Classification Model Code.ipynb

{"cells":[{"cell_type":"code","source":"!ls /datasets/acm-drive-data/'ACM DATA'","metadata":{"tags":[],"cell_id":"b0adad385e0e4e0899df485253d66441","source_hash":"a70ae7d3","execution_start":1681782591204,"execution_millis":1460,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"cardio_megaly\tCOVID\t   normal      Part8\t   yolo\nclassification\tinput.npy  output.npy  pneuomonia\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"import sklearn\nprint(sklearn.__version__)","metadata":{"cell_id":"ced98daf5b5c4cd6a797f296070b86b8","source_hash":"7f20c191","execution_start":1679631743715,"execution_millis":4383,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"1.1.2\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"!pip install opencv-python","metadata":{"tags":[],"cell_id":"423d07b841304170ae561f0ebd9c6b89","source_hash":"45b6a678","execution_start":1679631745317,"execution_millis":3988,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python in /root/venv/lib/python3.9/site-packages (4.7.0.72)\nRequirement already satisfied: numpy>=1.17.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from opencv-python) (1.23.4)\n\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.0.1 is available.\nYou should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"!pip install scikit-plot\n!pip install matplotlib","metadata":{"cell_id":"7598105473ab4e8c8a2fe3d1386cd0b9","source_hash":"f76141be","execution_start":1679631747981,"execution_millis":5135,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"Requirement already satisfied: scikit-plot in /root/venv/lib/python3.9/site-packages (0.3.7)\nRequirement already satisfied: joblib>=0.10 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from scikit-plot) (1.2.0)\nRequirement already satisfied: scipy>=0.9 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from scikit-plot) (1.9.3)\nRequirement already satisfied: matplotlib>=1.4.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from scikit-plot) (3.6.0)\nRequirement already satisfied: scikit-learn>=0.18 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from scikit-plot) (1.1.2)\nRequirement already satisfied: python-dateutil>=2.7 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\nRequirement already satisfied: numpy>=1.19 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (1.23.4)\nRequirement already satisfied: kiwisolver>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (1.4.4)\nRequirement already satisfied: pillow>=6.2.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (9.2.0)\nRequirement already satisfied: fonttools>=4.22.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (4.37.4)\nRequirement already satisfied: contourpy>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (1.0.5)\nRequirement already satisfied: cycler>=0.10 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (0.11.0)\nRequirement already satisfied: pyparsing>=2.2.1 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (3.0.9)\nRequirement already satisfied: packaging>=20.0 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib>=1.4.0->scikit-plot) (21.3)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from scikit-learn>=0.18->scikit-plot) (3.1.0)\nRequirement already satisfied: six>=1.5 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=1.4.0->scikit-plot) (1.16.0)\n\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.0.1 is available.\nYou should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0mRequirement already satisfied: matplotlib in /shared-libs/python3.9/py/lib/python3.9/site-packages (3.6.0)\nRequirement already satisfied: fonttools>=4.22.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib) (4.37.4)\nRequirement already satisfied: kiwisolver>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: numpy>=1.19 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib) (1.23.4)\nRequirement already satisfied: pillow>=6.2.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib) (9.2.0)\nRequirement already satisfied: cycler>=0.10 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: packaging>=20.0 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: contourpy>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib) (1.0.5)\nRequirement already satisfied: pyparsing>=2.2.1 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: six>=1.5 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.0.1 is available.\nYou should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"from PIL import Image\nimport os, sys\nimport pandas as pd\nimport numpy as np\n\n'''\nConverts all images in a directory to '.npy' format.\nUse np.save and np.load to save and load the images.\nUse it for training your neural networks in ML/DL projects. \n'''\n\n# Path to image directory\n#megaly_normal = '/datasets/acm-drive-data/ACM DATA/cardio_megaly/train/train/false'\n#lung_normal = '/datasets/acm-drive-data/ACM DATA/pneuomonia/chest_xray/train/NORMAL'\nnormal = '/datasets/acm-drive-data/ACM DATA/normal'\nCOVID = '/datasets/acm-drive-data/ACM DATA/COVID/images'\npnuemonia = '/datasets/acm-drive-data/ACM DATA/pneuomonia/chest_xray/train/PNEUMONIA'\ncardio_megaly = '/datasets/acm-drive-data/ACM DATA/cardio_megaly/train/train/true'\n\ninputValues = []\n#megaly_normal_dir = os.listdir(megaly_normal)\n#lung_normal_dir = os.listdir(lung_normal)\nnormal_dir = os.listdir(normal)\nCOVID_dir = os.listdir(COVID)\npneumonia_dir = os.listdir(pnuemonia)\ncardio_megaly_dir = os.listdir(cardio_megaly)\n\nfor pic in range(len(normal_dir)):\n    path = normal + '/' + normal_dir[pic]\n    inputValues.append(path)\nfor pic in range(len(COVID_dir)):\n    path = COVID + '/' + COVID_dir[pic]\n    inputValues.append(path)\nfor