DCGAN_Jan3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyM0OoRvmeNQhbj88tMGx0yI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fjadidi2001/Image_Inpaint/blob/main/NextGen_Jan2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 1: Load Image"
      ],
      "metadata": {
        "id": "TnFc4jlSR250"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Upload the file\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Open the image using PIL\n",
        "image = Image.open('Mona_Lisa.jpg')\n",
        "\n",
        "# Display the image\n",
        "plt.imshow(image)\n",
        "plt.axis('off')  # Hide the axis\n",
        "plt.title('Original Image')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "dj8pHukGRubE",
        "outputId": "7c5a1906-c184-4ed0-d24d-f9eb6cba8df3"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ba268e80-3512-4c15-9b59-61cecc72282a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ba268e80-3512-4c15-9b59-61cecc72282a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Mona_Lisa.jpg to Mona_Lisa.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 2: Preprocess Image\n"
      ],
      "metadata": {
        "id": "GJd8YD9JR_M4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "\n",
        "# Resize and normalize the image\n",
        "image = image.resize((64, 64))  # Resize to 64x64\n",
        "image = img_to_array(image)  # Convert to numpy array\n",
        "image = (image - 127.5) / 127.5  # Normalize to [-1, 1]\n",
        "\n",
        "# Display the preprocessed image\n",
        "plt.imshow((image + 1) / 2)  # Rescale to [0, 1] for display\n",
        "plt.axis('off')\n",
        "plt.title('Preprocessed Image')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "piqm78ajR8O0",
        "outputId": "d0245701-d16f-4a61-fa24-fd18d839a4aa"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 3: Create Mask\n"
      ],
      "metadata": {
        "id": "B5Ik3BklSRv9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import ImageDraw\n",
        "\n",
        "# Create a copy of the image to draw on\n",
        "masked_image = Image.fromarray(((image + 1) * 127.5).astype(np.uint8))\n",
        "\n",
        "# Define the square's position and size (left, upper, right, lower)\n",
        "left = 20\n",
        "upper = 20\n",
        "right = 40\n",
        "lower = 40\n",
        "\n",
        "# Draw the mask (a filled rectangle) on the image\n",
        "draw = ImageDraw.Draw(masked_image)\n",
        "draw.rectangle([left, upper, right, lower], fill=(0, 0, 0))  # Black mask\n",
        "\n",
        "# Convert back to numpy array and normalize\n",
        "masked_image = img_to_array(masked_image)\n",
        "masked_image = (masked_image - 127.5) / 127.5\n",
        "\n",
        "# Display the masked image\n",
        "plt.imshow((masked_image + 1) / 2)\n",
        "plt.axis('off')\n",
        "plt.title('Masked Image')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "_xXY8Uz-SFqk",
        "outputId": "017084bc-5f6e-4b2e-cebc-70ad5dec653c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 4: Define Model\n"
      ],
      "metadata": {
        "id": "0rxVRzA9Sg3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers, models\n",
        "\n",
        "# Define the Generator\n",
        "def build_generator(latent_dim):\n",
        "    model = models.Sequential()\n",
        "\n",
        "    # Start with a dense layer\n",
        "    model.add(layers.Dense(8 * 8 * 256, input_dim=latent_dim))\n",
        "    model.add(layers.Reshape((8, 8, 256)))\n",
        "\n",
        "    # Upsample to 16x16\n",
        "    model.add(layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding='same'))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU(alpha=0.2))\n",
        "\n",
        "    # Upsample to 32x32\n",
        "    model.add(layers.Conv2DTranspose(64, kernel_size=4, strides=2, padding='same'))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU(alpha=0.2))\n",
        "\n",
        "    # Upsample to 64x64\n",
        "    model.add(layers.Conv2DTranspose(3, kernel_size=4, strides=2, padding='same', activation='tanh'))\n",
        "\n",
        "    return model\n",
        "\n",
        "# Define the Discriminator\n",
        "def build_discriminator(img_shape):\n",
        "    model = models.Sequential()\n",
        "\n",
        "    # Input layer\n",
        "    model.add(layers.Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=img_shape))\n",
        "    model.add(layers.LeakyReLU(alpha=0.2))\n",
        "\n",
        "    # Downsample to 16x16\n",
        "    model.add(layers.Conv2D(128, kernel_size=4, strides=2, padding='same'))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU(alpha=0.2))\n",
        "\n",
        "    # Downsample to 8x8\n",
        "    model.add(layers.Conv2D(256, kernel_size=4, strides=2, padding='same'))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU(alpha=0.2))\n",
        "\n",
        "    # Output layer\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    return model\n",
        "\n",
        "# Parameters\n",
        "latent_dim = 100\n",
        "img_shape = (64, 64, 3)\n",
        "\n",
        "# Build models\n",
        "generator = build_generator(latent_dim)\n",
        "discriminator = build_discriminator(img_shape)"
      ],
      "metadata": {
        "id": "DK-cFz4ASWIi"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 5: Combine DCGAN Model\n"
      ],
      "metadata": {
