Brain Tumor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7c7010f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /home/rishav/anaconda3/lib/python3.9/site-packages (4.7.0.68)\r\n",
      "Requirement already satisfied: numpy>=1.19.3 in /home/rishav/anaconda3/lib/python3.9/site-packages (from opencv-python) (1.21.5)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b40a9f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71afdf8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "from PIL import Image\n",
    "from numpy import asarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3071a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "dirct ='/media/rishav/Local Disk/Personal/MachineLearningProjects/Brain-Tumor-Detection/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3a7c3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "path = os.listdir(dirct+'/Brain Tumor Data')\n",
    "classes = {'no_tumor':0, 'pituitary_tumor':1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fae456ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18880/3892889325.py:10: DeprecationWarning: FLIP_LEFT_RIGHT is deprecated and will be removed in Pillow 10 (2023-07-01). Use Transpose.FLIP_LEFT_RIGHT instead.\n",
      "  mirimg=img.transpose(Image.FLIP_LEFT_RIGHT)\n",
      "/tmp/ipykernel_18880/3892889325.py:13: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n",
      "  rotimg=img.rotate(90,Image.NEAREST, expand = 1)\n",
      "/tmp/ipykernel_18880/3892889325.py:16: DeprecationWarning: FLIP_LEFT_RIGHT is deprecated and will be removed in Pillow 10 (2023-07-01). Use Transpose.FLIP_LEFT_RIGHT instead.\n",
      "  mirrotimg=rotimg.transpose(Image.FLIP_LEFT_RIGHT)\n"
     ]
    }
   ],
   "source": [
    "no_tumor_frompath= dirct+'/Brain Tumor Data/Training/no_tumor'\n",
    "no_tumor_topath = dirct+'/Brain Tumor Data/Agumented_Data/Training/no_tumor/'\n",
    "no_tumor_images=glob(no_tumor_frompath+'/*')\n",
    "c=0\n",
    "for path in no_tumor_images:\n",
    "  img=Image.open(path)\n",
    "  img=img.resize((224,224))\n",
    "  img.save(no_tumor_topath+str(c)+'.jpg')\n",
    "  c+=1\n",
    "  mirimg=img.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "  mirimg.save(no_tumor_topath+str(c)+'.jpg')\n",
    "  c+=1\n",
    "  rotimg=img.rotate(90,Image.NEAREST, expand = 1)\n",
    "  rotimg.save(no_tumor_topath+str(c)+'.jpg')\n",
    "  c+=1\n",
    "  mirrotimg=rotimg.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "  mirrotimg.save(no_tumor_topath+str(c)+'.jpg')\n",
    "  c+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "386c29d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18880/1661648098.py:10: DeprecationWarning: FLIP_LEFT_RIGHT is deprecated and will be removed in Pillow 10 (2023-07-01). Use Transpose.FLIP_LEFT_RIGHT instead.\n",
      "  mirimg=img.transpose(Image.FLIP_LEFT_RIGHT)\n",
      "/tmp/ipykernel_18880/1661648098.py:13: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n",
      "  rotimg=img.rotate(90,Image.NEAREST, expand = 1)\n",
      "/tmp/ipykernel_18880/1661648098.py:16: DeprecationWarning: FLIP_LEFT_RIGHT is deprecated and will be removed in Pillow 10 (2023-07-01). Use Transpose.FLIP_LEFT_RIGHT instead.\n",
      "  mirrotimg=rotimg.transpose(Image.FLIP_LEFT_RIGHT)\n"
     ]
    }
   ],
   "source": [
    "frompath = dirct+'Brain Tumor Data/Training/pituitary_tumor'\n",
    "topath = dirct+'Brain Tumor Data/Agumented_Data/Training/pituitary_tumor/'\n",
    "images=glob(frompath+'/*')\n",
    "c=0\n",
    "for path in images:\n",
    "  img=Image.open(path)\n",
    "  img=img.resize((224,224))\n",
    "  img.save(topath+str(c)+'.jpg')\n",
    "  c+=1\n",
    "  mirimg=img.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "  mirimg.save(topath+str(c)+'.jpg')\n",
    "  c+=1\n",
    "  rotimg=img.rotate(90,Image.NEAREST, expand = 1)\n",
    "  rotimg.save(topath+str(c)+'.jpg')\n",
    "  c+=1\n",
    "  mirrotimg=rotimg.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "  mirrotimg.save(topath+str(c)+'.jpg')\n",
    "  c+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bf4990b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "X=[]\n",
    "Y=[]\n",
    "for cls in classes:\n",
    "  pth = dirct+'/Brain Tumor Data/Agumented_Data/Training/'+cls\n",
    "  for j in os.listdir(pth):\n",
    "    img = cv2.imread(pth+'/'+j)   \n",
    "    # img = cv2.resize(img,dsize=(224,224))\n",
    "    X.append(img)\n",
    "    Y.append(classes[cls])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64f33ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff66cbe9f10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[0],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "796c4237",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of an image in x_train:  (224, 224, 3)\n",
      "Shape of an image in x_test:  (224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,random_state=10,test_size=0.20)\n",
    "print (\"Shape of an image in x_train: \", x_train[0].shape)\n",