pic in range(len(pneumonia_dir)):\n    path = pnuemonia + '/' + pneumonia_dir[pic]\n    inputValues.append(path)\nfor pic in range(len(cardio_megaly_dir)):\n    path = cardio_megaly + '/' + cardio_megaly_dir[pic]\n    inputValues.append(path)\n\ninputValues = np.array(inputValues)\n\n# Display input values\ninputValues","metadata":{"tags":[],"cell_id":"7bf75560c539429bb5a7177e288e38a4","source_hash":"5991a6da","execution_start":1679631753120,"execution_millis":118,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"array(['/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1022.png',\n       '/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1278.png',\n       '/datasets/acm-drive-data/ACM DATA/normal/NORMAL_157.png', ...,\n       '/datasets/acm-drive-data/ACM DATA/cardio_megaly/train/train/true/487.png',\n       '/datasets/acm-drive-data/ACM DATA/cardio_megaly/train/train/true/1657.png',\n       '/datasets/acm-drive-data/ACM DATA/cardio_megaly/train/train/true/759.png'],\n      dtype='<U101')"},"metadata":{}}],"execution_count":null},{"cell_type":"code","source":"#output1 = ['Normal Megaly' for _ in range(1000)]\n#output2 = ['Normal Lung' for _ in range(1000)]\noutput1 = ['Normal' for _ in range(len(normal_dir))]\noutput2 = ['COVID' for _ in range(len(COVID_dir))]\noutput3 = ['Pnuemonia' for _ in range(len(pneumonia_dir))]\noutput4 = ['Cardiomegaly' for _ in range(len(cardio_megaly_dir))]\n\noutputValues = []\nfor o in output1:\n    outputValues.append(o)\nfor o in output2:\n    outputValues.append(o)\nfor o in output3:\n    outputValues.append(o)\nfor o in output4:\n    outputValues.append(o)\n\n\noutputValues = np.array(outputValues)\n\n# Print shape of both input and output\nprint(inputValues.shape, outputValues.shape)\n\n# Print Lenght of all outputs\n#print('Length of Normal Megaly: %d' % len(output1))\n#print('Length of Normal Lung: %d' % len(output2))\nprint('Length of Normal: %d' % len(output1))\nprint('Length of COVID: %d' % len(output2))\nprint('Length of Pnuemonia: %d' % len(output3))\nprint('Length of Cardio-megaly: %d' % len(output4))\n","metadata":{"tags":[],"cell_id":"9367028437db424180dd7b0a1cb19de2","source_hash":"c85631d7","execution_start":1679631753246,"execution_millis":31,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"(11512,) (11512,)\nLength of Normal: 1802\nLength of COVID: 3616\nLength of Pnuemonia: 3875\nLength of Cardio-megaly: 2219\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"df = pd.DataFrame({'inputValues':inputValues,\n                   'outputValues':outputValues})\n\n# Display dataframe\ndf","metadata":{"tags":[],"cell_id":"c301335c0ebf4642a59eff037d3304ce","source_hash":"4184c909","execution_start":1679631753260,"execution_millis":52,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":0},"deepnote_table_invalid":false,"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":2,"row_count":11512,"columns":[{"name":"inputValues","dtype":"object","stats":{"unique_count":11512,"nan_count":0,"categories":[{"name":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1022.png","count":1},{"name":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1278.png","count":1},{"name":"11510 others","count":11510}]}},{"name":"outputValues","dtype":"object","stats":{"unique_count":4,"nan_count":0,"categories":[{"name":"Pnuemonia","count":3875},{"name":"COVID","count":3616},{"name":"2 others","count":4021}]}},{"name":"_deepnote_index_column","dtype":"int64"}],"rows":[{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1022.png","outputValues":"Normal","_deepnote_index_column":"0"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1278.png","outputValues":"Normal","_deepnote_index_column":"1"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_157.png","outputValues":"Normal","_deepnote_index_column":"2"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1511.png","outputValues":"Normal","_deepnote_index_column":"3"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_350.png","outputValues":"Normal","_deepnote_index_column":"4"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1740.png","outputValues":"Normal","_deepnote_index_column":"5"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_240.png","outputValues":"Normal","_deepnote_index_column":"6"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1683.png","outputValues":"Normal","_deepnote_index_column":"7"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_74.png","outputValues":"Normal","_deepnote_index_column":"8"},{"inputValues":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1220.png","outputValues":"Normal","_deepnote_index_column":"9"}]},"text/plain":"                                             inputValues  outputValues\n0      /datasets/acm-drive-data/ACM DATA/normal/NORMA...        Normal\n1      /datasets/acm-drive-data/ACM DATA/normal/NORMA...        Normal\n2      /datasets/acm-drive-data/ACM DATA/normal/NORMA...        Normal\n3      /datasets/acm-drive-data/ACM DATA/normal/NORMA...        Normal\n4      /datasets/acm-drive-data/ACM DATA/normal/NORMA...        Normal\n...                                                  ...           ...\n11507  /datasets/acm-drive-data/ACM DATA/cardio_megal...  Cardiomegaly\n11508  /datasets/acm-drive-data/ACM DATA/cardio_megal...  Cardiomegaly\n11509  /datasets/acm-drive-data/ACM DATA/cardio_megal...  Cardiomegaly\n11510  /datasets/acm-drive-data/ACM DATA/cardio_megal...  Cardiomegaly\n11511  /datasets/acm-drive-data/ACM DATA/cardio_megal...  Cardiomegaly\n\n[11512 rows x 2 columns]","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>inputValues</th>\n      <th>outputValues</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>11507</th>\n      <td>/datasets/acm-drive-data/ACM DATA/cardio_megal...