CNN_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9259d8c5",
   "metadata": {},
   "source": [
    "<div style=\"display: flex; background-color: RGB(220, 102, 160);\" >\n",
    "<h1 style=\"margin: auto; padding: 30px; \">PROJECT CNN NUMBERS</h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a2b0ce7",
   "metadata": {},
   "source": [
    "Context : numbers to predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5e01df",
   "metadata": {},
   "source": [
    "<div style=\"background-color: RGB(165, 125, 186);\" >\n",
    "<h2 style=\"margin: auto; padding: 20px; color:#fff; \"> Data and Libraries Importation</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e65b0d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d68130c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-10 06:49:13.977280: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-05-10 06:49:14.711823: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-05-10 06:49:14.711893: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-05-10 06:49:14.801994: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-05-10 06:49:17.034760: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-05-10 06:49:17.035149: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-05-10 06:49:17.035197: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense\n",
    "\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from keras.utils.np_utils import to_categorical\n",
    "# from keras.preprocessing.image import ImageDataGenerator # the good one!!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "771981d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/train.csv\")\n",
    "train = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "812a6a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"data/test.csv\")\n",
    "test = df2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b122004",
   "metadata": {},
   "source": [
    "<div style=\"background-color: RGB(165, 125, 186);\" >\n",
    "<h2 style=\"margin: auto; padding: 20px; color:#fff; \">Dataset Exploration </h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bdb07043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 785)\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42000 entries, 0 to 41999\n",
      "Columns: 785 entries, label to pixel783\n",
      "dtypes: int64(785)\n",
      "memory usage: 251.5 MB\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>label</th>\n",
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       "<p>5 rows × 785 columns</p>\n",
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      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print('')\n",
    "train.info()\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9309d624",
   "metadata": {},
   "source": [
    "❓ La target est le label?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3bedba96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label       int64\n",
       "pixel0      int64\n",
       "pixel1      int64\n",
       "pixel2      int64\n",
       "pixel3      int64\n",
       "            ...  \n",
       "pixel779    int64\n",
       "pixel780    int64\n",
       "pixel781    int64\n",
       "pixel782    int64\n",
       "pixel783    int64\n",
       "Length: 785, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657b08bc",
   "metadata": {},
   "source": [
    "<div style=\"background-color: RGB(165, 125, 186);\" >\n",
    "<h2 style=\"margin: auto; padding: 20px; color:#fff; \">Model baseline</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0507bc8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/indi/.pyenv/versions/3.8.12/envs/simplon/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IS THE DATASET REPRESANTATIVE?\n",
    "\n",
    "numbers_count = sns.countplot(train.label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8572331b",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "We have similar counts for them all.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d9674d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separation Target\n",
    "Y = train[\"label\"]\n",
    "\n",
    "# Drop 'label' column\n",
    "X = train.drop(labels = [\"label\"],axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b01b324d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        1\n",
       "3        4\n",
       "4        0\n",
       "        ..\n",
       "41995    0\n",
       "41996    1\n",
       "41997    7\n",
       "41998    6\n",
       "41999    9\n",
       "Name: label, Length: 42000, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verifiaction\n",
    "\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd88aaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       784\n",
       "unique        1\n",
       "top       False\n",
       "freq        784\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data\n",
    "X.isnull().any().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "043cc4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.dtypes\n",
    "#Not Y since boolean??? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1df1155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "41995    False\n",
       "41996    False\n",
       "41997    False\n",
       "41998    False\n",
       "41999    False\n",
       "Name: label, Length: 42000, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.isnull()\n",
    "# Ok it's just normal..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e0a50a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "There is no missing values in the train dataset. So we can safely go ahead.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c769f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# observation feature \n",
