data/preferentialBO.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NTeLbyDoMi0-"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import warnings\n",
        "from itertools import combinations\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "# Suppress potential optimization warnings for cleaner notebook\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import DTLZ2_model\n",
        "import fischer_model\n",
        "from botorch.test_functions.multi_objective import DTLZ2\n",
        "from DTLZ2_model import neg_l1_dist\n",
        "from DTLZ2_model import predict_DTLZ2_model\n",
        "from fischer_model import predict_fischer_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor([[0.6455, 0.0591, 0.1161, 0.5071]])"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predict_fischer_model(torch.tensor([[0.5, 0.5, 0.5, 0.5]]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Mvj4PbSpNSjG"
      },
      "outputs": [],
      "source": [
        "# data generating helper functions\n",
        "#function that defines the comparisons\n",
        "def utility(X):\n",
        "    y=predict_fischer_model(X)\n",
        "    weighted_y = y * torch.sqrt(torch.arange(y.size(-1), dtype=torch.float) + 1)\n",
        "    y = torch.sum(weighted_y, dim=-1)\n",
        "    return y\n",
        "\n",
        "\n",
        "def generate_data(n, dim=4):\n",
        "    \"\"\"Generate data X and y\"\"\"\n",
        "    # X is randomly sampled from dim-dimentional unit cube\n",
        "    # we recommend using double as opposed to float tensor here for\n",
        "    # better numerical stability\n",
        "    X = torch.rand(n, dim, dtype=torch.float64)\n",
        "    y = utility(X)\n",
        "    return X, y\n",
        "\n",
        "#we need to change this to interaction with the llm\n",
        "def generate_comparisons(y, n_comp, noise=0.1, replace=False):\n",
        "    \"\"\"Create pairwise comparisons with noise\"\"\"\n",
        "    # generate all possible pairs of elements in y\n",
        "    all_pairs = np.array(list(combinations(range(y.shape[0]), 2)))\n",
        "    # randomly select n_comp pairs from all_pairs\n",
        "    comp_pairs = all_pairs[\n",
        "        np.random.choice(range(len(all_pairs)), n_comp, replace=replace)\n",
        "    ]\n",
        "    # add gaussian noise to the latent y values\n",
        "    c0 = y[comp_pairs[:, 0]] + np.random.standard_normal(len(comp_pairs)) * noise\n",
        "    c1 = y[comp_pairs[:, 1]] + np.random.standard_normal(len(comp_pairs)) * noise\n",
        "    reverse_comp = (c0 < c1).numpy()\n",
        "    comp_pairs[reverse_comp, :] = np.flip(comp_pairs[reverse_comp, :], 1)\n",
        "    comp_pairs = torch.tensor(comp_pairs).long()\n",
        "\n",
        "    return comp_pairs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "m5F2xn-8N-g9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
          ]
        }
      ],
      "source": [
        "#parameters\n",
        "torch.manual_seed(123)\n",
        "n = 50 if not SMOKE_TEST else 5\n",
        "m = 100 if not SMOKE_TEST else 10\n",
        "dim = 4\n",
        "noise = 0.1\n",
        "\n",
        "#generate data, initial data\n",
        "train_X, train_y = generate_data(n, dim=dim)\n",
        "#generating comparison based on the utility function\n",
        "train_comp = generate_comparisons(train_y, m, noise=noise)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "fMuLBRO-NVj3"
      },
      "outputs": [],
      "source": [
        "from botorch.fit import fit_gpytorch_mll\n",
        "from botorch.models.pairwise_gp import PairwiseGP, PairwiseLaplaceMarginalLogLikelihood\n",
        "from botorch.models.transforms.input import Normalize\n",
        "\n",
        "\n",
        "model = PairwiseGP(\n",
        "    train_X,\n",
        "    train_comp,\n",
        "    input_transform=Normalize(d=train_X.shape[-1]),\n",
        ")\n",
        "mll = PairwiseLaplaceMarginalLogLikelihood(model.likelihood, model)\n",
        "mll = fit_gpytorch_mll(mll)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdxhkF6yNZKp",
        "outputId": "0e3b833e-7a46-4413-f148-7e3fab6d8433"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step\n",
            "Test Kendall-Tau rank correlation: 0.8616\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import kendalltau\n",
        "\n",
        "\n",
        "# Kendall-Tau rank correlation\n",
        "def eval_kt_cor(model, test_X, test_y):\n",
        "    pred_y = model.posterior(test_X).mean.squeeze().detach().numpy()\n",
        "    return kendalltau(pred_y, test_y).correlation\n",
        "\n",
        "\n",
        "n_kendall = 1000 if not SMOKE_TEST else 10\n",
        "\n",
        "test_X, test_y = generate_data(n_kendall, dim=dim)\n",
        "kt_correlation = eval_kt_cor(model, test_X, test_y)\n",
        "\n",
        "print(f\"Test Kendall-Tau rank correlation: {kt_correlation:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "qEoN28RSNk2N"
      },
      "outputs": [],
      "source": [