</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>11508</th>\n      <td>/datasets/acm-drive-data/ACM DATA/cardio_megal...</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>11509</th>\n      <td>/datasets/acm-drive-data/ACM DATA/cardio_megal...</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>11510</th>\n      <td>/datasets/acm-drive-data/ACM DATA/cardio_megal...</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>11511</th>\n      <td>/datasets/acm-drive-data/ACM DATA/cardio_megal...</td>\n      <td>Cardiomegaly</td>\n    </tr>\n  </tbody>\n</table>\n<p>11512 rows × 2 columns</p>\n</div>"},"metadata":{}}],"execution_count":null},{"cell_type":"code","source":"from PIL import Image, ImageOps\n\nres = df.values \nX = []\ny = []\nfor i in range(len(res)):\n    image = Image.open(res[i][0])\n    new_image = image.resize((128, 128))\n    grey = ImageOps.grayscale(new_image)\n    data = np.asarray(grey)\n    X.append(data)\n    y.append(res[i][1])","metadata":{"tags":[],"cell_id":"0407e16f68be41b594a572d670ade153","source_hash":"e361caeb","execution_start":1679631753352,"execution_millis":5411369,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":0},{"cell_type":"code","source":"inputValues = np.array(X)\noutputValues = np.array(y)\nnp.save('input.npy', inputValues)\nnp.save('output.npy', outputValues)\n\n#df = pd.DataFrame({'inputValues':inputValues,\n#                   'outputValues':outputValues})\n#df.to_csv('data.csv', sep='\\t')","metadata":{"tags":[],"cell_id":"164aaaa5ff7242bd9fbdb9add75b0650","source_hash":"c244cbba","execution_start":1679637164761,"execution_millis":471,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\n\n'''\n(11512,) (11512,)\nLength of Normal: 1802\nLength of COVID: 3616\nLength of Pnuemonia: 3875\nLength of Cardio-megaly: 2219\n'''\n\n# Prepare Ouputs\ndef prepareTargets(y_train, y_val, y_test):\n\tle = LabelEncoder()\n\tle.fit(y_train)\n\ty_train_enc, y_val_enc, y_test_enc = le.transform(y_train), le.transform(y_val), le.transform(y_test)\n\treturn y_train_enc, y_val_enc, y_test_enc\n\nX = np.load('input.npy')\ny = np.load('output.npy')\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.125, random_state=0)\ny_train, y_val, y_test = prepareTargets(y_train, y_val, y_test)\n\n#print('Size of X_train: %d, X_train: %d' % (len(X_train), len(X_test)))\n#print('Size of X_test: %d, y_test: %d' % (len(y_train), len(y_test)))\n\nprint('Size of X_train: %d, X_val: %d, X_train: %d' % (len(X_train), len(X_val), len(X_test)))\nprint('Size of X_test: %d, y_val: %d, y_test: %d' % (len(y_train), len(y_val), len(y_test)))\n","metadata":{"tags":[],"cell_id":"be1b03aeda134bff9675c31bd914d437","source_hash":"2c640670","execution_start":1680573389378,"execution_millis":3182,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"Size of X_train: 8057, X_val: 1152, X_train: 2303\nSize of X_test: 8057, y_val: 1152, y_test: 2303\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n'''\nimage = Image.open(res[0][0])\nnew_image = image.resize((128, 128)).\ndata = np.asarray(new_image)\nplt.imshow(data)\n'''\n# Normalize data\nX_train = np.array(X_train)\nX_val = np.array(X_val)\nX_test = np.array(X_test)\nX_train = X_train/255.0\nX_val = X_val/255.0\nX_test = X_test/255.0","metadata":{"tags":[],"cell_id":"71644d2f0b1440da80e83af57a9f1bda","source_hash":"f852b281","execution_start":1680573408686,"execution_millis":6371,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import tensorflow as tf\n\n'''\n# Filters are kernels/nodes in first layer, kernel_size -> 3x3, padding -> max pooling for every value\nmodel = tf.keras.models.Sequential()\nmodel.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=[128, 128, 1]))\n\n# First convolutionary and max pooling layers\nmodel.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))\nmodel.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))\n# Dropout Layer\nmodel.add(tf.keras.layers.Dropout(0.5))\n\n'''\n'''\n# Second convolutionary and max pooling layers\nmodel.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\nmodel.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))\n'''\n'''\nmodel.add(tf.keras.layers.Flatten())\nmodel.add(tf.keras.layers.Dense(units=128, activation='relu'))\n\nmodel.add(tf.keras.layers.Dense(units=4, activation='softmax'))\n\nmodel.summary()\n'''\n\n# ResNet50 (transfer learning) Architecture Below\nresNet50 = tf.keras.models.Sequential()\ntransfer = tf.keras.applications.resnet50.ResNet50(\n                include_top=False,\n                weights=None,\n                input_shape=(128,128,1),\n                pooling='max',\n                classes=4\n            )\n\nresNet50.add(transfer)\nresNet50.add(tf.keras.layers.Flatten())\nresNet50.add(tf.keras.layers.Dense(units=128, activation='relu'))\nresNet50.add(tf.keras.layers.Dense(units=4, activation='softmax'))\n","metadata":{"tags":[],"cell_id":"720e62a06c8542d99cd74ac08c72b015","source_hash":"99c20f85","execution_start":1680573421294,"execution_millis":17830,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stderr","text":"2023-04-04 01:57:01.313940: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2023-04-04 01:57:01.778242: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n2023-04-04 01:57:01.778277: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n2023-04-04 01:57:01.872750: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2023-04-04 01:57:06.294320: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n2023-04-04 01:57:06.294500: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n2023-04-04 01:57:06.294517: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n2023-04-04 01:57:12.829074: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n2023-04-04 01:57:12.829126: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)\n2023-04-04 01:57:12.829155: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (p-72ca566f-42ef-466b-9ab4-9411088db224): /proc/driver/nvidia/version does not exist\n2023-04-04 01:57:12.829478: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"resNet50.