Tupolew.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c242103e-d19b-40ff-bf7f-2aae42077f8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-06 22:06:32.790103: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-06-06 22:06:32.820014: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-06-06 22:06:32.970915: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-06 22:06:33.993544: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, UpSampling2D, Input, MaxPooling2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n",
    "import warnings\n",
    "from PIL import Image\n",
    "from shutil import rmtree\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "69b5b593-6655-4df0-8208-8d5193476d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_original = '256X'\n",
    "path_blurred = 'Zgaussowane'\n",
    "\n",
    "# Funkcja do ładowania i przetwarzania obrazów\n",
    "def load_images(path, size=(256, 256)):\n",
    "    images = []\n",
    "    for filename in sorted(os.listdir(path)):\n",
    "        img_path = os.path.join(path, filename)\n",
    "        img = load_img(img_path, target_size=size, color_mode='rgb')\n",
    "        img = img_to_array(img) / 255.0  # Normalizacja obrazów do zakresu [0, 1]\n",
    "        images.append(img)\n",
    "    return np.array(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f249bb73-19a9-4bcb-9393-1e119827887c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isdir('256X/.ipynb_checkpoints'):\n",
    "    rmtree('256X/.ipynb_checkpoints')\n",
    "if os.path.isdir('Zgaussowane/.ipynb_checkpoints'):\n",
    "    rmtree('Zgaussowane/.ipynb_checkpoints')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6654087-2d04-42a3-9448-5112308fc88f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liczba załadowanych oryginalnych obrazów: 115\n",
      "Liczba załadowanych zblurowanych obrazów: 115\n"
     ]
    }
   ],
   "source": [
    "original_images = load_images(path_original)\n",
    "blurred_images = load_images(path_blurred)\n",
    "\n",
    "print(f'Liczba załadowanych oryginalnych obrazów: {len(original_images)}')\n",
    "print(f'Liczba załadowanych zblurowanych obrazów: {len(blurred_images)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b48e1b4-9cdf-4ca8-bd39-31e465d6a9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,731</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m1,792\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m36,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │       \u001b[38;5;34m147,584\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m73,792\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │         \u001b[38;5;34m1,731\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">335,683</span> (1.28 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m335,683\u001b[0m (1.28 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">335,683</span> (1.28 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m335,683\u001b[0m (1.28 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def build_model(input_shape):\n",
    "    model = Sequential()\n",
    "    model.add(Input(shape=input_shape))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
    "    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
    "    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
    "    model.add(Conv2D(3, (3, 3), activation='sigmoid', padding='same'))\n",
    "    \n",
    "    model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')\n",
    "    return model\n",
    "\n",
    "input_shape = (256, 256, 3)\n",
    "model = build_model(input_shape)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6868a8b-c6d4-4299-bfe9-c3d4f9cdb6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#def build_model(input_shape):\n",
    "#    model = Sequential()\n",
    "#    model.add(Input(shape=input_shape))\n",
    "#    model.add(UpSampling2D((2, 2)))\n",
    "#    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
    "#    model.add(MaxPooling2D((2, 2)))\n",
    "#    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
    "#    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
    "#    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
    "#    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
    "#    model.add(Conv2D(3, (3, 3), activation='sigmoid', padding='same'))\n",
    "#    \n",
    "#    model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')\n",
    "#    return model\n",
    "#\n",
    "#input_shape = (256, 256, 3)\n",
    "#model = build_model(input_shape)\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2059aef8-46c6-47d4-bb72-f64d91d8405d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 9s/step - loss: 0.0814 - val_loss: 0.0593\n",
      "Epoch 2/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 10s/step - loss: 0.0482 - val_loss: 0.0261\n",
