Crop_Train_Test_Split_Run.ipynb

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    "kaggle": {
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        "<a href=\"https://colab.research.google.com/github/masoudshahrian/DeepLearning-Code/blob/main/Crop_Train_Test_Split_Run.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "source": [
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "import kagglehub\n",
        "jessicali9530_celeba_dataset_path = kagglehub.dataset_download('jessicali9530/celeba-dataset')\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
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      "cell_type": "code",
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      "source": [
        "# # This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# # For example, here's several helpful packages to load\n",
        "\n",
        "# import numpy as np # linear algebra\n",
        "# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# # Input data files are available in the read-only \"../input/\" directory\n",
        "# # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "# import os\n",
        "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "#     for filename in filenames:\n",
        "#         print(os.path.join(dirname, filename))\n",
        "\n",
        "# # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
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      "cell_type": "code",
      "source": [
        "!pip install dlib"
      ],
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          "name": "stdout",
          "text": "Collecting dlib\n  Downloading dlib-19.24.6.tar.gz (3.4 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m33.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hBuilding wheels for collected packages: dlib\n  Building wheel for dlib (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for dlib: filename=dlib-19.24.6-cp310-cp310-linux_x86_64.whl size=3590658 sha256=dc6f5494188ab4cc4f90e79d2372886371272e4a9062cade39c50f2c9d618e37\n  Stored in directory: /root/.cache/pip/wheels/7c/1d/d1/e69ceb001441acedfa6156acda6c1856699e260ea1a9e6dcc4\nSuccessfully built dlib\nInstalling collected packages: dlib\nSuccessfully installed dlib-19.24.6\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import dlib\n",
        "import os\n",
        "\n",
        "# مسیر به دایرکتوری تصاویر CelebA\n",
        "input_dir = '/kaggle/input/celeba-dataset/img_align_celeba/img_align_celeba/'  # مسیر دایرکتوری تصاویر\n",
        "output_dir = '/kaggle/working/CelebA_Image_Croped/'  # مسیر دایرکتوری خروجی\n",
        "\n",
        "# ایجاد دایرکتوری خروجی در صورت عدم وجود\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "# بارگذاری مدل شناسایی صورت\n",
        "detector = dlib.get_frontal_face_detector()\n",
        "\n",
        "# لیست کردن تصاویر در دایرکتوری ورودی\n",
        "image_count = 0  # شمارنده برای تعداد تصاویر پردازش شده\n",
        "max_images = 100000  # حداکثر تعداد تصاویری که باید پردازش شوند\n",
        "\n",
        "for filename in os.listdir(input_dir):\n",
        "    if (filename.endswith('.jpg') or filename.endswith('.png')) and image_count < max_images:\n",
        "        # بارگذاری تصویر\n",
        "        image_path = os.path.join(input_dir, filename)\n",
        "        image = cv2.imread(image_path)\n",
        "\n",
        "        # تبدیل تصویر به خاکستری\n",
        "        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "        # شناسایی صورت‌ها\n",
        "        faces = detector(gray)\n",
        "\n",
        "        # پردازش هر صورت شناسایی شده\n",
        "        for face in faces:\n",
        "            # استخراج مختصات صورت\n",
        "            x, y, w, h = (face.left(), face.top(), face.width(), face.height())\n",
        "\n",
        "            # محاسبه مرکز صورت\n",
        "            center_x = x + w // 2\n",
        "            center_y = y + h // 2\n",
        "\n",
        "            # محاسبه مختصات جدید برای کراپ\n",
        "            crop_x1 = max(center_x - 64, 0)\n",
        "            crop_x2 = min(center_x + 64, image.shape[1])\n",
        "            crop_y1 = max(center_y - 64, 0)\n",
        "            crop_y2 = min(center_y + 64, image.shape[0])\n",
        "\n",
        "            # کراپ کردن تصویر\n",
        "            cropped_image = image[crop_y1:crop_y2, crop_x1:crop_x2]\n",
        "\n",
        "            # تغییر اندازه به 128x128\n",
        "            resized_image = cv2.resize(cropped_image, (128, 128))\n",
        "\n",
        "            # ذخیره تصویر کراپ شده\n",
        "            output_path = os.path.join(output_dir, f'cropped_{image_count}.jpg')  # نام فایل خروجی\n",
        "            cv2.imwrite(output_path, resized_image)\n",
        "\n",
        "            image_count += 1  # افزایش شمارنده\n",
        "\n",
        "            # اگر به حداکثر تعداد تصاویر رسیدیم، از حلقه خارج شویم\n",
        "            if image_count >= max_images:\n",
        "                break\n",
        "\n",
        "print(f\"تمامی {image_count} تصویر با موفقیت کراپ و ذخیره شدند.\")"
      ],
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          "name": "stdout",
          "text": "تمامی 100000 تصویر با موفقیت کراپ و ذخیره شدند.\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "import random"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# مسیر به دایرکتوری تصاویر ورودی\n",
        "input_dir = '/kaggle/working/CelebA_Image_Croped/'  # مسیر دایرکتوری تصاویر\n",
        "train_dir = '/kaggle/working/train/'  # مسیر دایرکتوری train\n",
        "test_dir = '/kaggle/working/test/'    # مسیر دایرکتوری test\n",
        "\n",
        ""
      ],
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        "trusted": true,
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    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# import os\n",
        "# import shutil\n",
        "# import random\n",
        "\n",
        "# # مسیر به دایرکتوری تصاویر ورودی\n",
        "# input_dir = '/content/drive/MyDrive/kaggle-celebA/images/'  # مسیر دایرکتوری تصاویر\n",
        "# train_dir = '/content/drive/MyDrive/kaggle-celebA/train/'  # مسیر دایرکتوری train\n",
        "# test_dir = '/content/drive/MyDrive/kaggle-celebA/test/'    # مسیر دایرکتوری test\n",
        "\n",
        "# ایجاد دایرکتوری‌های train و test در صورت عدم وجود\n",
        "os.makedirs(train_dir, exist_ok=True)\n",
        "os.makedirs(test_dir, exist_ok=True)\n",
        "\n",
        "# لیست کردن تصاویر در دایرکتوری ورودی\n",
        "images = [f for f in os.listdir(input_dir) if f.endswith('.jpg') or f.endswith('.png')]\n",
        "\n",
        "# # اطمینان از اینکه تعداد تصاویر 10000 تا است\n",
        "# if len(images) < 100000:\n",
        "#     print(f\"تعداد تصاویر موجود کمتر از 20 است: {len(images)}\")\n",
        "# else:\n",
        "#     # انتخاب 100000 تصویر تصادفی\n",
        "#     images = random.sample(images, 100000)\n",
        "\n",
        "# # تصادفی کردن لیست تصاویر\n",
        "# random.shuffle(images)\n",
        "\n",
        "# محاسبه تعداد تصاویر برای train و test\n",
        "train_size = int(0.8 * len(images))  # 80% برای train\n",
        "test_size = len(images) - train_size   # 20% برای test\n",
        "\n",
        "# تقسیم تصاویر به train و test\n",
        "train_images = images[:train_size]\n",
        "test_images = images[train_size:]\n",
        "\n",
        "# کپی کردن تصاویر به دایرکتوری‌های مربوطه\n",
        "for img in train_images:\n",
        "    shutil.copy(os.path.join(input_dir, img), os.path.join(train_dir, img))\n",
        "\n",
        "for img in test_images:\n",
        "    shutil.copy(os.path.join(input_dir, img), os.path.join(test_dir, img))\n",
        "\n",
        "print(f\"تعداد تصاویر در train: {len(train_images)}\")\n",
        "print(f\"تعداد تصاویر در test: {len(test_images)}\")\n",
        "print(\"تقسیم تصاویر با موفقیت انجام شد.\")"
      ],
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        {
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          "text": "تعداد تصاویر در train: 80000\nتعداد تصاویر در test: 20000\nتقسیم تصاویر با موفقیت انجام شد.\n",
          "output_type": "stream"
        }
      ],
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    {
      "cell_type": "code",
      "source": [
        "# import os\n",
        "# import shutil\n",
        "# import random\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import glob\n",
        "from skimage.metrics import structural_similarity as ssim"
      ],
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Set your image dataset paths\n",
        "train_path = '/kaggle/working/train'\n",
        "test_path = '/kaggle/working/test'"
      ],
      "metadata": {
        "trusted": true,
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    {
      "cell_type": "code",
      "source": [
        "# Load and preprocess the dataset\n",
        "def load_images(path, img_size=(64, 64)):\n",
        "    images = []\n",
        "    for file in glob.glob(path + \"/*.jpg\"):\n",
        "        img = cv2.imread(file)\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        img = cv2.resize(img, img_size)\n",
        "        images.append(img)\n",
        "    return np.array(images)\n",
        "\n",
