x-ray-classification-v2.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":23812,"sourceType":"datasetVersion","datasetId":17810}],"dockerImageVersionId":30747,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# using deep learning to classify x-ray image\n\n\n1. [Import Libraries](#import-libraries)\n2. [EDA](#EDA)\n3. [create dataset](#create-dataset)\n4. [images preprocess and use class weight to solve imbalance problem](#images-preprocess)\n5. [Training the Model](#training-the-model)\n6. [Plotting Training and Validation Accuracy and Loss](#plotting-training-and-validation-accuracy-and-loss)\n7. [Compute Accuracy](#compute-accuracy)\n8. [Loading the Model and Making Predictions](#loading-the-model-and-making-predictions)\n","metadata":{}},{"cell_type":"code","source":"#import-libraries\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport glob as gb\nimport cv2\nimport shutil\nimport pickle\nimport seaborn as sns\nfrom keras.applications.vgg16 import VGG16\n\nfrom keras.applications.vgg19 import VGG19\nfrom keras.layers import Dense, Conv2D, MaxPooling2D, AvgPool2D, Input, Dropout, Flatten, BatchNormalization\nfrom keras.models import Sequential, Model\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\nimport tensorflow as tf\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras import datasets, layers, models\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom mlxtend.plotting import plot_confusion_matrix\nfrom tensorflow.keras.utils import to_categorical\nfrom sklearn.metrics import classification_report, recall_score, f1_score, accuracy_score\n\n\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n #   for filename in filenames:\n  #      print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-07-31T22:56:04.268155Z","iopub.execute_input":"2024-07-31T22:56:04.268933Z","iopub.status.idle":"2024-07-31T22:56:04.279309Z","shell.execute_reply.started":"2024-07-31T22:56:04.268894Z","shell.execute_reply":"2024-07-31T22:56:04.278375Z"},"trusted":true},"execution_count":150,"outputs":[]},{"cell_type":"code","source":"BASE_DIR_train =\"/kaggle/input/chest-xray-pneumonia/chest_xray/train/\"\nBASE_DIR_test=\"/kaggle/input/chest-xray-pneumonia/chest_xray/test/\"\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:37:28.761429Z","iopub.execute_input":"2024-07-31T20:37:28.762153Z","iopub.status.idle":"2024-07-31T20:37:28.766083Z","shell.execute_reply.started":"2024-07-31T20:37:28.762124Z","shell.execute_reply":"2024-07-31T20:37:28.765195Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"#EDA\ntrain_img = pd.DataFrame.from_dict(\n    {'Normal': [len([os.path.join(BASE_DIR_train+'NORMAL', filename) \n                     for filename in os.listdir(BASE_DIR_train+'NORMAL')])], \n     'Pneumonia': [len([os.path.join(BASE_DIR_train+'PNEUMONIA', filename) \n                        for filename in os.listdir(BASE_DIR_train+'PNEUMONIA')])]})\n\n\nsns.barplot(data=train_img).set_title('Training data set', fontsize=20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:37:31.693745Z","iopub.execute_input":"2024-07-31T20:37:31.694382Z","iopub.status.idle":"2024-07-31T20:37:32.274060Z","shell.execute_reply.started":"2024-07-31T20:37:31.694354Z","shell.execute_reply":"2024-07-31T20:37:32.273235Z"},"trusted":true},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjAAAAG7CAYAAADdbq/pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA+7ElEQVR4nO3dfVwU9f7//yeguwK6KMqFJqDlJYpaWrrfyrzK1dBjJ+tomWJqpmGllno4n1LTc7IsL/OqS7HSvDhlmaSG12WURpFoZmkadnTRk8GqKSLM749+zHETUBDCscf9dpvbjZ33a97v9+wK+3RmdtbHMAxDAAAAFuJb0RMAAAAoKQIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMcAU6ePCgfHx85OPjo8TExHIbp0OHDvLx8VGHDh3KbYwrUb169eTj46OBAwdW9FQAlBIBBn8a54eCy1kAABWPAAMAZWzz5s1m4N28eXNFT+eKUPB8TJw4saKngqtEpYqeAPBHueaaa5Senl5ke0xMjCSpTZs2Wrhw4R81rULVq1dPf8T3rPLmCsCqCDD406hcubKaN29+0brAwMBLqgMAVBxOIQEAAMshwACX4Pef1vn+++81YsQINWzYUAEBAfLx8dHBgwfN+iNHjmjevHm6++671bBhQwUGBsput+uaa65Rr169tGzZMuXn5xc53sU+hTRx4kSvi4rPnDmj559/XjfccIOqVaumatWq6aabbtKcOXN07ty5S96vi80hOTlZPXv2VHh4uOx2u+rXr6/hw4frp59+uuhz+PPPP2vs2LFq3Lix/P39FRYWpttvv10rV66UJCUmJprjnf9clsaaNWt0xx13KCQkRAEBAWrUqJFGjx6t//znP5e0/Q8//KBp06apZ8+eqlevnvz9/eXv76+oqCj16dNHa9euLXS7guesY8eO5rqOHTtecCH471/TXbt26Z///KdcLpfq1q0ru92uqlWrqmHDhoqLi9Nnn31W6ufifN99950eeeQRNW/eXNWqVZPNZlOdOnXUqlUrDRo0SMuWLVNOTk6R27vdbv3f//2f2rRpo+DgYNntdkVEROhvf/ub1q9fX+g2BZ/4KvD0009f8HzwaTCUigHAMAzDkGRIMm677bYL2m677Taz7b333jMCAwPN+oLlwIEDhmEYxrlz5wxfX98L2n+/3H777caJEycKncuBAwfMuoULF17QPmHCBLPd7XYbrVq1KnKcnj17Gnl5eYWOc/5+XWwOf//734scIyQkxPjmm2+KfG537txphIWFFbn90KFDjYULF17wXJbGqFGjip3njh07jKioKEOSERcXd8H2P/zww0VfO0nG/fffb+Tm5hb5nBW3nP+abtq06ZK2+fvf/17q58QwDGP58uWGzWa76Djp6emFbv/WW28V+u/+/GXw4MEXPCcFz3VxS2GvA3AxXAMDlEBGRobuv/9+BQQE6KmnntKtt94qPz8/7dixQ1WrVpUk8+LbTp06qXv37oqJiVFISIhOnDihH374Qa+88opSUlKUnJys+Ph4LVq06LLmdNddd+mbb77Ro48+qp49eyo4OFh79+7V5MmTtWfPHn3wwQd65ZVX9NBDD5V6jFdeeUWffvqpbrvtNj300ENq1KiRsrKy9MYbb+iNN97QsWPHNGjQIKWkpFywbVZWlrp166bMzExJUv/+/XXfffcpJCRE+/bt06xZs/Tyyy/r66+/LvX8CsycOVMzZsyQJNWpU0cJCQm66aabdObMGSUlJWnmzJm655579OuvvxbZR15enmw2m1wul26//XZFR0crODhYx48f13fffae5c+dq9+7deuutt3Tttdfq6aefNrctuFB8x44dGjRokCTp9ddf14033ug1Rt26dc2fz507p8DAQMXGxqpTp05q0qSJHA6Hjh49qt27d2v27Nn68ccf9eyzz6pRo0Z64IEHSvy8ZGZm6oEHHtDZs2cVGhqqESNGqF27dqpVq5ZOnz6tffv2acuWLXrvvfcK3X758uXq37+/DMPQtddeqxEjRig6OlohISE6ePCgXnvtNX344Yd67bXX5HA4NH36dHPbjz76SGfPnjUvkh8+fLgefvhhr/5r1KhR4n0COAID/P90CUdgJBl16tQxfvzxxyL7yc/PN77//vtixxo/frwhyfDx8TG+++67C9pLcgSmcuXKxqZNmy6o+fnnn82jHi1atCh0Hpd6BEaS8eCDDxr5+fkX1A0ZMsSs+fLLLy9oHzlypNk+c+bMC9rPnTtn9OrVq9CjWSWRmZlpBAQEGJKMqKgo48iRIxfUbNiwwahUqVKx//M/efKkcfjw4SLHyc/PNwYOHGhIMgIDA42srKwLas4/qlLYa3O+Y8eOGb/88kuR7Tk5Ocbtt99u7te5c+eK7a8wr7322kWPsBiGYfz666/Gr7/+esH8goKCDEnGoEGDLjjCUuAf//iHIcnw9fU1vv322wvaC8afMGFCiecPFIZrYIASevbZZxUZGVlku4+Pjxo0aFBsH+PHj1etWrVkGIZWrVp1WfN55JFHCr2GJTg42Pzfenp6urKzs0s9Ru3atfXiiy8WeiO/J554wvz5448/9mrLyckxr/e48cYb9dhjj12wvZ+fn1566SVVqVKl1POTpEWLFplHVqZNm6bw8PALajp16qQHH3yw2H4CAwNVu3btItt9fHw0bdo0+fn56dSpU0Ve+3GpatWqperVqxfZbrPZ9Pzzz0uSfvzxR6WlpZV4DLfbLem3Ix3FfcKu4Fqf882fP1/Z2dm65pprNG/ePFWqVPiB+6efflrXXHON8vPz9cYbb5R4jkBJEWCAErDZbLrnnntKtE1+fr4OHz6svXv3ateuXdq1a5f27Nljnka43FMn/fr1K7KtdevWkn47rXXgwIFSj3H33XfLbrcX2ta4cWPz9NkPP/zg1fbFF18oKytLknT//fcX2X9YWJhcLlep5yfJDBI1atRQr169iqwrOLVzqXJzc/XTTz9pz5495ut3+PBh1axZU9Llv36/l5OTo4yMDH3zzTfmeMZ59wQqzXgFgeyXX37R+++/X6JtCwJ2jx49ivw3IEmVKlWS0+mUpEJPJQJljWtggBJo2LDhJR0pMAxDixcv1muvvabPP/9cp0+fLrL2v//972XNqUmTJkW2BQcHmz+fOHGiXMaQfgsNJ0+evGCMXbt2mT8XhKmitGnTpsRvrucruEnh9ddfX+RRAklq1aqVbDabzp49W2RNbm6uXn75Zb355pv66quviq293NdPkk6dOqXZs2dr6dKl2r17t/Ly8sp0vL/85S+qXr26srKy9Ne//lUdOnRQz5491b59e7Vq1Up+fn6FbpeXl2ce8XnppZf00ksvXdJ4BUd8gPJEgAFK4FIuNjxz5ozuuusurVmz5pL6LC7cXIqAgIAi23x9/3eQtbg3xcsZ4/xxfj/GL7/8Yv4cEhJSbB8Xa7+Y48ePS5JCQ0OLratUqZKCg4OLfJM9fvy4unbtqtTU1Esa93Jfv4MHD6pTp06XfISsNOPVrFlTq1at0r333qv//Oc/2rRpkzZt2iRJcjgc6ty5swYNGqQePXp4bXf8+PFiP4ZflOIukgbKCgEGKIGi/qd6vn/9619meLntttsUHx+vG264QeHh4fL39zff7Nu3b6+PP/74D/nKgD+Ty/3Czccee8wML3feeacGDRqkFi1aKDQ0VFWqVDH7j4yM1KFDhy779evfv78OHDggHx8fPfDAA+rbt6+aNm2qkJAQ2Ww2+fj4KD8/3/y3V9rxbr31Vu3bt0/vvPOOPvzwQ23dulU//fSTPB6PVq5cqZUrV8rlcundd981A+v5gXTIkCGFXsNUGJvNVqo5AiVBgAHKkGEYevXVVyX99oaxceNGr6Mg5ys4YnA1O/+I1bFjx9SoUaMia48dO3bZY7ndbvPj2kU5d+5ckc+9x+PRsmXLJP12bdFbb71VZD/nH10qrW+//VaffPKJJOkf//iH/vnPfxZaV1b/VqpUqaJ+/fqZ100dOHBASUlJevHFF/Xdd99p3bp1+r//+z/zo+jnn4I0DIOv2MAVhYt4gTJ0/Phx89TEPffcU2R4OXnypPbu3ftHTq1CNGvWzPz5Yqdkvvjii8saq+A+I2lpacWe9vj666+LvKbl+++/V25uriSpT58+Rfbx7bff6uTJk0W2X+pRoN27d5s/Fzfe5T43Ralfv75GjBihHTt2mBeVL1++3Gy32Wzma7ht27ZymQNQWgQYoAyd/8Z56tSpIuteffXVUl1bYDVt2rRRUFCQJBV7NCMzM1Pr1q27rLG6dOki6bcQ+cEHHxRZ9/rrrxfZdqmv34IFC4qdy/kXehd3a/6yGu9yORwO82Z7v79I+C9/+Yuk30Lb5bxGBc9Jcc8HUBIEGKAMhYSEmPf0ePvttwv9Y71jxw499dRTf/DMKkaVKlU0YMAASb/t96xZsy6oyc/P10MPPaQzZ85c1lhxcXHmPUxGjx5d6KmkLVu26OWXXy6yjwYNGphHTxYtWlTo9SYffPCB5syZU+xczr+PzP79+4usa9iwoflzYd95Jf12H5bL+XSWJK1bt05Hjhwpsj07O1vbt2+X9NtRmfM99thj5sfkH3jgAa+jRoVJSkrSzp07L1hf8JwU93wAJUGAAcqQr6+veX3Bzp07dcstt+jtt9/WF198oQ0bNujxxx9X+/btVaVKlWKvB7maTJw40byp3MiRIzVgwACtW7dOX375pZYvX65bb71V77//vm666SZzm9JciBsWFqbJkydL+u2TPa1bt9bcuXO1Y8cOffzxx0pISJDL5dI111xT5CeeatasqTvuuEOStHbtWnXt2lXvvvuuUlNTtWbNGg0ZMkR//etfde211xb7qanIyEjzlMwLL7ygVatWae/evdq3b5/27dtnftz8+uuvN68reemll9SnTx+tXr1aqampev/993XPPffo4Ycf1s0331zi5+N8b7/9tqKiohQbG6tZs2Zpw4YN+uqrr7R161bNmzdPTqfT/KLLYcOGeW0bFhamRYsWycfHR0eOHFGbNm00fPhwrVq1Sl9++aU+//xzvfPOOxo3bpyuu+469ejRQxkZGRfM4f/9v/8n6bf7yrz00kvatWuX+XwcPXr0svYPf1IVdQtg4EqjS/gqgcLafi8rK6vYL1cMDg42tmzZUqIvUvy9879KoDgXu6X95czhfMV9OaJhGEZaWpoREhJS5HMycOBAr9vdu93uYscrzqOPPlrkOLVq1TK2b99e7HwzMjKMyMjIIvuIjIw0du/efdF9njdvXpF9nP98fvXVV0aNGjWKrI2JiTEOHz58Wbfij4uLu+gXKkoyhg0bVuQXf65atcoIDg6+aB++vr7Gxo0bL9j+q6++Mux2e6Hb8GWOKA2OwABlLCgoSNu2bdPkyZMVExOjKlWqqGrVqmratKmeeOIJff3112rfvn1FT/MP1bJlS33zzTd6/PHH1bBhQ9ntdtWqVUsdO3bUkiVLtHDhQnk8HrO+4LqZ0pg1a5aSkpLkcrkUHBysKlWqqEGDBnr00Uf11VdfXfDFir8XERGhL7/8UmPGjFGjRo1kt9sVFBSkli1basKECUpLS1N0dPRF5zF8+HC988476tq1q0JDQ4u8uV6rVq2UlpamYcOGKSoqSpUrV1ZwcLBuuukmvfDCC9q+fXuxX21wKWbMmKG33npLgwYNUps2bXTNNdfIZrPJ399fjRo1UlxcnD7++GPNnz+/yAvPe/bsqQMHDuiFF15Qp06dFBYWpsqVK8vf31/169dXjx49NH36dB08eFAdO3YsdD9TUlJ07733KjIysti7+gKXwscwuAkFgIo3ZMgQvfbaa6pbt64OHTpU0dMBcIXjCAyACnf69GnzQtV27dpV8GwAWAEBBkC5279/f5F3kM3Ly9Pw4cPNj+/GxcX9kVMDYFGcQgJQ7gYOHKjt27erb9++atu2rUJDQ3X69Gnt3LlTr7zyir788ktJv93L5aOPPrrsrwMAcPXjqwQA/CH27NmjCRMmFNl+8803a+nSpYQXAJeEIzAAyt3evXv1zjvvaP369Tp48KCOHTum3Nxc1axZU23atFGfPn3Ut2/fIj8BAwC/R4ABAACWc9WeQsrPz9fhw4dVrVo1DkkDAGARhmHoxIkTqlOnTrFHZa/aAHP48GFFRERU9DQAAEApHDp0yPxKjsJctQGmWrVqkn57AhwORwXPBgAAXAqPx6OIiAjzfbxIl/M9BFOmTDEkGY899pi57vTp08bDDz9sBAcHG4GBgcZdd911wfea/Pjjj8Ydd9xh+Pv7GyEhIcYTTzxh5ObmetVs2rTJuP766w2bzWZcd911F/0ult/Lzs42JBnZ2dml3T0AAPAHu9T371Jf8r9jxw699NJLatGihdf6UaNG6YMPPtCKFSu0ZcsWHT58WHfddZfZnpeXp9jYWJ09e1affvqpFi1apMTERI0fP96sOXDggGJjY9WxY0elpaVp5MiRGjJkiNatW1fa6QIAgKtJadLRiRMnjIYNGxrJycnGbbfdZh6BycrKMipXrmysWLHCrN2zZ48hyUhJSTEMwzA+/PBDw9fX1+uozPz58w2Hw2Hk5OQYhmEYY8eONZo1a+Y1Zp8+fQyXy3XJc+QIDAAA1lOuR2Di4+MVGxurLl26eK1PTU1Vbm6u1/omTZooMjJSKSkpkqSUlBTFxMQoLCzMrHG5XPJ4PNq9e7dZ8/u+XS6X2UdhcnJy5PF4vBYAAHB1KvFFvEuXLtWXX36pHTt2XNDmdrtls9lUvXp1r/VhYWFyu91mzfnhpaC9oK24Go/Ho9OnT8vf3/+CsadMmaKnn366pLsDAAAsqERHYA4dOqTHHntMixcvVpUqVcprTqWSkJCg7Oxsczl06FBFTwkAAJSTEgWY1NRUHT16VDfccIMqVaqkSpUqacuWLZo9e7YqVaqksLAwnT17VllZWV7bZWZmKjw8XJIUHh6uzMzMC9oL2oqrcTgchR59kSS73S6Hw+G1AACAq1OJAkznzp2Vnp6utLQ0c2nTpo369etn/ly5cmVt2LDB3Gbv3r3KyMiQ0+mUJDmdTqWnp+vo0aNmTXJyshwOh6Kjo82a8/soqCnoAwAA/LmV6BqYatWqqXnz5l7rAgMDVbNmTXP94MGDNXr0aAUHB8vhcOiRRx6R0+lUu3btJEldu3ZVdHS0+vfvr6lTp8rtduvJJ59UfHy87Ha7JGnYsGGaM2eOxo4dq0GDBmnjxo1avny5kpKSymKfAQCAxZX5nXhnzJghX19f9e7dWzk5OXK5XJo3b57Z7ufnp9WrV2v48OFyOp0KDAxUXFycJk2aZNbUr19fSUlJGjVqlGbNmqW6devq1VdflcvlKuvpAgAAC7pqv43a4/EoKChI2dnZXA8DAIBFXOr7d6nvxAsAAFBRCDAAAMByCDAAAMByCDAAAMByyvxTSABwtciYFFPRUwCuOJHj0yt6CpI4AgMAACyIAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACynRAFm/vz5atGihRwOhxwOh5xOp9asWWO2d+jQQT4+Pl7LsGHDvPrIyMhQbGysAgICFBoaqjFjxujcuXNeNZs3b9YNN9wgu92uBg0aKDExsfR7CAAArjqVSlJct25dPfvss2rYsKEMw9CiRYvUq1cvffXVV2rWrJkk6cEHH9SkSZPMbQICAsyf8/LyFBsbq/DwcH366ac6cuSIBgwYoMqVK+uZZ56RJB04cECxsbEaNmyYFi9erA0bNmjIkCGqXbu2XC5XWewzAACwOB/DMIzL6SA4OFjPP/+8Bg8erA4dOqhVq1aaOXNmobVr1qxRjx49dPjwYYWFhUmSFixYoHHjxunYsWOy2WwaN26ckpKStGvXLnO7vn37KisrS2vXrr3keXk8HgUFBSk7O1sOh+NydhHAn1TGpJiKngJwxYkcn16u/V/q+3epr4HJy8vT0qVLderUKTmdTnP94sWLVatWLTVv3lwJCQn69ddfzbaUlBTFxMSY4UWSXC6XPB6Pdu/ebdZ06dLFayyXy6WUlJRi55OTkyOPx+O1AACAq1OJTiFJUnp6upxOp86cOaOqVatq5cqVio6OliTdd999ioqKUp06dbRz506NGzdOe/fu1bvvvitJcrvdXuFFkvnY7XYXW+PxeHT69Gn5+/sXOq8pU6bo6aefLunuAAAACypxgGncuLHS0tKUnZ2tf//734qLi9OWLVsUHR2toUOHmnUxMTGqXbu2OnfurP379+u6664r04n/XkJCgkaPHm0+9ng8ioiIKNcxAQBAxSjxKSSbzaYGDRqodevWmjJlilq2bKlZs2YVWtu2bVtJ0r59+yRJ4eHhyszM9KopeBweHl5sjcPhKPLoiyTZ7Xbz01EFCwAAuDpd9n1g8vPzlZOTU2hbWlqaJKl27dqSJKfTqfT0dB09etSsSU5OlsPhME9DOZ1Obdiwwauf5ORkr+tsAADAn1uJTiElJCSoe/fuioyM1IkTJ7RkyRJt3rxZ69at0/79+7VkyRLdcccdqlmzpnbu3KlRo0apffv2atGihSSpa9euio6OVv/+/TV16lS53W49+eSTio+Pl91ulyQNGzZMc+bM0dixYzVo0CBt3LhRy5cvV1JSUtnvPQAAsKQSBZijR49qwIABOnLkiIKCgtSiRQutW7dOt99+uw4dOqT169dr5syZOnXqlCIiItS7d289+eST5vZ+fn5avXq1hg8fLqfTqcDAQMXFxXndN6Z+/fpKSkrSqFGjNGvWLNWtW1evvvoq94ABAACmy74PzJWK+8AAuFzcBwa4kOXvAwMAAFBRCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByShRg5s+frxYtWsjhcMjhcMjpdGrNmjVm+5kzZxQfH6+aNWuqatWq6t27tzIzM736yMjIUGxsrAICAhQaGqoxY8bo3LlzXjWbN2/WDTfcILvdrgYNGigxMbH0ewgAAK46JQowdevW1bPPPqvU1FR98cUX6tSpk3r16qXdu3dLkkaNGqUPPvhAK1as0JYtW3T48GHddddd5vZ5eXmKjY3V2bNn9emnn2rRokVKTEzU+PHjzZoDBw4oNjZWHTt2VFpamkaOHKkhQ4Zo3bp1ZbTLAADA6nwMwzAup4Pg4GA9//zzuvvuuxUSEqIlS5bo7rvvliR9++23atq0qVJSUtSuXTutWbNGPXr00OHDhxUWFiZJWrBggcaNG6djx47JZrNp3LhxSkpK0q5du8wx+vbtq6ysLK1du/aS5+XxeBQUFKTs7Gw5HI7L2UUAf1IZk2IqegrAFSdyfHq59n+p79+lvgYmLy9PS5cu1alTp+R0OpWamqrc3Fx16dLFrGnSpIkiIyOVkpIiSUpJSVFMTIwZXiTJ5XLJ4/GYR3FSUlK8+iioKeijKDk5OfJ4PF4LAAC4OpU4wKSnp6tq1aqy2+0aNmyYVq5cqejoaLndbtlsNlWvXt2rPiwsTG63W5Lkdru9wktBe0FbcTUej0enT58ucl5TpkxRUFCQuURERJR01wAAgEWUOMA0btxYaWlp+vzzzzV8+HDFxcXpm2++KY+5lUhCQoKys7PN5dChQxU9JQAAUE4qlXQDm82mBg0aSJJat26tHTt2aNasWerTp4/Onj2rrKwsr6MwmZmZCg8PlySFh4dr+/btXv0VfErp/Jrff3IpMzNTDodD/v7+Rc7LbrfLbreXdHcAAIAFXfZ9YPLz85WTk6PWrVurcuXK2rBhg9m2d+9eZWRkyOl0SpKcTqfS09N19OhRsyY5OVkOh0PR0dFmzfl9FNQU9AEAAFCiIzAJCQnq3r27IiMjdeLECS1ZskSbN2/WunXrFBQUpMGDB2v06NEKDg6Ww+HQI488IqfTqXbt2kmSunbtqujoaPXv319Tp06V2+3Wk08+qfj4ePPoybBhwzRnzhyNHTtWgwYN0saNG7V8+XIlJSWV/d4DAABLKlGAOXr0qAYMGKAjR44oKChILVq00Lp163T77bdLkmbMmCFfX1/17t1bOTk5crlcmjdvnrm9n5+fVq9ereHDh8vpdCowMFBxcXGaNGmSWVO/fn0lJSVp1KhRmjVrlurWratXX31VLperjHYZAABY3WXfB+ZKxX1gAFwu7gMDXMjy94EBAACoKAQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOSUKMFOmTNGNN96oatWqKTQ0VHfeeaf27t3rVdOhQwf5+Ph4LcOGDfOqycjIUGxsrAICAhQaGqoxY8bo3LlzXjWbN2/WDTfcILvdrgYNGigxMbF0ewgAAK46JQowW7ZsUXx8vD777DMlJycrNzdXXbt21alTp7zqHnzwQR05csRcpk6darbl5eUpNjZWZ8+e1aeffqpFixYpMTFR48ePN2sOHDig2NhYdezYUWlpaRo5cqSGDBmidevWXebuAgCAq0GlkhSvXbvW63FiYqJCQ0OVmpqq9u3bm+sDAgIUHh5eaB8fffSRvvnmG61fv15hYWFq1aqVJk+erHHjxmnixImy2WxasGCB6tevr2nTpkmSmjZtqk8++UQzZsyQy+Uq6T4CAICrzGVdA5OdnS1JCg4O9lq/ePFi1apVS82bN1dCQoJ+/fVXsy0lJUUxMTEKCwsz17lcLnk8Hu3evdus6dKli1efLpdLKSkpRc4lJydHHo/HawEAAFenEh2BOV9+fr5Gjhypm2++Wc2bNzfX33fffYqKilKdOnW0c+dOjRs3Tnv37tW7774rSXK73V7hRZL52O12F1vj8Xh0+vRp+fv7XzCfKVOm6Omnny7t7gAAAAspdYCJj4/Xrl279Mknn3itHzp0qPlzTEyMateurc6dO2v//v267rrrSj/Ti0hISNDo0aPNxx6PRxEREeU2HgAAqDilOoU0YsQIrV69Wps2bVLdunWLrW3btq0kad++fZKk8PBwZWZmetUUPC64bqaoGofDUejRF0my2+1yOBxeCwAAuDqVKMAYhqERI0Zo5cqV2rhxo+rXr3/RbdLS0iRJtWvXliQ5nU6lp6fr6NGjZk1ycrIcDoeio6PNmg0bNnj1k5ycLKfTWZLpAgCAq1SJAkx8fLzeeustLVmyRNWqVZPb7Zbb7dbp06clSfv379fkyZOVmpqqgwcPatWqVRowYIDat2+vFi1aSJK6du2q6Oho9e/fX19//bXWrVunJ598UvHx8bLb7ZKkYcOG6YcfftDYsWP17bffat68eVq+fLlGjRpVxrsPAACsqEQBZv78+crOzlaHDh1Uu3Ztc1m2bJkkyWazaf369eratauaNGmixx9/XL1799YHH3xg9uHn56fVq1fLz89PTqdT999/vwYMGKBJkyaZNfXr11dSUpKSk5PVsmVLTZs2Ta+++iofoQYAAJIkH8MwjIqeRHnweDwKCgpSdnY218MAKJWMSTEVPQXgihM5Pr1c+7/U92++CwkAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFhOiQLMlClTdOONN6patWoKDQ3VnXfeqb1793rVnDlzRvHx8apZs6aqVq2q3r17KzMz06smIyNDsbGxCggIUGhoqMaMGaNz58551WzevFk33HCD7Ha7GjRooMTExNLtIQAAuOqUKMBs2bJF8fHx+uyzz5ScnKzc3Fx17dpVp06dMmtGjRqlDz74QCtWrNCWLVt0+PBh3XXXXWZ7Xl6eYmNjdfbsWX366adatGiREhMTNX78eLPmwIEDio2NVceOHZWWlqaRI0dqyJAhWrduXRnsMgAAsDofwzCM0m587NgxhYaGasuWLWrfvr2ys7MVEhKiJUuW6O6775Ykffvtt2ratKlSUlLUrl07rVmzRj169NDhw4cVFhYmSVqwYIHGjRunY8eOyWazady4cUpKStKuXbvMsfr27ausrCytXbv2kubm8XgUFBSk7OxsORyO0u4igD+xjEkxFT0F4IoTOT69XPu/1Pfvy7oGJjs7W5IUHBwsSUpNTVVubq66dOli1jRp0kSRkZFKSUmRJKWkpCgmJsYML5Lkcrnk8Xi0e/dus+b8PgpqCvooTE5Ojjwej9cCAACuTqUOMPn5+Ro5cqRuvvlmNW/eXJLkdrtls9lUvXp1r9qwsDC53W6z5vzwUtBe0FZcjcfj0enTpwudz5QpUxQUFGQuERERpd01AABwhSt1gImPj9euXbu0dOnSspxPqSUkJCg7O9tcDh06VNFTAgAA5aRSaTYaMWKEVq9era1bt6pu3brm+vDwcJ09e1ZZWVleR2EyMzMVHh5u1mzfvt2rv4JPKZ1f8/tPLmVmZsrhcMjf37/QOdntdtnt9tLsDgAAsJgSHYExDEMjRozQypUrtXHjRtWvX9+rvXXr1qpcubI2bNhgrtu7d68yMjLkdDolSU6nU+np6Tp69KhZk5ycLIfDoejoaLPm/D4Kagr6AAAAf24lOgITHx+vJUuW6P3331e1atXMa1aCgoLk7++voKAgDR48WKNHj1ZwcLAcDoceeeQROZ1OtWvXTpLUtWtXRUdHq3///po6darcbreefPJJxcfHm0dQhg0bpjlz5mjs2LEaNGiQNm7cqOXLlyspKamMdx8AAFhRiY7AzJ8/X9nZ2erQoYNq165tLsuWLTNrZsyYoR49eqh3795q3769wsPD9e6775rtfn5+Wr16tfz8/OR0OnX//fdrwIABmjRpkllTv359JSUlKTk5WS1bttS0adP06quvyuVylcEuAwAAq7us+8BcybgPDIDLxX1ggAtdFfeBAQAAqAgEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDklDjBbt25Vz549VadOHfn4+Oi9997zah84cKB8fHy8lm7dunnVHD9+XP369ZPD4VD16tU1ePBgnTx50qtm586duvXWW1WlShVFRERo6tSpJd87AABwVSpxgDl16pRatmypuXPnFlnTrVs3HTlyxFzefvttr/Z+/fpp9+7dSk5O1urVq7V161YNHTrUbPd4POratauioqKUmpqq559/XhMnTtTLL79c0ukCAICrUKWSbtC9e3d179692Bq73a7w8PBC2/bs2aO1a9dqx44datOmjSTpxRdf1B133KEXXnhBderU0eLFi3X27Fm9/vrrstlsatasmdLS0jR9+nSvoAMAAP6cyuUamM2bNys0NFSNGzfW8OHD9fPPP5ttKSkpql69uhleJKlLly7y9fXV559/bta0b99eNpvNrHG5XNq7d69++eWXQsfMycmRx+PxWgAAwNWpzANMt27d9MYbb2jDhg167rnntGXLFnXv3l15eXmSJLfbrdDQUK9tKlWqpODgYLndbrMmLCzMq6bgcUHN702ZMkVBQUHmEhERUda7BgAArhAlPoV0MX379jV/jomJUYsWLXTddddp8+bN6ty5c1kPZ0pISNDo0aPNxx6PhxADAMBVqtw/Rn3ttdeqVq1a2rdvnyQpPDxcR48e9ao5d+6cjh8/bl43Ex4erszMTK+agsdFXVtjt9vlcDi8FgAAcHUq9wDz008/6eeff1bt2rUlSU6nU1lZWUpNTTVrNm7cqPz8fLVt29as2bp1q3Jzc82a5ORkNW7cWDVq1CjvKQMAgCtciQPMyZMnlZaWprS0NEnSgQMHlJaWpoyMDJ08eVJjxozRZ599poMHD2rDhg3q1auXGjRoIJfLJUlq2rSpunXrpgcffFDbt2/Xtm3bNGLECPXt21d16tSRJN13332y2WwaPHiwdu/erWXLlmnWrFlep4gAAMCfV4kDzBdffKHrr79e119/vSRp9OjRuv766zV+/Hj5+flp586d+stf/qJGjRpp8ODBat26tT7++GPZ7Xazj8WLF6tJkybq3Lmz7rjjDt1yyy1e93gJCgrSRx99pAMHDqh169Z6/PHHNX78eD5CDQAAJEk+hmEYFT2J8uDxeBQUFKTs7GyuhwFQKhmTYip6CsAVJ3J8ern2f6nv33wXEgAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsBwCDAAAsJwy/zLHP5vWY96o6CkAV5zU5wdU9BQAXOU4AgMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACynxAFm69at6tmzp+rUqSMfHx+99957Xu2GYWj8+PGqXbu2/P391aVLF33//fdeNcePH1e/fv3kcDhUvXp1DR48WCdPnvSq2blzp2699VZVqVJFERERmjp1asn3DgAAXJVKHGBOnTqlli1bau7cuYW2T506VbNnz9aCBQv0+eefKzAwUC6XS2fOnDFr+vXrp927dys5OVmrV6/W1q1bNXToULPd4/Goa9euioqKUmpqqp5//nlNnDhRL7/8cil2EQAAXG0qlXSD7t27q3v37oW2GYahmTNn6sknn1SvXr0kSW+88YbCwsL03nvvqW/fvtqzZ4/Wrl2rHTt2qE2bNpKkF198UXfccYdeeOEF1alTR4sXL9bZs2f1+uuvy2azqVmzZkpLS9P06dO9gs75cnJylJOTYz72eDwl3TUAAGARZXoNzIEDB+R2u9WlSxdzXVBQkNq2bauUlBRJUkpKiqpXr26GF0nq0qWLfH199fnnn5s17du3l81mM2tcLpf27t2rX375pdCxp0yZoqCgIHOJiIgoy10DAABXkDINMG63W5IUFhbmtT4sLMxsc7vdCg0N9WqvVKmSgoODvWoK6+P8MX4vISFB2dnZ5nLo0KHL3yEAAHBFKvEppCuV3W6X3W6v6GkAAIA/QJkegQkPD5ckZWZmeq3PzMw028LDw3X06FGv9nPnzun48eNeNYX1cf4YAADgz6tMA0z9+vUVHh6uDRs2mOs8Ho8+//xzOZ1OSZLT6VRWVpZSU1PNmo0bNyo/P19t27Y1a7Zu3arc3FyzJjk5WY0bN1aNGjXKcsoAAMCCShxgTp48qbS0NKWlpUn67cLdtLQ0ZWRkyMfHRyNHjtQ///lPrVq1Sunp6RowYIDq1KmjO++8U5LUtGlTdevWTQ8++KC2b9+ubdu2acSIEerbt6/q1KkjSbrvvvtks9k0ePBg7d69W8uWLdOsWbM0evToMttxAABgXSW+BuaLL75Qx44dzccFoSIuLk6JiYkaO3asTp06paFDhyorK0u33HKL1q5dqypVqpjbLF68WCNGjFDnzp3l6+ur3r17a/bs2WZ7UFCQPvroI8XHx6t169aqVauWxo8fX+RHqAEAwJ+Lj2EYRkVPojx4PB4FBQUpOztbDoej3MZpPeaNcusbsKrU5wdU9BTKRMakmIqeAnDFiRyfXq79X+r7N9+FBAAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALIcAAwAALKfMA8zEiRPl4+PjtTRp0sRsP3PmjOLj41WzZk1VrVpVvXv3VmZmplcfGRkZio2NVUBAgEJDQzVmzBidO3eurKcKAAAsqlJ5dNqsWTOtX7/+f4NU+t8wo0aNUlJSklasWKGgoCCNGDFCd911l7Zt2yZJysvLU2xsrMLDw/Xpp5/qyJEjGjBggCpXrqxnnnmmPKYLAAAsplwCTKVKlRQeHn7B+uzsbL322mtasmSJOnXqJElauHChmjZtqs8++0zt2rXTRx99pG+++Ubr169XWFiYWrVqpcmTJ2vcuHGaOHGibDZbeUwZAABYSLlcA/P999+rTp06uvbaa9WvXz9lZGRIklJTU5Wbm6suXbqYtU2aNFFkZKRSUlIkSSkpKYqJiVFYWJhZ43K55PF4tHv37iLHzMnJkcfj8VoAAMDVqcwDTNu2bZWYmKi1a9dq/vz5OnDggG699VadOHFCbrdbNptN1atX99omLCxMbrdbkuR2u73CS0F7QVtRpkyZoqCgIHOJiIgo2x0DAABXjDI/hdS9e3fz5xYtWqht27aKiorS8uXL5e/vX9bDmRISEjR69GjzscfjIcQAAHCVKvePUVevXl2NGjXSvn37FB4errNnzyorK8urJjMz07xmJjw8/IJPJRU8Luy6mgJ2u10Oh8NrAQAAV6dyDzAnT57U/v37Vbt2bbVu3VqVK1fWhg0bzPa9e/cqIyNDTqdTkuR0OpWenq6jR4+aNcnJyXI4HIqOji7v6QIAAAso81NITzzxhHr27KmoqCgdPnxYEyZMkJ+fn+69914FBQVp8ODBGj16tIKDg+VwOPTII4/I6XSqXbt2kqSuXbsqOjpa/fv319SpU+V2u/Xkk08qPj5edru9rKcLAAAsqMwDzE8//aR7771XP//8s0JCQnTLLbfos88+U0hIiCRpxowZ8vX1Ve/evZWTkyOXy6V58+aZ2/v5+Wn16tUaPny4nE6nAgMDFRcXp0mTJpX1VAEAgEWVeYBZunRpse1VqlTR3LlzNXfu3CJroqKi9OGHH5b11AAAwFWC70ICAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWc0UHmLlz56pevXqqUqWK2rZtq+3bt1f0lAAAwBXgig0wy5Yt0+jRozVhwgR9+eWXatmypVwul44ePVrRUwMAABXsig0w06dP14MPPqgHHnhA0dHRWrBggQICAvT6669X9NQAAEAFq1TREyjM2bNnlZqaqoSEBHOdr6+vunTpopSUlEK3ycnJUU5Ojvk4OztbkuTxeMp1rnk5p8u1f8CKyvv37o9y4kxeRU8BuOKU9+93Qf+GYRRbd0UGmP/+97/Ky8tTWFiY1/qwsDB9++23hW4zZcoUPf300xesj4iIKJc5Aiha0IvDKnoKAMrLlKA/ZJgTJ04oKKjosa7IAFMaCQkJGj16tPk4Pz9fx48fV82aNeXj41OBM8MfwePxKCIiQocOHZLD4ajo6QAoQ/x+/7kYhqETJ06oTp06xdZdkQGmVq1a8vPzU2Zmptf6zMxMhYeHF7qN3W6X3W73Wle9evXymiKuUA6Hgz9wwFWK3+8/j+KOvBS4Ii/itdlsat26tTZs2GCuy8/P14YNG+R0OitwZgAA4EpwRR6BkaTRo0crLi5Obdq00U033aSZM2fq1KlTeuCBByp6agAAoIJdsQGmT58+OnbsmMaPHy+3261WrVpp7dq1F1zYC0i/nUKcMGHCBacRAVgfv98ojI9xsc8pAQAAXGGuyGtgAAAAikOAAQAAlkOAAQAAlkOAAQAAlkOAAYqxefNm+fj4KCsrq6KnAuAPUq9ePc2cObOip4GLIMDgDzNw4ED5+Pjo2Wef9Vr/3nvv8XUPwBWk4HfVx8dHNptNDRo00KRJk3Tu3LmKntofYseOHRo6dGhFTwMXQYDBH6pKlSp67rnn9Msvv5RZn2fPni2zvgD8plu3bjpy5Ii+//57Pf7445o4caKef/75ip7WHyIkJEQBAQEVPQ1cBAEGf6guXbooPDxcU6ZMKbLmnXfeUbNmzWS321WvXj1NmzbNq71evXqaPHmyBgwYIIfDoaFDhyoxMVHVq1fX6tWr1bhxYwUEBOjuu+/Wr7/+qkWLFqlevXqqUaOGHn30UeXl5Zl9vfnmm2rTpo2qVaum8PBw3XfffTp69Gi57T9gFXa7XeHh4YqKitLw4cPVpUsXrVq1SgMHDtSdd96pF154QbVr11bNmjUVHx+v3Nxcc9ucnBw98cQTuuaaaxQYGKi2bdtq8+bNZvvEiRPVqlUrr/FmzpypevXqmY8LxnnmmWcUFham6tWrm0eBxowZo+DgYNWtW1cLFy706ic9PV2dOnWSv7+/atasqaFDh+rkyZMX9Fvc/H9/Cmn69OmKiYlRYGCgIiIi9PDDD3v1iYpBgMEfys/PT88884xefPFF/fTTTxe0p6am6m9/+5v69u2r9PR0TZw4UU899ZQSExO96l544QW1bNlSX331lZ566ilJ0q+//qrZs2dr6dKlWrt2rTZv3qy//vWv+vDDD/Xhhx/qzTff1EsvvaR///vfZj+5ubmaPHmyvv76a7333ns6ePCgBg4cWJ5PAWBJ/v7+5tHOTZs2af/+/dq0aZMWLVqkxMREr9/RESNGKCUlRUuXLtXOnTt1zz33qFu3bvr+++9LNObGjRt1+PBhbd26VdOnT9eECRPUo0cP1ahRQ59//rmGDRumhx56yPxbcurUKblcLtWoUUM7duzQihUrtH79eo0YMcKr34vN//d8fX01e/Zs7d69W4sWLdLGjRs1duzYEu0LyoEB/EHi4uKMXr16GYZhGO3atTMGDRpkGIZhrFy50ij4p3jfffcZt99+u9d2Y8aMMaKjo83HUVFRxp133ulVs3DhQkOSsW/fPnPdQw89ZAQEBBgnTpww17lcLuOhhx4qco47duwwJJnbbNq0yZBk/PLLLyXfYcCizv9dzc/PN5KTkw273W488cQTRlxcnBEVFWWcO3fOrL/nnnuMPn36GIZhGD/++KPh5+dn/Oc///Hqs3PnzkZCQoJhGIYxYcIEo2XLll7tM2bMMKKiorzmEBUVZeTl5ZnrGjdubNx6663m43PnzhmBgYHG22+/bRiGYbz88stGjRo1jJMnT5o1SUlJhq+vr+F2u736LWr+hvHb35gZM2YU+fysWLHCqFmzZpHt+GNwBAYV4rnnntOiRYu0Z88er/V79uzRzTff7LXu5ptv1vfff+916qdNmzYX9BkQEKDrrrvOfBwWFqZ69eqpatWqXuvOP0WUmpqqnj17KjIyUtWqVdNtt90mScrIyLi8HQQsbvXq1apataqqVKmi7t27q0+fPpo4caIkqVmzZvLz8zNra9eubf5epaenKy8vT40aNVLVqlXNZcuWLdq/f3+J5tCsWTP5+v7vbSosLEwxMTHmYz8/P9WsWdMce8+ePWrZsqUCAwPNmptvvln5+fnau3evV79Fzb8w69evV+fOnXXNNdeoWrVq6t+/v37++Wf9+uuvJdoflK0r9ssccXVr3769XC6XEhISSnXK5vw/UAUqV67s9djHx6fQdfn5+ZL+d7jZ5XJp8eLFCgkJUUZGhlwuFxcG40+vY8eOmj9/vmw2m+rUqaNKlf73dlHc79XJkyfl5+en1NRUr5AgyfzPhK+vr4zffQ3f+degFDdOcWNfqpL0cfDgQfXo0UPDhw/Xv/71LwUHB+uTTz7R4MGDdfbsWS72rUAEGFSYZ599Vq1atVLjxo3NdU2bNtW2bdu86rZt26ZGjRpd8Mfwcn377bf6+eef9eyzzyoiIkKS9MUXX5TpGIBVBQYGqkGDBiXe7vrrr1deXp6OHj2qW2+9tdCakJAQud1uGYZh3kIhLS3tcqYr6be/H4mJiTp16pT5n5xt27bJ19fX6+9MSaSmpio/P1/Tpk0zjwYtX778sueKy8cpJFSYmJgY9evXT7NnzzbXPf7449qwYYMmT56s7777TosWLdKcOXP0xBNPlPn4kZGRstlsevHFF/XDDz9o1apVmjx5cpmPA/yZNGrUSP369dOAAQP07rvv6sCBA9q+fbumTJmipKQkSVKHDh107NgxTZ06Vfv379fcuXO1Zs2ayx67X79+qlKliuLi4rRr1y5t2rRJjzzyiPr376+wsLBS9dmgQQPl5uaafyfefPNNLViw4LLnistHgEGFmjRpkteh2xtuuEHLly/X0qVL1bx5c40fP16TJk0ql08GhYSEKDExUStWrFB0dLSeffZZvfDCC2U+DvBns3DhQg0YMECPP/64GjdurDvvvFM7duxQZGSkpN+OlMybN09z585Vy5YttX379jL5T0pAQIDWrVun48eP68Ybb9Tdd9+tzp07a86cOaXus2XLlpo+fbqee+45NW/eXIsXLy72NhD44/gYvz8RCQAAcIXjCAwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALCc/w+Yc1Wxj8+jlAAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"markdown","source":"as