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])","metadata":{"tags":[],"cell_id":"cc872027fb3e4273ae64db768d2d71d8","source_hash":"3655ef50","execution_start":1680573444474,"execution_millis":115,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"'''\nCropping, Horizontal Flip, Cloudiness\nAccuracy: 95% minimum (AUC)\n'''\n\nresNet50.fit(X_train, y_train, epochs=10)","metadata":{"tags":[],"cell_id":"d3eec88235544155a4700c9e8425e385","source_hash":"50921472","execution_start":1680573448020,"execution_millis":342527,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stderr","text":"2023-04-04 01:57:29.449137: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 528023552 exceeds 10% of free system memory.\nEpoch 1/10\n 17/252 [=>............................] - ETA: 1:08:09 - loss: 4.9599 - accuracy: 0.4743","output_type":"stream"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn [7], line 6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124;03mCropping, Horizontal Flip, Cloudiness\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03mAccuracy: 95% minimum (AUC)\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mresNet50\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/keras/engine/training.py:1564\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1556\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1557\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1558\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1561\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1562\u001b[0m ):\n\u001b[1;32m   1563\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1564\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1565\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1566\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    944\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    945\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    946\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    949\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    950\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    951\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/tensorflow/python/eager/function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2493\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m   2494\u001b[0m   (graph_function,\n\u001b[1;32m   2495\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2497\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/tensorflow/python/eager/function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1858\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1859\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1860\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1861\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1862\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1863\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1864\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1865\u001b[0m     args,\n\u001b[1;32m   1866\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1867\u001b[0m     executing_eagerly)\n\u001b[1;32m   1868\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/tensorflow/python/eager/function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    498\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    505\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    506\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    507\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m    508\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    511\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m    512\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n","File \u001b[0;32m/shared-libs/python3.9/py/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"execution_count":null},{"cell_type":"code","source":"# Test accuracy of testing to make sure it doesn't overfit\nmodel.evaluate(X_val, y_val)","metadata":{"tags":[],"cell_id":"2152b88b460f4bd9afd00737ae0716e9","source_hash":"fb9bf3da","execution_start":1679638991983,"execution_millis":5233,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"36/36 [==============================] - 5s 139ms/step - loss: 0.1741 - accuracy: 0.9592\n","output_type":"stream"},{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"[0.1741374433040619, 0.9592013955116272]"},"metadata":{}}],"execution_count":null},{"cell_type":"code","source":"y_pred = model.predict(X_test)","metadata":{"tags":[],"cell_id":"06bb235a06f84693a5f9fb4c9dfdb8cb","source_hash":"1febaa2d","execution_start":1679638997214,"execution_millis":10302,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"72/72 [==============================] - 10s 138ms/step\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import classification_report\n\n'''\nSpecificity : Precision\nSensitivity : Recall\n'''\ny_predict = []\nROW = len(y_pred)\nCOL = len(y_pred[0])\nfor i in range(ROW):\n    maximum = -1\n    index = -1\n    for j in range(COL):\n        if y_pred[i][j] > maximum:\n            maximum = y_pred[i][j]\n            index = j\n    y_predict.append(index)\n\ny_predict = np.array(y_predict)\nprint(classification_report(y_test, y_predict))\n\n'''\nNormal: 1\nCOVID: 2\nPnuemonia: 3\nCardio-megaly: 