    "obs  = np.array(X)\n",
    "img= obs[4] #  selects the 5th image in the dataset and assigns it to the variable img.\n",
    "img = img.reshape((28,28))\n",
    "#reshapes the 1D array representing the image into a 2D array with shape (28,28). \n",
    "#This is necessary because the images in the dataset are originally flattened into 1D arrays of length 784.\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22bf325f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb41b52",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "*Normalisation*\n",
    "    \n",
    "Normalisation is done to reduce the scale of the input values. The pixel value ranges from 0 to 255 which specify gradient of gray. The CNN will converge more faster on values 0 to 1 than 0 to 255. So we divide every value by 255 to scale the data from [0..255] to [0..1]. It helps the model to better learning of features by decreasing computational complexities if we have data that scales bigger.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "017da905",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data (to reduce the effect of illumination's differences and CNN converg faster on [0..1] data than on [0..255])\n",
    "\n",
    "# X_train = X / 255.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ad039f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Reshape\n",
    "The array of pixel values are reshaped into a (28,28,1) matrix. We are feeding the CNN model with input_shape of 28x28x1 matrix.\n",
    "    \n",
    "Train and test images (28px x 28px) has been stock into pandas.Dataframe as 1D vectors of 784 values. We reshape all data to 28x28x1 3D matrices.\n",
    "\n",
    "Keras requires an extra dimension in the end which correspond to channels. MNIST images are gray scaled so it use only one channel. For RGB images, there is 3 channels, we would have reshaped 784px vectors to 28x28x3 3D matrices.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51e7ae8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "60a23fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\n",
    "\n",
    "# X_train = X_train.values.reshape(-1,28,28,1)\n",
    "# test = test.values.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8781f3a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Label Encoding\n",
    "\n",
    "Since the CNN model will give results in a vector of predictions for each classes. The label (numbers) are encoded into hot vector for prediction by the model. So that we can train the CNN with the encoded outputs and the parameters are tuned accordingly\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f321bd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The .values attribute is used to convert the Pandas DataFrame \\nto a NumPy array, and the reshape function is used to perform the \\nreshaping operation. The data is then normalized by dividing all \\npixel values by 255.0 to scale them to the range [0, 1]. \\nThis is a common practice in deep learning to ensure that the inputs are \\nin a similar range, which makes the training process more stable and \\nefficient.'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split training and val\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X,Y,test_size=0.33,random_state=42)\n",
    "\n",
    "# reshape et normalisation  des data\n",
    "X_train = ((X_train).values.reshape((-1,28,28,1)))/255.0\n",
    "X_val = ((X_val).values.reshape((-1 ,28,28,1)))/255.0\n",
    "\n",
    "'''The .values attribute is used to convert the Pandas DataFrame \n",
    "to a NumPy array, and the reshape function is used to perform the \n",
    "reshaping operation. The data is then normalized by dividing all \n",
    "pixel values by 255.0 to scale them to the range [0, 1]. \n",
    "This is a common practice in deep learning to ensure that the inputs are \n",
    "in a similar range, which makes the training process more stable and \n",
    "efficient.'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b1cac2b",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"> USE  ONE HOT OR NOT??? </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df90a572",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y_train = to_categorical(Y_train, num_classes = 10)\n",
    "\n",
    "#To enable label into hot vector. For Eg.7 -> [0,0,0,0,0,0,0,1,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62eba7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x450 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PREVIEW IMAGES\n",
    "plt.figure(figsize=(15,4.5))\n",
    "for i in range(30):  \n",
    "    plt.subplot(3, 10, i+1)\n",
    "    plt.imshow(X_train[i].reshape((28,28)),cmap=plt.cm.binary)\n",
    "    plt.axis('off')\n",
    "plt.subplots_adjust(wspace=-0.1, hspace=-0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "81a86173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28140,)\n",
      "(13860,)\n",
      "(28140, 28, 28, 1)\n",
      "(13860, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(Y_train.shape)\n",
    "print(Y_val.shape)\n",
    "print(X_train.shape)\n",
    "print(X_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "de6c40ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL 1\n",
    "\n",
    "model = Sequential()\n",
    "# initializes an empty model and sets it to the variable model\n",
    "\n",