we can see here there is aproblem in dataset which is implanced datatset\nto solve this problem we have many solution:\n- class weight\n- SMOTE\n- over sample\n- down sample\n","metadata":{}},{"cell_type":"code","source":"#create-dataset\n#first creat function that take the path of the imags and return data frame contain images\n#and label then encode label using labelencoder\ndef img_path(path):\n    normal_imgs = os.listdir(os.path.join(path,\"NORMAL\"))\n    pneumonia_imgs = os.listdir(os.path.join(path,\"PNEUMONIA\"))\n    normal_paths = [os.path.join(path,\"NORMAL\",filename) for filename in normal_imgs]\n    pneumonia_paths = [os.path.join(path,\"PNEUMONIA\",filename) for filename in  pneumonia_imgs]\n    normal_labels = [\"NORMAL\" for i in range( len(normal_paths) )]\n    pneumonia_labels = [\"PNEUMONIA\" for i in range(len(pneumonia_paths))]\n    df = pd.DataFrame({\n    \"images\":[*normal_paths , *pneumonia_paths],\n    \"target\":[*normal_labels,*pneumonia_labels]})\n    labelencoder = LabelEncoder()\n    df['target_encoded'] = labelencoder.fit_transform(df['target'])\n    return df\n    ","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:37:40.246191Z","iopub.execute_input":"2024-07-31T20:37:40.246727Z","iopub.status.idle":"2024-07-31T20:37:40.254700Z","shell.execute_reply.started":"2024-07-31T20:37:40.246532Z","shell.execute_reply":"2024-07-31T20:37:40.253753Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"df=img_path(\"/kaggle/input/chest-xray-pneumonia/chest_xray/train/\")\ndf.shape","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:37:49.394685Z","iopub.execute_input":"2024-07-31T20:37:49.395365Z","iopub.status.idle":"2024-07-31T20:37:49.422174Z","shell.execute_reply.started":"2024-07-31T20:37:49.395310Z","shell.execute_reply":"2024-07-31T20:37:49.421266Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"(5216, 3)"},"metadata":{}}]},{"cell_type":"code","source":"df.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:37:53.304130Z","iopub.execute_input":"2024-07-31T20:37:53.304951Z","iopub.status.idle":"2024-07-31T20:37:53.320046Z","shell.execute_reply.started":"2024-07-31T20:37:53.304918Z","shell.execute_reply":"2024-07-31T20:37:53.319244Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"                                              images  target  target_encoded\n0  /kaggle/input/chest-xray-pneumonia/chest_xray/...  NORMAL               0\n1  /kaggle/input/chest-xray-pneumonia/chest_xray/...  NORMAL               0\n2  /kaggle/input/chest-xray-pneumonia/chest_xray/...  NORMAL               0\n3  /kaggle/input/chest-xray-pneumonia/chest_xray/...  NORMAL               0\n4  /kaggle/input/chest-xray-pneumonia/chest_xray/...  NORMAL               0","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>images</th>\n      <th>target</th>\n      <th>target_encoded</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>/kaggle/input/chest-xray-pneumonia/chest_xray/...</td>\n      <td>NORMAL</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>/kaggle/input/chest-xray-pneumonia/chest_xray/...</td>\n      <td>NORMAL</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>/kaggle/input/chest-xray-pneumonia/chest_xray/...</td>\n      <td>NORMAL</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>/kaggle/input/chest-xray-pneumonia/chest_xray/...</td>\n      <td>NORMAL</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>/kaggle/input/chest-xray-pneumonia/chest_xray/...</td>\n      <td>NORMAL</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#i will depend on the train image for both train and validation i will split images to train and validation\ntrain, validation = train_test_split(df, test_size=0.2, random_state=42,stratify=df['target_encoded'])\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:38:01.071460Z","iopub.execute_input":"2024-07-31T20:38:01.072270Z","iopub.status.idle":"2024-07-31T20:38:01.082981Z","shell.execute_reply.started":"2024-07-31T20:38:01.072238Z","shell.execute_reply":"2024-07-31T20:38:01.082193Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"#images-preprocess\n#create preprocess function and add class weight to solve imbalanced class problem\ndef img_preprocess(df):\n    new_size = 224    \n    X = []\n    y = []\n    \n    for idx, row in df.iterrows():\n        file = row['images']\n        image_class = row['target_encoded']\n        original_image = cv2.imread(file)\n        \n        if original_image is not None:\n            image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)\n            resized_image = cv2.resize(image, (new_size, new_size))\n            X.append(resized_image)\n            y.append(image_class)\n        else:\n            print(f\"Warning: Image {file} not found or unable to read\")\n    \n    X = np.array(X)\n    y = np.array(y)\n    \n    # Normalize image \n    X = X / 255.0\n    # use class weights to avoid imbanced problem\n    class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(y), y=y)\n    class_weights_dict = dict(enumerate(class_weights))\n\n    print(\"Class Weights: \", class_weights_dict)\n\n    # Convert labels to categorical format\n    y = to_categorical(y, num_classes=2)\n    \n    return X, y,class_weights_dict\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:52:42.802033Z","iopub.execute_input":"2024-07-31T20:52:42.802758Z","iopub.status.idle":"2024-07-31T20:52:42.810748Z","shell.execute_reply.started":"2024-07-31T20:52:42.802727Z","shell.execute_reply":"2024-07-31T20:52:42.809770Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"X_train, y_train,class_weights_dict= img_preprocess(train)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:52:57.275856Z","iopub.execute_input":"2024-07-31T20:52:57.276216Z","iopub.status.idle":"2024-07-31T20:53:42.530619Z","shell.execute_reply.started":"2024-07-31T20:52:57.276187Z","shell.execute_reply":"2024-07-31T20:53:42.529652Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Class Weights:  {0: 1.9440820130475303, 1: 0.6731203614069055}\n","output_type":"stream"}]},{"cell_type":"code","source":"X_val, y_val,class_weights_dict= img_preprocess(validation)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:53:55.151528Z","iopub.execute_input":"2024-07-31T20:53:55.152421Z","iopub.status.idle":"2024-07-31T20:54:06.425076Z","shell.execute_reply.started":"2024-07-31T20:53:55.152389Z","shell.execute_reply":"2024-07-31T20:54:06.424180Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Class Weights:  {0: 1.9477611940298507, 1: 0.6726804123711341}\n","output_type":"stream"}]},{"cell_type":"code","source":"test=img_path(\"/kaggle/input/chest-xray-pneumonia/chest_xray/test/\")\nX_test, y_test,class_weights_dict= img_preprocess(test)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T20:54:06.426587Z","iopub.execute_input":"2024-07-31T20:54:06.426868Z","iopub.status.idle":"2024-07-31T20:54:12.638779Z","shell.execute_reply.started":"2024-07-31T20:54:06.426844Z","shell.execute_reply":"2024-07-31T20:54:12.637740Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Class Weights:  {0: 1.3333333333333333, 1: 0.8}\n","output_type":"stream"}]},{"cell_type":"code","source":"#training-the-model\n#try basic cnn mosel\n# Define the CNN model\nmodel = models.Sequential([\n    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),\n    layers.MaxPooling2D((2, 2)),\n    layers.Flatten(),\n    layers.Dense(2, activation='softmax')\n])\nmodel.summary()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:19:03.884431Z","iopub.execute_input":"2024-07-31T21:19:03.884887Z","iopub.status.idle":"2024-07-31T21:19:03.927066Z","shell.execute_reply.started":"2024-07-31T21:19:03.884857Z","shell.execute_reply":"2024-07-31T21:19:03.926200Z"},"trusted":true},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_4\"\u001b[0m\n","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_4\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"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;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m394272\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;34m2\u001b[0m)              │       \u001b[38;5;34m788,546\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","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\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</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\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_4 (<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\">394272</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\">2</span>)              │       <span style=\"color: #00af00; text-decoration-color: #00af00\">788,546</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m789,442\u001b[0m (3.01 MB)\n","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\">789,442</span> (3.01 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m789,442\u001b[0m (3.01 MB)\n","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\">789,442</span> (3.01 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","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"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\nearlystop = EarlyStopping(\"val_accuracy\", patience=30, verbose=1, restore_best_weights=True,)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:19:09.196195Z","iopub.execute_input":"2024-07-31T21:19:09.196557Z","iopub.status.idle":"2024-07-31T21:19:09.206510Z","shell.execute_reply.started":"2024-07-31T21:19:09.196523Z","shell.execute_reply":"2024-07-31T21:19:09.205634Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"history = model.fit(\n    X_train, y_train,\n    validation_data=(X_val, y_val),\n    epochs=50,\n    callbacks=[earlystop],\n    class_weight=class_weights_dict\n)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:24:39.184345Z","iopub.execute_input":"2024-07-31T21:24:39.185135Z","iopub.status.idle":"2024-07-31T21:26:32.459388Z","shell.execute_reply.started":"2024-07-31T21:24:39.185103Z","shell.execute_reply":"2024-07-31T21:26:32.458395Z"},"trusted":true},"execution_count":54,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 39ms/step - accuracy: 0.9335 - loss: 0.3548 - val_accuracy: 0.9540 - val_loss: 0.1444\nEpoch 2/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 0.9777 - loss: 0.0677 - val_accuracy: 0.9569 - val_loss: 0.1122\nEpoch 3/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 0.9871 - loss: 0.0387 - val_accuracy: 0.9684 - val_loss: 0.1039\nEpoch 4/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 0.9920 - loss: 0.0214 - val_accuracy: 0.9550 - val_loss: 0.1339\nEpoch 5/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 0.9929 - loss: 0.0195 - val_accuracy: 0.9665 - val_loss: 0.0947\nEpoch 6/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0032 - val_accuracy: 0.9693 - val_loss: 0.1017\nEpoch 7/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9713 - val_loss: 0.0996\nEpoch 8/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.9722 - val_loss: 0.1017\nEpoch 9/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 8.2631e-04 - val_accuracy: 0.9703 - val_loss: 0.1040\nEpoch 10/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 4.7054e-04 - val_accuracy: 0.9703 - val_loss: 0.1049\nEpoch 11/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 4.2972e-04 - val_accuracy: 0.9713 - val_loss: 0.1107\nEpoch 12/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 3.1463e-04 - val_accuracy: 0.9703 - val_loss: 0.1081\nEpoch 13/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 2.5707e-04 - val_accuracy: 0.9732 - val_loss: 0.1095\nEpoch 14/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 2.3105e-04 - val_accuracy: 0.9713 - val_loss: 0.1137\nEpoch 15/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 2.1352e-04 - val_accuracy: 0.9732 - val_loss: 0.1123\nEpoch 16/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 1.6746e-04 - val_accuracy: 0.9703 - val_loss: 0.1128\nEpoch 17/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 1.4774e-04 - val_accuracy: 0.9713 - val_loss: 0.1140\nEpoch 18/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.2021e-04 - val_accuracy: 0.9703 - val_loss: 0.1143\nEpoch 19/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 1.0621e-04 - val_accuracy: 0.9722 - val_loss: 0.1138\nEpoch 20/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 9.0911e-05 - val_accuracy: 0.9674 - val_loss: 0.1149\nEpoch 21/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 8.2896e-05 - val_accuracy: 0.9713 - val_loss: 0.1154\nEpoch 22/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 8.3784e-05 - val_accuracy: 0.9722 - val_loss: 0.1153\nEpoch 23/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 6.4313e-05 - val_accuracy: 0.9713 - val_loss: 0.1182\nEpoch 24/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 6.3681e-05 - val_accuracy: 0.9741 - val_loss: 0.1169\nEpoch 25/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 5.6438e-05 - val_accuracy: 0.9713 - val_loss: 0.1174\nEpoch 26/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 4.8982e-05 - val_accuracy: 0.9713 - val_loss: 0.1188\nEpoch 27/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 5.0677e-05 - val_accuracy: 0.9732 - val_loss: 0.1191\nEpoch 28/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 4.1578e-05 - val_accuracy: 0.9713 - val_loss: 0.1199\nEpoch 29/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 3.8973e-05 - val_accuracy: 0.9713 - val_loss: 0.1204\nEpoch 30/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 3.7778e-05 - val_accuracy: 0.9713 - val_loss: 0.1215\nEpoch 30: early stopping\nRestoring model weights from the end of the best epoch: 1.\n","output_type":"stream"}]},{"cell_type":"code","source":"#compute-accuracy\ndef model_accuracy():\n    y_test_pred = model.predict(X_test)\n    y_pred = np.argmax(y_test_pred, axis=1)\n    y_true=np.argmax(y_test, axis=1) \n    cm = confusion_matrix(y_true, y_pred)\n    print(\"confusion matrix:\",cm)\n    fig, ax = plot_confusion_matrix(conf_mat=cm ,  figsize=(5, 5))\n    plt.show()\n    print(classification_report(y_true, y_pred, target_names=['Normal', 'Pneumonia']))\n    accuracy = accuracy_score(y_true, y_pred)\n    recall = recall_score(y_true, y_pred, average='macro')\n    f1 = f1_score(y_true, y_pred, average='macro')\n    print(f\"Accuracy: {accuracy}\")\n    print(f\"Recall: {recall}\")\n    print(f\"F1 Score: {f1}\")","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:28:31.144017Z","iopub.execute_input":"2024-07-31T21:28:31.144708Z","iopub.status.idle":"2024-07-31T21:28:31.151524Z","shell.execute_reply.started":"2024-07-31T21:28:31.144678Z","shell.execute_reply":"2024-07-31T21:28:31.150687Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"model.evaluate(X_train,y_train)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:28:43.255264Z","iopub.execute_input":"2024-07-31T21:28:43.256006Z","iopub.status.idle":"2024-07-31T21:28:52.328521Z","shell.execute_reply.started":"2024-07-31T21:28:43.255975Z","shell.execute_reply":"2024-07-31T21:28:52.327583Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stdout","text":"\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9741 - loss: 0.0741\n","output_type":"stream"},{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"[0.07216078042984009, 0.9743528366088867]"},"metadata":{}}]},{"cell_type":"code","source":"model.evaluate(X_val,y_val)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:29:15.068250Z","iopub.execute_input":"2024-07-31T21:29:15.068968Z","iopub.status.idle":"2024-07-31T21:29:17.379750Z","shell.execute_reply.started":"2024-07-31T21:29:15.068938Z","shell.execute_reply":"2024-07-31T21:29:17.378860Z"},"trusted":true},"execution_count":57,"outputs":[{"name":"stdout","text":"\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9547 - loss: 0.1362\n","output_type":"stream"},{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"[0.14444972574710846, 0.954023003578186]"},"metadata":{}}]},{"cell_type":"code","source":"model_accuracy()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:29:27.427245Z","iopub.execute_input":"2024-07-31T21:29:27.427613Z","iopub.status.idle":"2024-07-31T21:29:29.183246Z","shell.execute_reply.started":"2024-07-31T21:29:27.427586Z","shell.execute_reply":"2024-07-31T21:29:29.181873Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"\u001b[1m19/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1722461368.633821     206 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step\nconfusion matrix: [[108 126]\n [ 12 378]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x500 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"              precision    recall  f1-score   support\n\n      Normal       0.90      0.46      0.61       234\n   Pneumonia       0.75      0.97      0.85       390\n\n    accuracy                           0.78       624\n   macro avg       0.82      0.72      0.73       624\nweighted avg       0.81      0.78      0.76       624\n\nAccuracy: 0.7788461538461539\nRecall: 0.7153846153846154\nF1 Score: 0.7279035377090206\n","output_type":"stream"}]},{"cell_type":"code","source":"#plotting-training-and-validation-accuracy-and-loss\n# Plot training and validation accuracy\nplt.plot(history.history['accuracy'], label='accuracy')\nplt.plot(history.history['val_accuracy'], label='val_accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:29:48.831563Z","iopub.execute_input":"2024-07-31T21:29:48.831929Z","iopub.status.idle":"2024-07-31T21:29:49.093743Z","shell.execute_reply.started":"2024-07-31T21:29:48.831899Z","shell.execute_reply":"2024-07-31T21:29:49.092802Z"},"trusted":true},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"#lets make some fine tuning by adding dropout layer\nmodel = models.Sequential([\n    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),\n    layers.MaxPooling2D((2, 2)),\n    layers.Flatten(),\n    layers.Dropout(0.2),\n    layers.Dense(2, activation='softmax')\n])\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:30:48.597475Z","iopub.execute_input":"2024-07-31T21:30:48.597848Z","iopub.status.idle":"2024-07-31T21:30:48.642931Z","shell.execute_reply.started":"2024-07-31T21:30:48.597821Z","shell.execute_reply":"2024-07-31T21:30:48.642158Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:107: 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__(activity_regularizer=activity_regularizer, **kwargs)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_5\"\u001b[0m\n","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_5\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"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_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m394272\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m394272\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │       \u001b[38;5;34m788,546\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","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_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\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</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\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_5 (<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\">394272</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">394272</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_6 (<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\">2</span>)              │       <span style=\"color: #00af00; text-decoration-color: #00af00\">788,546</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m789,442\u001b[0m (3.01 MB)\n","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\">789,442</span> (3.01 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m789,442\u001b[0m (3.01 MB)\n","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\">789,442</span> (3.01 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","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"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\nearlystop = EarlyStopping(\"val_accuracy\", patience=30, verbose=1, restore_best_weights=True,)\nhistory = model.fit(\n    X_train, y_train,\n    validation_data=(X_val, y_val),\n    epochs=50,\n    callbacks=[earlystop],\n    class_weight=class_weights_dict\n)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:30:59.791372Z","iopub.execute_input":"2024-07-31T21:30:59.792029Z","iopub.status.idle":"2024-07-31T21:33:52.363969Z","shell.execute_reply.started":"2024-07-31T21:30:59.791997Z","shell.execute_reply":"2024-07-31T21:33:52.363122Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m  5/131\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 31ms/step - accuracy: 0.4720 - loss: 14.9204","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1722461468.585383     205 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.7609 - loss: 3.6140","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1722461475.258943     204 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 56ms/step - accuracy: 0.7617 - loss: 3.5953 - val_accuracy: 0.9531 - val_loss: 0.1218\nEpoch 2/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 0.9693 - loss: 0.0843 - val_accuracy: 0.9626 - val_loss: 0.0923\nEpoch 3/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 0.9780 - loss: 0.0622 - val_accuracy: 0.9684 - val_loss: 0.0807\nEpoch 4/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 0.9900 - loss: 0.0320 - val_accuracy: 0.9684 - val_loss: 0.0920\nEpoch 5/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 27ms/step - accuracy: 0.9892 - loss: 0.0370 - val_accuracy: 0.9684 - val_loss: 0.0929\nEpoch 6/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 0.9992 - loss: 0.0089 - val_accuracy: 0.9713 - val_loss: 0.0835\nEpoch 7/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 0.9998 - loss: 0.0066 - val_accuracy: 0.9722 - val_loss: 0.0847\nEpoch 8/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0052 - val_accuracy: 0.9722 - val_loss: 0.0911\nEpoch 9/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0020 - val_accuracy: 0.9722 - val_loss: 0.0919\nEpoch 10/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0019 - val_accuracy: 0.9674 - val_loss: 0.1009\nEpoch 11/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9703 - val_loss: 