        "from botorch.acquisition.preference import AnalyticExpectedUtilityOfBestOption\n",
        "from botorch.optim import optimize_acqf\n",
        "\n",
        "\n",
        "def init_and_fit_model(X, comp):\n",
        "    \"\"\"Model fitting helper function\"\"\"\n",
        "    model = PairwiseGP(\n",
        "        X,\n",
        "        comp,\n",
        "        input_transform=Normalize(d=X.shape[-1]),\n",
        "    )\n",
        "    mll = PairwiseLaplaceMarginalLogLikelihood(model.likelihood, model)\n",
        "    fit_gpytorch_mll(mll)\n",
        "    return mll, model\n",
        "\n",
        "\n",
        "def make_new_data(X, next_X, comps, q_comp):\n",
        "    \"\"\"Given X and next_X,\n",
        "    generate q_comp new comparisons between next_X\n",
        "    and return the concatenated X and comparisons\n",
        "    \"\"\"\n",
        "    # next_X is float by default; cast it to the dtype of X (i.e., double)\n",
        "    next_X = next_X.to(X)\n",
        "    next_y = utility(next_X)\n",
        "    next_comps = generate_comparisons(next_y, n_comp=q_comp, noise=noise)\n",
        "    comps = torch.cat([comps, next_comps + X.shape[-2]])\n",
        "    X = torch.cat([X, next_X])\n",
        "    return X, comps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "1pTEh0DMNv1w"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 916us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 708us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 742us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 714us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 644us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 881us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 851us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 697us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 879us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 754us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 648us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 694us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 679us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 659us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 648us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 705us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 674us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 737us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 790us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 699us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 730us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 923us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 675us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 708us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 682us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 723us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 695us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 714us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 690us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 718us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 708us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 799us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 708us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 726us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 942us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 692us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 732us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 879us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 797us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 873us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 927us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 701us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 723us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 698us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 799us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 959us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 722us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 688us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 861us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 689us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 647us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 643us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 630us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 925us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 735us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 679us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 683us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 641us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 640us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 745us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 633us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 740us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 649us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 640us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 733us/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 858us/step\n"
