symulowanie_cnn/cnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 160,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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",
    "#     resized = resized.flatten()\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",
    "        else:\n",
    "            print(\"chuj\")\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": 163,
   "id": "b9df2aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = readData(data_links)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "a8977fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "6764d3d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "675\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "97e8bc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 80, 3)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "2df14985",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 2, 2, 2, 2, 1, 1, 0, 0, 2, 0, 2, 2, 0, 2, 2, 0, 1, 2, 1, 0, 1, 2, 0, 1, 0, 2, 0, 0, 2, 1, 0, 2, 1, 0, 1, 1, 1, 2, 0, 0, 1, 0, 2, 0, 0, 2, 1, 0, 0, 0, 1, 0, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 0, 1, 2, 2, 2, 1, 1, 1, 2, 2, 0, 2, 2, 1, 0, 2, 0, 1, 2, 0, 1, 0, 1, 1, 2, 0, 0, 2, 1, 2, 0, 1, 1, 2, 0, 2, 0, 1, 1, 0, 0, 0, 1, 1, 2, 2, 2, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 2, 0, 1, 2, 2, 2, 2, 0, 1, 1, 1, 1, 2, 0, 1, 2, 2, 0, 0, 1, 0, 0, 0, 2, 1, 2, 2, 2, 2, 2, 1, 2, 0, 0, 0, 0, 0, 1, 1, 2, 0, 2, 1, 0, 2, 1, 2, 1, 0, 2, 0, 2, 0, 1, 1, 2, 0, 2, 2, 1, 0, 2, 1, 1, 1, 1, 2, 1, 0, 2, 1, 1, 2, 2, 2, 2, 0, 1, 1, 2, 0, 1, 2, 1, 0, 0, 1, 1, 1, 0, 0, 2, 1, 2, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 2, 0, 0, 2, 0, 0, 0, 2, 1, 1, 1, 1, 0, 0, 0, 1, 2, 2, 0, 1, 0, 2, 0, 2, 1, 1, 0, 1, 1, 1, 2, 0, 2, 2, 1, 2, 2, 1, 0, 0, 2, 0, 2, 0, 1, 1, 1, 0, 2, 1, 0, 0, 0, 2, 1, 2, 2, 1, 2, 1, 0, 2, 1, 0, 1, 1, 2, 2, 1, 0, 0, 1, 2, 1, 1, 0, 1, 1, 0, 1, 0, 2, 0, 1, 2, 0, 2, 1, 2, 1, 0, 2, 0, 1, 0, 2, 1, 1, 0, 1, 2, 0, 0, 2, 0, 1, 2, 0, 0, 0, 0, 0, 2, 2, 2, 1, 2, 1, 2, 1, 0, 1, 1, 0, 2, 1, 0, 0, 2, 0, 2, 2, 1, 2, 2, 1, 2, 0, 1, 2, 0, 2, 1, 0, 1, 2, 0, 2, 1, 0, 0, 1, 1, 1, 1, 2, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 2, 0, 1, 1, 1, 2, 2, 1, 0, 0, 1, 0, 0, 1, 2, 2, 2, 1, 1, 1, 0, 0, 2, 2, 1, 1, 1, 1, 0, 2, 2, 2, 0, 1, 2, 0, 2, 1, 2, 1, 1, 2, 0, 1, 0, 0, 1, 2, 2, 0, 0, 1, 0, 1, 2, 0, 2, 1, 1, 2, 2, 0, 1, 0, 1, 0, 2, 0, 0, 0, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 0, 1, 2, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 0, 2, 0, 1, 2, 2, 1, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 2, 0, 1, 1, 0, 0, 0, 1, 2, 1, 2, 1, 1, 1, 2, 0, 2, 1, 1, 0, 0, 0, 2, 2, 0, 1, 2, 2, 1, 1, 1, 2, 2, 2, 0, 0, 1, 1, 0, 2, 1, 2, 0, 1, 2, 1, 1, 0, 2, 1, 0, 2, 0, 2, 1, 1, 0, 0, 0, 1, 2, 2, 2, 0, 1, 1, 0, 0, 1, 0, 1, 0, 2, 0, 1, 0, 2, 1, 2, 0, 1, 1, 0, 1, 2, 1, 0, 1, 2, 1, 1, 0, 2, 0, 2, 2, 0, 2, 1, 0, 2, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 2, 2, 1, 2, 0, 1, 1, 0, 2, 0, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 0, 2, 0, 2, 2, 2, 0, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 0, 2, 2, 1, 1, 1, 0, 1, 0, 1, 0, 0, 2]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "2d3f1e5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 2 0 0 0 2 2 1 2 2 2 2 0 1 2 0 2 1 1 0 0 2 0 1 0 1 0 1 0 0 2 2 0 1 1 1\n",