    "print (\"Shape of an image in x_test: \", x_test[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "958606fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-01 11:05:24.282765: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-01 11:05:24.435193: W tensorflow/compiler/xla/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; LD_LIBRARY_PATH: /home/rishav/anaconda3/lib/python3.9/site-packages/cv2/../../lib64:\n",
      "2023-04-01 11:05:24.435216: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-04-01 11:05:24.898201: W tensorflow/compiler/xla/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; LD_LIBRARY_PATH: /home/rishav/anaconda3/lib/python3.9/site-packages/cv2/../../lib64:\n",
      "2023-04-01 11:05:24.898259: W tensorflow/compiler/xla/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; LD_LIBRARY_PATH: /home/rishav/anaconda3/lib/python3.9/site-packages/cv2/../../lib64:\n",
      "2023-04-01 11:05:24.898264: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train Shape:  (3910, 224, 224, 3)\n",
      "x_test Shape:  (978, 224, 224, 3)\n",
      "y_train Shape:  (3910, 2)\n",
      "y_test Shape:  (978, 2)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "import tensorflow as tf\n",
    "le = preprocessing.LabelEncoder()\n",
    "y_train = le.fit_transform(y_train)\n",
    "y_test = le.fit_transform(y_test)\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes=2)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes=2)\n",
    "y_train = np.array(y_train)\n",
    "x_train = np.array(x_train)\n",
    "y_test = np.array(y_test)\n",
    "x_test = np.array(x_test) \n",
    "print(\"x_train Shape: \", x_train.shape) \n",
    "print(\"x_test Shape: \", x_test.shape)\n",
    "print(\"y_train Shape: \", y_train.shape) \n",
    "print(\"y_test Shape: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "416ae972",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-01 11:05:28.499303: W tensorflow/compiler/xla/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; LD_LIBRARY_PATH: /home/rishav/anaconda3/lib/python3.9/site-packages/cv2/../../lib64:\n",
      "2023-04-01 11:05:28.499331: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-04-01 11:05:28.499352: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (rishav): /proc/driver/nvidia/version does not exist\n",
      "2023-04-01 11:05:28.499523: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import vgg16\n",
    "\n",
    "img_rows,img_cols=224,224\n",
    "\n",
    "vgg=vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(img_rows,img_cols,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22ee074c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in vgg.layers:\n",
    "  layer.trainable=False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ce5bc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers.pooling.global_average_pooling2d import GlobalAveragePooling2D\n",
    "def lw(bottom_model,num_classes):\n",
    "  top_model=bottom_model.output\n",
    "  top_model=GlobalAveragePooling2D()(top_model)\n",
    "  top_model=Dense(1024,activation='relu')(top_model)\n",
    "  top_model=Dense(1024,activation='relu')(top_model)\n",
    "  top_model=Dense(512,activation='relu')(top_model)\n",
    "  top_model=Dense(num_classes,activation='softmax')(top_model)\n",
    "  return top_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27c15116",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout,Activation,Flatten,GlobalAveragePooling2D\n",
    "from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7871d8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "\n",
    "def make_keras_picklable():\n",
    "    def __getstate__(self):\n",
    "        model_str = \"\"\n",
    "        with tempfile.NamedTemporaryFile(suffix='.hdf5', delete=True) as fd:\n",
    "            save_model(self, fd.name, overwrite=True)\n",
    "            model_str = fd.read()\n",
    "        d = {'model_str': model_str}\n",
    "        return d\n",
    "\n",
    "    def __setstate__(self, state):\n",
    "        with tempfile.NamedTemporaryFile(suffix='.hdf5', delete=True) as fd:\n",
    "            fd.write(state['model_str'])\n",
    "            fd.flush()\n",
    "            model = load_model(fd.name)\n",
    "        self.__dict__ = model.__dict__\n",
    "\n",
    "\n",
    "    cls = Model\n",
    "    cls.__getstate__ = __getstate__\n",
    "    cls.__setstate__ = __setstate__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86aae42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "make_keras_picklable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "08689dab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 512)              0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1024)              525312    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1024)              1049600   \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 512)               524800    \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 2)                 1026      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16,815,426\n",
