Malaria Detection-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "702b1230",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "690ac991",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'cell_images'\n",
    "num_classes = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3f5b60d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Conv2D(16, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    \n",
    "    Conv2D(32, (3, 3), activation='relu'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    \n",
    "    Conv2D(64, (3, 3), activation='relu'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    \n",
    "    Flatten(),\n",
    "    \n",
    "    Dense(64, activation=\"relu\"),\n",
    "    Dense(num_classes, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c2c4f8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b64d8281",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 27558 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "    rescale=1./255, \n",
    "    shear_range=0.2, \n",
    "    zoom_range=0.2, \n",
    "    horizontal_flip=True\n",
    ")\n",
    "\n",
    "data_generator = datagen.flow_from_directory(\n",
    "    data_dir,\n",
    "    target_size=(150, 150),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "23214ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-01 21:40:15.957469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 21s 206ms/step - loss: 0.6933 - accuracy: 0.4859\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 20s 198ms/step - loss: 0.6932 - accuracy: 0.4891\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 20s 198ms/step - loss: 0.6931 - accuracy: 0.5019\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 20s 199ms/step - loss: 0.6934 - accuracy: 0.4800\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 20s 198ms/step - loss: 0.6932 - accuracy: 0.4925\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 20s 197ms/step - loss: 0.6932 - accuracy: 0.4972\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 20s 201ms/step - loss: 0.6932 - accuracy: 0.4884\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 20s 198ms/step - loss: 0.6931 - accuracy: 0.4959\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 20s 200ms/step - loss: 0.6933 - accuracy: 0.4959\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 20s 198ms/step - loss: 0.6928 - accuracy: 0.5222\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    data_generator,\n",
    "    steps_per_epoch=100,\n",
    "    epochs=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26193915",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.693311</td>\n",
       "      <td>0.485938</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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      "text/plain": [
       "       loss  accuracy\n",
       "0  0.693311  0.485938\n",
       "1  0.693226  0.489062\n",
       "2  0.693064  0.501875\n",
       "3  0.693369  0.480000\n",
       "4  0.693175  0.492500"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_df = pd.DataFrame(history.history)\n",
    "history_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc091f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"malaria.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1da9fdbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "28d45adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e92e6be3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-01 21:45:35.206179: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "862/862 [==============================] - 133s 154ms/step - loss: 0.6934 - accuracy: 0.5000\n",
      "Test accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(data_generator)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d32d991",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}