        "id": "bBBBLVJwTZb5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Define the GAN\n",
        "def build_gan(generator, discriminator):\n",
        "    discriminator.trainable = False  # Freeze discriminator during generator training\n",
        "    model = models.Sequential()\n",
        "    model.add(generator)\n",
        "    model.add(discriminator)\n",
        "    return model\n",
        "\n",
        "# Compile models\n",
        "discriminator.compile(optimizer=Adam(learning_rate=0.0002, beta_1=0.5), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "gan = build_gan(generator, discriminator)\n",
        "gan.compile(optimizer=Adam(learning_rate=0.0002, beta_1=0.5), loss='binary_crossentropy')"
      ],
      "metadata": {
        "id": "-ZPlLUBqTTVz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 6: Train Model\n"
      ],
      "metadata": {
        "id": "lGutL2OZTp1o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RbqcIAyqT0Ip",
        "outputId": "fa1da7c0-5809-4ba5-e26b-6304e4c530c9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.1)\n",
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            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
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            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.image import img_to_array, array_to_img # Import array_to_img\n"
      ],
      "metadata": {
        "id": "C4UOXto1UVzK"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Training parameters\n",
        "epochs = 1000\n",
        "batch_size = 1\n",
        "sample_interval = 1000\n",
        "\n",
        "# Adversarial ground truths\n",
        "real = np.ones((batch_size, 1))\n",
        "fake = np.zeros((batch_size, 1))\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(epochs):\n",
        "    # Train Discriminator\n",
        "    noise = np.random.normal(0, 1, (batch_size, latent_dim))\n",
        "    gen_imgs = generator.predict(noise)\n",
        "\n",
        "    d_loss_real = discriminator.train_on_batch(image[np.newaxis, ...], real)\n",
        "    d_loss_fake = discriminator.train_on_batch(gen_imgs, fake)\n",
        "    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "    # Train Generator\n",
        "    noise = np.random.normal(0, 1, (batch_size, latent_dim))\n",
        "    g_loss = gan.train_on_batch(noise, real)\n",
        "\n",
        "    # Print progress\n",
        "    if epoch % sample_interval == 0:\n",
        "        print(f\"{epoch} [D loss: {d_loss[0]}, acc.: {100 * d_loss[1]}%] [G loss: {g_loss}]\")\n",
        "        # Save generated images\n",
        "        img = array_to_img(gen_imgs[0] * 127.5 + 127.5, scale=False) # Now array_to_img is defined\n",
        "        img.save(f\"generated_{epoch}.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vnw1247VTgKh",
        "outputId": "03c73451-3753-471e-c70c-c3e0635c595f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "0 [D loss: 0.6557990312576294, acc.: 52.77777910232544%] [G loss: [array(0.6578026, dtype=float32), array(0.6578026, dtype=float32), array(0.5, dtype=float32)]]\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
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            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
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            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
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            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from skimage.metrics import peak_signal_noise_ratio as psnr\n",
        "from skimage.metrics import structural_similarity as ssim\n",
        "\n",
        "# Generate inpainted image\n",
        "noise = np.random.normal(0, 1, (1, latent_dim))\n",
        "inpainted_image = generator.predict(noise)\n",
        "\n",
        "# Rescale images to [0, 1]\n",
        "original_image = (image + 1) / 2\n",
        "inpainted_image = (inpainted_image[0] + 1) / 2\n",
        "\n",
        "# Calculate PSNR\n",
        "psnr_value = psnr(original_image, inpainted_image, data_range=1.0)  # data_range=1.0 for [0, 1]\n",
        "\n",
        "# Calculate SSIM\n",
        "ssim_value = ssim(original_image, inpainted_image, channel_axis=-1, data_range=1.0)  # data_range=1.0 for [0, 1]\n",
        "\n",
        "print(f\"PSNR: {psnr_value}, SSIM: {ssim_value}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jB5JDM50UUky",
        "outputId": "1c6ea55d-a785-43ce-f564-45d83dc99fc1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "PSNR: 6.278585985260319, SSIM: 0.007382684852927923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display original, masked, and inpainted images\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "# Original Image\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.imshow((image + 1) / 2)\n",
        "plt.axis('off')\n",
        "plt.title('Original Image')\n",
        "\n",
        "# Masked Image\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.imshow((masked_image + 1) / 2)\n",
        "plt.axis('off')\n",
        "plt.title('Masked Image')\n",
        "\n",
        "# Inpainted Image\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.imshow(inpainted_image)\n",
        "plt.axis('off')\n",
        "plt.title('Inpainted Image')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6NrPMZRrXvOC",
        "outputId": "6035e8a0-d9ac-456a-c656-f5e02235960d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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}