    "model.add(Conv2D(8, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "# adds a 2D convolutional layer to the model with 8 filters of size 3x3\n",
    "# the input_shape parameter specifies the shape of the input images.\n",
    "\n",
    "model.add(MaxPool2D((2, 2)))\n",
    "#  adds a 2D max pooling layer to the model with a pool size of 2x2\n",
    "\n",
    "model.add(Conv2D(16, (3, 3), activation='relu'))\n",
    "# adds another 2D convolutional layer to the model with 16 filters of size 3x3 and the ReLU activation function\n",
    "\n",
    "model.add(MaxPool2D((2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "model.add(Flatten())\n",
    "# flattens the output of the previous layer into a 1D vector.\n",
    "\n",
    "model.add(Dense(128, activation='relu'))\n",
    "# adds a fully connected (dense) layer to the model with 128 units and the ReLU activation function.\n",
    "\n",
    "model.add(Dense(len(train.label.unique()), activation='softmax'))\n",
    "# adds another dense layer to the model with a number of units equal to the number of unique labels in the training data. The softmax activation function is used to ensure that the output of the model can be interpreted as probabilities over the different classes\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6892466",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"> This CNN model consists of three convolutional layers, followed by max pooling layers, a flattening layer, and two fully connected (dense) layers. It's a relatively simple architecture that can be effective for image classification tasks. </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f3cf1ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c2a460d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 8)         80        \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 8)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 16)        1168      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 16)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 3, 3, 32)          4640      \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 288)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               36992     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 44,170\n",
      "Trainable params: 44,170\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cea522bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=280x201>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "display-im6.q16: unable to open X server `' @ error/display.c/DisplayImageCommand/412.\n"
     ]
    }
   ],
   "source": [
    "import visualkeras\n",
    "\n",
    "\n",
    "visualkeras.layered_view(model,legend=True).show() # display using your system viewer\n",
    "visualkeras.layered_view(model,legend=True, to_file='output.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0741b30e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "880/880 [==============================] - 15s 16ms/step - loss: 0.3019 - accuracy: 0.9061 - val_loss: 0.0958 - val_accuracy: 0.9689\n",
      "Epoch 2/10\n",
      "880/880 [==============================] - 12s 13ms/step - loss: 0.0895 - accuracy: 0.9727 - val_loss: 0.0742 - val_accuracy: 0.9762\n",
      "Epoch 3/10\n",
      "880/880 [==============================] - 16s 18ms/step - loss: 0.0638 - accuracy: 0.9798 - val_loss: 0.0729 - val_accuracy: 0.9763\n",
      "Epoch 4/10\n",
      "880/880 [==============================] - 11s 12ms/step - loss: 0.0500 - accuracy: 0.9837 - val_loss: 0.0495 - val_accuracy: 0.9845\n",
      "Epoch 5/10\n",
      "880/880 [==============================] - 10s 12ms/step - loss: 0.0424 - accuracy: 0.9863 - val_loss: 0.0493 - val_accuracy: 0.9840\n",
      "Epoch 6/10\n",
      "880/880 [==============================] - 11s 12ms/step - loss: 0.0341 - accuracy: 0.9885 - val_loss: 0.0494 - val_accuracy: 0.9850\n",
      "Epoch 7/10\n",
      "880/880 [==============================] - 12s 13ms/step - loss: 0.0296 - accuracy: 0.9900 - val_loss: 0.0589 - val_accuracy: 0.9814\n",
      "Epoch 8/10\n",
      "880/880 [==============================] - 12s 14ms/step - loss: 0.0239 - accuracy: 0.9926 - val_loss: 0.0496 - val_accuracy: 0.9866\n",
      "Epoch 9/10\n",
      "880/880 [==============================] - 13s 15ms/step - loss: 0.0205 - accuracy: 0.9937 - val_loss: 0.0430 - val_accuracy: 0.9876\n",
      "Epoch 10/10\n",
      "880/880 [==============================] - 16s 18ms/step - loss: 0.0183 - accuracy: 0.9941 - val_loss: 0.0466 - val_accuracy: 0.9877\n"
     ]
    }
   ],
   "source": [
    "# train neural network and have it automatically stop on 95% accuracy\n",
    "history = model.fit(X_train, Y_train, epochs=10,\n",
    "                    validation_data=(X_val, Y_val),\n",
    "                    batch_size=32, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d032f53f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training vs validation accruacy\n",
    "history_frame = pd.DataFrame(history.history)\n",
    "#The pd.DataFrame function is used to convert this dictionary to a pandas dataframe. \n",
    "\n",
    "history_frame.loc[:, ['loss', 'val_loss']].plot()\n",
    "history_frame.loc[:, ['accuracy', 'val_accuracy']].plot()\n",
    "# The loc function is used to select only the loss values for both training and validation sets. \n",
    "\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.legend(['training', 'validation'], loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a53ac4",
   "metadata": {},
   "source": [
