OptimizersFinal.ipynb

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      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets,layers,models,losses,metrics\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_datasets as tfds"
      ]
    },
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          "text": [
            "Downloading and preparing dataset 162.17 MiB (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\n"
          ]
        },
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            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
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              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
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              "Extraction completed...: 0 file [00:00, ? file/s]"
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            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
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            "text/plain": [
              "Generating train examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]"
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            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incomplete71ZCEX/cifar10-train.tfrecord*...:   0%|          …"
            ]
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              "Generating test examples...:   0%|          | 0/10000 [00:00<?, ? examples/s]"
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              "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incomplete71ZCEX/cifar10-test.tfrecord*...:   0%|          |…"
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\n",
            "Train Set Size: 45000\n",
            "Validation Set Size: 5000\n",
            "Test Set Size: 10000\n",
            "Train Labels Size: 45000\n",
            "Validation Labels Size: 5000\n",
            "Test Labels Size: 10000\n"
          ]
        }
      ],
      "source": [
        "\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "# Load the CIFAR-10 dataset\n",
        "dataset, info = tfds.load(\"cifar10\", split=\"train\", as_supervised=True, with_info=True)\n",
        "\n",
        "# Split the training set into validation and training sets\n",
        "validation_percentage = 10  # Percentage of data to use for validation\n",
        "num_validation_samples = int(len(dataset) * (validation_percentage / 100.0))\n",
        "\n",
        "train_set = dataset.skip(num_validation_samples)\n",
        "valid_set = dataset.take(num_validation_samples)\n",
        "\n",
        "# Load the test set\n",
        "test_set = tfds.load(\"cifar10\", split=\"test\", as_supervised=True)\n",
        "\n",
        "# Print the number of samples in each set\n",
        "print(\"Train Set Size:\", len(train_set))\n",
        "print(\"Validation Set Size:\", len(valid_set))\n",
        "print(\"Test Set Size:\", len(test_set))\n",
        "\n",
        "# Extract the labels from the dataset\n",
        "train_labels = [label for _, label in train_set]\n",
        "valid_labels = [label for _, label in valid_set]\n",
        "test_labels = [label for _, label in test_set]\n",
        "\n",
        "# Print the number of labels in each set\n",
        "print(\"Train Labels Size:\", len(train_labels))\n",
        "print(\"Validation Labels Size:\", len(valid_labels))\n",
        "print(\"Test Labels Size:\", len(test_labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LckhSmhsRV-d",
        "outputId": "40714693-799b-4e1f-e509-72e6d2de0176"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Images Size: 45000\n",
            "Validation Images Size: 5000\n",
            "Test Images Size: 10000\n"
          ]
        }
      ],
      "source": [
        "\n",
        "train_images = [image for image,_ in train_set]\n",
        "valid_images = [image for image,_ in valid_set]\n",
        "test_images = [image for image,_ in test_set]\n",
        "\n",
        "\n",
        "\n",
        "print(\"Train Images Size:\", len(train_images))\n",
        "print(\"Validation Images Size:\", len(valid_images))\n",
        "print(\"Test Images Size:\", len(test_images))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "19gfXyziRcVz"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "train_images=np.array(train_images\n",
        "                       )\n",
        "valid_images=np.array(valid_images\n",
        "                       )\n",
        "test_images=np.array(test_images\n",
        "                       )\n",
        "\n",
        "\n",
        "train_images,valid_images,test_images=train_images.astype('float32')/255.0,valid_images.astype('float32')/255.0,test_images.astype('float32')/255.0\n",
        "\n",
        "\n",
        "\n",
        "train_labels=np.array(train_labels\n",
        "                       )\n",
        "valid_labels=np.array(valid_labels\n",
        "                       )\n",
        "test_labels=np.array(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1pchXJZRiA6",
        "outputId": "2a6ecffb-d12c-46bf-e547-957374046bf5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(9000, 32, 32, 3)\n",
            "(9000, 32, 32, 3)\n",
            "(9000, 32, 32, 3)\n",
            "(9000, 32, 32, 3)\n",
            "(9000, 32, 32, 3)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "train_b1,train_b2,train_b3,train_b4,train_b5=train_images[0:9000],train_images[9000:18000],train_images[18000:27000],train_images[27000:36000],train_images[36000:]\n",
        "print(train_b1.shape)\n",
        "print(train_b2.shape)\n",
        "print(train_b3.shape)\n",
        "print(train_b4.shape)\n",
        "print(train_b5.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-DkZ_DXRps-",
        "outputId": "58d4f20b-6ae3-4e5f-fce1-1297e56a9a66"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(9000,)\n",
            "(9000,)\n",
            "(9000,)\n",
            "(9000,)\n",
            "(9000,)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "train_l1,train_l2,train_l3,train_l4,train_l5=train_labels[0:9000],train_labels[9000:18000],train_labels[18000:27000],train_labels[27000:36000],train_labels[36000:]\n",
        "print(train_l1.shape)\n",
        "print(train_l2.shape)\n",
        "print(train_l3.shape)\n",
        "print(train_l4.shape)\n",
        "print(train_l5.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "eIqPo86pRtOw"
      },
      "outputs": [],
      "source": [
        "classes1,classes2,classes3,classes4,classes5 = train_l1,train_l2,train_l3,train_l4,train_l5\n",
        "numberofclasses = 10\n",
        "onehotencb1 = tf.one_hot(classes1,numberofclasses)\n",
        "onehotencb2 = tf.one_hot(classes2,numberofclasses)\n",
        "onehotencb3 = tf.one_hot(classes3,numberofclasses)\n",
        "onehotencb4 = tf.one_hot(classes4,numberofclasses)\n",
        "onehotencb5 = tf.one_hot(classes5,numberofclasses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "FM9eCQmtRyNM"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "from keras.engine.sequential import Sequential\n",
        "final_labels_encoded=np.concatenate((onehotencb1,onehotencb2,onehotencb3,onehotencb4,onehotencb5))\n",
        "valid_lfinal=tf.one_hot(valid_labels,numberofclasses)\n",
        "test_labels_f=tf.one_hot(test_labels,numberofclasses)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oj45pYQIZzQi"
      },
      "source": [
        "## Hyperparameter: Optimizers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_8rp9GtTYoZ",
        "outputId": "fe726761-db70-4966-84fa-3bdc084c5e58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 20s 9ms/step - loss: 1.7383 - categorical_crossentropy: 1.7383 - val_loss: 1.4725 - val_categorical_crossentropy: 1.4725\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3832 - categorical_crossentropy: 1.3832 - val_loss: 1.3883 - val_categorical_crossentropy: 1.3883\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2280 - categorical_crossentropy: 1.2280 - val_loss: 1.2386 - val_categorical_crossentropy: 1.2386\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.1239 - categorical_crossentropy: 1.1239 - val_loss: 1.1231 - val_categorical_crossentropy: 1.1231\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0357 - categorical_crossentropy: 1.0357 - val_loss: 1.0411 - val_categorical_crossentropy: 1.0411\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9626 - categorical_crossentropy: 0.9626 - val_loss: 0.9854 - val_categorical_crossentropy: 0.9854\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8997 - categorical_crossentropy: 0.8997 - val_loss: 0.9793 - val_categorical_crossentropy: 0.9793\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.8456 - categorical_crossentropy: 0.8456 - val_loss: 0.9158 - val_categorical_crossentropy: 0.9158\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.7914 - categorical_crossentropy: 0.7914 - val_loss: 0.9219 - val_categorical_crossentropy: 0.9219\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7400 - categorical_crossentropy: 0.7400 - val_loss: 0.8815 - val_categorical_crossentropy: 0.8815\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.6988 - categorical_crossentropy: 0.6988 - val_loss: 0.9088 - val_categorical_crossentropy: 0.9088\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.6585 - categorical_crossentropy: 0.6585 - val_loss: 0.9038 - val_categorical_crossentropy: 0.9038\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.6126 - categorical_crossentropy: 0.6126 - val_loss: 0.8575 - val_categorical_crossentropy: 0.8575\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.5628 - categorical_crossentropy: 0.5628 - val_loss: 0.8878 - val_categorical_crossentropy: 0.8878\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.5197 - categorical_crossentropy: 0.5197 - val_loss: 0.9076 - val_categorical_crossentropy: 0.9076\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.4830 - categorical_crossentropy: 0.4830 - val_loss: 0.9215 - val_categorical_crossentropy: 0.9215\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.4454 - categorical_crossentropy: 0.4454 - val_loss: 0.9367 - val_categorical_crossentropy: 0.9367\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4033 - categorical_crossentropy: 0.4033 - val_loss: 0.9314 - val_categorical_crossentropy: 0.9314\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 8s 12ms/step - loss: 0.3591 - categorical_crossentropy: 0.3591 - val_loss: 0.9582 - val_categorical_crossentropy: 0.9582\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.3160 - categorical_crossentropy: 0.3160 - val_loss: 0.9794 - val_categorical_crossentropy: 0.9794\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.2893 - categorical_crossentropy: 0.2893 - val_loss: 1.0605 - val_categorical_crossentropy: 1.0605\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 0.2486 - categorical_crossentropy: 0.2486 - val_loss: 1.1144 - val_categorical_crossentropy: 1.1144\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.2083 - categorical_crossentropy: 0.2083 - val_loss: 1.1834 - val_categorical_crossentropy: 1.1834\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.1772 - categorical_crossentropy: 0.1772 - val_loss: 1.2484 - val_categorical_crossentropy: 1.2484\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.1573 - categorical_crossentropy: 0.1573 - val_loss: 1.2569 - val_categorical_crossentropy: 1.2569\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.1220 - categorical_crossentropy: 0.1220 - val_loss: 1.3290 - val_categorical_crossentropy: 1.3290\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0986 - categorical_crossentropy: 0.0986 - val_loss: 1.4202 - val_categorical_crossentropy: 1.4202\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0743 - categorical_crossentropy: 0.0743 - val_loss: 1.4840 - val_categorical_crossentropy: 1.4840\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0606 - categorical_crossentropy: 0.0606 - val_loss: 1.6157 - val_categorical_crossentropy: 1.6157\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0408 - categorical_crossentropy: 0.0408 - val_loss: 1.6467 - val_categorical_crossentropy: 1.6467\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0290 - categorical_crossentropy: 0.0290 - val_loss: 1.7366 - val_categorical_crossentropy: 1.7366\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0185 - categorical_crossentropy: 0.0185 - val_loss: 1.7905 - val_categorical_crossentropy: 1.7905\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0162 - categorical_crossentropy: 0.0162 - val_loss: 1.8505 - val_categorical_crossentropy: 1.8505\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0070 - categorical_crossentropy: 0.0070 - val_loss: 1.9279 - val_categorical_crossentropy: 1.9279\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0046 - categorical_crossentropy: 0.0046 - val_loss: 1.9942 - val_categorical_crossentropy: 1.9942\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0038 - categorical_crossentropy: 0.0038 - val_loss: 2.0332 - val_categorical_crossentropy: 2.0332\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0028 - categorical_crossentropy: 0.0028 - val_loss: 2.0779 - val_categorical_crossentropy: 2.0779\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0030 - categorical_crossentropy: 0.0030 - val_loss: 2.1024 - val_categorical_crossentropy: 2.1024\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0022 - categorical_crossentropy: 0.0022 - val_loss: 2.1421 - val_categorical_crossentropy: 2.1421\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0020 - categorical_crossentropy: 0.0020 - val_loss: 2.1757 - val_categorical_crossentropy: 2.1757\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0017 - categorical_crossentropy: 0.0017 - val_loss: 2.2009 - val_categorical_crossentropy: 2.2009\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 0.0016 - categorical_crossentropy: 0.0016 - val_loss: 2.2272 - val_categorical_crossentropy: 2.2272\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0019 - categorical_crossentropy: 0.0019 - val_loss: 2.2399 - val_categorical_crossentropy: 2.2399\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 7s 11ms/step - loss: 0.0013 - categorical_crossentropy: 0.0013 - val_loss: 2.2684 - val_categorical_crossentropy: 2.2684\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0013 - categorical_crossentropy: 0.0013 - val_loss: 2.2884 - val_categorical_crossentropy: 2.2884\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 0.0011 - categorical_crossentropy: 0.0011 - val_loss: 2.3096 - val_categorical_crossentropy: 2.3096\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0010 - categorical_crossentropy: 0.0010 - val_loss: 2.3292 - val_categorical_crossentropy: 2.3292\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 9.5380e-04 - categorical_crossentropy: 9.5380e-04 - val_loss: 2.3453 - val_categorical_crossentropy: 2.3453\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 8.8685e-04 - categorical_crossentropy: 8.8685e-04 - val_loss: 2.3603 - val_categorical_crossentropy: 2.3603\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 8.3759e-04 - categorical_crossentropy: 8.3759e-04 - val_loss: 2.3780 - val_categorical_crossentropy: 2.3780\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 7.9938e-04 - categorical_crossentropy: 7.9938e-04 - val_loss: 2.3921 - val_categorical_crossentropy: 2.3921\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 7.5502e-04 - categorical_crossentropy: 7.5502e-04 - val_loss: 2.4085 - val_categorical_crossentropy: 2.4085\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 7.1629e-04 - categorical_crossentropy: 7.1629e-04 - val_loss: 2.4223 - val_categorical_crossentropy: 2.4223\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 8s 12ms/step - loss: 6.8626e-04 - categorical_crossentropy: 6.8626e-04 - val_loss: 2.4355 - val_categorical_crossentropy: 2.4355\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 6.6020e-04 - categorical_crossentropy: 6.6020e-04 - val_loss: 2.4480 - val_categorical_crossentropy: 2.4480\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 6.6379e-04 - categorical_crossentropy: 6.6379e-04 - val_loss: 2.4596 - val_categorical_crossentropy: 2.4596\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 6.2284e-04 - categorical_crossentropy: 6.2284e-04 - val_loss: 2.4711 - val_categorical_crossentropy: 2.4711\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 5.8303e-04 - categorical_crossentropy: 5.8303e-04 - val_loss: 2.4852 - val_categorical_crossentropy: 2.4852\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 5.6170e-04 - categorical_crossentropy: 5.6170e-04 - val_loss: 2.4973 - val_categorical_crossentropy: 2.4973\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 5.4233e-04 - categorical_crossentropy: 5.4233e-04 - val_loss: 2.5059 - val_categorical_crossentropy: 2.5059\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 5.2323e-04 - categorical_crossentropy: 5.2323e-04 - val_loss: 2.5191 - val_categorical_crossentropy: 2.5191\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 5.0746e-04 - categorical_crossentropy: 5.0746e-04 - val_loss: 2.5263 - val_categorical_crossentropy: 2.5263\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 4.8992e-04 - categorical_crossentropy: 4.8992e-04 - val_loss: 2.5394 - val_categorical_crossentropy: 2.5394\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 4.7143e-04 - categorical_crossentropy: 4.7143e-04 - val_loss: 2.5466 - val_categorical_crossentropy: 2.5466\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 4.6029e-04 - categorical_crossentropy: 4.6029e-04 - val_loss: 2.5557 - val_categorical_crossentropy: 2.5557\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 4.4545e-04 - categorical_crossentropy: 4.4545e-04 - val_loss: 2.5650 - val_categorical_crossentropy: 2.5650\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 4.3235e-04 - categorical_crossentropy: 4.3235e-04 - val_loss: 2.5732 - val_categorical_crossentropy: 2.5732\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 4.2362e-04 - categorical_crossentropy: 4.2362e-04 - val_loss: 2.5834 - val_categorical_crossentropy: 2.5834\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 7s 11ms/step - loss: 4.1048e-04 - categorical_crossentropy: 4.1048e-04 - val_loss: 2.5917 - val_categorical_crossentropy: 2.5917\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 3.9749e-04 - categorical_crossentropy: 3.9749e-04 - val_loss: 2.5985 - val_categorical_crossentropy: 2.5985\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 3.8642e-04 - categorical_crossentropy: 3.8642e-04 - val_loss: 2.6083 - val_categorical_crossentropy: 2.6083\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 3.7640e-04 - categorical_crossentropy: 3.7640e-04 - val_loss: 2.6166 - val_categorical_crossentropy: 2.6166\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 3.6487e-04 - categorical_crossentropy: 3.6487e-04 - val_loss: 2.6233 - val_categorical_crossentropy: 2.6233\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 3.5776e-04 - categorical_crossentropy: 3.5776e-04 - val_loss: 2.6317 - val_categorical_crossentropy: 2.6317\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 8s 12ms/step - loss: 3.4892e-04 - categorical_crossentropy: 3.4892e-04 - val_loss: 2.6392 - val_categorical_crossentropy: 2.6392\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 3.4085e-04 - categorical_crossentropy: 3.4085e-04 - val_loss: 2.6469 - val_categorical_crossentropy: 2.6469\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 10s 14ms/step - loss: 3.3225e-04 - categorical_crossentropy: 3.3225e-04 - val_loss: 2.6523 - val_categorical_crossentropy: 2.6523\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 3.2403e-04 - categorical_crossentropy: 3.2403e-04 - val_loss: 2.6599 - val_categorical_crossentropy: 2.6599\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 3.1733e-04 - categorical_crossentropy: 3.1733e-04 - val_loss: 2.6673 - val_categorical_crossentropy: 2.6673\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 3.1089e-04 - categorical_crossentropy: 3.1089e-04 - val_loss: 2.6744 - val_categorical_crossentropy: 2.6744\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 8s 12ms/step - loss: 3.0374e-04 - categorical_crossentropy: 3.0374e-04 - val_loss: 2.6807 - val_categorical_crossentropy: 2.6807\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 2.9726e-04 - categorical_crossentropy: 2.9726e-04 - val_loss: 2.6883 - val_categorical_crossentropy: 2.6883\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 2.9127e-04 - categorical_crossentropy: 2.9127e-04 - val_loss: 2.6943 - val_categorical_crossentropy: 2.6943\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 2.8564e-04 - categorical_crossentropy: 2.8564e-04 - val_loss: 2.7004 - val_categorical_crossentropy: 2.7004\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 2.7943e-04 - categorical_crossentropy: 2.7943e-04 - val_loss: 2.7072 - val_categorical_crossentropy: 2.7072\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 2.7349e-04 - categorical_crossentropy: 2.7349e-04 - val_loss: 2.7129 - val_categorical_crossentropy: 2.7129\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 9s 12ms/step - loss: 2.6877e-04 - categorical_crossentropy: 2.6877e-04 - val_loss: 2.7181 - val_categorical_crossentropy: 2.7181\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.6368e-04 - categorical_crossentropy: 2.6368e-04 - val_loss: 2.7243 - val_categorical_crossentropy: 2.7243\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.5776e-04 - categorical_crossentropy: 2.5776e-04 - val_loss: 2.7307 - val_categorical_crossentropy: 2.7307\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.5265e-04 - categorical_crossentropy: 2.5265e-04 - val_loss: 2.7368 - val_categorical_crossentropy: 2.7368\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 2.4888e-04 - categorical_crossentropy: 2.4888e-04 - val_loss: 2.7422 - val_categorical_crossentropy: 2.7422\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.4508e-04 - categorical_crossentropy: 2.4508e-04 - val_loss: 2.7483 - val_categorical_crossentropy: 2.7483\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.4007e-04 - categorical_crossentropy: 2.4007e-04 - val_loss: 2.7524 - val_categorical_crossentropy: 2.7524\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3605e-04 - categorical_crossentropy: 2.3605e-04 - val_loss: 2.7578 - val_categorical_crossentropy: 2.7578\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 2.3168e-04 - categorical_crossentropy: 2.3168e-04 - val_loss: 2.7626 - val_categorical_crossentropy: 2.7626\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.2788e-04 - categorical_crossentropy: 2.2788e-04 - val_loss: 2.7686 - val_categorical_crossentropy: 2.7686\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.2421e-04 - categorical_crossentropy: 2.2421e-04 - val_loss: 2.7735 - val_categorical_crossentropy: 2.7735\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.2085e-04 - categorical_crossentropy: 2.2085e-04 - val_loss: 2.7790 - val_categorical_crossentropy: 2.7790\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.1684e-04 - categorical_crossentropy: 2.1684e-04 - val_loss: 2.7840 - val_categorical_crossentropy: 2.7840\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.1359e-04 - categorical_crossentropy: 2.1359e-04 - val_loss: 2.7900 - val_categorical_crossentropy: 2.7900\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modelsgd=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimsgd=tf.keras.optimizers.SGD(learning_rate=0.001,momentum=0.9)\n",
        "modelsgd.compile(optimizer=optimsgd, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "trainersgd= modelsgd.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultsgd = modelsgd.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B67AYvhJTbpJ",
        "outputId": "8f06ee35-eb23-4ebe-d698-fde305cd7012"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 8s 8ms/step - loss: 2.2662 - categorical_crossentropy: 2.2662 - val_loss: 2.2132 - val_categorical_crossentropy: 2.2132\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.1805 - categorical_crossentropy: 2.1805 - val_loss: 2.1451 - val_categorical_crossentropy: 2.1451\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.1177 - categorical_crossentropy: 2.1177 - val_loss: 2.0897 - val_categorical_crossentropy: 2.0897\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.0644 - categorical_crossentropy: 2.0644 - val_loss: 2.0411 - val_categorical_crossentropy: 2.0411\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.0184 - categorical_crossentropy: 2.0184 - val_loss: 1.9982 - val_categorical_crossentropy: 1.9982\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.9769 - categorical_crossentropy: 1.9769 - val_loss: 1.9624 - val_categorical_crossentropy: 1.9624\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.9424 - categorical_crossentropy: 1.9424 - val_loss: 1.9313 - val_categorical_crossentropy: 1.9313\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.9137 - categorical_crossentropy: 1.9137 - val_loss: 1.9075 - val_categorical_crossentropy: 1.9075\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.8887 - categorical_crossentropy: 1.8887 - val_loss: 1.8837 - val_categorical_crossentropy: 1.8837\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.8668 - categorical_crossentropy: 1.8668 - val_loss: 1.8645 - val_categorical_crossentropy: 1.8645\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.8472 - categorical_crossentropy: 1.8472 - val_loss: 1.8476 - val_categorical_crossentropy: 1.8476\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.8291 - categorical_crossentropy: 1.8291 - val_loss: 1.8304 - val_categorical_crossentropy: 1.8304\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.8121 - categorical_crossentropy: 1.8121 - val_loss: 1.8152 - val_categorical_crossentropy: 1.8152\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.7967 - categorical_crossentropy: 1.7967 - val_loss: 1.8000 - val_categorical_crossentropy: 1.8000\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.7819 - categorical_crossentropy: 1.7819 - val_loss: 1.7864 - val_categorical_crossentropy: 1.7864\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.7676 - categorical_crossentropy: 1.7676 - val_loss: 1.7740 - val_categorical_crossentropy: 1.7740\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.7542 - categorical_crossentropy: 1.7542 - val_loss: 1.7605 - val_categorical_crossentropy: 1.7605\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.7414 - categorical_crossentropy: 1.7414 - val_loss: 1.7490 - val_categorical_crossentropy: 1.7490\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.7289 - categorical_crossentropy: 1.7289 - val_loss: 1.7363 - val_categorical_crossentropy: 1.7363\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.7170 - categorical_crossentropy: 1.7170 - val_loss: 1.7249 - val_categorical_crossentropy: 1.7249\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.7054 - categorical_crossentropy: 1.7054 - val_loss: 1.7154 - val_categorical_crossentropy: 1.7154\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6943 - categorical_crossentropy: 1.6943 - val_loss: 1.7035 - val_categorical_crossentropy: 1.7035\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.6834 - categorical_crossentropy: 1.6834 - val_loss: 1.6932 - val_categorical_crossentropy: 1.6932\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.6729 - categorical_crossentropy: 1.6729 - val_loss: 1.6842 - val_categorical_crossentropy: 1.6842\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6629 - categorical_crossentropy: 1.6629 - val_loss: 1.6724 - val_categorical_crossentropy: 1.6724\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.6532 - categorical_crossentropy: 1.6532 - val_loss: 1.6639 - val_categorical_crossentropy: 1.6639\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6437 - categorical_crossentropy: 1.6437 - val_loss: 1.6535 - val_categorical_crossentropy: 1.6535\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.6344 - categorical_crossentropy: 1.6344 - val_loss: 1.6462 - val_categorical_crossentropy: 1.6462\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.6253 - categorical_crossentropy: 1.6253 - val_loss: 1.6365 - val_categorical_crossentropy: 1.6365\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6166 - categorical_crossentropy: 1.6166 - val_loss: 1.6287 - val_categorical_crossentropy: 1.6287\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.6082 - categorical_crossentropy: 1.6082 - val_loss: 1.6206 - val_categorical_crossentropy: 1.6206\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6000 - categorical_crossentropy: 1.6000 - val_loss: 1.6119 - val_categorical_crossentropy: 1.6119\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.5921 - categorical_crossentropy: 1.5921 - val_loss: 1.6050 - val_categorical_crossentropy: 1.6050\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.5844 - categorical_crossentropy: 1.5844 - val_loss: 1.5974 - val_categorical_crossentropy: 1.5974\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.5767 - categorical_crossentropy: 1.5767 - val_loss: 1.5909 - val_categorical_crossentropy: 1.5909\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.5693 - categorical_crossentropy: 1.5693 - val_loss: 1.5827 - val_categorical_crossentropy: 1.5827\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.5619 - categorical_crossentropy: 1.5619 - val_loss: 1.5747 - val_categorical_crossentropy: 1.5747\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.5547 - categorical_crossentropy: 1.5547 - val_loss: 1.5680 - val_categorical_crossentropy: 1.5680\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.5478 - categorical_crossentropy: 1.5478 - val_loss: 1.5618 - val_categorical_crossentropy: 1.5618\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.5411 - categorical_crossentropy: 1.5411 - val_loss: 1.5553 - val_categorical_crossentropy: 1.5553\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.5346 - categorical_crossentropy: 1.5346 - val_loss: 1.5495 - val_categorical_crossentropy: 1.5495\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.5282 - categorical_crossentropy: 1.5282 - val_loss: 1.5420 - val_categorical_crossentropy: 1.5420\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.5217 - categorical_crossentropy: 1.5217 - val_loss: 1.5359 - val_categorical_crossentropy: 1.5359\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.5155 - categorical_crossentropy: 1.5155 - val_loss: 1.5300 - val_categorical_crossentropy: 1.5300\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.5096 - categorical_crossentropy: 1.5096 - val_loss: 1.5253 - val_categorical_crossentropy: 1.5253\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.5036 - categorical_crossentropy: 1.5036 - val_loss: 1.5186 - val_categorical_crossentropy: 1.5186\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.4978 - categorical_crossentropy: 1.4978 - val_loss: 1.5135 - val_categorical_crossentropy: 1.5135\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.4920 - categorical_crossentropy: 1.4920 - val_loss: 1.5091 - val_categorical_crossentropy: 1.5091\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4864 - categorical_crossentropy: 1.4864 - val_loss: 1.5025 - val_categorical_crossentropy: 1.5025\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.4808 - categorical_crossentropy: 1.4808 - val_loss: 1.4968 - val_categorical_crossentropy: 1.4968\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4754 - categorical_crossentropy: 1.4754 - val_loss: 1.4923 - val_categorical_crossentropy: 1.4923\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.4700 - categorical_crossentropy: 1.4700 - val_loss: 1.4877 - val_categorical_crossentropy: 1.4877\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4649 - categorical_crossentropy: 1.4649 - val_loss: 1.4830 - val_categorical_crossentropy: 1.4830\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.4600 - categorical_crossentropy: 1.4600 - val_loss: 1.4779 - val_categorical_crossentropy: 1.4779\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.4549 - categorical_crossentropy: 1.4549 - val_loss: 1.4753 - val_categorical_crossentropy: 1.4753\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.4501 - categorical_crossentropy: 1.4501 - val_loss: 1.4688 - val_categorical_crossentropy: 1.4688\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.4452 - categorical_crossentropy: 1.4452 - val_loss: 1.4630 - val_categorical_crossentropy: 1.4630\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4404 - categorical_crossentropy: 1.4404 - val_loss: 1.4593 - val_categorical_crossentropy: 1.4593\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.4356 - categorical_crossentropy: 1.4356 - val_loss: 1.4545 - val_categorical_crossentropy: 1.4545\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4312 - categorical_crossentropy: 1.4312 - val_loss: 1.4523 - val_categorical_crossentropy: 1.4523\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.4266 - categorical_crossentropy: 1.4266 - val_loss: 1.4472 - val_categorical_crossentropy: 1.4472\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.4221 - categorical_crossentropy: 1.4221 - val_loss: 1.4437 - val_categorical_crossentropy: 1.4437\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4177 - categorical_crossentropy: 1.4177 - val_loss: 1.4394 - val_categorical_crossentropy: 1.4394\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.4136 - categorical_crossentropy: 1.4136 - val_loss: 1.4347 - val_categorical_crossentropy: 1.4347\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4093 - categorical_crossentropy: 1.4093 - val_loss: 1.4310 - val_categorical_crossentropy: 1.4310\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.4050 - categorical_crossentropy: 1.4050 - val_loss: 1.4267 - val_categorical_crossentropy: 1.4267\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4011 - categorical_crossentropy: 1.4011 - val_loss: 1.4227 - val_categorical_crossentropy: 1.4227\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3970 - categorical_crossentropy: 1.3970 - val_loss: 1.4191 - val_categorical_crossentropy: 1.4191\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3929 - categorical_crossentropy: 1.3929 - val_loss: 1.4151 - val_categorical_crossentropy: 1.4151\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3890 - categorical_crossentropy: 1.3890 - val_loss: 1.4132 - val_categorical_crossentropy: 1.4132\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3853 - categorical_crossentropy: 1.3853 - val_loss: 1.4073 - val_categorical_crossentropy: 1.4073\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3814 - categorical_crossentropy: 1.3814 - val_loss: 1.4042 - val_categorical_crossentropy: 1.4042\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3778 - categorical_crossentropy: 1.3778 - val_loss: 1.4025 - val_categorical_crossentropy: 1.4025\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3741 - categorical_crossentropy: 1.3741 - val_loss: 1.3981 - val_categorical_crossentropy: 1.3981\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3704 - categorical_crossentropy: 1.3704 - val_loss: 1.3940 - val_categorical_crossentropy: 1.3940\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3668 - categorical_crossentropy: 1.3668 - val_loss: 1.3908 - val_categorical_crossentropy: 1.3908\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3633 - categorical_crossentropy: 1.3633 - val_loss: 1.3868 - val_categorical_crossentropy: 1.3868\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3600 - categorical_crossentropy: 1.3600 - val_loss: 1.3843 - val_categorical_crossentropy: 1.3843\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3566 - categorical_crossentropy: 1.3566 - val_loss: 1.3815 - val_categorical_crossentropy: 1.3815\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3533 - categorical_crossentropy: 1.3533 - val_loss: 1.3796 - val_categorical_crossentropy: 1.3796\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3500 - categorical_crossentropy: 1.3500 - val_loss: 1.3760 - val_categorical_crossentropy: 1.3760\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3468 - categorical_crossentropy: 1.3468 - val_loss: 1.3740 - val_categorical_crossentropy: 1.3740\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3436 - categorical_crossentropy: 1.3436 - val_loss: 1.3721 - val_categorical_crossentropy: 1.3721\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3405 - categorical_crossentropy: 1.3405 - val_loss: 1.3657 - val_categorical_crossentropy: 1.3657\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3374 - categorical_crossentropy: 1.3374 - val_loss: 1.3640 - val_categorical_crossentropy: 1.3640\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3342 - categorical_crossentropy: 