        "# Split images into upper and lower halves\n",
        "def split_images(images):\n",
        "    upper_half = []\n",
        "    lower_half = []\n",
        "    for img in images:\n",
        "        h, w, _ = img.shape\n",
        "        upper_half.append(img[:h//2, :])\n",
        "        lower_half.append(img[h//2:, :])\n",
        "    return np.array(upper_half), np.array(lower_half)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# import tensorflow as tf\n",
        "# from tensorflow.keras import layers, models\n",
        "\n",
        "# Initiate autoencoder\n",
        "def build_autoencoder(input_shape):\n",
        "    encoder_input = layers.Input(shape=input_shape)\n",
        "\n",
        "    # Encoder\n",
        "    x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(encoder_input)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.MaxPooling2D((2, 2), padding='same')(x)\n",
        "    x = layers.Dropout(0.2)(x)  # Dropout layer to reduce overfitting\n",
        "\n",
        "    x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.MaxPooling2D((2, 2), padding='same')(x)\n",
        "    x = layers.Dropout(0.2)(x)  # Dropout layer to reduce overfitting\n",
        "\n",
        "    x = layers.Flatten()(x)\n",
        "    encoder_output = layers.Dense(256, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01))(x)  # L2 Regularization\n",
        "    encoder = models.Model(encoder_input, encoder_output)\n",
        "\n",
        "    # Decoder\n",
        "    decoder_input = layers.Input(shape=(256,))\n",
        "    x = layers.Dense((input_shape[0]//4) * (input_shape[1]//4) * 64, activation='relu')(decoder_input)\n",
        "    x = layers.Reshape((input_shape[0]//4, input_shape[1]//4, 64))(x)\n",
        "    x = layers.Conv2DTranspose(64, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = layers.UpSampling2D((2, 2))(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "\n",
        "    x = layers.Conv2DTranspose(32, (3, 3), activation='relu', padding='same')(x)\n",
        "    x = layers.UpSampling2D((2, 2))(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "\n",
        "    decoder_output = layers.Conv2DTranspose(3, (3, 3), activation='sigmoid', padding='same')(x)\n",
        "\n",
        "    decoder = models.Model(decoder_input, decoder_output)\n",
        "\n",
        "    # Complete Autoencoder\n",
        "    autoencoder_input = layers.Input(shape=input_shape)\n",
        "    encoded_img = encoder(autoencoder_input)\n",
        "    decoded_img = decoder(encoded_img)\n",
        "\n",
        "    autoencoder = models.Model(autoencoder_input, decoded_img)\n",
        "    autoencoder.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "    return autoencoder"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T17:58:28.705383Z",
          "iopub.execute_input": "2024-11-18T17:58:28.705657Z",
          "iopub.status.idle": "2024-11-18T17:58:28.717375Z",
          "shell.execute_reply.started": "2024-11-18T17:58:28.705632Z",
          "shell.execute_reply": "2024-11-18T17:58:28.716552Z"
        },
        "id": "1x_CyiFoJ5zt"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and preprocess the training dataset\n",
        "train_images = load_images(train_path)\n",
        "upper_half_train, lower_half_train = split_images(train_images)\n",
        "\n",
        "# Normalize Data\n",
        "upper_half_train = upper_half_train / 255.0\n",
        "lower_half_train = lower_half_train / 255.0\n",
        "\n",
        "# Build and train the autoencoder\n",
        "input_shape = upper_half_train.shape[1:]\n",
        "autoencoder = build_autoencoder(input_shape)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T17:58:28.719304Z",
          "iopub.execute_input": "2024-11-18T17:58:28.71959Z",
          "iopub.status.idle": "2024-11-18T17:58:48.698427Z",
          "shell.execute_reply.started": "2024-11-18T17:58:28.719551Z",
          "shell.execute_reply": "2024-11-18T17:58:48.697475Z"
        },
        "id": "uH4fVOm0J5zt"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# input_shape"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T17:58:48.699605Z",
          "iopub.execute_input": "2024-11-18T17:58:48.699893Z",
          "iopub.status.idle": "2024-11-18T17:58:48.703838Z",
          "shell.execute_reply.started": "2024-11-18T17:58:48.699866Z",
          "shell.execute_reply": "2024-11-18T17:58:48.702968Z"
        },
        "id": "mgmXW4WtJ5zu"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_images)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T17:58:48.704802Z",
          "iopub.execute_input": "2024-11-18T17:58:48.705061Z",
          "iopub.status.idle": "2024-11-18T17:58:48.717643Z",
          "shell.execute_reply.started": "2024-11-18T17:58:48.705038Z",
          "shell.execute_reply": "2024-11-18T17:58:48.716991Z"
        },
        "id": "kIoTiPnmJ5zu",
        "outputId": "25eddb76-1bc8-4a18-97c9-703131698d65"
      },
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "80000"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "autoencoder.fit(upper_half_train, lower_half_train, epochs=3, batch_size=32, validation_split=0.1)\n",
        "\n",
        "# # Save the model\n",
        "# autoencoder.save('autoencoder_model.h5')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T17:58:48.71848Z",
          "iopub.execute_input": "2024-11-18T17:58:48.718773Z",
          "iopub.status.idle": "2024-11-18T17:59:42.390777Z",
          "shell.execute_reply.started": "2024-11-18T17:58:48.718747Z",
          "shell.execute_reply": "2024-11-18T17:59:42.389805Z"
        },
        "id": "fuXYxsXAJ5zu",
        "outputId": "2a49df2f-d836-4071-eec7-297af4f7c108"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Epoch 1/3\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1731952741.985836    1431 service.cc:145] XLA service 0x7d50b80018a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1731952741.985898    1431 service.cc:153]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "\u001b[1m  33/2250\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - loss: 2.5494",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "I0000 00:00:1731952747.820950    1431 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 6ms/step - loss: 0.1558 - val_loss: 0.0390\nEpoch 2/3\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0382 - val_loss: 0.0376\nEpoch 3/3\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0362 - val_loss: 0.0346\n",
          "output_type": "stream"
        },
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<keras.src.callbacks.history.History at 0x7d5342f57550>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "autoencoder.fit(upper_half_train, lower_half_train, epochs=1000, batch_size=32, validation_split=0.1)\n",
        "\n",
        "# Save the model\n",
        "# autoencoder.save('autoencoder_model.h5')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T18:01:00.366224Z",
          "iopub.execute_input": "2024-11-18T18:01:00.366949Z"
        },
        "id": "7YWy0tD_J5zu",
        "outputId": "1349bb74-0920-4cb8-ab63-9d26fca59adf"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Epoch 1/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0356 - val_loss: 0.0367\nEpoch 2/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0352 - val_loss: 0.0341\nEpoch 3/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0352 - val_loss: 0.0339\nEpoch 4/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0352 - val_loss: 0.0345\nEpoch 5/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0348 - val_loss: 0.0343\nEpoch 6/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0349 - val_loss: 0.0353\nEpoch 7/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0347 - val_loss: 0.0346\nEpoch 8/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0349 - val_loss: 0.0335\nEpoch 9/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0348 - val_loss: 0.0334\nEpoch 10/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0346 - val_loss: 0.0342\nEpoch 11/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0346 - val_loss: 0.0343\nEpoch 12/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0345 - val_loss: 0.0341\nEpoch 13/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0346 - val_loss: 0.0345\nEpoch 14/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0347 - val_loss: 0.0338\nEpoch 15/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0344 - val_loss: 0.0337\nEpoch 16/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0344 - val_loss: 0.0340\nEpoch 17/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0343 - val_loss: 0.0334\nEpoch 18/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0345 - val_loss: 0.0339\nEpoch 19/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0344 - val_loss: 0.0339\nEpoch 20/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0343 - val_loss: 0.0337\nEpoch 21/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0343 - val_loss: 0.0343\nEpoch 22/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0340\nEpoch 23/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0344 - val_loss: 0.0336\nEpoch 24/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0338\nEpoch 25/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0342\nEpoch 26/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0340\nEpoch 