4\n'''","metadata":{"tags":[],"cell_id":"a4dca0afd7da41fd984639be3da2cd73","source_hash":"16682e7c","execution_start":1679639007534,"execution_millis":9,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.95      0.99      0.97       720\n           1       1.00      0.93      0.96       435\n           2       0.97      0.94      0.96       340\n           3       0.96      0.98      0.97       808\n\n    accuracy                           0.97      2303\n   macro avg       0.97      0.96      0.97      2303\nweighted avg       0.97      0.97      0.97      2303\n\n","output_type":"stream"},{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"'\\nNormal: 1\\nCOVID: 2\\nPnuemonia: 3\\nCardio-megaly: 4\\n'"},"metadata":{}}],"execution_count":null},{"cell_type":"code","source":"# Source: https://leslietj.github.io/2020/06/22/Draw-Confusion-Matrix-for-CNN-models/\n\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools\n\ndef plot_confusion_matrix(cm, target_names, title='Confusion matrix', cmap=None, normalize=False):\n    if cmap is None:\n        cmap = plt.get_cmap('Oranges')\n\n    plt.figure(figsize=(8, 6))\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    \n    if target_names is not None:\n        tick_marks = np.arange(len(target_names))\n        plt.xticks(tick_marks, target_names, rotation=45)\n        plt.yticks(tick_marks, target_names)\n\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n\n    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        if normalize:\n            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n                     horizontalalignment=\"center\",\n                     color=\"white\" if cm[i, j] > thresh else \"black\")\n        else:\n            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n                     horizontalalignment=\"center\",\n                     color=\"white\" if cm[i, j] > thresh else \"black\")\n\n\n    plt.tight_layout()\n    plt.ylim(len(target_names)-0.5, -0.5)\n    plt.ylabel('True Labels')\n    plt.xlabel('Predicted Labels')\n    plt.savefig('classfication_confusion_matrix.png', dpi=500, bbox_inches = 'tight')\n    plt.show()\n    \n    \n# a tuple for all the class names\ncm = confusion_matrix(y_test, y_predict)\ntarget_names = ('Normal', 'COVID', 'Pnuemonia', 'Cardiomegaly')\nplot_confusion_matrix(cm, target_names)","metadata":{"tags":[],"cell_id":"428fb4928de544ea96be3cfc275b699e","source_hash":"a654d8ef","execution_start":1679639007535,"execution_millis":1470,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":699,"height":609}},"output_type":"display_data"}],"execution_count":null},{"cell_type":"code","source":"import pickle\n\n# Dump everything in pickle file\nwith open('classification', 'wb') as f:\n    pickle.dump(model, f)","metadata":{"tags":[],"cell_id":"a74a967089fa4312a73f6784e07c8fc8","source_hash":"ea6a328c","execution_start":1679639374365,"execution_millis":2214,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stderr","text":"WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 2 of 2). These functions will not be directly callable after loading.\nINFO:tensorflow:Assets written to: ram://a6fcd450-b027-47ae-a94b-0bc8001c8c8e/assets\nINFO:tensorflow:Assets written to: ram://a6fcd450-b027-47ae-a94b-0bc8001c8c8e/assets\n","output_type":"stream"}],"execution_count":null},{"cell_type":"markdown","source":"Philip what is the error that occurs in the block above this one?","metadata":{"cell_id":"c939767a464d476f88501f93bc6e645d","is_collapsed":false,"formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"I'm trying to create a ROC curve but it only works for binary classification. I need to find a multiclassification version.","metadata":{"cell_id":"997f47a6-b01c-4f22-81c1-09385dda6948","is_collapsed":false,"formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools\n\n\ndef plot_confusion_matrix(cm, target_names, title='Confusion Matrix', cmap=None, normalize=False):\n    if cmap is None:\n        cmap = plt.get_cmap('Oranges')\n\n    plt.figure(figsize=(8, 6))\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    \n    if target_names is not None:\n        tick_marks = np.arange(len(target_names))\n        plt.xticks(tick_marks, target_names, rotation=45)\n        plt.yticks(tick_marks, target_names)\n\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n\n    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        if normalize:\n            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n                     horizontalalignment=\"center\",\n                     color=\"white\" if cm[i, j] > thresh else \"black\")\n        else:\n            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n                     horizontalalignment=\"center\",\n                     color=\"white\" if cm[i, j] > thresh else \"black\")\n\n\n    plt.tight_layout()\n    plt.ylim(len(target_names)-0.5, -0.5)\n    plt.ylabel('True Labels')\n    plt.xlabel('Predicted Labels')\n    plt.savefig('classfication_confusion_matrix.png', dpi=500, bbox_inches = 'tight')\n    plt.show()\n\n\n# Prepare Ouputs\ndef prepareTargets(y_train, y_val, y_test):\n\tle = LabelEncoder()\n\tle.fit(y_train)\n\ty_train_enc, y_val_enc, y_test_enc = le.transform(y_train), le.transform(y_val), le.transform(y_test)\n\treturn y_train_enc, y_val_enc, y_test_enc\n\nX = np.load('input.npy')\ny = np.load('output.npy')\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.125, random_state=0)\ny_train, y_val, y_test = prepareTargets(y_train, y_val, y_test)\n\n# Normalize data\nX_train = np.array(X_train)\nX_val = np.array(X_val)\nX_test = np.array(X_test)\nX_train = X_train/255.0\nX_val = X_val/255.0\nX_test = X_test/255.0","metadata":{"tags":[],"cell_id":"aa668f5f591b4096bca758915218d369","source_hash":"6709bae6","execution_start":1682478881534,"execution_millis":4488,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pickle\n\nwith open('classification', 'rb') as f:\n    model = pickle.load(f)\n\ny_pred = model.predict(X_test)\n\ny_predict = []\nROW = len(y_pred)\nCOL = len(y_pred[0])\nfor i in range(ROW):\n    maximum = -1\n    index = -1\n    for j in range(COL):\n        if y_pred[i][j] > maximum:\n            maximum = y_pred[i][j]\n            index = j\n    y_predict.append(index)\n\ny_predict = np.array(y_predict)\n\ncm = confusion_matrix(y_test, y_predict)\ntarget_names = ('Normal', 'COVID', 'Pnuemonia', 'Cardiomegaly')\nplot_confusion_matrix(cm, target_names)","metadata":{"cell_id":"fbc5915005b9487d951b302d8108f799","source_hash":"904f0c40","execution_start":1682478896497,"execution_millis":19749,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stderr","text":"2023-04-26 03:14:56.538794: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2023-04-26 03:14:56.670286: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n2023-04-26 03:14:56.675334: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n2023-04-26 03:14:56.675351: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n2023-04-26 03:14:56.710579: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2023-04-26 03:14:58.238663: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n2023-04-26 03:14:58.238750: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n2023-04-26 03:14:58.238759: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n2023-04-26 03:15:03.298377: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n2023-04-26 03:15:03.298429: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)\n2023-04-26 03:15:03.298453: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (p-72ca566f-42ef-466b-9ab4-9411088db224): /proc/driver/nvidia/version does not exist\n2023-04-26 03:15:03.298835: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n72/72 [==============================] - 10s 135ms/step\n","output_type":"stream"},{"data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":699,"height":609}},"output_type":"display_data"}],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import roc_curve, auc\nfrom sklearn.preprocessing import label_binarize\nfrom itertools import cycle\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# y_test: true class labels\n# y_score: predicted scores for each class\n\nclasses = ['Normal Image', 'Pneumonia', 'COVID-19', 'Cardiomegaly']\n\n# Binarize the output\nn_classes = len(np.unique(y_test))\ny_test_bin = label_binarize(y_test, classes=np.arange(n_classes))\n\n# Compute ROC curve and ROC area for each class\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nfor i in range(n_classes):\n    fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_pred[:, i])\n    roc_auc[i] = auc(fpr[i], tpr[i])\n\n# Compute micro-average ROC curve and ROC area\nfpr_micro, tpr_micro, _ = roc_curve(y_test_bin.ravel(), y_pred.ravel())\nroc_auc_micro = auc(fpr_micro, tpr_micro)\n\n# Compute macro-average ROC curve and ROC area\n# First aggregate all false positive rates\nall_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n\n# Then interpolate all ROC curves at this points\nmean_tpr = np.zeros_like(all_fpr)\nfor i in range(n_classes):\n    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n\n# Finally average it and compute AUC\nmean_tpr /= n_classes\nfpr_macro = all_fpr\ntpr_macro = mean_tpr\nroc_auc_macro = auc(fpr_macro, tpr_macro)\n\n# Plot ROC curve\nplt.figure(figsize=(8, 6))\n\ncolors = cycle(['red', 'darkorange', 'darkred', 'orange'])\nfor i, color in zip(range(n_classes), colors):\n    plt.plot(fpr[i], tpr[i], color=color, lw=2,\n             label='ROC curve of ' + classes[i] + ' (Area = {1:0.3f})'\n             ''.format(i, roc_auc[i]))\n\nplt.plot([0, 1], [0, 1], 'k--', lw=2)\nplt.xlim([0, 0.12])\nplt.ylim([0.9, 1.01])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Chest X-Ray ROC Curve')\nplt.legend(loc=\"lower right\")\nplt.show()\n","metadata":{"cell_id":"c7a9f5c035104867855e605268f130a9","source_hash":"a9499402","execution_start":1682482494177,"execution_millis":346,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":715,"height":547}},"output_type":"display_data"}],"execution_count":null},{"cell_type":"code","source":"import pandas as pd","metadata":{"cell_id":"d766ad4f27824a099d2fe7fd09962a84","deepnote_cell_type":"code"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import seaborn as sns\nfrom sklearn.metrics import roc_curve, auc\n\n# y_true is the true labels and y_score is the predicted scores for each class\nfpr = {}\ntpr = {}\nroc_auc = {}\n\nclasses = ['Normal Image', 'Pneumonia', 'COVID-19', 'Cardiomegaly']\n\n# Binarize the output\nn_classes = len(np.unique(y_test))\ny_test_bin = label_binarize(y_test, classes=np.arange(n_classes))\n\n# Compute ROC curve and ROC area for each class\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nfor i in range(n_classes):\n    fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_pred[:, i])\n    roc_auc[i] = auc(fpr[i], tpr[i])\n\n# Plot ROC curves for each class\nsns.set_style('whitegrid')\nplt.figure(figsize=(8, 6))\nfor i in range(n_classes):\n    sns.lineplot(x=fpr[i], y=tpr[i], label=f'Class {i} (AUC = {roc_auc[i]:.2f})')\nplt.plot([0, 1], [0, 1], 'k--')\nplt.xlim([-0.01, 1.0])\nplt.ylim([0.0, 1.01])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic (ROC) Curve')\nplt.legend(loc=\"lower right\")\nplt.show()\n","metadata":{"cell_id":"5311463c36db4eb98dd0fbaeee53214b","source_hash":"9d334c96","execution_start":1682481640365,"execution_millis":99,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"output_type":"error","ename":"TypeError","evalue":"lineplot() takes from 0 to 1 positional arguments but 2 were given","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn [10], line 27\u001b[0m\n\u001b[1;32m     25\u001b[0m sns\u001b[38;5;241m.