n",
      "Epoch 9/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5730 - loss: 0.9150 - val_accuracy: 0.5840 - val_loss: 0.8764\n",
      "Epoch 10/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.5925 - loss: 0.8851 - val_accuracy: 0.5760 - val_loss: 0.9396\n",
      "Epoch 11/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.5834 - loss: 0.8890 - val_accuracy: 0.4520 - val_loss: 1.1106\n",
      "Epoch 12/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5804 - loss: 0.9128 - val_accuracy: 0.5780 - val_loss: 0.9306\n",
      "Epoch 13/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.6019 - loss: 0.8648 - val_accuracy: 0.3460 - val_loss: 1.2233\n",
      "Epoch 14/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.5750 - loss: 0.9236 - val_accuracy: 0.4810 - val_loss: 1.0841\n",
      "Epoch 15/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.6015 - loss: 0.8847 - val_accuracy: 0.5650 - val_loss: 0.9543\n",
      "Epoch 16/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.6115 - loss: 0.8475 - val_accuracy: 0.5800 - val_loss: 0.8794\n",
      "Epoch 17/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.6302 - loss: 0.8182 - val_accuracy: 0.5400 - val_loss: 1.1330\n",
      "Epoch 18/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5858 - loss: 0.9071 - val_accuracy: 0.5320 - val_loss: 0.9494\n",
      "Epoch 19/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.6331 - loss: 0.8303 - val_accuracy: 0.4710 - val_loss: 1.0729\n",
      "Epoch 20/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.6254 - loss: 0.8410 - val_accuracy: 0.6000 - val_loss: 0.8780\n",
      "Epoch 21/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.6074 - loss: 0.8488 - val_accuracy: 0.6040 - val_loss: 0.8211\n",
      "Epoch 22/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.6289 - loss: 0.8076 - val_accuracy: 0.4530 - val_loss: 1.1053\n",
      "Epoch 23/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.6375 - loss: 0.8383 - val_accuracy: 0.6090 - val_loss: 0.8047\n",
      "Epoch 24/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.6188 - loss: 0.8196 - val_accuracy: 0.4800 - val_loss: 1.0166\n",
      "Epoch 25/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.6523 - loss: 0.7984 - val_accuracy: 0.6050 - val_loss: 1.2154\n",
      "Epoch 26/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.6369 - loss: 0.8484 - val_accuracy: 0.4960 - val_loss: 1.0424\n",
      "Epoch 27/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.6315 - loss: 0.8183 - val_accuracy: 0.4880 - val_loss: 1.0632\n",
      "Epoch 28/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.6472 - loss: 0.8093 - val_accuracy: 0.5010 - val_loss: 1.0304\n",
      "Epoch 29/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.6445 - loss: 0.7994 - val_accuracy: 0.5200 - val_loss: 1.0021\n",
      "Epoch 30/30\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.6428 - loss: 0.7935 - val_accuracy: 0.5700 - val_loss: 0.9189\n"
     ]
    }
   ],
   "source": [
    "# training the model and saving metrics in history\n",
    "# minibatch\n",
    "history = model.fit(X_train, y_train,\n",
    "          batch_size=128, epochs=30,\n",
    "          verbose=1,\n",
    "          validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.6139 - loss: 0.8674 - val_accuracy: 0.6410 - val_loss: 0.7506\n",
      "Epoch 2/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.6387 - loss: 0.7995 - val_accuracy: 0.6760 - val_loss: 0.7094\n",
      "Epoch 3/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.6559 - loss: 0.7610 - val_accuracy: 0.6440 - val_loss: 0.7282\n",
      "Epoch 4/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.6629 - loss: 0.7450 - val_accuracy: 0.6650 - val_loss: 0.7127\n",
      "Epoch 5/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.6909 - loss: 0.7076 - val_accuracy: 0.6740 - val_loss: 0.7005\n",
      "Epoch 6/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.6923 - loss: 0.6929 - val_accuracy: 0.6700 - val_loss: 0.6774\n",
      "Epoch 7/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.6947 - loss: 0.6833 - val_accuracy: 0.7120 - val_loss: 0.6381\n",
      "Epoch 8/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7156 - loss: 0.6734 - val_accuracy: 0.6200 - val_loss: 0.9855\n",
      "Epoch 9/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.7331 - loss: 0.6533 - val_accuracy: 0.7560 - val_loss: 0.5743\n",
      "Epoch 10/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7322 - loss: 0.6283 - val_accuracy: 0.7660 - val_loss: 0.5607\n",
      "Epoch 11/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.7455 - loss: 0.6017 - val_accuracy: 0.7590 - val_loss: 0.5704\n",
      "Epoch 12/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.7499 - loss: 0.5804 - val_accuracy: 0.7000 - val_loss: 0.6339\n",
      "Epoch 13/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7675 - loss: 0.5472 - val_accuracy: 0.7810 - val_loss: 0.5379\n",
      "Epoch 14/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7571 - loss: 0.5501 - val_accuracy: 0.7640 - val_loss: 0.5935\n",
      "Epoch 15/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7722 - loss: 0.5201 - val_accuracy: 0.7990 - val_loss: 0.4852\n",
      "Epoch 16/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7876 - loss: 0.5195 - val_accuracy: 0.8440 - val_loss: 0.4512\n",