27/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0343 - val_loss: 0.0352\nEpoch 28/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0336\nEpoch 29/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0343 - val_loss: 0.0336\nEpoch 30/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0336\nEpoch 31/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0341\nEpoch 32/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0360\nEpoch 33/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0352\nEpoch 34/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0339\nEpoch 35/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0352\nEpoch 36/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0348\nEpoch 37/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0335\nEpoch 38/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0333\nEpoch 39/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0332\nEpoch 40/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 41/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 42/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0332\nEpoch 43/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0340\nEpoch 44/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0341\nEpoch 45/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0334\nEpoch 46/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 47/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0346\nEpoch 48/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0335\nEpoch 49/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 50/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0344\nEpoch 51/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 52/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0329\nEpoch 53/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 54/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0344\nEpoch 55/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0342\nEpoch 56/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0340\nEpoch 57/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0331\nEpoch 58/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 59/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 60/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0353\nEpoch 61/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 62/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0352\nEpoch 63/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0343\nEpoch 64/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0342\nEpoch 65/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 66/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0340\nEpoch 67/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 68/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 69/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 70/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 71/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 72/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0346\nEpoch 73/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0329\nEpoch 74/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0336\nEpoch 75/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0329\nEpoch 76/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 77/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0343\nEpoch 78/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 79/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0333\nEpoch 80/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0334\nEpoch 81/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 82/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 83/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 84/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0342 - val_loss: 0.0334\nEpoch 85/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 86/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0336\nEpoch 87/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 88/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 89/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 90/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 91/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0339\nEpoch 92/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0336\nEpoch 93/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 94/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0332\nEpoch 95/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0329\nEpoch 96/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0329\nEpoch 97/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0330\nEpoch 98/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 99/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 100/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 101/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0338\nEpoch 102/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 103/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 104/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 105/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 106/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0340\nEpoch 107/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 108/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0338\nEpoch 109/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 110/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0335\nEpoch 111/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 112/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 113/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0334\nEpoch 114/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 115/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0327\nEpoch 116/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 117/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 118/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 119/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 120/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 121/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0340\nEpoch 122/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0330\nEpoch 123/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 124/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0329\nEpoch 125/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 126/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 127/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 128/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 129/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 130/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 131/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 132/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0348\nEpoch 133/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0339\nEpoch 134/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 135/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 136/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0331\nEpoch 137/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0330\nEpoch 138/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0330\nEpoch 139/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 140/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 141/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0329\nEpoch 142/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0349\nEpoch 143/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 144/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 145/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 146/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 147/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 148/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 149/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 150/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 151/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0344\nEpoch 152/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 153/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 154/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0326\nEpoch 155/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 156/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 157/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0327\nEpoch 158/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0332\nEpoch 159/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 160/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 161/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 162/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0329\nEpoch 163/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 164/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 165/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 166/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 167/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 168/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 169/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 170/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 171/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 172/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 173/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 174/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 175/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 176/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 