1.3342 - val_loss: 1.3611 - val_categorical_crossentropy: 1.3611\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3313 - categorical_crossentropy: 1.3313 - val_loss: 1.3589 - val_categorical_crossentropy: 1.3589\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3282 - categorical_crossentropy: 1.3282 - val_loss: 1.3579 - val_categorical_crossentropy: 1.3579\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3253 - categorical_crossentropy: 1.3253 - val_loss: 1.3533 - val_categorical_crossentropy: 1.3533\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3224 - categorical_crossentropy: 1.3224 - val_loss: 1.3501 - val_categorical_crossentropy: 1.3501\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3195 - categorical_crossentropy: 1.3195 - val_loss: 1.3503 - val_categorical_crossentropy: 1.3503\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3166 - categorical_crossentropy: 1.3166 - val_loss: 1.3463 - val_categorical_crossentropy: 1.3463\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3139 - categorical_crossentropy: 1.3139 - val_loss: 1.3454 - val_categorical_crossentropy: 1.3454\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3110 - categorical_crossentropy: 1.3110 - val_loss: 1.3391 - val_categorical_crossentropy: 1.3391\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3083 - categorical_crossentropy: 1.3083 - val_loss: 1.3391 - val_categorical_crossentropy: 1.3391\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 1.3057 - categorical_crossentropy: 1.3057 - val_loss: 1.3365 - val_categorical_crossentropy: 1.3365\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 1.3032 - categorical_crossentropy: 1.3032 - val_loss: 1.3348 - val_categorical_crossentropy: 1.3348\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 1.3001 - categorical_crossentropy: 1.3001 - val_loss: 1.3323 - val_categorical_crossentropy: 1.3323\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 1.2978 - categorical_crossentropy: 1.2978 - val_loss: 1.3289 - val_categorical_crossentropy: 1.3289\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.2952 - categorical_crossentropy: 1.2952 - val_loss: 1.3259 - val_categorical_crossentropy: 1.3259\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modelada=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimada=tf.keras.optimizers.Adadelta(learning_rate=0.001)\n",
        "modelada.compile(optimizer=optimada, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "trainerada= modelada.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultada = modelada.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAF_qzZ-ToZi",
        "outputId": "20e6b2a3-1bec-4ea0-9f84-daaf5369416d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 11s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: nan - categorical_crossentropy: nan - val_loss: nan - val_categorical_crossentropy: nan\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modeladf=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimadf=tf.keras.optimizers.Adafactor(learning_rate=0.001)\n",
        "modeladf.compile(optimizer=optimadf, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "traineradf= modeladf.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultadf = modeladf.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1vrID6bTzjO",
        "outputId": "3f8b7b54-ee20-4749-cace-1c97b535684a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 8s 8ms/step - loss: 1.9243 - categorical_crossentropy: 1.9243 - val_loss: 1.8146 - val_categorical_crossentropy: 1.8146\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6360 - categorical_crossentropy: 1.6360 - val_loss: 1.6150 - val_categorical_crossentropy: 1.6150\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.5292 - categorical_crossentropy: 1.5292 - val_loss: 1.5253 - val_categorical_crossentropy: 1.5253\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.4579 - categorical_crossentropy: 1.4579 - val_loss: 1.5251 - val_categorical_crossentropy: 1.5251\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4037 - categorical_crossentropy: 1.4037 - val_loss: 1.4234 - val_categorical_crossentropy: 1.4234\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3599 - categorical_crossentropy: 1.3599 - val_loss: 1.4171 - val_categorical_crossentropy: 1.4171\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3253 - categorical_crossentropy: 1.3253 - val_loss: 1.3543 - val_categorical_crossentropy: 1.3543\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.2963 - categorical_crossentropy: 1.2963 - val_loss: 1.3666 - val_categorical_crossentropy: 1.3666\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2701 - categorical_crossentropy: 1.2701 - val_loss: 1.3129 - val_categorical_crossentropy: 1.3129\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2489 - categorical_crossentropy: 1.2489 - val_loss: 1.2910 - val_categorical_crossentropy: 1.2910\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.2270 - categorical_crossentropy: 1.2270 - val_loss: 1.3551 - val_categorical_crossentropy: 1.3551\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2094 - categorical_crossentropy: 1.2094 - val_loss: 1.2389 - val_categorical_crossentropy: 1.2389\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1919 - categorical_crossentropy: 1.1919 - val_loss: 1.2421 - val_categorical_crossentropy: 1.2421\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1752 - categorical_crossentropy: 1.1752 - val_loss: 1.2472 - val_categorical_crossentropy: 1.2472\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1587 - categorical_crossentropy: 1.1587 - val_loss: 1.2315 - val_categorical_crossentropy: 1.2315\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1462 - categorical_crossentropy: 1.1462 - val_loss: 1.2075 - val_categorical_crossentropy: 1.2075\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1323 - categorical_crossentropy: 1.1323 - val_loss: 1.2076 - val_categorical_crossentropy: 1.2076\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1196 - categorical_crossentropy: 1.1196 - val_loss: 1.2585 - val_categorical_crossentropy: 1.2585\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1068 - categorical_crossentropy: 1.1068 - val_loss: 1.1930 - val_categorical_crossentropy: 1.1930\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0947 - categorical_crossentropy: 1.0947 - val_loss: 1.1444 - val_categorical_crossentropy: 1.1444\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0835 - categorical_crossentropy: 1.0835 - val_loss: 1.1438 - val_categorical_crossentropy: 1.1438\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0727 - categorical_crossentropy: 1.0727 - val_loss: 1.1690 - val_categorical_crossentropy: 1.1690\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0632 - categorical_crossentropy: 1.0632 - val_loss: 1.1413 - val_categorical_crossentropy: 1.1413\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0532 - categorical_crossentropy: 1.0532 - val_loss: 1.1149 - val_categorical_crossentropy: 1.1149\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.0446 - categorical_crossentropy: 1.0446 - val_loss: 1.1434 - val_categorical_crossentropy: 1.1434\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0355 - categorical_crossentropy: 1.0355 - val_loss: 1.1093 - val_categorical_crossentropy: 1.1093\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0264 - categorical_crossentropy: 1.0264 - val_loss: 1.1390 - val_categorical_crossentropy: 1.1390\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0187 - categorical_crossentropy: 1.0187 - val_loss: 1.1095 - val_categorical_crossentropy: 1.1095\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0110 - categorical_crossentropy: 1.0110 - val_loss: 1.0956 - val_categorical_crossentropy: 1.0956\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0027 - categorical_crossentropy: 1.0027 - val_loss: 1.1453 - val_categorical_crossentropy: 1.1453\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9958 - categorical_crossentropy: 0.9958 - val_loss: 1.0877 - val_categorical_crossentropy: 1.0877\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9888 - categorical_crossentropy: 0.9888 - val_loss: 1.0866 - val_categorical_crossentropy: 1.0866\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.9818 - categorical_crossentropy: 0.9818 - val_loss: 1.0747 - val_categorical_crossentropy: 1.0747\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9754 - categorical_crossentropy: 0.9754 - val_loss: 1.0950 - val_categorical_crossentropy: 1.0950\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.9691 - categorical_crossentropy: 0.9691 - val_loss: 1.0800 - val_categorical_crossentropy: 1.0800\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9628 - categorical_crossentropy: 0.9628 - val_loss: 1.0613 - val_categorical_crossentropy: 1.0613\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9563 - categorical_crossentropy: 0.9563 - val_loss: 1.1025 - val_categorical_crossentropy: 1.1025\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.9514 - categorical_crossentropy: 0.9514 - val_loss: 1.0754 - val_categorical_crossentropy: 1.0754\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9449 - categorical_crossentropy: 0.9449 - val_loss: 1.1321 - val_categorical_crossentropy: 1.1321\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.9392 - categorical_crossentropy: 0.9392 - val_loss: 1.0421 - val_categorical_crossentropy: 1.0421\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9346 - categorical_crossentropy: 0.9346 - val_loss: 1.0490 - val_categorical_crossentropy: 1.0490\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.9292 - categorical_crossentropy: 0.9292 - val_loss: 1.0851 - val_categorical_crossentropy: 1.0851\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.9243 - categorical_crossentropy: 0.9243 - val_loss: 1.0370 - val_categorical_crossentropy: 1.0370\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9189 - categorical_crossentropy: 0.9189 - val_loss: 1.0281 - val_categorical_crossentropy: 1.0281\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.9134 - categorical_crossentropy: 0.9134 - val_loss: 1.0698 - val_categorical_crossentropy: 1.0698\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9093 - categorical_crossentropy: 0.9093 - val_loss: 1.0237 - val_categorical_crossentropy: 1.0237\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.9045 - categorical_crossentropy: 0.9045 - val_loss: 1.0188 - val_categorical_crossentropy: 1.0188\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9003 - categorical_crossentropy: 0.9003 - val_loss: 1.0311 - val_categorical_crossentropy: 1.0311\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8956 - categorical_crossentropy: 0.8956 - val_loss: 1.0111 - val_categorical_crossentropy: 1.0111\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.8913 - categorical_crossentropy: 0.8913 - val_loss: 1.0334 - val_categorical_crossentropy: 1.0334\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8866 - categorical_crossentropy: 0.8866 - val_loss: 1.0175 - val_categorical_crossentropy: 1.0175\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.8830 - categorical_crossentropy: 0.8830 - val_loss: 1.0214 - val_categorical_crossentropy: 1.0214\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8793 - categorical_crossentropy: 0.8793 - val_loss: 1.0163 - val_categorical_crossentropy: 1.0163\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8748 - categorical_crossentropy: 0.8748 - val_loss: 1.0034 - val_categorical_crossentropy: 1.0034\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8709 - categorical_crossentropy: 0.8709 - val_loss: 1.0081 - val_categorical_crossentropy: 1.0081\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8667 - categorical_crossentropy: 0.8667 - val_loss: 1.0064 - val_categorical_crossentropy: 1.0064\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8635 - categorical_crossentropy: 0.8635 - val_loss: 0.9938 - val_categorical_crossentropy: 0.9938\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8599 - categorical_crossentropy: 0.8599 - val_loss: 0.9945 - val_categorical_crossentropy: 0.9945\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8564 - categorical_crossentropy: 0.8564 - val_loss: 1.0188 - val_categorical_crossentropy: 1.0188\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8529 - categorical_crossentropy: 0.8529 - val_loss: 0.9987 - val_categorical_crossentropy: 0.9987\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8492 - categorical_crossentropy: 0.8492 - val_loss: 0.9837 - val_categorical_crossentropy: 0.9837\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.8459 - categorical_crossentropy: 0.8459 - val_loss: 0.9942 - val_categorical_crossentropy: 0.9942\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8420 - categorical_crossentropy: 0.8420 - val_loss: 0.9919 - val_categorical_crossentropy: 0.9919\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.8395 - categorical_crossentropy: 0.8395 - val_loss: 1.0053 - val_categorical_crossentropy: 1.0053\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.8358 - categorical_crossentropy: 0.8358 - val_loss: 0.9853 - val_categorical_crossentropy: 0.9853\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 0.8324 - categorical_crossentropy: 0.8324 - val_loss: 0.9739 - val_categorical_crossentropy: 0.9739\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.8296 - categorical_crossentropy: 0.8296 - val_loss: 0.9836 - val_categorical_crossentropy: 0.9836\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.8264 - categorical_crossentropy: 0.8264 - val_loss: 0.9721 - val_categorical_crossentropy: 0.9721\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.8229 - categorical_crossentropy: 0.8229 - val_loss: 0.9745 - val_categorical_crossentropy: 0.9745\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 9s 13ms/step - loss: 0.8199 - categorical_crossentropy: 0.8199 - val_loss: 0.9837 - val_categorical_crossentropy: 0.9837\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.8172 - categorical_crossentropy: 0.8172 - val_loss: 0.9844 - val_categorical_crossentropy: 0.9844\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 8s 11ms/step - loss: 0.8138 - categorical_crossentropy: 0.8138 - val_loss: 0.9806 - val_categorical_crossentropy: 0.9806\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8107 - categorical_crossentropy: 0.8107 - val_loss: 0.9680 - val_categorical_crossentropy: 0.9680\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.8084 - categorical_crossentropy: 0.8084 - val_loss: 0.9752 - val_categorical_crossentropy: 0.9752\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8053 - categorical_crossentropy: 0.8053 - val_loss: 0.9804 - val_categorical_crossentropy: 0.9804\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8026 - categorical_crossentropy: 0.8026 - val_loss: 0.9834 - val_categorical_crossentropy: 0.9834\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7994 - categorical_crossentropy: 0.7994 - val_loss: 0.9627 - val_categorical_crossentropy: 0.9627\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7973 - categorical_crossentropy: 0.7973 - val_loss: 0.9843 - val_categorical_crossentropy: 0.9843\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.7946 - categorical_crossentropy: 0.7946 - val_loss: 0.9647 - val_categorical_crossentropy: 0.9647\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7919 - categorical_crossentropy: 0.7919 - val_loss: 0.9703 - val_categorical_crossentropy: 0.9703\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.7890 - categorical_crossentropy: 0.7890 - val_loss: 0.9764 - val_categorical_crossentropy: 0.9764\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7862 - categorical_crossentropy: 0.7862 - val_loss: 0.9740 - val_categorical_crossentropy: 0.9740\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7842 - categorical_crossentropy: 0.7842 - val_loss: 0.9738 - val_categorical_crossentropy: 0.9738\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7814 - categorical_crossentropy: 0.7814 - val_loss: 0.9772 - val_categorical_crossentropy: 0.9772\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7788 - categorical_crossentropy: 0.7788 - val_loss: 0.9503 - val_categorical_crossentropy: 0.9503\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.7759 - categorical_crossentropy: 0.7759 - val_loss: 0.9571 - val_categorical_crossentropy: 0.9571\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7740 - categorical_crossentropy: 0.7740 - val_loss: 0.9505 - val_categorical_crossentropy: 0.9505\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7712 - categorical_crossentropy: 0.7712 - val_loss: 0.9513 - val_categorical_crossentropy: 0.9513\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7689 - categorical_crossentropy: 0.7689 - val_loss: 0.9553 - val_categorical_crossentropy: 0.9553\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7669 - categorical_crossentropy: 0.7669 - val_loss: 0.9612 - val_categorical_crossentropy: 0.9612\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7641 - categorical_crossentropy: 0.7641 - val_loss: 0.9529 - val_categorical_crossentropy: 0.9529\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7620 - categorical_crossentropy: 0.7620 - val_loss: 1.0558 - val_categorical_crossentropy: 1.0558\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.7598 - categorical_crossentropy: 0.7598 - val_loss: 0.9537 - val_categorical_crossentropy: 0.9537\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7578 - categorical_crossentropy: 0.7578 - val_loss: 0.9493 - val_categorical_crossentropy: 0.9493\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7545 - categorical_crossentropy: 0.7545 - val_loss: 0.9517 - val_categorical_crossentropy: 0.9517\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.7526 - categorical_crossentropy: 0.7526 - val_loss: 0.9617 - val_categorical_crossentropy: 0.9617\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7507 - categorical_crossentropy: 0.7507 - val_loss: 0.9550 - val_categorical_crossentropy: 0.9550\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7487 - categorical_crossentropy: 0.7487 - val_loss: 0.9405 - val_categorical_crossentropy: 0.9405\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7470 - categorical_crossentropy: 0.7470 - val_loss: 0.9459 - val_categorical_crossentropy: 0.9459\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7444 - categorical_crossentropy: 0.7444 - val_loss: 0.9469 - val_categorical_crossentropy: 0.9469\n"