    "The training and validation accuracy graphs show that the model is learning and improving over time, with the training accuracy increasing to around 98% and the validation accuracy plateauing at around 98%.\n",
    "\n",
    "This suggests that the model is not overfitting the training data, as the validation accuracy is not significantly lower than the training accuracy.\n",
    "\n",
    "The loss graphs show that the model is also improving in terms of minimizing the loss function. The training and validation loss both decrease over the 2 epochs, indicating that the model is learning to make better predictions.\n",
    "\n",
    "Overall, this learning curve suggests that the CNN model is performing well and learning effectively, achieving high accuracy and low loss on both the training and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ae659b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Loss: 0.0183\n",
      "Minimum Validation Loss: 0.0430\n",
      "Accuracy: 0.9061\n",
      "Validation Accuracy: 0.9689\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum Loss: {:0.4f}\".format(history_frame['loss'].min()));\n",
    "print(\"Minimum Validation Loss: {:0.4f}\".format(history_frame['val_loss'].min()));\n",
    "print(\"Accuracy: {:0.4f}\".format(history_frame['accuracy'].min()));\n",
    "print(\"Validation Accuracy: {:0.4f}\".format(history_frame['val_accuracy'].min()));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9b9f7d",
   "metadata": {},
   "source": [
    "Our model achieved a minimum loss of 0.0196 and a minimum validation loss of 0.0512, indicating that it is performing well in terms of minimizing error. Additionally, our model achieved an accuracy of 0.9060 on the training set and a validation accuracy of 0.9665 on the test set, which means that it is able to accurately predict the classes of new data. These results demonstrate that our model is robust and reliable, and can be used with confidence in real-world applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c8da32e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOT CONFUSION MATRIX??? SUCH A GOOD IDEA!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f4c80366",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # CONFUSION MATRIX\n",
    "\n",
    "# val_pred = model.predict(X_val)\n",
    "# # generate predictions for the validation data (X_val), which is a set of images that the model has not seen during training\n",
    "# val_predicted_labels = np.argmax(val_pred, axis=1)\n",
    "# #  argmax() function to obtain the index of the class with the highest probability,\n",
    "# #  The axis=1 argument specifies that the function should find the index of the maximum value in each row (i.e., for each image in X_val), which corresponds to the predicted class label.\n",
    "# val_true_labels = Y_val\n",
    "# # assigns the true class labels (Y_val) to a new variable (val_true_labels) \n",
    "# cm = confusion_matrix(val_true_labels, val_predicted_labels)\n",
    "# # The function takes the true class labels (val_true_labels) and predicted class labels (val_predicted_labels) as input, and outputs a matrix that shows the number of true positives, false positives, true negatives, and false negatives for each class.\n",
    "# cmd = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "# # display\n",
    "# cmd.plot(include_values=True, cmap='YlGnBu', ax=None, xticks_rotation='horizontal')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4ddb1bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "434/434 [==============================] - 3s 6ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CONFUSION MATRIX\n",
    "\n",
    "Y_pred  = model.predict(X_val)\n",
    "# generate predictions for the validation data (X_val), which is a set of images that the model has not seen during training\n",
    "Y_pred_classes  = np.argmax(Y_pred, axis=1)\n",
    "#  argmax() function to obtain the index of the class with the highest probability,\n",
    "#  The axis=1 argument specifies that the function should find the index of the maximum value in each row (i.e., for each image in X_val), which corresponds to the predicted class label.\n",
    "Y_true  = Y_val\n",
    "# assigns the true class labels (Y_val) to a new variable (val_true_labels) \n",
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "# The function takes the true class labels (val_true_labels) and predicted class labels (val_predicted_labels) as input, and outputs a matrix that shows the number of true positives, false positives, true negatives, and false negatives for each class.\n",
    "cmd = ConfusionMatrixDisplay(confusion_matrix=confusion_mtx)\n",
    "cmd.plot(include_values=True, cmap='YlGnBu', ax=None, xticks_rotation='horizontal')\n",
    "plt.show()\n",
    "# plot_confusion_matrix(confusion_mtx, classes = range(10)) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3efce5",
   "metadata": {},
   "source": [
    "We can see that 0 is mistaken 13 times for 6. \n",
    "The diagonal line is true positives and true negatives (TP and TN).\n",
    "The cell at (0,1) shows the number of false positives (FP) (rows)\n",
    "where the model incorrectly predicted a positive case when the actual label was negative\n",
    "\n",
    "The cell at (1,0) shows the number of false negatives (FN) (columns)\n",
    "where the model incorrectly predicted a negative case when the actual label was positive"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c2db74",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"> UPDATE COMMENT!!! MODEL IMPROVED!!! </div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ee48ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model: pickle ou h5\n",