177/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0332\nEpoch 178/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 179/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0330\nEpoch 180/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 181/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 182/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0335\nEpoch 183/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 184/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 185/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 186/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 187/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 188/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 189/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 190/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 191/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 192/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 193/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 194/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 195/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 196/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 197/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 198/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 199/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 200/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 201/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 202/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0339\nEpoch 203/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 204/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 205/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 206/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 207/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 208/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 209/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 210/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0343\nEpoch 211/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 212/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0331\nEpoch 213/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 214/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 215/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 216/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0330\nEpoch 217/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 218/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 219/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 220/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 221/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 222/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0328\nEpoch 223/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 224/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 225/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0342\nEpoch 226/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 227/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0344\nEpoch 228/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 229/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 230/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 231/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 232/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 233/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 234/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 6ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 235/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0349\nEpoch 236/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 237/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 238/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0331\nEpoch 239/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 240/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 241/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0327\nEpoch 242/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 243/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 244/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0335\nEpoch 245/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0330\nEpoch 246/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 247/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 248/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0329\nEpoch 249/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 250/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 251/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 252/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 253/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0341\nEpoch 254/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 255/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 256/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 257/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 258/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 259/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 260/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 261/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 262/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 263/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 264/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 265/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 266/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 267/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 268/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 269/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 270/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 271/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0331\nEpoch 272/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 273/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 274/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 275/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 276/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0330\nEpoch 277/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0332\nEpoch 278/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 279/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 280/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 281/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 282/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 283/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 284/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 285/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0330\nEpoch 286/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 287/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0346\nEpoch 288/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 289/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 290/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 291/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 292/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 293/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 294/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0335\nEpoch 295/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 296/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 297/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 298/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 299/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 300/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0331\nEpoch 301/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 302/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0336\nEpoch 303/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 304/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 305/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 306/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 307/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 308/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 309/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 310/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 311/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 312/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 313/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0340\nEpoch 314/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 315/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 316/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 317/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 318/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0343\nEpoch 319/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 320/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 321/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 322/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 323/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 324/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 325/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 326/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 327/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 328/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 329/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 330/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 331/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 332/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 333/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 