      "Trainable params: 2,100,738\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "num_classes=2\n",
    "FC_Head=lw(vgg,num_classes)\n",
    "model=Model(inputs=vgg.input,outputs=FC_Head)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "76686499",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1ed748a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-01 11:05:47.269400: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 588564480 exceeds 10% of free system memory.\n",
      "2023-04-01 11:05:48.240458: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 19267584 exceeds 10% of free system memory.\n",
      "2023-04-01 11:05:48.249078: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 411041792 exceeds 10% of free system memory.\n",
      "2023-04-01 11:05:48.394642: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 411041792 exceeds 10% of free system memory.\n",
      "2023-04-01 11:05:49.150746: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 102760448 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123/123 [==============================] - 786s 6s/step - loss: 0.3884 - accuracy: 0.9353 - val_loss: 0.0687 - val_accuracy: 0.9785\n",
      "Epoch 2/5\n",
      "123/123 [==============================] - 834s 7s/step - loss: 0.0546 - accuracy: 0.9826 - val_loss: 0.0425 - val_accuracy: 0.9836\n",
      "Epoch 3/5\n",
      "123/123 [==============================] - 856s 7s/step - loss: 0.0299 - accuracy: 0.9913 - val_loss: 0.0884 - val_accuracy: 0.9683\n",
      "Epoch 4/5\n",
      "123/123 [==============================] - 889s 7s/step - loss: 0.0329 - accuracy: 0.9895 - val_loss: 0.0485 - val_accuracy: 0.9806\n",
      "Epoch 5/5\n",
      "123/123 [==============================] - 886s 7s/step - loss: 0.0250 - accuracy: 0.9900 - val_loss: 0.0073 - val_accuracy: 0.9980\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(x_train,y_train,epochs=5,validation_data=(x_test,y_test),verbose=1,initial_epoch=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ddda4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename= 'brain_tumor_model.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dc7a4bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......conv2d\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_10\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_11\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_12\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_6\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_7\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_8\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_9\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......global_average_pooling2d\n",
      ".........vars\n",
      "......input_layer\n",
      ".........vars\n",
      "......max_pooling2d\n",
      ".........vars\n",
      "......max_pooling2d_1\n",
      ".........vars\n",
      "......max_pooling2d_2\n",
      ".........vars\n",
      "......max_pooling2d_3\n",
      ".........vars\n",
      "......max_pooling2d_4\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
      ".........12\n",
      ".........13\n",
      ".........14\n",
      ".........15\n",
      ".........16\n",
      ".........2\n",
      ".........3\n",
      ".........4\n",
      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-04-01 12:20:56     84145360\n",
      "config.json                                    2023-04-01 12:20:56        12542\n",
      "metadata.json                                  2023-04-01 12:20:56           64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pickle.dump(model,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "832c931f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-21 21:20:40.564385: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-21 21:20:40.671616: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-03-21 21:20:40.671637: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-03-21 21:20:41.160193: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-03-21 21:20:41.160243: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-03-21 21:20:41.160247: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras model archive loading:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-03-20 22:41:38     84145360\n",
      "config.json                                    2023-03-20 22:41:36        12542\n",
      "metadata.json                                  2023-03-20 22:41:36           64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-21 21:20:42.250624: W tensorflow/compiler/xla/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\n",
      "2023-03-21 21:20:42.250650: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-03-21 21:20:42.250667: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (rishav): /proc/driver/nvidia/version does not exist\n",
      "2023-03-21 21:20:42.250832: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r)>) loading:\n",