      " 2 0 2 2 2 0 1 1 0 0 0 2 2 2 1 0 0 1 0 1 2 1 0 2 0 1 0 2 0 2 1 0 0 0 0 1 0\n",
      " 1 0 1 2 0 2 0 2 1 1 1 2 1 2 1 2 0 2 0 2 2 1 1 0 1 2 1 2 2 2 2 0 0 0 0 1 1\n",
      " 0 0 0 0 0 0 2 0 2 2 0 1 1 2 1 1 2 2 1 2 2 1 0 1 2 1 2 0 2 1 0 2 1 1 2 2 2\n",
      " 2 1 2 2 0 0 0 0 0 1 1 1 0 2 2 2 2 2 2 0 0 0 2 2 0 2 0 1 0 1 1 1 2 0 1 0 1\n",
      " 1 0 0 0 1 1 1 2 0 2 1 2 0 2 2 0 2 0 2 2 2 1 2 1 2 0 2 1 2 2 2 2 0 1 0 1 2\n",
      " 2 1 1]\n"
     ]
    }
   ],
   "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)\n",
    "\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(3, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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": 172,
   "id": "802da4db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "22/22 [==============================] - 2s 98ms/step - loss: 1.1007 - accuracy: 0.4000 - val_loss: 0.9776 - val_accuracy: 0.5422\n",
      "Epoch 2/10\n",
      "22/22 [==============================] - 2s 90ms/step - loss: 0.8537 - accuracy: 0.6667 - val_loss: 0.7531 - val_accuracy: 0.6489\n",
      "Epoch 3/10\n",
      "22/22 [==============================] - 2s 89ms/step - loss: 0.7288 - accuracy: 0.6948 - val_loss: 1.5778 - val_accuracy: 0.4978\n",
      "Epoch 4/10\n",
      "22/22 [==============================] - 2s 91ms/step - loss: 0.7752 - accuracy: 0.6593 - val_loss: 0.7269 - val_accuracy: 0.7333\n",
      "Epoch 5/10\n",
      "22/22 [==============================] - 2s 89ms/step - loss: 0.5980 - accuracy: 0.7585 - val_loss: 0.6848 - val_accuracy: 0.7067\n",
      "Epoch 6/10\n",
      "22/22 [==============================] - 2s 91ms/step - loss: 0.4614 - accuracy: 0.8326 - val_loss: 0.7548 - val_accuracy: 0.6978\n",
      "Epoch 7/10\n",
      "22/22 [==============================] - 2s 95ms/step - loss: 0.4666 - accuracy: 0.8119 - val_loss: 0.4485 - val_accuracy: 0.8356\n",
      "Epoch 8/10\n",
      "22/22 [==============================] - 2s 98ms/step - loss: 0.2721 - accuracy: 0.9156 - val_loss: 0.3949 - val_accuracy: 0.8311\n",
      "Epoch 9/10\n",
      "22/22 [==============================] - 2s 92ms/step - loss: 0.2106 - accuracy: 0.9289 - val_loss: 0.3096 - val_accuracy: 0.8933\n",
      "Epoch 10/10\n",
      "22/22 [==============================] - 2s 93ms/step - loss: 0.1317 - accuracy: 0.9556 - val_loss: 0.2744 - val_accuracy: 0.8800\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=10, \n",
    "                    validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a27e69ff",
   "metadata": {},
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   "execution_count": null,
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   "execution_count": null,
   "id": "ebd5b1c1",
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   "execution_count": null,
   "id": "57828ac4",
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  {
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   "id": "a1448617",
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