334/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0343\nEpoch 335/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 336/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 337/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0345\nEpoch 338/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0335\nEpoch 339/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0343\nEpoch 340/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 341/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 342/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 343/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0343\nEpoch 344/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 345/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 346/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 347/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 348/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0329\nEpoch 349/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0339\nEpoch 350/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 351/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 352/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 353/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 354/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 355/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 356/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 357/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 358/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 359/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0344\nEpoch 360/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 361/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 362/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 363/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 364/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 365/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 366/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 367/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 368/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 369/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 370/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0345\nEpoch 371/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 372/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 373/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 374/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 375/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 376/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 377/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 378/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 379/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0333\nEpoch 380/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0336\nEpoch 381/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 382/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0337\nEpoch 383/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 384/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 385/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 386/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 387/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 388/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0343\nEpoch 389/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 390/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 391/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 392/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0335\nEpoch 393/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 394/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 395/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0342\nEpoch 396/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0337\nEpoch 397/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 398/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 399/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 400/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 401/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 402/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 403/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 404/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 405/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0341\nEpoch 406/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 407/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 408/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 409/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0339\nEpoch 410/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 411/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 412/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 413/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 414/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 415/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 416/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 417/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 418/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 419/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 420/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 421/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 422/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 423/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 424/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0330\nEpoch 425/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 426/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0346\nEpoch 427/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 428/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 429/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 430/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 431/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 432/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 433/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 434/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 435/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 436/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 437/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 438/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0342\nEpoch 439/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 440/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 441/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 442/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 443/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 444/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0345\nEpoch 445/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0334\nEpoch 446/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 447/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 448/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 449/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0335\nEpoch 450/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0335\nEpoch 451/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 452/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 453/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0330\nEpoch 454/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 455/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 456/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 457/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 458/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 459/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 460/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 461/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 462/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 463/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 464/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 465/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 466/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 467/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0335\nEpoch 468/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0329\nEpoch 469/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 470/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 471/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0336\nEpoch 472/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 473/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0343\nEpoch 474/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 475/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 476/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 477/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0338\nEpoch 478/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 479/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 480/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0341 - val_loss: 0.0337\nEpoch 481/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 482/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 483/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 484/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 485/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 486/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 487/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 488/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 489/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 