      "...layers\n",
      "......conv2d\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_10\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_11\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_12\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_6\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_7\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_8\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_9\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......global_average_pooling2d\n",
      ".........vars\n",
      "......input_layer\n",
      ".........vars\n",
      "......max_pooling2d\n",
      ".........vars\n",
      "......max_pooling2d_1\n",
      ".........vars\n",
      "......max_pooling2d_2\n",
      ".........vars\n",
      "......max_pooling2d_3\n",
      ".........vars\n",
      "......max_pooling2d_4\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
      ".........12\n",
      ".........13\n",
      ".........14\n",
      ".........15\n",
      ".........16\n",
      ".........2\n",
      ".........3\n",
      ".........4\n",
      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n"
     ]
    }
   ],
   "source": [
    "load_model = pickle.load(open(filename,'rb'))\n",
    "#load_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb10b79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLP for Pima Indians Dataset Serialize to JSON and HDF5\n",
    "from tensorflow.keras.models import model_from_json\n",
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ea644bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "json_file = open('model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "34a350c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 186s 6s/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[9.9999994e-01, 4.7917198e-10],\n",
       "       [8.7774271e-07, 9.9999911e-01],\n",
       "       [9.9999005e-01, 9.8449254e-06],\n",
       "       ...,\n",
       "       [7.0458255e-04, 9.9929541e-01],\n",
       "       [1.3789128e-10, 1.0000000e+00],\n",
       "       [5.6625770e-05, 9.9994338e-01]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08146434",
   "metadata": {},
   "outputs": [],
   "source": [
    "dec= {0: 'No Tumor', 1:'Positive Tumor'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b7edea1",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/media/rishav/Local Disk/Personal/MachineLearningProjects/Brain-Tumor-Detection/Brain Tumor Data/Training/pituitary_tumor/p (99).jpgBrain Tumor Data/Training/pituitary_tumor'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_18880/1707451006.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'Brain Tumor Data/Training/pituitary_tumor'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpredict_img\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'Brain Tumor Data/Training/pituitary_tumor/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/media/rishav/Local Disk/Personal/MachineLearningProjects/Brain-Tumor-Detection/Brain Tumor Data/Training/pituitary_tumor/p (99).jpgBrain Tumor Data/Training/pituitary_tumor'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,12))\n",
    "p=os.listdir(path+'Brain Tumor Data/Training/pituitary_tumor')\n",
    "c=1\n",
    "predict_img=[]\n",
    "for i in os.listdir(path+'Brain Tumor Data/Training/pituitary_tumor/')[:16]:\n",
    "  plt.subplot(4,4,c)\n",
    "\n",
    "  img=cv2.imread(path+'Brain Tumor Data/Training/pituitary_tumor/'+i)\n",
    "  img1 = cv2.resize(img,(224,224))\n",
    "  predict_img.append(img1)\n",
    "  p= load_model.predict(np.array(predict_img))\n",
    "  result = np.argmax(p, axis =1)\n",
    "  plt.title(dec[result[c-1]])\n",
    "  plt.imshow(img,cmap='gray')\n",
    "  plt.axis('off')\n",
    "  c+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c3d6408",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_16159/3463644309.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/home/rishav/Brain Tumor Detection/Brain Tumor Data/Training/no_tumor'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpredict_img\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/home/rishav/Brain Tumor Detection/Brain Tumor Data/Training/no_tumor/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(18,12))\n",
    "p=os.listdir('/home/rishav/Brain Tumor Detection/Brain Tumor Data/Training/no_tumor')\n",
    "c=1\n",
    "predict_img=[]\n",
    "for i in os.listdir('/home/rishav/Brain Tumor Detection/Brain Tumor Data/Training/no_tumor/')[:16]:\n",
    "  plt.subplot(4,4,c)\n",
    "\n",
    "  img=cv2.imread('/home/rishav/Brain Tumor Detection/Brain Tumor Data/Training/no_tumor/'+i)\n",
    "  img1 = cv2.resize(img,(224,224))\n",
    "  predict_img.append(img1)\n",
    "  p= model.predict(np.array(predict_img))\n",
    "  result = np.argmax(p, axis =1)\n",
    "  plt.title(dec[result[c-1]])\n",
    "  plt.imshow(img,cmap='gray')\n",
    "  plt.axis('off')\n",
    "  c+=1"
   ]
  }
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