symulowanie_cnn/cnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "b026b65f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "f4b3c889",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(img):\n",
    "    scale_percent = 10\n",
    "    width = int(img.shape[1] * scale_percent / 100)\n",
    "    height = int(img.shape[0] * scale_percent / 100)\n",
    "    dim = (width, height)\n",
    "    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n",
    "    return resized\n",
    "\n",
    "def readData(data_links):\n",
    "    x, y = [], []\n",
    "    for link in data_links:\n",
    "        img = cv2.imread(link, cv2.IMREAD_COLOR)\n",
    "        img = preprocess(img)\n",
    "        label = link.split(\"/\")[1].split('_')[1]\n",
    "        new_label = 4\n",
    "        \n",
    "        if label == '3':\n",
    "            new_label = 0\n",
    "        elif label == '5':\n",
    "            new_label = 1\n",
    "        elif label == '8':\n",
    "            new_label = 2\n",
    "        \n",
    "        x.append(img)\n",
    "        y.append(new_label)\n",
    "\n",
    "    return x, y\n",
    "\n",
    "data_links = os.listdir(\"data/\")\n",
    "data_links = [\"data/\" + x for x in data_links]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "b9df2aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = readData(data_links)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "a8977fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "97e8bc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 80, 3)"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "2d3f1e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array([i / 255.0 for i in X_train], dtype=np.float64)\n",
    "X_test = np.array([i / 255.0 for i in X_test], dtype=np.float64)\n",
    "y_train = np.array(y_train, dtype=np.int64)\n",
    "y_test = np.array(y_test, dtype=np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "2a10ce01",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(60, 80, 3)))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dense(3, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "67f6cef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "802da4db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "20/20 [==============================] - 2s 70ms/step - loss: 1.0857 - accuracy: 0.3730 - val_loss: 1.0336 - val_accuracy: 0.4926\n",
      "Epoch 2/20\n",
      "20/20 [==============================] - 1s 64ms/step - loss: 0.9725 - accuracy: 0.5127 - val_loss: 0.8676 - val_accuracy: 0.6037\n",
      "Epoch 3/20\n",
      "20/20 [==============================] - 1s 65ms/step - loss: 0.8294 - accuracy: 0.6524 - val_loss: 0.7766 - val_accuracy: 0.6852\n",
      "Epoch 4/20\n",
      "20/20 [==============================] - 1s 65ms/step - loss: 0.8491 - accuracy: 0.6476 - val_loss: 0.7487 - val_accuracy: 0.6815\n",
      "Epoch 5/20\n",
      "20/20 [==============================] - 1s 66ms/step - loss: 0.7602 - accuracy: 0.6667 - val_loss: 0.8408 - val_accuracy: 0.5741\n",
      "Epoch 6/20\n",
      "20/20 [==============================] - 1s 66ms/step - loss: 0.7095 - accuracy: 0.7048 - val_loss: 0.6505 - val_accuracy: 0.7296\n",
      "Epoch 7/20\n",
      "20/20 [==============================] - 1s 65ms/step - loss: 0.6341 - accuracy: 0.7349 - val_loss: 0.5629 - val_accuracy: 0.7852\n",
      "Epoch 8/20\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 0.5052 - accuracy: 0.7921 - val_loss: 0.4398 - val_accuracy: 0.8444\n",
      "Epoch 9/20\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 0.3686 - accuracy: 0.8508 - val_loss: 0.3397 - val_accuracy: 0.8593\n",
      "Epoch 10/20\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 0.2249 - accuracy: 0.9317 - val_loss: 0.2998 - val_accuracy: 0.8889\n",
      "Epoch 11/20\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 0.4271 - accuracy: 0.8365 - val_loss: 0.4476 - val_accuracy: 0.8296\n",
      "Epoch 12/20\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 0.2457 - accuracy: 0.9270 - val_loss: 0.2269 - val_accuracy: 0.9074\n",
      "Epoch 13/20\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 0.1181 - accuracy: 0.9635 - val_loss: 0.1664 - val_accuracy: 0.9407\n",
      "Epoch 14/20\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 0.0965 - accuracy: 0.9714 - val_loss: 0.1522 - val_accuracy: 0.9444\n",
      "Epoch 15/20\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 0.0577 - accuracy: 0.9810 - val_loss: 0.1595 - val_accuracy: 0.9444\n",
      "Epoch 16/20\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 0.1496 - accuracy: 0.9444 - val_loss: 0.3178 - val_accuracy: 0.8963\n",
      "Epoch 17/20\n",
      "20/20 [==============================] - 1s 66ms/step - loss: 0.0731 - accuracy: 0.9794 - val_loss: 0.0890 - val_accuracy: 0.9704\n",
      "Epoch 18/20\n",
      "20/20 [==============================] - 1s 66ms/step - loss: 0.0253 - accuracy: 0.9937 - val_loss: 0.0744 - val_accuracy: 0.9704\n",
      "Epoch 19/20\n",
      "20/20 [==============================] - 1s 65ms/step - loss: 0.0138 - accuracy: 0.9984 - val_loss: 0.0656 - val_accuracy: 0.9778\n",
      "Epoch 20/20\n",
      "20/20 [==============================] - 1s 65ms/step - loss: 0.0092 - accuracy: 0.9984 - val_loss: 0.0616 - val_accuracy: 0.9741\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=20, \n",
    "                    validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "a27e69ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 - 0s - loss: 0.0616 - accuracy: 0.9741 - 153ms/epoch - 17ms/step\n",
      "0.06160588562488556\n",
      "0.9740740656852722\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.5, 1])\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "test_loss, test_acc = model.evaluate(X_test,  y_test, verbose=2)\n",
    "\n",
    "print(test_loss)\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21696ef3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd5b1c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57828ac4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1893f9da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1448617",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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