          ]
        }
      ],
      "source": [
        "algos = [\"EUBO\", \"rand\"]\n",
        "\n",
        "NUM_TRIALS = 3 if not SMOKE_TEST else 2\n",
        "NUM_BATCHES = 30 if not SMOKE_TEST else 2\n",
        "\n",
        "dim = 4\n",
        "NUM_RESTARTS = 3\n",
        "RAW_SAMPLES = 512 if not SMOKE_TEST else 8\n",
        "q = 2  # number of points per query\n",
        "q_comp = 1  # number of comparisons per query\n",
        "\n",
        "# initial evals\n",
        "best_vals = {}  # best observed values\n",
        "for algo in algos:\n",
        "    best_vals[algo] = []\n",
        "\n",
        "# average over multiple trials\n",
        "for i in range(NUM_TRIALS):\n",
        "    torch.manual_seed(i)\n",
        "    np.random.seed(i)\n",
        "    data = {}\n",
        "    models = {}\n",
        "\n",
        "    # Create initial data\n",
        "    init_X, init_y = generate_data(q, dim=dim)\n",
        "    comparisons = generate_comparisons(init_y, q_comp, noise=noise)\n",
        "    # X are within the unit cube\n",
        "    bounds = torch.stack([torch.zeros(dim), torch.ones(dim)])\n",
        "\n",
        "    for algo in algos:\n",
        "        best_vals[algo].append([])\n",
        "        data[algo] = (init_X, comparisons)\n",
        "        _, models[algo] = init_and_fit_model(init_X, comparisons)\n",
        "\n",
        "        best_next_y = utility(init_X).max().item()\n",
        "        best_vals[algo][-1].append(best_next_y)\n",
        "\n",
        "    # we make additional NUM_BATCHES comparison queries after the initial observation\n",
        "    for j in range(1, NUM_BATCHES + 1):\n",
        "        for algo in algos:\n",
        "            model = models[algo]\n",
        "            if algo == \"EUBO\":\n",
        "                # create the acquisition function object\n",
        "                acq_func = AnalyticExpectedUtilityOfBestOption(pref_model=model)\n",
        "                # optimize and get new observation\n",
        "                next_X, acq_val = optimize_acqf(\n",
        "                    acq_function=acq_func,\n",
        "                    bounds=bounds,\n",
        "                    q=q,\n",
        "                    num_restarts=NUM_RESTARTS,\n",
        "                    raw_samples=RAW_SAMPLES,\n",
        "                )\n",
        "            else:\n",
        "                # randomly sample data\n",
        "                next_X, _ = generate_data(q, dim=dim)\n",
        "\n",
        "            # update data\n",
        "            X, comps = data[algo]\n",
        "            X, comps = make_new_data(X, next_X, comps, q_comp)\n",
        "            data[algo] = (X, comps)\n",
        "\n",
        "            # refit models\n",
        "            _, models[algo] = init_and_fit_model(X, comps)\n",
        "\n",
        "            # record the best observed values so far\n",
        "            max_val = utility(X).max().item()\n",
        "            best_vals[algo][-1].append(max_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "eRLF3-dFNzyW",
        "outputId": "9c83df50-e355-42b9-860e-b4b51d63f2c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x2860b6e10>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "plt.rcParams.update({\"font.size\": 14})\n",
        "\n",
        "algo_labels = {\n",
        "    \"rand\": \"Random Exploration\",\n",
        "    \"EUBO\": \"EUBO\",\n",
        "}\n",
        "\n",
        "\n",
        "def ci(y):\n",
        "    return 1.96 * y.std(axis=0) / np.sqrt(y.shape[0])\n",
        "\n",
        "\n",
        "# the utility function is maximized at the full vector of 1\n",
        "optimal_val = utility(torch.tensor([[1] * dim])).item()\n",
        "iters = list(range(NUM_BATCHES + 1))\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
        "# plot the optimal value\n",
        "ax.plot(\n",
        "    iters,\n",
        "    [optimal_val] * len(iters),\n",
        "    label=\"Optimal Function Value\",\n",
        "    color=\"black\",\n",
        "    linewidth=1.5,\n",
        ")\n",
        "\n",
        "# plot the the best observed value from each algorithm\n",
        "for algo in algos:\n",
        "    ys = np.vstack(best_vals[algo])\n",
        "    ax.errorbar(\n",
        "        iters, ys.mean(axis=0), yerr=ci(ys), label=algo_labels[algo], linewidth=1.5\n",
        "    )\n",
        "\n",
        "ax.set(\n",
        "    xlabel=f\"Number of queries (q = {q}, num_comparisons = {q_comp})\",\n",
        "    ylabel=\"Best observed value\",\n",
        "    title=f\"{dim}-dim weighted vector sum\",\n",
        ")\n",
        "ax.legend(loc=\"best\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "47P-W6iDOiHp"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "botorcj_mar2024",
      "language": "python",
      "name": "botorcj_mar2024"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}