490/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 491/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0343\nEpoch 492/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0341\nEpoch 493/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 494/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 495/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 496/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 497/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 498/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 499/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 500/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0339\nEpoch 501/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 502/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 503/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 504/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 505/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 506/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 507/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 508/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 509/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 510/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 511/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 512/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0330\nEpoch 513/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0339\nEpoch 514/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 515/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 516/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 517/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 518/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 519/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 520/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 521/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 522/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 523/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 524/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0347\nEpoch 525/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 526/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0345\nEpoch 527/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 528/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 529/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 530/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 531/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 532/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 533/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 534/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0331\nEpoch 535/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 536/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 537/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0336\nEpoch 554/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 555/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 556/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0342\nEpoch 557/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 558/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 559/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0344\nEpoch 560/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0336\nEpoch 561/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 568/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 569/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 570/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 571/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0330\nEpoch 572/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 573/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 574/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 575/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 576/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 577/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 578/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 579/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 580/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0343\nEpoch 581/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 582/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 583/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 584/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 585/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 586/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 587/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 588/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 589/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 590/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 591/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 592/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 593/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 594/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 595/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 614/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 615/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 616/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 617/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 618/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 619/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 620/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 621/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 622/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 629/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 630/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 631/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 632/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 633/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 634/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 635/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 636/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 637/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 638/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 639/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 647/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 648/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 649/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 650/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 651/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 652/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 653/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 654/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 655/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0336\nEpoch 656/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 669/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 670/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 671/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 672/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 673/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 674/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 675/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 676/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 677/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 678/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 679/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 689/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 690/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 691/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 692/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 693/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 694/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 695/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 696/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 697/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 698/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 699/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 711/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 712/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 713/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 714/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 715/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 716/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 717/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 718/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 719/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 720/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 721/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 722/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 723/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 724/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 725/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 732/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 733/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 734/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 735/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 736/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 737/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 738/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 739/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 740/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 