    "\n",
    "# import pickle\n",
    "\n",
    "# with open('../model.pickle', 'wb') as f:\n",
    "#     pickle.dump(model, f)\n",
    "#     f.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "67883a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('model_Indi_2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70662e2",
   "metadata": {},
   "source": [
    "<div style=\"background-color: RGB(165, 125, 186);\" >\n",
    "<h2 style=\"margin: auto; padding: 20px; color:#fff; \">Model TEST DATASET</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3243771b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.array(pd.read_csv('data/test.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "54454623",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_pd_df\n",
    "\n",
    "# df2 = pd.read_csv(\"data/test.csv\")\n",
    "# test = df2.copy("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d833abcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28000, 784)\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28000 entries, 0 to 27999\n",
      "Columns: 784 entries, pixel0 to pixel783\n",
      "dtypes: int64(784)\n",
      "memory usage: 167.5 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0        0       0       0       0       0       0       0       0       0   \n",
       "1        0       0       0       0       0       0       0       0       0   \n",
       "2        0       0       0       0       0       0       0       0       0   \n",
       "3        0       0       0       0       0       0       0       0       0   \n",
       "4        0       0       0       0       0       0       0       0       0   \n",
       "5        0       0       0       0       0       0       0       0       0   \n",
       "6        0       0       0       0       0       0       0       0       0   \n",
       "7        0       0       0       0       0       0       0       0       0   \n",
       "8        0       0       0       0       0       0       0       0       0   \n",
       "9        0       0       0       0       0       0       0       0       0   \n",
       "10       0       0       0       0       0       0       0       0       0   \n",
       "11       0       0       0       0       0       0       0       0       0   \n",
       "12       0       0       0       0       0       0       0       0       0   \n",
       "13       0       0       0       0       0       0       0       0       0   \n",
       "14       0       0       0       0       0       0       0       0       0   \n",
       "15       0       0       0       0       0       0       0       0       0   \n",
       "16       0       0       0       0       0       0       0       0       0   \n",
       "17       0       0       0       0       0       0       0       0       0   \n",
       "18       0       0       0       0       0       0       0       0       0   \n",
       "19       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "    pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0        0  ...         0         0         0         0         0         0   \n",
       "1        0  ...         0         0         0         0         0         0   \n",
       "2        0  ...         0         0         0         0         0         0   \n",
       "3        0  ...         0         0         0         0         0         0   \n",
       "4        0  ...         0         0         0         0         0         0   \n",
       "5        0  ...         0         0         0         0         0         0   \n",
       "6        0  ...         0         0         0         0         0         0   \n",
       "7        0  ...         0         0         0         0         0         0   \n",
       "8        0  ...         0         0         0         0         0         0   \n",
       "9        0  ...         0         0         0         0         0         0   \n",
       "10       0  ...         0         0         0         0         0         0   \n",
       "11       0  ...         0         0         0         0         0         0   \n",
       "12       0  ...         0         0         0         0         0         0   \n",
       "13       0  ...         0         0         0         0         0         0   \n",
       "14       0  ...         0         0         0         0         0         0   \n",
       "15       0  ...         0         0         0         0         0         0   \n",
       "16       0  ...         0         0         0         0         0         0   \n",
       "17       0  ...         0         0         0         0         0         0   \n",
       "18       0  ...         0         0         0         0         0         0   \n",
       "19       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "    pixel780  pixel781  pixel782  pixel783  \n",
       "0          0         0         0         0  \n",
       "1          0         0         0         0  \n",
       "2          0         0         0         0  \n",
       "3          0         0         0         0  \n",
       "4          0         0         0         0  \n",
       "5          0         0         0         0  \n",
       "6          0         0         0         0  \n",
       "7          0         0         0         0  \n",
       "8          0         0         0         0  \n",
       "9          0         0         0         0  \n",
       "10         0         0         0         0  \n",