0.0989\nEpoch 12/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 8.3598e-04 - val_accuracy: 0.9741 - val_loss: 0.0981\nEpoch 13/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 7.6112e-04 - val_accuracy: 0.9751 - val_loss: 0.0974\nEpoch 14/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 5.3179e-04 - val_accuracy: 0.9751 - val_loss: 0.0993\nEpoch 15/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 4.2466e-04 - val_accuracy: 0.9761 - val_loss: 0.1023\nEpoch 16/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 3.9226e-04 - val_accuracy: 0.9732 - val_loss: 0.1031\nEpoch 17/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 3.3392e-04 - val_accuracy: 0.9741 - val_loss: 0.1028\nEpoch 18/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 3.1844e-04 - val_accuracy: 0.9741 - val_loss: 0.1027\nEpoch 19/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 3.1790e-04 - val_accuracy: 0.9741 - val_loss: 0.1046\nEpoch 20/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 2.0040e-04 - val_accuracy: 0.9751 - val_loss: 0.1046\nEpoch 21/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.7810e-04 - val_accuracy: 0.9732 - val_loss: 0.1050\nEpoch 22/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.6225e-04 - val_accuracy: 0.9722 - val_loss: 0.1096\nEpoch 23/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.7741e-04 - val_accuracy: 0.9665 - val_loss: 0.1181\nEpoch 24/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.6269e-04 - val_accuracy: 0.9741 - val_loss: 0.1085\nEpoch 25/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.3273e-04 - val_accuracy: 0.9732 - val_loss: 0.1095\nEpoch 26/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.5517e-04 - val_accuracy: 0.9732 - val_loss: 0.1100\nEpoch 27/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.1215e-04 - val_accuracy: 0.9722 - val_loss: 0.1134\nEpoch 28/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.0581e-04 - val_accuracy: 0.9713 - val_loss: 0.1150\nEpoch 29/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 6.5903e-05 - val_accuracy: 0.9722 - val_loss: 0.1148\nEpoch 30/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 8.5396e-05 - val_accuracy: 0.9703 - val_loss: 0.1202\nEpoch 31/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 6.4702e-05 - val_accuracy: 0.9703 - val_loss: 0.1175\nEpoch 32/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 7.6181e-05 - val_accuracy: 0.9703 - val_loss: 0.1223\nEpoch 33/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 5.8651e-05 - val_accuracy: 0.9713 - val_loss: 0.1194\nEpoch 34/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 5.4549e-05 - val_accuracy: 0.9722 - val_loss: 0.1228\nEpoch 35/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 4.3734e-05 - val_accuracy: 0.9722 - val_loss: 0.1196\nEpoch 36/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 4.4500e-05 - val_accuracy: 0.9713 - val_loss: 0.1225\nEpoch 37/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 4.5303e-05 - val_accuracy: 0.9693 - val_loss: 0.1288\nEpoch 38/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 4.0500e-05 - val_accuracy: 0.9722 - val_loss: 0.1236\nEpoch 39/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 2.9268e-05 - val_accuracy: 0.9713 - val_loss: 0.1255\nEpoch 40/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 2.8074e-05 - val_accuracy: 0.9713 - val_loss: 0.1236\nEpoch 41/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 2.9443e-05 - val_accuracy: 0.9722 - val_loss: 0.1254\nEpoch 42/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 3.2590e-05 - val_accuracy: 0.9703 - val_loss: 0.1257\nEpoch 43/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 2.2409e-05 - val_accuracy: 0.9703 - val_loss: 0.1287\nEpoch 44/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.9241e-05 - val_accuracy: 0.9703 - val_loss: 0.1290\nEpoch 45/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 1.7368e-05 - val_accuracy: 0.9703 - val_loss: 0.1290\nEpoch 45: early stopping\nRestoring model weights from the end of the best epoch: 15.\n","output_type":"stream"}]},{"cell_type":"code","source":"model.evaluate(X_train,y_train)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:33:52.365851Z","iopub.execute_input":"2024-07-31T21:33:52.366140Z","iopub.status.idle":"2024-07-31T21:34:01.050659Z","shell.execute_reply.started":"2024-07-31T21:33:52.366116Z","shell.execute_reply":"2024-07-31T21:34:01.049659Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 1.0000 - loss: 3.2961e-04\n","output_type":"stream"},{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"[0.00032371358247473836, 1.0]"},"metadata":{}}]},{"cell_type":"code","source":"model.evaluate(X_val,y_val)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:34:01.051834Z","iopub.execute_input":"2024-07-31T21:34:01.052129Z","iopub.status.idle":"2024-07-31T21:34:03.271767Z","shell.execute_reply.started":"2024-07-31T21:34:01.052103Z","shell.execute_reply":"2024-07-31T21:34:03.270823Z"},"trusted":true},"execution_count":63,"outputs":[{"name":"stdout","text":"\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9774 - loss: 0.0915\n","output_type":"stream"},{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"[0.10232555121183395, 0.9760536551475525]"},"metadata":{}}]},{"cell_type":"code","source":"# Plot training and validation accuracy\nplt.plot(history.history['accuracy'], label='accuracy')\nplt.plot(history.history['val_accuracy'], label='val_accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:36:28.709494Z","iopub.execute_input":"2024-07-31T21:36:28.709848Z","iopub.status.idle":"2024-07-31T21:36:28.961457Z","shell.execute_reply.started":"2024-07-31T21:36:28.709821Z","shell.execute_reply":"2024-07-31T21:36:28.960584Z"},"trusted":true},"execution_count":65,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"model_accuracy()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:36:10.503662Z","iopub.execute_input":"2024-07-31T21:36:10.504278Z","iopub.status.idle":"2024-07-31T21:36:12.230118Z","shell.execute_reply.started":"2024-07-31T21:36:10.504241Z","shell.execute_reply":"2024-07-31T21:36:12.229041Z"},"trusted":true},"execution_count":64,"outputs":[{"name":"stdout","text":"\u001b[1m18/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1722461771.722696     205 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step\nconfusion matrix: [[ 68 166]\n [  1 389]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x500 with 1 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"},"metadata":{}},{"name":"stdout","text":"              precision    recall  f1-score   support\n\n      Normal       0.99      0.29      0.45       234\n   Pneumonia       0.70      1.00      0.82       390\n\n    accuracy                           0.73       624\n   macro avg       0.84      0.64      0.64       624\nweighted avg       0.81      0.73      0.68       624\n\nAccuracy: 0.7323717948717948\nRecall: 0.6440170940170941\nF1 Score: 0.6360626538844361\n","output_type":"stream"}]},{"cell_type":"code","source":"#lets make some fine tuning by adding more dense layer\nmodel = models.Sequential([\n    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),\n    layers.MaxPooling2D((2, 2)),\n    layers.Flatten(),\n    Dense(128, activation='relu'),\n    layers.Dense(2, activation='softmax')\n])\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:40:11.216867Z","iopub.execute_input":"2024-07-31T21:40:11.217471Z","iopub.status.idle":"2024-07-31T21:40:11.269707Z","shell.execute_reply.started":"2024-07-31T21:40:11.217439Z","shell.execute_reply":"2024-07-31T21:40:11.268808Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:107: 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__(activity_regularizer=activity_regularizer, **kwargs)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_6\"\u001b[0m\n","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_6\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"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_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_6 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m394272\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │    \u001b[38;5;34m50,466,944\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │           \u001b[38;5;34m258\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","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_6 (<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\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_6 (<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\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_6 (<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\">394272</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_7 (<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\">50,466,944</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_8 (<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\">2</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">258</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m50,468,098\u001b[0m (192.52 MB)\n","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\">50,468,098</span> (192.52 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m50,468,098\u001b[0m (192.52 MB)\n","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\">50,468,098</span> (192.52 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","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"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\nearlystop = EarlyStopping(\"val_accuracy\", patience=30, verbose=1, restore_best_weights=True,)\nhistory = model.fit(\n    X_train, y_train,\n    validation_data=(X_val, y_val),\n    epochs=50,\n    callbacks=[earlystop],\n    class_weight=class_weights_dict\n)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:40:31.203158Z","iopub.execute_input":"2024-07-31T21:40:31.203905Z","iopub.status.idle":"2024-07-31T21:44:53.858147Z","shell.execute_reply.started":"2024-07-31T21:40:31.203870Z","shell.execute_reply":"2024-07-31T21:44:53.857107Z"},"trusted":true},"execution_count":69,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 72ms/step - accuracy: 0.7610 - loss: 7.0137 - val_accuracy: 0.9636 - val_loss: 0.0957\nEpoch 2/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 0.9676 - loss: 0.0829 - val_accuracy: 0.9090 - val_loss: 0.2345\nEpoch 3/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 0.9854 - loss: 0.0463 - val_accuracy: 0.9713 - val_loss: 0.0774\nEpoch 4/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 0.9942 - loss: 0.0218 - val_accuracy: 0.9569 - val_loss: 0.1141\nEpoch 5/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 0.9932 - loss: 0.0196 - val_accuracy: 0.9693 - val_loss: 0.0880\nEpoch 6/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 0.0034 - val_accuracy: 0.9713 - val_loss: 0.0945\nEpoch 7/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 0.0019 - val_accuracy: 0.9703 - val_loss: 0.0968\nEpoch 8/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9751 - val_loss: 0.0923\nEpoch 9/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 8.1927e-04 - val_accuracy: 0.9722 - val_loss: 0.0946\nEpoch 10/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 7.1869e-04 - val_accuracy: 0.9761 - val_loss: 0.0940\nEpoch 11/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 4.5792e-04 - val_accuracy: 0.9751 - val_loss: 0.0960\nEpoch 