761/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0341\nEpoch 762/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 763/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 764/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 765/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 766/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0341\nEpoch 767/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 768/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 769/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 770/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 771/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 783/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 784/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 785/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0343\nEpoch 786/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 787/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 788/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 789/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0344\nEpoch 790/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0336\nEpoch 791/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 792/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 793/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 794/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 804/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0336 - val_loss: 0.0337\nEpoch 805/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 806/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0339\nEpoch 807/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 808/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 809/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0334\nEpoch 810/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 811/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 812/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 813/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0337\nEpoch 814/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0331\nEpoch 824/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0339\nEpoch 825/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 826/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 827/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0342\nEpoch 828/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 829/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 830/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 831/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 832/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 833/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0346\nEpoch 834/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0338\nEpoch 835/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0343\nEpoch 845/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 846/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 847/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0333\nEpoch 848/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 849/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0332\nEpoch 850/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0339\nEpoch 851/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 852/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 853/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 854/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0331\nEpoch 855/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0338\nEpoch 856/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0330\nEpoch 857/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0336\nEpoch 867/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 868/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0340\nEpoch 869/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 870/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 871/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0338\nEpoch 872/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 873/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 874/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 875/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 891/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 892/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 893/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 894/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 895/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 896/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0342\nEpoch 897/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0334\nEpoch 898/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0335\nEpoch 899/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 900/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0333\nEpoch 913/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0330\nEpoch 914/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 915/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0341\nEpoch 916/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0332\nEpoch 917/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 918/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 919/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 920/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0338\nEpoch 921/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0346\nEpoch 922/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0345\nEpoch 936/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 937/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0336\nEpoch 938/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0343\nEpoch 939/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 940/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 941/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 942/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 943/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 944/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0337\nEpoch 945/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0337\nEpoch 946/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 947/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0335\nEpoch 958/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0340 - val_loss: 0.0335\nEpoch 959/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0340\nEpoch 960/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0332\nEpoch 961/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0340\nEpoch 962/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0337\nEpoch 963/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 964/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0335\nEpoch 965/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 966/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0341\nEpoch 967/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0330\nEpoch 982/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0334\nEpoch 983/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0342\nEpoch 984/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0339 - val_loss: 0.0343\nEpoch 985/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0336\nEpoch 986/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 987/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0334\nEpoch 988/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0333\nEpoch 989/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0338 - val_loss: 0.0344\nEpoch 990/1000\n\u001b[1m2250/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - loss: 0.0337 - val_loss: 0.0339\nEpoch 991/1000\n\u001b[1m2079/2250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0338",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and preprocess the test dataset\n",
        "test_images = load_images(test_path)\n",
        "upper_half_test, lower_half_test = split_images(test_images)\n",
        "\n",
        "# Normalize test data\n",
        "upper_half_test = upper_half_test / 255.0\n",
        "\n",
        "# Predict the lower half using the model\n",
        "lower_half_pred = autoencoder.predict(upper_half_test)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T21:14:21.839599Z",
          "iopub.execute_input": "2024-11-18T21:14:21.84027Z",
          "iopub.status.idle": "2024-11-18T21:14:30.32346Z",
          "shell.execute_reply.started": "2024-11-18T21:14:21.840239Z",
          "shell.execute_reply": "2024-11-18T21:14:30.322392Z"
        },
        "id": "ARwfcL7NJ5zu",
        "outputId": "7e96fdb4-b0ec-40dd-d4ce-06e8dd526508"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_images)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T21:14:38.886002Z",
          "iopub.execute_input": "2024-11-18T21:14:38.886823Z",
          "iopub.status.idle": "2024-11-18T21:14:38.891811Z",
          "shell.execute_reply.started": "2024-11-18T21:14:38.886788Z",
          "shell.execute_reply": "2024-11-18T21:14:38.890898Z"
        },
        "id": "4_49oW32J5zv",
        "outputId": "ce3e1a9c-dd19-44c4-cd02-2310c3724505"
      },
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "20000"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "def display_results(model, upper_half_test, lower_half_pred):\n",
        "    predictions = model.predict(upper_half_test)\n",
        "\n",
        "    fig, axes = plt.subplots(3, 10, figsize=(15, 5))\n",