      "Epoch 17/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7891 - loss: 0.4976 - val_accuracy: 0.6870 - val_loss: 1.1138\n",
      "Epoch 18/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8091 - loss: 0.5060 - val_accuracy: 0.8670 - val_loss: 0.4077\n",
      "Epoch 19/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8127 - loss: 0.4744 - val_accuracy: 0.8040 - val_loss: 0.4542\n",
      "Epoch 20/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8436 - loss: 0.4168 - val_accuracy: 0.7530 - val_loss: 0.5259\n",
      "Epoch 21/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8234 - loss: 0.4369 - val_accuracy: 0.6820 - val_loss: 1.0509\n",
      "Epoch 22/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8549 - loss: 0.4063 - val_accuracy: 0.6790 - val_loss: 0.9448\n",
      "Epoch 23/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8490 - loss: 0.4049 - val_accuracy: 0.9030 - val_loss: 0.3370\n",
      "Epoch 24/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8635 - loss: 0.3724 - val_accuracy: 0.6960 - val_loss: 1.7054\n",
      "Epoch 25/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8482 - loss: 0.4695 - val_accuracy: 0.6900 - val_loss: 0.6777\n",
      "Epoch 26/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8112 - loss: 0.5302 - val_accuracy: 0.5560 - val_loss: 0.9366\n",
      "Epoch 27/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8686 - loss: 0.3744 - val_accuracy: 0.7260 - val_loss: 0.5419\n",
      "Epoch 28/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8451 - loss: 0.3959 - val_accuracy: 0.8910 - val_loss: 0.3255\n",
      "Epoch 29/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8348 - loss: 0.4606 - val_accuracy: 0.7100 - val_loss: 1.4278\n",
      "Epoch 30/30\n",
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8406 - loss: 0.4623 - val_accuracy: 0.8830 - val_loss: 0.3188\n"
     ]
    }
   ],
   "source": [
    "#stochastic gradient decent\n",
    "historysgd = model.fit(X_train, y_train, epochs=30,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'verbose': 1, 'epochs': 30, 'steps': 33}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n"
     ]
    }
   ],
   "source": [
    "print(history.epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 1)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(historysgd.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 1)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1279, 128, 128) (1279,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7772 - loss: 0.5261\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.2890703678131104, 0.5887411832809448]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 2: Convolutional Neural Network (CNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building a CNN\n",
    "\n",
    "A convolution layer is defined with    \n",
    "    \n",
    "    `Conv2D(nb_filters, kernel_size,\n",
    "                 padding='valid',\n",
    "                 input_shape=input_shape,\n",
    "                 activation='relu')`\n",
    "                 \n",
    "Where\n",
    "\n",
    "* `nb_filters` number of convolution filters.\n",
    "* `kernel_size` size of each filter, say [5,5] for a $5\\times 5$ filter.\n",
    "* `padding` When we convolve, we tend to lower the image size. We can choose to pad the image back to its original size or not. \n",
    "* `input_shape` shape of the inputed training data, only required for the first layer. \n",
    "* `activation` the activation layer following the convolution layer. \n",
    "\n",
    "For a pooling layer we only specify the pool size:\n",
    "\n",
    "* `MaxPooling2D(pool_size=pool_size)` where `pool_size = [2,2]` down-samples by 2 in each direction. \n",
    "\n",
    "After we down-sample enough, we flatten and feed the network into a dense layer to do the fitting. The final architecture looks like the cartoon from class:\n",
    "\n",
    "<img width= 700 src=\"https://raw.githubusercontent.com/tipthederiver/Math-7243-2020/master/Labs/Lab%205/L12CNN2.PNG\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonzhang/Documents/Classes/Spring2024/MATH4570/.venv/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">122</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">122</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,600</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">59</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">59</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26912</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,444,864</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m122\u001b[0m, \u001b[38;5;34m122\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m1,600\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m59\u001b[0m, \u001b[38;5;34m59\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26912\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m3,444,864\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,457,002</span> (13.19 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,457,002\u001b[0m (13.19 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,457,002</span> (13.19 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,457,002\u001b[0m (13.19 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, MaxPooling2D, Conv2D \n",
    "\n",