      "Epoch 3/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 10s/step - loss: 0.0194 - val_loss: 0.0102\n",
      "Epoch 4/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 10s/step - loss: 0.0119 - val_loss: 0.0085\n",
      "Epoch 5/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 10s/step - loss: 0.0085 - val_loss: 0.0070\n",
      "Epoch 6/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 9s/step - loss: 0.0068 - val_loss: 0.0060\n",
      "Epoch 7/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 10s/step - loss: 0.0065 - val_loss: 0.0055\n",
      "Epoch 8/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 10s/step - loss: 0.0051 - val_loss: 0.0052\n",
      "Epoch 9/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 10s/step - loss: 0.0053 - val_loss: 0.0048\n",
      "Epoch 10/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 10s/step - loss: 0.0049 - val_loss: 0.0049\n",
      "Epoch 11/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 10s/step - loss: 0.0044 - val_loss: 0.0052\n",
      "Epoch 12/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 10s/step - loss: 0.0050 - val_loss: 0.0047\n",
      "Epoch 13/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 10s/step - loss: 0.0042 - val_loss: 0.0043\n",
      "Epoch 14/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 10s/step - loss: 0.0039 - val_loss: 0.0044\n",
      "Epoch 15/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 10s/step - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 16/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 9s/step - loss: 0.0040 - val_loss: 0.0043\n",
      "Epoch 17/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 10s/step - loss: 0.0050 - val_loss: 0.0047\n",
      "Epoch 18/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 10s/step - loss: 0.0040 - val_loss: 0.0042\n",
      "Epoch 19/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 10s/step - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 20/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 10s/step - loss: 0.0037 - val_loss: 0.0040\n",
      "Epoch 21/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 9s/step - loss: 0.0035 - val_loss: 0.0041\n",
      "Epoch 22/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 9s/step - loss: 0.0038 - val_loss: 0.0041\n",
      "Epoch 23/36\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 13s/step - loss: 0.0033 - val_loss: 0.0040\n",
      "Epoch 24/36\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ilosc_epok = 36\n",
    "# Podział danych na treningowe i walidacyjne\n",
    "X_train, X_val, y_train, y_val = train_test_split(blurred_images, original_images, random_state=42)\n",
    "\n",
    "# Trenowanie modelu\n",
    "history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=ilosc_epok, batch_size=8)\n",
    "\n",
    "# Zapisywanie modelu\n",
    "# model.save('deblur_cnn_model.h5')\n",
    "keras.saving.save_model(model, f'deblur_cnn_model_redux{ilosc_epok}.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd502441-6e12-4fd4-8bf5-35b85aef80c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_test = 'Test_Gauss/'\n",
    "test_images = load_images(path_test)\n",
    "X_test, y_test = train_test_split(test_images, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2014b3f2-cffa-4251-b0ae-9c64632318fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_path = 'deblur_cnn_model_redux.keras'\n",
    "# keras.saving.load_model(model_path, custom_objects=None, compile=True, safe_mode=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df08c8bf-d7fe-4f19-b2b9-ad0fb28f917b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7437dde-b706-4376-8a8a-b703f73c3745",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "937527e5-c421-4a26-95cd-42cb427d64b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    restored_image = model.predict(np.expand_dims(X_val[i], axis=0))\n",
    "\n",
    "    restored_image = restored_image[0] * 255\n",
    "    restored_image = restored_image.astype('uint8')\n",
    "\n",
    "    restored_image_pil = Image.fromarray(restored_image)\n",
    "    restored_image_pil.save(f'Restored/{i}_{ilosc_epok}.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "424e1e5b-568b-48e5-abfd-63f47a8bae80",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(3):\n",
    "    test_image = model.predict(np.expand_dims(X_test[i], axis=0))\n",
    "\n",
    "    test_image = test_image[0] * 255\n",
    "    test_image = test_image.astype('uint8')\n",
    "\n",
    "    test_image_pil = Image.fromarray(test_image)\n",
    "    test_image_pil.save(f'Restored/Test_{i}_{ilosc_epok}.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb33d882-c32a-441a-9a76-e0c26051545f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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