12/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 5.0437e-04 - val_accuracy: 0.9751 - val_loss: 0.0966\nEpoch 13/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 3.6200e-04 - val_accuracy: 0.9741 - val_loss: 0.0969\nEpoch 14/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 2.9185e-04 - val_accuracy: 0.9693 - val_loss: 0.1048\nEpoch 15/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 2.9353e-04 - val_accuracy: 0.9761 - val_loss: 0.0980\nEpoch 16/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.9746e-04 - val_accuracy: 0.9761 - val_loss: 0.0985\nEpoch 17/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.7461e-04 - val_accuracy: 0.9761 - val_loss: 0.0990\nEpoch 18/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.4510e-04 - val_accuracy: 0.9741 - val_loss: 0.0996\nEpoch 19/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.3497e-04 - val_accuracy: 0.9741 - val_loss: 0.1002\nEpoch 20/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 1.1741e-04 - val_accuracy: 0.9761 - val_loss: 0.1005\nEpoch 21/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.0292e-04 - val_accuracy: 0.9741 - val_loss: 0.1023\nEpoch 22/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 9.2435e-05 - val_accuracy: 0.9751 - val_loss: 0.1025\nEpoch 23/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 8.3735e-05 - val_accuracy: 0.9761 - val_loss: 0.1022\nEpoch 24/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 7.5486e-05 - val_accuracy: 0.9751 - val_loss: 0.1024\nEpoch 25/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 6.9233e-05 - val_accuracy: 0.9741 - val_loss: 0.1031\nEpoch 26/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 6.1232e-05 - val_accuracy: 0.9770 - val_loss: 0.1040\nEpoch 27/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 5.7613e-05 - val_accuracy: 0.9761 - val_loss: 0.1052\nEpoch 28/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 5.3861e-05 - val_accuracy: 0.9751 - val_loss: 0.1052\nEpoch 29/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 4.5810e-05 - val_accuracy: 0.9751 - val_loss: 0.1045\nEpoch 30/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 4.5347e-05 - val_accuracy: 0.9741 - val_loss: 0.1052\nEpoch 31/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 4.1100e-05 - val_accuracy: 0.9770 - val_loss: 0.1074\nEpoch 32/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 3.3025e-05 - val_accuracy: 0.9741 - val_loss: 0.1066\nEpoch 33/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 2.9723e-05 - val_accuracy: 0.9751 - val_loss: 0.1069\nEpoch 34/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 3.1125e-05 - val_accuracy: 0.9761 - val_loss: 0.1101\nEpoch 35/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 2.8239e-05 - val_accuracy: 0.9761 - val_loss: 0.1096\nEpoch 36/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 2.4076e-05 - val_accuracy: 0.9751 - val_loss: 0.1093\nEpoch 37/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 2.3474e-05 - val_accuracy: 0.9713 - val_loss: 0.1126\nEpoch 38/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 1.9741e-05 - val_accuracy: 0.9751 - val_loss: 0.1095\nEpoch 39/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.8100e-05 - val_accuracy: 0.9732 - val_loss: 0.1105\nEpoch 40/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 1.7144e-05 - val_accuracy: 0.9761 - val_loss: 0.1120\nEpoch 41/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.6577e-05 - val_accuracy: 0.9732 - val_loss: 0.1104\nEpoch 42/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.3827e-05 - val_accuracy: 0.9751 - val_loss: 0.1122\nEpoch 43/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.4096e-05 - val_accuracy: 0.9732 - val_loss: 0.1120\nEpoch 44/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.2105e-05 - val_accuracy: 0.9751 - val_loss: 0.1126\nEpoch 45/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.1785e-05 - val_accuracy: 0.9732 - val_loss: 0.1131\nEpoch 46/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.1079e-05 - val_accuracy: 0.9741 - val_loss: 0.1127\nEpoch 47/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 9.4605e-06 - val_accuracy: 0.9751 - val_loss: 0.1137\nEpoch 48/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 9.2357e-06 - val_accuracy: 0.9741 - val_loss: 0.1137\nEpoch 49/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 7.5999e-06 - val_accuracy: 0.9732 - val_loss: 0.1155\nEpoch 50/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 7.6429e-06 - val_accuracy: 0.9741 - val_loss: 0.1152\nRestoring model weights from the end of the best epoch: 26.\n","output_type":"stream"}]},{"cell_type":"code","source":"model.evaluate(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:45:32.923904Z","iopub.execute_input":"2024-07-31T21:45:32.928244Z","iopub.status.idle":"2024-07-31T21:45:42.241521Z","shell.execute_reply.started":"2024-07-31T21:45:32.928207Z","shell.execute_reply":"2024-07-31T21:45:42.240596Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 1.0000 - loss: 5.3568e-05\n","output_type":"stream"},{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"[5.225718268775381e-05, 1.0]"},"metadata":{}}]},{"cell_type":"code","source":"model.evaluate(X_val,y_val)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:45:42.243199Z","iopub.execute_input":"2024-07-31T21:45:42.247496Z","iopub.status.idle":"2024-07-31T21:45:44.557008Z","shell.execute_reply.started":"2024-07-31T21:45:42.247468Z","shell.execute_reply":"2024-07-31T21:45:44.556084Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9786 - loss: 0.0906\n","output_type":"stream"},{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"[0.10403118282556534, 0.977011501789093]"},"metadata":{}}]},{"cell_type":"code","source":"# Plot training and validation accuracy\nplt.plot(history.history['accuracy'], label='accuracy')\nplt.plot(history.history['val_accuracy'], label='val_accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:45:44.559469Z","iopub.execute_input":"2024-07-31T21:45:44.559780Z","iopub.status.idle":"2024-07-31T21:45:44.806290Z","shell.execute_reply.started":"2024-07-31T21:45:44.559755Z","shell.execute_reply":"2024-07-31T21:45:44.805516Z"},"trusted":true},"execution_count":72,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"model_accuracy()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:45:52.347027Z","iopub.execute_input":"2024-07-31T21:45:52.347452Z","iopub.status.idle":"2024-07-31T21:45:54.712174Z","shell.execute_reply.started":"2024-07-31T21:45:52.347419Z","shell.execute_reply":"2024-07-31T21:45:54.710819Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step\nconfusion matrix: [[ 74 160]\n [  1 389]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x500 with 1 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"},"metadata":{}},{"name":"stdout","text":"              precision    recall  f1-score   support\n\n      Normal       0.99      0.32      0.48       234\n   Pneumonia       0.71      1.00      0.83       390\n\n    accuracy                           0.74       624\n   macro avg       0.85      0.66      0.65       624\nweighted avg       0.81      0.74      0.70       624\n\nAccuracy: 0.7419871794871795\nRecall: 0.6568376068376068\nF1 Score: 0.6537527011797306\n","output_type":"stream"}]},{"cell_type":"code","source":"#try using vgg19\n#try using some pretrained model\n\n\nvgg = VGG19(input_shape=(224, 224, 3), weights='imagenet', include_top=False)\nfor layer in vgg.layers:\n  layer.trainable = False\nflat_layer = Flatten()(vgg.output)\nfinal_layer = Dense(2, activation='softmax')(flat_layer)\nmodel = Model(inputs=vgg.input, outputs=final_layer)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:04:49.313395Z","iopub.execute_input":"2024-07-31T22:04:49.313819Z","iopub.status.idle":"2024-07-31T22:04:50.489582Z","shell.execute_reply.started":"2024-07-31T22:04:49.313792Z","shell.execute_reply":"2024-07-31T22:04:50.488713Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m80134624/80134624\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_8\"\u001b[0m\n","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: \"functional_8\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"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│ input_layer_8 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m1,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │       \u001b[38;5;34m147,584\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv4 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m1,180,160\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv4 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv4 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_8 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │        \u001b[38;5;34m50,178\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","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│ input_layer_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (<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\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (<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\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (<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\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (<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\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (<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\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv4 (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv4 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv4 (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (<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\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_8 (<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\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_10 (<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\">2</span>)              │        <span style=\"color: #00af00; text-decoration-color: #00af00\">50,178</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,074,562\u001b[0m (76.58 MB)\n","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\">20,074,562</span> (76.58 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m50,178\u001b[0m (196.01 KB)\n","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\">50,178</span> (196.01 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20,024,384\u001b[0m (76.39 MB)\n","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\">20,024,384</span> (76.39 MB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\nearlystop = EarlyStopping(\"val_accuracy\", patience=30, verbose=1, restore_best_weights=True,)\nhistory = model.fit(\n    X_train, y_train,\n    validation_data=(X_val, y_val),\n    epochs=50,\n    callbacks=[earlystop],\n    class_weight=class_weights_dict\n)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:05:07.955538Z","iopub.execute_input":"2024-07-31T22:05:07.956303Z","iopub.status.idle":"2024-07-31T22:25:39.531118Z","shell.execute_reply.started":"2024-07-31T22:05:07.956272Z","shell.execute_reply":"2024-07-31T22:25:39.530035Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 203ms/step - accuracy: 0.8445 - loss: 0.4115 - val_accuracy: 0.9502 - val_loss: 0.1267\nEpoch 2/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 176ms/step - accuracy: 0.9695 - loss: 0.0734 - val_accuracy: 0.9751 - val_loss: 0.0624\nEpoch 3/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 178ms/step - accuracy: 0.9850 - loss: 0.0458 - val_accuracy: 0.9818 - val_loss: 0.0590\nEpoch 4/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 178ms/step - accuracy: 0.9904 - loss: 0.0316 - val_accuracy: 0.9559 - val_loss: 0.1053\nEpoch 5/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 178ms/step - accuracy: 0.9836 - loss: 0.0436 - val_accuracy: 0.9780 - val_loss: 0.0569\nEpoch 6/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 179ms/step - accuracy: 0.9911 - loss: 0.0241 - val_accuracy: 0.9780 - val_loss: 0.0510\nEpoch 7/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 179ms/step - accuracy: 0.9949 - loss: 0.0181 - val_accuracy: 0.9847 - val_loss: 0.0573\nEpoch 8/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 180ms/step - accuracy: 0.9906 - loss: 0.0305 - val_accuracy: 0.9818 - val_loss: 0.0492\nEpoch 9/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 180ms/step - accuracy: 0.9955 - loss: 0.0131 - val_accuracy: 0.9799 - val_loss: 0.0568\nEpoch 10/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 181ms/step - accuracy: 0.9980 - loss: 0.0112 - val_accuracy: 0.9789 - val_loss: 0.0548\nEpoch 11/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 181ms/step - accuracy: 0.9979 - loss: 0.0093 - val_accuracy: 0.9847 - val_loss: 0.0405\nEpoch 12/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 0.9977 - loss: 0.0070 - val_accuracy: 0.9837 - val_loss: 0.0378\nEpoch 13/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 178ms/step - accuracy: 0.9995 - loss: 0.0045 - val_accuracy: 0.9847 - val_loss: 0.0383\nEpoch 14/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 179ms/step - accuracy: 0.9995 - loss: 0.0044 - val_accuracy: 0.9847 - val_loss: 0.0391\nEpoch 15/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 180ms/step - accuracy: 0.9998 - loss: 0.0031 - val_accuracy: 0.9665 - val_loss: 0.1156\nEpoch 16/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 180ms/step - accuracy: 0.9970 - loss: 0.0078 - val_accuracy: 0.9847 - val_loss: 0.0417\nEpoch 17/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 181ms/step - accuracy: 0.9998 - loss: 0.0024 - val_accuracy: 0.9847 - val_loss: 0.0408\nEpoch 18/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 181ms/step - accuracy: 1.0000 - loss: 0.0024 - val_accuracy: 0.9856 - val_loss: 0.0403\nEpoch 19/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 181ms/step - accuracy: 0.9998 - loss: 0.0026 - val_accuracy: 0.9856 - val_loss: 0.0399\nEpoch 20/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 0.0019 - val_accuracy: 0.9847 - val_loss: 0.0376\nEpoch 21/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9866 - val_loss: 0.0390\nEpoch 22/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9847 - val_loss: 0.0382\nEpoch 23/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 0.0017 - val_accuracy: 0.9837 - val_loss: 0.0453\nEpoch 24/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9856 - val_loss: 0.0424\nEpoch 25/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9856 - val_loss: 0.0377\nEpoch 26/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9847 - val_loss: 0.0382\nEpoch 27/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.9847 - val_loss: 0.0383\nEpoch 28/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9847 - val_loss: 0.0384\nEpoch 29/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 8.4751e-04 - val_accuracy: 0.9856 - val_loss: 0.0389\nEpoch 30/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 8.2386e-04 - val_accuracy: 0.9847 - val_loss: 0.0400\nEpoch 31/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 7.0773e-04 - val_accuracy: 0.9847 - val_loss: 0.0389\nEpoch 32/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 7.1859e-04 - val_accuracy: 0.9856 - val_loss: 0.0388\nEpoch 33/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 7.0280e-04 - val_accuracy: 0.9847 - val_loss: 0.0399\nEpoch 34/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 8.3306e-04 - val_accuracy: 0.9837 - val_loss: 0.0389\nEpoch 35/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 6.4986e-04 - val_accuracy: 0.9818 - val_loss: 0.0547\nEpoch 36/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 6.8001e-04 - val_accuracy: 0.9866 - val_loss: 0.0426\nEpoch 37/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 5.9750e-04 - val_accuracy: 0.9866 - val_loss: 0.0407\nEpoch 38/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 5.0442e-04 - val_accuracy: 0.9875 - val_loss: 0.0406\nEpoch 39/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 4.3437e-04 - val_accuracy: 0.9828 - val_loss: 0.0463\nEpoch 40/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 7.4092e-04 - val_accuracy: 0.9856 - val_loss: 0.0391\nEpoch 41/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 5.6324e-04 - val_accuracy: 0.9856 - val_loss: 0.0393\nEpoch 42/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 3.7001e-04 - val_accuracy: 0.9847 - val_loss: 0.0397\nEpoch 43/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 183ms/step - accuracy: 1.0000 - loss: 3.1650e-04 - val_accuracy: 0.9856 - val_loss: 0.0391\nEpoch 44/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 178ms/step - accuracy: 1.0000 - loss: 3.0376e-04 - val_accuracy: 0.9847 - val_loss: 0.0404\nEpoch 45/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 179ms/step - accuracy: 1.0000 - loss: 2.9300e-04 - val_accuracy: 0.9837 - val_loss: 0.0413\nEpoch 46/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 180ms/step - accuracy: 1.0000 - loss: 3.4082e-04 - val_accuracy: 0.9856 - val_loss: 0.0394\nEpoch 47/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 180ms/step - accuracy: 1.0000 - loss: 2.4019e-04 - val_accuracy: 0.9856 - val_loss: 0.0395\nEpoch 48/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 181ms/step - accuracy: 1.0000 - loss: 2.5382e-04 - val_accuracy: 0.9856 - val_loss: 0.0395\nEpoch 49/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 2.3222e-04 - val_accuracy: 0.9847 - val_loss: 0.0495\nEpoch 50/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 182ms/step - accuracy: 1.0000 - loss: 2.4757e-04 - val_accuracy: 0.9856 - val_loss: 0.0397\nRestoring model weights from the end of the best epoch: 38.\n","output_type":"stream"}]},{"cell_type":"code","source":"model.evaluate(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:25:39.533210Z","iopub.execute_input":"2024-07-31T22:25:39.533546Z","iopub.status.idle":"2024-07-31T22:26:05.590233Z","shell.execute_reply.started":"2024-07-31T22:25:39.533519Z","shell.execute_reply":"2024-07-31T22:26:05.589368Z"},"trusted":true},"execution_count":85,"outputs":[{"name":"stdout","text":"\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 148ms/step - accuracy: 1.0000 - loss: 5.7105e-04\n","output_type":"stream"},{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"[0.0005020098760724068, 1.0]"},"metadata":{}}]},{"cell_type":"code","source":"model.evaluate(X_val,y_val)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:26:05.591203Z","iopub.execute_input":"2024-07-31T22:26:05.591491Z","iopub.status.idle":"2024-07-31T22:26:12.056792Z","shell.execute_reply.started":"2024-07-31T22:26:05.591466Z","shell.execute_reply":"2024-07-31T22:26:12.055891Z"},"trusted":true},"execution_count":86,"outputs":[{"name":"stdout","text":"\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 142ms/step - accuracy: 0.9915 - loss: 0.0278\n","output_type":"stream"},{"execution_count":86,"output_type":"execute_result","data":{"text/plain":"[0.040617071092128754, 0.9875478744506836]"},"metadata":{}}]},{"cell_type":"code","source":"# Plot training and validation accuracy\nplt.plot(history.history['accuracy'], label='accuracy')\nplt.plot(history.history['val_accuracy'], label='val_accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:26:12.058939Z","iopub.execute_input":"2024-07-31T22:26:12.059391Z","iopub.status.idle":"2024-07-31T22:26:12.257525Z","shell.execute_reply.started":"2024-07-31T22:26:12.059356Z","shell.execute_reply":"2024-07-31T22:26:12.256614Z"},"trusted":true},"execution_count":87,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"model_accuracy()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:26:12.258717Z","iopub.execute_input":"2024-07-31T22:26:12.259060Z","iopub.status.idle":"2024-07-31T22:26:17.387206Z","shell.execute_reply.started":"2024-07-31T22:26:12.259028Z","shell.execute_reply":"2024-07-31T22:26:17.384471Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 167ms/step\nconfusion matrix: [[ 97 137]\n [  0 390]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x500 with 1 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"},"metadata":{}},{"name":"stdout","text":"              precision    recall  f1-score   support\n\n      Normal       1.00      0.41      0.59       234\n   Pneumonia       0.74      1.00      0.85       390\n\n    accuracy                           0.78       624\n   macro avg       0.87      0.71      0.72       624\nweighted avg       0.84      0.78      0.75       624\n\nAccuracy: 0.780448717948718\nRecall: 0.7072649572649573\nF1 Score: 0.7183512504653622\n","output_type":"stream"}]},{"cell_type":"code","source":"#try using vgg16\n#try using some pretrained model\n\n\nvgg = VGG16(input_shape=(224, 224, 3), weights='imagenet', include_top=False)\nfor layer in vgg.layers:\n  layer.trainable = False\nflat_layer = Flatten()(vgg.output)\nfinal_layer = Dense(2, activation='softmax')(flat_layer)\nmodel = Model(inputs=vgg.input, outputs=final_layer)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:48:11.609018Z","iopub.execute_input":"2024-07-31T21:48:11.609833Z","iopub.status.idle":"2024-07-31T21:48:12.351826Z","shell.execute_reply.started":"2024-07-31T21:48:11.609800Z","shell.execute_reply":"2024-07-31T21:48:12.350905Z"},"trusted":true},"execution_count":77,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_7\"\u001b[0m\n","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: \"functional_7\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"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│ input_layer_7 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m1,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │       \u001b[38;5;34m147,584\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m1,180,160\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_7 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │        \u001b[38;5;34m50,178\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","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│ input_layer_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (<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\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (<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\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (<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\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (<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\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (<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\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (<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\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (<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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (<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\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_7 (<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\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_9 (<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\">2</span>)              │        <span style=\"color: #00af00; text-decoration-color: #00af00\">50,178</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,764,866\u001b[0m (56.32 MB)\n","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\">14,764,866</span> (56.32 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m50,178\u001b[0m (196.01 KB)\n","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\">50,178</span> (196.01 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n","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\">14,714,688</span> (56.13 MB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\nearlystop = EarlyStopping(\"val_accuracy\", patience=30, verbose=1, restore_best_weights=True,)\nhistory = model.fit(\n    X_train, y_train,\n    validation_data=(X_val, y_val),\n    epochs=50,\n    callbacks=[earlystop],\n    class_weight=class_weights_dict\n)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T21:48:33.325102Z","iopub.execute_input":"2024-07-31T21:48:33.325479Z","iopub.status.idle":"2024-07-31T22:02:44.975224Z","shell.execute_reply.started":"2024-07-31T21:48:33.325451Z","shell.execute_reply":"2024-07-31T22:02:44.974387Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 424ms/step - accuracy: 0.8695 - loss: 0.3686 - val_accuracy: 0.9741 - val_loss: 0.0683\nEpoch 2/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9730 - loss: 0.0731 - val_accuracy: 0.9751 - val_loss: 0.0743\nEpoch 3/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9808 - loss: 0.0556 - val_accuracy: 0.9741 - val_loss: 0.0556\nEpoch 4/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9899 - loss: 0.0312 - val_accuracy: 0.9866 - val_loss: 0.0389\nEpoch 5/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9933 - loss: 0.0193 - val_accuracy: 0.9761 - val_loss: 0.0567\nEpoch 6/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9928 - loss: 0.0173 - val_accuracy: 0.9875 - val_loss: 0.0361\nEpoch 7/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9973 - loss: 0.0098 - val_accuracy: 0.9866 - val_loss: 0.0372\nEpoch 8/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9994 - loss: 0.0082 - val_accuracy: 0.9828 - val_loss: 0.0381\nEpoch 9/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9998 - loss: 0.0057 - val_accuracy: 0.9904 - val_loss: 0.0327\nEpoch 10/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9986 - loss: 0.0067 - val_accuracy: 0.9885 - val_loss: 0.0338\nEpoch 11/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9995 - loss: 0.0051 - val_accuracy: 0.9885 - val_loss: 0.0321\nEpoch 12/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 0.9997 - loss: 0.0044 - val_accuracy: 0.9847 - val_loss: 0.0366\nEpoch 13/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0033 - val_accuracy: 0.9875 - val_loss: 0.0319\nEpoch 14/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0026 - val_accuracy: 0.9885 - val_loss: 0.0322\nEpoch 15/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0028 - val_accuracy: 0.9885 - val_loss: 0.0317\nEpoch 16/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0023 - val_accuracy: 0.9799 - val_loss: 0.0436\nEpoch 17/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0020 - val_accuracy: 0.9885 - val_loss: 0.0318\nEpoch 18/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9885 - val_loss: 0.0320\nEpoch 19/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9875 - val_loss: 0.0326\nEpoch 20/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9885 - val_loss: 0.0320\nEpoch 21/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.9808 - val_loss: 0.0401\nEpoch 22/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.9866 - val_loss: 0.0338\nEpoch 23/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9875 - val_loss: 0.0335\nEpoch 24/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 9.4022e-04 - val_accuracy: 0.9856 - val_loss: 0.0348\nEpoch 25/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 9.9481e-04 - val_accuracy: 0.9875 - val_loss: 0.0323\nEpoch 26/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 7.0951e-04 - val_accuracy: 0.9856 - val_loss: 0.0356\nEpoch 27/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 7.4080e-04 - val_accuracy: 0.9875 - val_loss: 0.0331\nEpoch 28/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 6.3450e-04 - val_accuracy: 0.9866 - val_loss: 0.0348\nEpoch 29/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 6.0705e-04 - val_accuracy: 0.9885 - val_loss: 0.0334\nEpoch 30/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 5.6933e-04 - val_accuracy: 0.9885 - val_loss: 0.0324\nEpoch 31/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 4.6158e-04 - val_accuracy: 0.9885 - val_loss: 0.0326\nEpoch 32/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 5.1648e-04 - val_accuracy: 0.9885 - val_loss: 0.0329\nEpoch 33/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 3.8557e-04 - val_accuracy: 0.9885 - val_loss: 0.0325\nEpoch 34/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 4.0233e-04 - val_accuracy: 0.9856 - val_loss: 0.0367\nEpoch 35/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 4.0564e-04 - val_accuracy: 0.9885 - val_loss: 0.0334\nEpoch 36/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 3.7441e-04 - val_accuracy: 0.9875 - val_loss: 0.0336\nEpoch 37/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 153ms/step - accuracy: 1.0000 - loss: 3.5641e-04 - val_accuracy: 0.9885 - val_loss: 0.0328\nEpoch 38/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 3.2988e-04 - val_accuracy: 0.9885 - val_loss: 0.0350\nEpoch 39/50\n\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - accuracy: 1.0000 - loss: 2.7495e-04 - val_accuracy: 0.9875 - val_loss: 0.0339\nEpoch 39: early stopping\nRestoring model weights from the end of the best epoch: 9.\n","output_type":"stream"}]},{"cell_type":"code","source":"model.evaluate(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:02:44.977038Z","iopub.execute_input":"2024-07-31T22:02:44.977386Z","iopub.status.idle":"2024-07-31T22:03:12.011661Z","shell.execute_reply.started":"2024-07-31T22:02:44.977352Z","shell.execute_reply":"2024-07-31T22:03:12.010852Z"},"trusted":true},"execution_count":79,"outputs":[{"name":"stdout","text":"\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 125ms/step - accuracy: 1.0000 - loss: 0.0055\n","output_type":"stream"},{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"[0.004372948780655861, 1.0]"},"metadata":{}}]},{"cell_type":"code","source":"model.evaluate(X_val,y_val)","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:03:12.013092Z","iopub.execute_input":"2024-07-31T22:03:12.013419Z","iopub.status.idle":"2024-07-31T22:03:17.818264Z","shell.execute_reply.started":"2024-07-31T22:03:12.013393Z","shell.execute_reply":"2024-07-31T22:03:17.817362Z"},"trusted":true},"execution_count":80,"outputs":[{"name":"stdout","text":"\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 121ms/step - accuracy: 0.9892 - loss: 0.0315\n","output_type":"stream"},{"execution_count":80,"output_type":"execute_result","data":{"text/plain":"[0.03270931541919708, 0.99042147397995]"},"metadata":{}}]},{"cell_type":"code","source":"# Plot training and validation accuracy\nplt.plot(history.history['accuracy'], label='accuracy')\nplt.plot(history.history['val_accuracy'], label='val_accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:03:17.819614Z","iopub.execute_input":"2024-07-31T22:03:17.819994Z","iopub.status.idle":"2024-07-31T22:03:18.099212Z","shell.execute_reply.started":"2024-07-31T22:03:17.819960Z","shell.execute_reply":"2024-07-31T22:03:18.098223Z"},"trusted":true},"execution_count":81,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"model_accuracy()","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:03:18.101071Z","iopub.execute_input":"2024-07-31T22:03:18.101387Z","iopub.status.idle":"2024-07-31T22:03:38.840263Z","shell.execute_reply.started":"2024-07-31T22:03:18.101358Z","shell.execute_reply":"2024-07-31T22:03:38.838928Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 993ms/step\nconfusion matrix: [[103 131]\n [  1 389]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x500 with 1 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"},"metadata":{}},{"name":"stdout","text":"              precision    recall  f1-score   support\n\n      Normal       0.99      0.44      0.61       234\n   Pneumonia       0.75      1.00      0.85       390\n\n    accuracy                           0.79       624\n   macro avg       0.87      0.72      0.73       624\nweighted avg       0.84      0.79      0.76       624\n\nAccuracy: 0.7884615384615384\nRecall: 0.7188034188034188\nF1 Score: 0.7322062552831784\n","output_type":"stream"}]},{"cell_type":"code","source":"#loading-the-model-and-making-predictions\n#function that take image and return prediction\ndef prediction(path):\n    original_image = cv2.imread(path)       \n    image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)\n    resized_image = cv2.resize(image, (224, 224))  \n    img = np.array(resized_image)\n    img= img / 255.0\n    img = np.expand_dims(img, axis=0)\n    pred = model.predict(img)\n    class_names = ['NORMAL', 'PNEUMONIA']\n    predicted_class_index = (pred[0] > 0.5).astype(int)[0]\n    predicted_class_name = class_names[predicted_class_index]  \n    print(f'The predicted class for the image is: {predicted_class_name}')\n","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:55:18.371933Z","iopub.execute_input":"2024-07-31T22:55:18.372674Z","iopub.status.idle":"2024-07-31T22:55:18.379342Z","shell.execute_reply.started":"2024-07-31T22:55:18.372642Z","shell.execute_reply":"2024-07-31T22:55:18.378376Z"},"trusted":true},"execution_count":148,"outputs":[]},{"cell_type":"code","source":"prediction('/kaggle/input/chest-xray-pneumonia/chest_xray/test/NORMAL/IM-0003-0001.jpeg')","metadata":{"execution":{"iopub.status.busy":"2024-07-31T22:55:20.155384Z","iopub.execute_input":"2024-07-31T22:55:20.155735Z","iopub.status.idle":"2024-07-31T22:55:20.248249Z","shell.execute_reply.started":"2024-07-31T22:55:20.155708Z","shell.execute_reply":"2024-07-31T22:55:20.247416Z"},"trusted":true},"execution_count":149,"outputs":[{"name":"stdout","text":"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\nThe predicted class for the image is: NORMAL\nRaw prediction probabilities: [0.04547908 0.954521  ]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## conclusion \n# we try several model :\n1. first model: simple deep learning model \n :: Accuracy: 0.78 -  Recall: 0.72  - F1 Score: 0.73\n2. second model we did some fine tuning by adding drop out layer:\n::Accuracy: 0.73  -   Recall: 0.64  - F1 Score: 0.64\n3. third model anther fine tuning by adding dense layer:\n ::Accuracy: 0.74 - Recall: 0.66 - F1 Score: 0.65\n4. anther pretrained model vgg19\n::Accuracy: 0.78  - Recall: 0.70 -F1 Score: 0.72\n5. then we try pretrained model vgg16\n ::Accuracy: 0.79   -    Recall: 0.72  - F1 Score: 0.73\n # the highest accuracy and f1score is vgg16 so we use this model in reference\n","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}