    "## Construct Network\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (7,7), padding = 'valid', input_shape=(128,128,1), activation = 'relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Conv2D(32, (3,3), padding = 'valid', activation = 'relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128))\n",
    "model.add(Dense(10))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">122</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">122</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,600</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">59</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">59</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26912</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,444,864</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m122\u001b[0m, \u001b[38;5;34m122\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m1,600\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m59\u001b[0m, \u001b[38;5;34m59\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26912\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m3,444,864\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,457,002</span> (13.19 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,457,002\u001b[0m (13.19 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,457,002</span> (13.19 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,457,002\u001b[0m (13.19 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Construct Network\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (7,7), padding = 'valid', input_shape=(128,128,1), activation = 'relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Conv2D(32, (3,3), padding = 'valid', activation = 'relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128))\n",
    "model.add(Dense(10))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "NUM_LABELS = 4 # 4 classes\n",
    "\n",
    "## We have to add an extra dimension to allow for the multiple images we will be creating\n",
    "X_train = X_train.reshape(-1, 128, 128, 1)\n",
    "X_test = X_test.reshape(-1, 128, 128, 1)\n",
    "\n",
    "Y_train = to_categorical(y_train, NUM_LABELS)\n",
    "Y_test = to_categorical(y_test, NUM_LABELS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">122</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">122</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,600</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">59</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">59</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26912</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,444,864</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">516</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m122\u001b[0m, \u001b[38;5;34m122\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m1,600\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m59\u001b[0m, \u001b[38;5;34m59\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26912\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m3,444,864\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │           \u001b[38;5;34m516\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,456,228</span> (13.18 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,456,228\u001b[0m (13.18 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,456,228</span> (13.18 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,456,228\u001b[0m (13.18 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, (7,7),\n",
    "                 padding='valid',\n",
    "                 input_shape=(128, 128,1),\n",
    "                 activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(32, (3,3),\n",
    "                 activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    " \n",
    "model.add(Flatten())\n",
    "model.add(Dense(units=128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=NUM_LABELS, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4121, 4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 453ms/step - accuracy: 0.4056 - loss: 1.3474 - val_accuracy: 0.5004 - val_loss: 1.0690\n",
      "Epoch 2/5\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 441ms/step - accuracy: 0.5154 - loss: 1.0435 - val_accuracy: 0.5035 - val_loss: 1.0021\n",
      "Epoch 3/5\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 426ms/step - accuracy: 0.5545 - loss: 0.9538 - val_accuracy: 0.5090 - val_loss: 0.9841\n",
      "Epoch 4/5\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 427ms/step - accuracy: 0.5850 - loss: 0.8951 - val_accuracy: 0.5465 - val_loss: 0.9919\n",
      "Epoch 5/5\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 432ms/step - accuracy: 0.5945 - loss: 0.8554 - val_accuracy: 0.5786 - val_loss: 0.9317\n"
     ]
    }
   ],
   "source": [
    "epochs = 5\n",
    "\n",
    "history = model.fit(X_train, \n",
    "                    Y_train, \n",
    "                    epochs=epochs, \n",
    "                    batch_size=128,\n",
    "                    verbose=1,\n",
    "                    validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0.],\n",
       "       [1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 1., 0., 0.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the metrics\n",
    "fig = plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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