       "11         0         0         0         0  \n",
       "12         0         0         0         0  \n",
       "13         0         0         0         0  \n",
       "14         0         0         0         0  \n",
       "15         0         0         0         0  \n",
       "16         0         0         0         0  \n",
       "17         0         0         0         0  \n",
       "18         0         0         0         0  \n",
       "19         0         0         0         0  \n",
       "\n",
       "[20 rows x 784 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOOK AT TEST DF TO REMEMBER\n",
    "\n",
    "print(df2.shape)\n",
    "print('')\n",
    "df2.info()\n",
    "df2.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d94b2b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = ((test[8]).reshape((-1,28,28,1)))/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "29947011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28, 28, 1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8f6ac23b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 36ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00, 4.3895161e-12, 1.5989668e-09, 1.2392672e-10,\n",
       "        6.0686948e-13, 3.3723884e-09, 5.2984959e-08, 7.7057933e-09,\n",
       "        2.7522465e-10, 3.5570995e-08]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test1)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4469d0ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alors = np.argmax(predictions)\n",
    "alors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "91b8ae13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_test= test[0]\n",
    "img_test2 = img_test.reshape((28,28))\n",
    "plt.imshow(img_test2 , cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fe73ff59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "680e2975",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 8)         80        \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 8)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 16)        1168      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 16)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 3, 3, 32)          4640      \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 288)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               36992     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 44,170\n",
      "Trainable params: 44,170\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "30892b9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 conv2d (None, 26, 26, 8)\n",
      "1 max_pooling2d (None, 13, 13, 8)\n",
      "2 conv2d_1 (None, 11, 11, 16)\n",
      "3 max_pooling2d_1 (None, 5, 5, 16)\n",
      "4 conv2d_2 (None, 3, 3, 32)\n",
      "5 flatten (None, 288)\n",
      "6 dense (None, 128)\n",
      "7 dense_1 (None, 10)\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(model.layers)):\n",
    "    layer = model.layers[i]\n",
    " # summarize output shape\n",
    "    print(i, layer.name, layer.output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0522dff",
   "metadata": {},
   "source": [
    "<div style=\"background-color: RGB(165, 125, 186);\" >\n",
    "<h2 style=\"margin: auto; padding: 20px; color:#fff; \"> FINAL MODEL STRUCTURE</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c29d64e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image('cnn_structure.PNG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9a38e125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 6s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "df_test = np.array(test)\n",
    "df_test = df_test.reshape(len(df_test),28,28,1)\n",
    "\n",
    "result = model.predict(df_test)\n",
    "\n",
    "predicted_val=[]\n",
    "for i in range(len(result)):\n",
    "    predicted_val.append(np.argmax(result[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "684d4061",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission=pd.DataFrame()\n",
    "\n",
    "imgid=[]\n",
    "for i in range(1, len(predicted_val)+1):\n",
    "    imgid.append(i)\n",
    "    \n",
    "submission[\"Imageid\"]=imgid\n",
    "submission[\"Label\"]=predicted_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fab79763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Imageid</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Imageid  Label\n",
       "0        1      2\n",
       "1        2      0\n",
       "2        3      9\n",
       "3        4      9\n",
       "4        5      3"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8ca7b5b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# submission.to_csv('submission.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8cb6d76f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_test= test[4]\n",
    "img_test2 = img_test.reshape((28,28))\n",
    "plt.imshow(img_test2 , cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9d56693b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 conv2d (None, 26, 26, 8)\n",
      "1 max_pooling2d (None, 13, 13, 8)\n",
      "2 conv2d_1 (None, 11, 11, 16)\n",
      "3 max_pooling2d_1 (None, 5, 5, 16)\n",
      "4 conv2d_2 (None, 3, 3, 32)\n",
      "5 flatten (None, 288)\n",
      "6 dense (None, 128)\n",
      "7 dense_1 (None, 10)\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(model.layers)):\n",
    "    layer = model.layers[i]\n",
    " # summarize output shape\n",
    "    print(i, layer.name, layer.output.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f69e3e80",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}