\u001b[39mset_palette(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhusl\u001b[39m\u001b[38;5;124m'\u001b[39m, n_colors\u001b[38;5;241m=\u001b[39mn_classes)\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_classes):\n\u001b[0;32m---> 27\u001b[0m     \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlineplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfpr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtpr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mROC curve of class \u001b[39;49m\u001b[38;5;132;43;01m%s\u001b[39;49;00m\u001b[38;5;124;43m (AUC = \u001b[39;49m\u001b[38;5;132;43;01m%0.2f\u001b[39;49;00m\u001b[38;5;124;43m)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mclasses\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroc_auc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     28\u001b[0m sns\u001b[38;5;241m.\u001b[39mlineplot([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m], [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnavy\u001b[39m\u001b[38;5;124m'\u001b[39m, lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     29\u001b[0m sns\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFalse Positive Rate\u001b[39m\u001b[38;5;124m'\u001b[39m)\n","\u001b[0;31mTypeError\u001b[0m: lineplot() takes from 0 to 1 positional arguments but 2 were given"]}],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\ndf = pd.read_csv('object_df1.csv')\ndf","metadata":{"cell_id":"0047131151224352a7fc9f9d96be22c8","source_hash":"2db25618","execution_start":1682488390367,"execution_millis":233,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","execution_count":1,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":4,"row_count":10804,"columns":[{"name":"Unnamed: 0","dtype":"int64","stats":{"unique_count":10804,"nan_count":0,"min":"0","max":"10803","histogram":[{"bin_start":0,"bin_end":1080.3,"count":1081},{"bin_start":1080.3,"bin_end":2160.6,"count":1080},{"bin_start":2160.6,"bin_end":3240.8999999999996,"count":1080},{"bin_start":3240.8999999999996,"bin_end":4321.2,"count":1081},{"bin_start":4321.2,"bin_end":5401.5,"count":1080},{"bin_start":5401.5,"bin_end":6481.799999999999,"count":1080},{"bin_start":6481.799999999999,"bin_end":7562.099999999999,"count":1081},{"bin_start":7562.099999999999,"bin_end":8642.4,"count":1080},{"bin_start":8642.4,"bin_end":9722.699999999999,"count":1080},{"bin_start":9722.699999999999,"bin_end":10803,"count":1081}]}},{"name":"Input","dtype":"object","stats":{"unique_count":10804,"nan_count":0,"categories":[{"name":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1535.png","count":1},{"name":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_364.png","count":1},{"name":"10802 others","count":10802}]}},{"name":"Bounding Boxes","dtype":"object","stats":{"unique_count":6029,"nan_count":1802,"categories":[{"name":"['c7d389ff-e2e1-40ed-b940-aef09bae009a' 273.0 168.0 225.0 478.0 1]","count":2854},{"name":"6028 others","count":6148},{"name":"Missing","count":1802}]}},{"name":"Output","dtype":"object","stats":{"unique_count":4,"nan_count":0,"categories":[{"name":"Pneumonia","count":6012},{"name":"COVID Pneumonia","count":2854},{"name":"2 others","count":1938}]}},{"name":"_deepnote_index_column","dtype":"int64"}],"rows":[{"Unnamed: 0":"0","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1535.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"0"},{"Unnamed: 0":"1","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_364.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"1"},{"Unnamed: 0":"2","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1718.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"2"},{"Unnamed: 0":"3","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1694.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"3"},{"Unnamed: 0":"4","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_678.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"4"},{"Unnamed: 0":"5","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_979.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"5"},{"Unnamed: 0":"6","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1472.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"6"},{"Unnamed: 0":"7","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_347.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"7"},{"Unnamed: 0":"8","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_983.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"8"},{"Unnamed: 0":"9","Input":"/datasets/acm-drive-data/ACM DATA/normal/NORMAL_1595.png","Bounding Boxes":"nan","Output":"Normal","_deepnote_index_column":"9"}]},"text/plain":"       Unnamed: 0                                              Input  \\\n0               0  /datasets/acm-drive-data/ACM DATA/normal/NORMA...   \n1               1  /datasets/acm-drive-data/ACM DATA/normal/NORMA...   \n2               2  /datasets/acm-drive-data/ACM DATA/normal/NORMA...   \n3               3  /datasets/acm-drive-data/ACM DATA/normal/NORMA...   \n4               4  /datasets/acm-drive-data/ACM DATA/normal/NORMA...   \n...           ...                                                ...   \n10799       10799  /datasets/acm-drive-data/ACM DATA/Part8/000302...   \n10800       10800  /datasets/acm-drive-data/ACM DATA/Part8/000285...   \n10801       10801  /datasets/acm-drive-data/ACM DATA/Part8/000286...   \n10802       10802  /datasets/acm-drive-data/ACM DATA/Part8/000302...   \n10803       10803  /datasets/acm-drive-data/ACM DATA/Part8/000298...   \n\n      Bounding Boxes        Output  \n0                NaN        Normal  \n1                NaN        Normal  \n2                NaN        Normal  \n3                NaN        Normal  \n4                NaN        Normal  \n...              ...           ...  \n10799             []  Cardiomegaly  \n10800             []  Cardiomegaly  \n10801             []  Cardiomegaly  \n10802             []  Cardiomegaly  \n10803             []  Cardiomegaly  \n\n[10804 rows x 4 columns]","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>Unnamed: 0</th>\n      <th>Input</th>\n      <th>Bounding Boxes</th>\n      <th>Output</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>NaN</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>NaN</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>NaN</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>NaN</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>/datasets/acm-drive-data/ACM DATA/normal/NORMA...</td>\n      <td>NaN</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>10799</th>\n      <td>10799</td>\n      <td>/datasets/acm-drive-data/ACM DATA/Part8/000302...</td>\n      <td>[]</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>10800</th>\n      <td>10800</td>\n      <td>/datasets/acm-drive-data/ACM DATA/Part8/000285...</td>\n      <td>[]</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>10801</th>\n      <td>10801</td>\n      <td>/datasets/acm-drive-data/ACM DATA/Part8/000286...</td>\n      <td>[]</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>10802</th>\n      <td>10802</td>\n      <td>/datasets/acm-drive-data/ACM DATA/Part8/000302...</td>\n      <td>[]</td>\n      <td>Cardiomegaly</td>\n    </tr>\n    <tr>\n      <th>10803</th>\n      <td>10803</td>\n      <td>/datasets/acm-drive-data/ACM DATA/Part8/000298...</td>\n      <td>[]</td>\n      <td>Cardiomegaly</td>\n    </tr>\n  </tbody>\n</table>\n<p>10804 rows × 4 columns</p>\n</div>"},"metadata":{}}],"execution_count":1},{"cell_type":"code","source":"pneum = df[df['Output'] == 'Pneumonia']\ncovid = df[df['Output'] == 'COVID Pneumonia']\ncardio = df[df['Output'] == 'Cardiomegaly']\n\np = pneum.values\nc = covid.values\nca = cardio.values","metadata":{"cell_id":"7552091866ce439ebbcb817502c66f5e","source_hash":"b7bd54c8","execution_start":1682488782918,"execution_millis":10,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":3},{"cell_type":"code","source":"print(p[0][1])","metadata":{"cell_id":"64899a7ef5be40d1adbecab2e1b561f9","source_hash":"353f749b","execution_start":1682488860449,"execution_millis":86,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"/datasets/acm-drive-data/ACM DATA/yolo/pneumonia/pneumonia_images/604a0eca-1494-47b8-a49b-713d24d070f9.dcm\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"path = 'pneumoniaPics/' + \nimage = Image.open(path)\nnew_image = image.resize((256, 256))\ngrey = new_image.convert('RGB')\n#grey = ImageOps.grayscale(new_image)\ndraw = ImageDraw.Draw(grey)\n#print(new_x_min, new_y_min, new_x_max, new_y_max)\nbounding_boxes = [(x1_min, y1_min, x1_max, y1_max)]\n#bounding_boxes = [(10,20,30,40)]\nfor bbox in bounding_boxes:\n    draw.rectangle(bbox, outline=\"red\", width=3)\ngrey.show()","metadata":{"cell_id":"0de03f5d6a8348fabb442c9d582e406c","deepnote_cell_type":"code"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=72ca566f-42ef-466b-9ab4-9411088db224' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHN2ZyB3aWR0aD0iODBweCIgaGVpZ2h0PSI4MHB4IiB2aWV3Qm94PSIwIDAgODAgODAiIHZlcnNpb249IjEuMSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayI+CiAgICA8IS0tIEdlbmVyYXRvcjogU2tldGNoIDU0LjEgKDc2NDkwKSAtIGh0dHBzOi8vc2tldGNoYXBwLmNvbSAtLT4KICAgIDx0aXRsZT5Hcm91cCAzPC90aXRsZT4KICAgIDxkZXNjPkNyZWF0ZWQgd2l0aCBTa2V0Y2guPC9kZXNjPgogICAgPGcgaWQ9IkxhbmRpbmciIHN0cm9rZT0ibm9uZSIgc3Ryb2tlLXdpZHRoPSIxIiBmaWxsPSJub25lIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiPgogICAgICAgIDxnIGlkPSJBcnRib2FyZCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTEyMzUuMDAwMDAwLCAtNzkuMDAwMDAwKSI+CiAgICAgICAgICAgIDxnIGlkPSJHcm91cC0zIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgxMjM1LjAwMDAwMCwgNzkuMDAwMDAwKSI+CiAgICAgICAgICAgICAgICA8cG9seWdvbiBpZD0iUGF0aC0yMCIgZmlsbD0iIzAyNjVCNCIgcG9pbnRzPSIyLjM3NjIzNzYyIDgwIDM4LjA0NzY2NjcgODAgNTcuODIxNzgyMiA3My44MDU3NTkyIDU3LjgyMTc4MjIgMzIuNzU5MjczOSAzOS4xNDAyMjc4IDMxLjY4MzE2ODMiPjwvcG9seWdvbj4KICAgICAgICAgICAgICAgIDxwYXRoIGQ9Ik0zNS4wMDc3MTgsODAgQzQyLjkwNjIwMDcsNzYuNDU0OTM1OCA0Ny41NjQ5MTY3LDcxLjU0MjI2NzEgNDguOTgzODY2LDY1LjI2MTk5MzkgQzUxLjExMjI4OTksNTUuODQxNTg0MiA0MS42NzcxNzk1LDQ5LjIxMjIyODQgMjUuNjIzOTg0Niw0OS4yMTIyMjg0IEMyNS40ODQ5Mjg5LDQ5LjEyNjg0NDggMjkuODI2MTI5Niw0My4yODM4MjQ4IDM4LjY0NzU4NjksMzEuNjgzMTY4MyBMNzIuODcxMjg3MSwzMi41NTQ0MjUgTDY1LjI4MDk3Myw2Ny42NzYzNDIxIEw1MS4xMTIyODk5LDc3LjM3NjE0NCBMMzUuMDA3NzE4LDgwIFoiIGlkPSJQYXRoLTIyIiBmaWxsPSIjMDAyODY4Ij48L3BhdGg+CiAgICAgICAgICAgICAgICA8cGF0aCBkPSJNMCwzNy43MzA0NDA1IEwyNy4xMTQ1MzcsMC4yNTcxMTE0MzYgQzYyLjM3MTUxMjMsLTEuOTkwNzE3MDEgODAsMTAuNTAwMzkyNyA4MCwzNy43MzA0NDA1IEM4MCw2NC45NjA0ODgyIDY0Ljc3NjUwMzgsNzkuMDUwMzQxNCAzNC4zMjk1MTEzLDgwIEM0Ny4wNTUzNDg5LDc3LjU2NzA4MDggNTMuNDE4MjY3Nyw3MC4zMTM2MTAzIDUzLjQxODI2NzcsNTguMjM5NTg4NSBDNTMuNDE4MjY3Nyw0MC4xMjg1NTU3IDM2LjMwMzk1NDQsMzcuNzMwNDQwNSAyNS4yMjc0MTcsMzcuNzMwNDQwNSBDMTcuODQzMDU4NiwzNy43MzA0NDA1IDkuNDMzOTE5NjYsMzcuNzMwNDQwNSAwLDM3LjczMDQ0MDUgWiIgaWQ9IlBhdGgtMTkiIGZpbGw9IiMzNzkzRUYiPjwvcGF0aD4KICAgICAgICAgICAgPC9nPgogICAgICAgIDwvZz4KICAgIDwvZz4KPC9zdmc+' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"deepnote":{},"orig_nbformat":2,"deepnote_notebook_id":"d7b49d3a9a064b7b9b54e2350181d760","deepnote_persisted_session":{"createdAt":"2023-04-26T06:04:20.333Z"},"deepnote_execution_queue":[]}}