        "\n",
        "    for i in range(10):\n",
        "        # چاپ ابعاد نیمه بالایی و پیش‌بینی\n",
        "        print(f\"Upper half shape: {upper_half_test[i].shape}, Prediction shape: {predictions[i].shape}\")\n",
        "\n",
        "        # نمایش نیمه بالایی\n",
        "        axes[0, i].imshow(upper_half_test[i])\n",
        "        axes[0, i].axis('off')\n",
        "\n",
        "        # نمایش نیمه پایینی\n",
        "        axes[1, i].imshow(lower_half_pred[i])\n",
        "        axes[1, i].axis('off')\n",
        "\n",
        "        # ترکیب نیمه بالایی و پیش‌بینی\n",
        "        combined_image = np.vstack((upper_half_test[i], predictions[i]))\n",
        "\n",
        "        # نمایش تصویر ترکیبی\n",
        "        axes[2, i].imshow(combined_image)\n",
        "        axes[2, i].axis('off')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "# فرض کنید که autoencoder، upper_half و lower_half از قبل تعریف شده‌اند\n",
        "display_results(autoencoder, upper_half_test, lower_half_test)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T21:14:48.154742Z",
          "iopub.execute_input": "2024-11-18T21:14:48.155105Z",
          "iopub.status.idle": "2024-11-18T21:14:52.524667Z",
          "shell.execute_reply.started": "2024-11-18T21:14:48.155075Z",
          "shell.execute_reply": "2024-11-18T21:14:52.523757Z"
        },
        "id": "amd1PqVRJ5zv",
        "outputId": "11e16d46-1c5c-438d-9c62-44df405ed7bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\nUpper half shape: (32, 64, 3), Prediction shape: (32, 64, 3)\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x500 with 30 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "trusted": true,
        "id": "anfCGO--J5zv"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "from skimage.metrics import structural_similarity as ssim\n",
        "import numpy as np\n",
        "\n",
        "# Function to calculate PSNR\n",
        "def calculate_psnr(original, predicted):\n",
        "    original = original.astype(np.float32)\n",
        "    predicted = predicted.astype(np.float32)\n",
        "    mse = np.mean((original - predicted) ** 2)\n",
        "    if mse == 0:\n",
        "        return float('inf')  # Infinite PSNR if images are identical\n",
        "    max_pixel = 1.0  # Assuming images are normalized to [0, 1]\n",
        "    psnr = 20 * np.log10(max_pixel / np.sqrt(mse))\n",
        "    return psnr\n",
        "\n",
        "# Function to calculate SSIM with a custom window size\n",
        "def calculate_ssim(image1, image2):\n",
        "    # print(f\"Image1 shape: {image1.shape}, Image2 shape: {image2.shape}\")\n",
        "    if image1.shape[0] < 7 or image1.shape[1] < 7:\n",
        "        print(\"Image dimensions are too small for SSIM calculation.\")\n",
        "        return None\n",
        "    else:\n",
        "        return ssim(image1, image2, multichannel=True, win_size=3, data_range=1)  # Assuming images are in range [0, 1]\n",
        "\n",
        "# Calculate metrics\n",
        "psnr_values = []\n",
        "ssim_values = []\n",
        "\n",
        "for i in range(len(lower_half_test)):\n",
        "    psnr_value = calculate_psnr(lower_half_test[i], lower_half_pred[i])\n",
        "    ssim_value = calculate_ssim(lower_half_test[i], lower_half_pred[i])  # Use the custom function\n",
        "\n",
        "    psnr_values.append(psnr_value)\n",
        "    if ssim_value is not None:  # Only append if SSIM was calculated\n",
        "        ssim_values.append(ssim_value)\n",
        "\n",
        "# Print average metrics\n",
        "print(f\"Average PSNR: {np.mean(psnr_values)}\")\n",
        "if ssim_values:  # Check if SSIM values were calculated\n",
        "    print(f\"Average SSIM: {np.mean(ssim_values)}\")"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T21:15:13.731632Z",
          "iopub.execute_input": "2024-11-18T21:15:13.732009Z",
          "iopub.status.idle": "2024-11-18T21:15:29.865964Z",
          "shell.execute_reply.started": "2024-11-18T21:15:13.731979Z",
          "shell.execute_reply": "2024-11-18T21:15:29.865081Z"
        },
        "id": "u3V1eQlcJ5zv",
        "outputId": "e45add47-68e8-4f5a-ac29-e50053256a95"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Average PSNR: -41.94657195787587\nAverage SSIM: 0.00016065911236765582\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model\n",
        "autoencoder.save('autoencoder_model.h5')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T21:16:54.270794Z",
          "iopub.execute_input": "2024-11-18T21:16:54.271148Z",
          "iopub.status.idle": "2024-11-18T21:16:54.402755Z",
          "shell.execute_reply.started": "2024-11-18T21:16:54.271118Z",
          "shell.execute_reply": "2024-11-18T21:16:54.401802Z"
        },
        "id": "CLwo_pLHJ5zv"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "autoencoder.summary()"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-18T21:17:35.757821Z",
          "iopub.execute_input": "2024-11-18T21:17:35.758393Z",
          "iopub.status.idle": "2024-11-18T21:17:35.777042Z",
          "shell.execute_reply.started": "2024-11-18T21:17:35.758358Z",
          "shell.execute_reply": "2024-11-18T21:17:35.776293Z"
        },
        "id": "USA0Uc8iJ5zv",
        "outputId": "275df3f2-2bff-448a-a5b9-842fc21fd948"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1mModel: \"functional_5\"\u001b[0m\n",
            "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\">Model: \"functional_5\"</span>\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "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│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ functional_1 (\u001b[38;5;33mFunctional\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │     \u001b[38;5;34m2,117,184\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ functional_3 (\u001b[38;5;33mFunctional\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │     \u001b[38;5;34m2,161,987\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
            "text/html": "<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│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ functional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</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\">2,117,184</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ functional_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,161,987</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m12,836,747\u001b[0m (48.97 MB)\n",
            "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\">12,836,747</span> (48.97 MB)\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,278,787\u001b[0m (16.32 MB)\n",
            "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\">4,278,787</span> (16.32 MB)\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m384\u001b[0m (1.50 KB)\n",
            "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\">384</span> (1.50 KB)\n</pre>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m8,557,576\u001b[0m (32.64 MB)\n",
            "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\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,557,576</span> (32.64 MB)\n</pre>\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# # Load and preprocess the new image\n",
        "# def load_and_preprocess_image(image_path, img_size=(64, 64)):\n",
        "#     img = cv2.imread(image_path)\n",
        "#     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "#     img = cv2.resize(img, img_size)\n",
        "#     return img\n",
        "\n",
        "\n",
        "# # Split the image into upper half\n",
        "# def split_image(img):\n",
        "#     h, w, _ = img.shape\n",
        "#     upper_half = img[:h//2, :]\n",
        "#     return upper_half\n",
        "\n",
        "# # ترکیب نیمه بالا و پایین و نمایش تصویر کامل\n",
        "# def combine_and_display(upper_half, lower_half_pred):\n",
        "#     full_face = np.vstack((upper_half, lower_half_pred))\n",
        "#     plt.imshow(full_face)\n",
        "#     plt.axis('off')\n",
        "#     plt.show()\n",
        "\n",
        "\n",
        "# # تابع پیش‌بینی و نمایش صورت کامل\n",
        "# def predict_and_display_full_face(autoencoder, image_path):\n",
        "#     new_image = load_and_preprocess_image(new_image_path)\n",
        "#     upper_half = split_image(new_image)\n",
        "#     upper_half_normalized = (upper_half.astype(np.float32) - 127.5) / 127.5\n",
        "#     upper_half_normalized = np.expand_dims(upper_half_normalized, axis=0)\n",
        "\n",
        "#     lower_half_pred = autoencoder.predict(upper_half_normalized)[0]\n",
        "#     lower_half_pred = (lower_half_pred * 127.5 + 127.5).astype(np.uint8)\n",
        "\n",
        "#     # combined_image = np.vstack((upper_half, lower_half_pred))\n",
        "\n",
        "#     combine_and_display(upper_half, lower_half_pred)\n",
        "\n",
        "# # مسیر تصویر جدید\n",
        "# new_image_path =\"/content/195579.jpg\"# \"/path/to/new/image.jpg\"\n",
        "# predict_and_display_full_face(autoencoder, new_image_path)"
      ],
      "metadata": {
        "trusted": true,
        "id": "QsGE-_1EJ5zw"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "l2dbamaeKQzq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}