          ]
        }
      ],
      "source": [
        "modeladd=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimadd=tf.keras.optimizers.Adagrad(learning_rate=0.001)\n",
        "modeladd.compile(optimizer=optimadd, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "traineradd= modeladd.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultadd = modeladd.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pEN08LKuT4di",
        "outputId": "5ff501d1-9fbb-4e60-dd82-12cb778ead0d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 9s 9ms/step - loss: 212.7890 - categorical_crossentropy: 212.7890 - val_loss: 2.3140 - val_categorical_crossentropy: 2.3140\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3133 - val_categorical_crossentropy: 2.3133\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 2.3108 - categorical_crossentropy: 2.3108 - val_loss: 2.3095 - val_categorical_crossentropy: 2.3095\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3115 - categorical_crossentropy: 2.3115 - val_loss: 2.3113 - val_categorical_crossentropy: 2.3113\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3121 - categorical_crossentropy: 2.3121 - val_loss: 2.3156 - val_categorical_crossentropy: 2.3156\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3133 - categorical_crossentropy: 2.3133 - val_loss: 2.3137 - val_categorical_crossentropy: 2.3137\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3125 - categorical_crossentropy: 2.3125 - val_loss: 2.3101 - val_categorical_crossentropy: 2.3101\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3114 - categorical_crossentropy: 2.3114 - val_loss: 2.3045 - val_categorical_crossentropy: 2.3045\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3110 - categorical_crossentropy: 2.3110 - val_loss: 2.3107 - val_categorical_crossentropy: 2.3107\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3122 - categorical_crossentropy: 2.3122 - val_loss: 2.3103 - val_categorical_crossentropy: 2.3103\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3121 - categorical_crossentropy: 2.3121 - val_loss: 2.3161 - val_categorical_crossentropy: 2.3161\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3106 - categorical_crossentropy: 2.3106 - val_loss: 2.3096 - val_categorical_crossentropy: 2.3096\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3129 - categorical_crossentropy: 2.3129 - val_loss: 2.3115 - val_categorical_crossentropy: 2.3115\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3120 - categorical_crossentropy: 2.3120 - val_loss: 2.3073 - val_categorical_crossentropy: 2.3073\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3114 - categorical_crossentropy: 2.3114 - val_loss: 2.3048 - val_categorical_crossentropy: 2.3048\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3118 - categorical_crossentropy: 2.3118 - val_loss: 2.3091 - val_categorical_crossentropy: 2.3091\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3110 - categorical_crossentropy: 2.3110 - val_loss: 2.3189 - val_categorical_crossentropy: 2.3189\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3134 - categorical_crossentropy: 2.3134 - val_loss: 2.3134 - val_categorical_crossentropy: 2.3134\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3125 - categorical_crossentropy: 2.3125 - val_loss: 2.3049 - val_categorical_crossentropy: 2.3049\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3112 - categorical_crossentropy: 2.3112 - val_loss: 2.3082 - val_categorical_crossentropy: 2.3082\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3127 - categorical_crossentropy: 2.3127 - val_loss: 2.3060 - val_categorical_crossentropy: 2.3060\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3118 - categorical_crossentropy: 2.3118 - val_loss: 2.3057 - val_categorical_crossentropy: 2.3057\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3113 - categorical_crossentropy: 2.3113 - val_loss: 2.3146 - val_categorical_crossentropy: 2.3146\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3115 - categorical_crossentropy: 2.3115 - val_loss: 2.3112 - val_categorical_crossentropy: 2.3112\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3110 - categorical_crossentropy: 2.3110 - val_loss: 2.3053 - val_categorical_crossentropy: 2.3053\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3122 - categorical_crossentropy: 2.3122 - val_loss: 2.3089 - val_categorical_crossentropy: 2.3089\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3114 - categorical_crossentropy: 2.3114 - val_loss: 2.3091 - val_categorical_crossentropy: 2.3091\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3124 - categorical_crossentropy: 2.3124 - val_loss: 2.3056 - val_categorical_crossentropy: 2.3056\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3157 - val_categorical_crossentropy: 2.3157\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3138 - val_categorical_crossentropy: 2.3138\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3115 - categorical_crossentropy: 2.3115 - val_loss: 2.3082 - val_categorical_crossentropy: 2.3082\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3082 - val_categorical_crossentropy: 2.3082\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3121 - categorical_crossentropy: 2.3121 - val_loss: 2.3126 - val_categorical_crossentropy: 2.3126\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3113 - categorical_crossentropy: 2.3113 - val_loss: 2.3121 - val_categorical_crossentropy: 2.3121\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3054 - val_categorical_crossentropy: 2.3054\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3109 - categorical_crossentropy: 2.3109 - val_loss: 2.3214 - val_categorical_crossentropy: 2.3214\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3115 - categorical_crossentropy: 2.3115 - val_loss: 2.3112 - val_categorical_crossentropy: 2.3112\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3118 - categorical_crossentropy: 2.3118 - val_loss: 2.3050 - val_categorical_crossentropy: 2.3050\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3108 - categorical_crossentropy: 2.3108 - val_loss: 2.3076 - val_categorical_crossentropy: 2.3076\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3104 - categorical_crossentropy: 2.3104 - val_loss: 2.3133 - val_categorical_crossentropy: 2.3133\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3119 - categorical_crossentropy: 2.3119 - val_loss: 2.3096 - val_categorical_crossentropy: 2.3096\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3124 - categorical_crossentropy: 2.3124 - val_loss: 2.3168 - val_categorical_crossentropy: 2.3168\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3125 - categorical_crossentropy: 2.3125 - val_loss: 2.3163 - val_categorical_crossentropy: 2.3163\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3133 - val_categorical_crossentropy: 2.3133\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3127 - categorical_crossentropy: 2.3127 - val_loss: 2.3048 - val_categorical_crossentropy: 2.3048\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3131 - val_categorical_crossentropy: 2.3131\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3074 - val_categorical_crossentropy: 2.3074\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3121 - val_categorical_crossentropy: 2.3121\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3118 - categorical_crossentropy: 2.3118 - val_loss: 2.3141 - val_categorical_crossentropy: 2.3141\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3109 - categorical_crossentropy: 2.3109 - val_loss: 2.3097 - val_categorical_crossentropy: 2.3097\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3122 - categorical_crossentropy: 2.3122 - val_loss: 2.3143 - val_categorical_crossentropy: 2.3143\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3111 - categorical_crossentropy: 2.3111 - val_loss: 2.3040 - val_categorical_crossentropy: 2.3040\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3121 - categorical_crossentropy: 2.3121 - val_loss: 2.3186 - val_categorical_crossentropy: 2.3186\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3114 - categorical_crossentropy: 2.3114 - val_loss: 2.3080 - val_categorical_crossentropy: 2.3080\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3119 - categorical_crossentropy: 2.3119 - val_loss: 2.3170 - val_categorical_crossentropy: 2.3170\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3120 - categorical_crossentropy: 2.3120 - val_loss: 2.3117 - val_categorical_crossentropy: 2.3117\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3101 - val_categorical_crossentropy: 2.3101\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3144 - val_categorical_crossentropy: 2.3144\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3121 - categorical_crossentropy: 2.3121 - val_loss: 2.3062 - val_categorical_crossentropy: 2.3062\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3120 - categorical_crossentropy: 2.3120 - val_loss: 2.3084 - val_categorical_crossentropy: 2.3084\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3129 - categorical_crossentropy: 2.3129 - val_loss: 2.3115 - val_categorical_crossentropy: 2.3115\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3112 - categorical_crossentropy: 2.3112 - val_loss: 2.3160 - val_categorical_crossentropy: 2.3160\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3112 - categorical_crossentropy: 2.3112 - val_loss: 2.3103 - val_categorical_crossentropy: 2.3103\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3137 - val_categorical_crossentropy: 2.3137\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3114 - categorical_crossentropy: 2.3114 - val_loss: 2.3075 - val_categorical_crossentropy: 2.3075\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3111 - categorical_crossentropy: 2.3111 - val_loss: 2.3099 - val_categorical_crossentropy: 2.3099\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3112 - categorical_crossentropy: 2.3112 - val_loss: 2.3140 - val_categorical_crossentropy: 2.3140\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3120 - categorical_crossentropy: 2.3120 - val_loss: 2.3116 - val_categorical_crossentropy: 2.3116\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3112 - categorical_crossentropy: 2.3112 - val_loss: 2.3092 - val_categorical_crossentropy: 2.3092\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3084 - val_categorical_crossentropy: 2.3084\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3126 - categorical_crossentropy: 2.3126 - val_loss: 2.3086 - val_categorical_crossentropy: 2.3086\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 2.3126 - categorical_crossentropy: 2.3126 - val_loss: 2.3105 - val_categorical_crossentropy: 2.3105\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3112 - categorical_crossentropy: 2.3112 - val_loss: 2.3115 - val_categorical_crossentropy: 2.3115\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3123 - categorical_crossentropy: 2.3123 - val_loss: 2.3064 - val_categorical_crossentropy: 2.3064\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3109 - categorical_crossentropy: 2.3109 - val_loss: 2.3160 - val_categorical_crossentropy: 2.3160\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3108 - categorical_crossentropy: 2.3108 - val_loss: 2.3070 - val_categorical_crossentropy: 2.3070\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3119 - categorical_crossentropy: 2.3119 - val_loss: 2.3135 - val_categorical_crossentropy: 2.3135\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3111 - categorical_crossentropy: 2.3111 - val_loss: 2.3095 - val_categorical_crossentropy: 2.3095\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3114 - categorical_crossentropy: 2.3114 - val_loss: 2.3094 - val_categorical_crossentropy: 2.3094\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3121 - categorical_crossentropy: 2.3121 - val_loss: 2.3150 - val_categorical_crossentropy: 2.3150\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3100 - val_categorical_crossentropy: 2.3100\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3113 - categorical_crossentropy: 2.3113 - val_loss: 2.3068 - val_categorical_crossentropy: 2.3068\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3120 - categorical_crossentropy: 2.3120 - val_loss: 2.3245 - val_categorical_crossentropy: 2.3245\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3113 - categorical_crossentropy: 2.3113 - val_loss: 2.3149 - val_categorical_crossentropy: 2.3149\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3115 - categorical_crossentropy: 2.3115 - val_loss: 2.3071 - val_categorical_crossentropy: 2.3071\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3112 - categorical_crossentropy: 2.3112 - val_loss: 2.3115 - val_categorical_crossentropy: 2.3115\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3122 - val_categorical_crossentropy: 2.3122\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3113 - categorical_crossentropy: 2.3113 - val_loss: 2.3131 - val_categorical_crossentropy: 2.3131\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3113 - categorical_crossentropy: 2.3113 - val_loss: 2.3168 - val_categorical_crossentropy: 2.3168\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3066 - val_categorical_crossentropy: 2.3066\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3102 - categorical_crossentropy: 2.3102 - val_loss: 2.3228 - val_categorical_crossentropy: 2.3228\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3124 - categorical_crossentropy: 2.3124 - val_loss: 2.3163 - val_categorical_crossentropy: 2.3163\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3117 - categorical_crossentropy: 2.3117 - val_loss: 2.3183 - val_categorical_crossentropy: 2.3183\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3102 - categorical_crossentropy: 2.3102 - val_loss: 2.3133 - val_categorical_crossentropy: 2.3133\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3109 - categorical_crossentropy: 2.3109 - val_loss: 2.3100 - val_categorical_crossentropy: 2.3100\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3113 - categorical_crossentropy: 2.3113 - val_loss: 2.3169 - val_categorical_crossentropy: 2.3169\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3119 - categorical_crossentropy: 2.3119 - val_loss: 2.3153 - val_categorical_crossentropy: 2.3153\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3116 - categorical_crossentropy: 2.3116 - val_loss: 2.3127 - val_categorical_crossentropy: 2.3127\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3120 - categorical_crossentropy: 2.3120 - val_loss: 2.3087 - val_categorical_crossentropy: 2.3087\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3127 - categorical_crossentropy: 2.3127 - val_loss: 2.3080 - val_categorical_crossentropy: 2.3080\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modeladm=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimadm=tf.keras.optimizers.Adam(learning_rate=0.1)\n",
        "modeladm.compile(optimizer=optimadm, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "traineradm= modeladm.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultadm = modeladm.evaluate(test_images, test_labels_f, verbose=0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8PY8OarUb-h",
        "outputId": "80f1fc21-39c6-4779-b3c0-ed26cd1cf00b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 8s 8ms/step - loss: 1.4975 - categorical_crossentropy: 1.4975 - val_loss: 1.3232 - val_categorical_crossentropy: 1.3232\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1014 - categorical_crossentropy: 1.1014 - val_loss: 1.0659 - val_categorical_crossentropy: 1.0659\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9412 - categorical_crossentropy: 0.9412 - val_loss: 0.9478 - val_categorical_crossentropy: 0.9478\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.8187 - categorical_crossentropy: 0.8187 - val_loss: 0.9095 - val_categorical_crossentropy: 0.9095\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7286 - categorical_crossentropy: 0.7286 - val_loss: 0.9146 - val_categorical_crossentropy: 0.9146\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.6487 - categorical_crossentropy: 0.6487 - val_loss: 0.8645 - val_categorical_crossentropy: 0.8645\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.5706 - categorical_crossentropy: 0.5706 - val_loss: 0.8819 - val_categorical_crossentropy: 0.8819\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.5001 - categorical_crossentropy: 0.5001 - val_loss: 0.8720 - val_categorical_crossentropy: 0.8720\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.4280 - categorical_crossentropy: 0.4280 - val_loss: 0.8710 - val_categorical_crossentropy: 0.8710\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3582 - categorical_crossentropy: 0.3582 - val_loss: 0.9260 - val_categorical_crossentropy: 0.9260\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.2982 - categorical_crossentropy: 0.2982 - val_loss: 0.9957 - val_categorical_crossentropy: 0.9957\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.2479 - categorical_crossentropy: 0.2479 - val_loss: 1.0592 - val_categorical_crossentropy: 1.0592\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.1906 - categorical_crossentropy: 0.1906 - val_loss: 1.1740 - val_categorical_crossentropy: 1.1740\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.1538 - categorical_crossentropy: 0.1538 - val_loss: 1.2400 - val_categorical_crossentropy: 1.2400\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.1192 - categorical_crossentropy: 0.1192 - val_loss: 1.3538 - val_categorical_crossentropy: 1.3538\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.1043 - categorical_crossentropy: 0.1043 - val_loss: 1.3931 - val_categorical_crossentropy: 1.3931\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0834 - categorical_crossentropy: 0.0834 - val_loss: 1.5149 - val_categorical_crossentropy: 1.5149\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0801 - categorical_crossentropy: 0.0801 - val_loss: 1.5696 - val_categorical_crossentropy: 1.5696\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0666 - categorical_crossentropy: 0.0666 - val_loss: 1.6825 - val_categorical_crossentropy: 1.6825\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0565 - categorical_crossentropy: 0.0565 - val_loss: 1.8265 - val_categorical_crossentropy: 1.8265\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0564 - categorical_crossentropy: 0.0564 - val_loss: 1.7474 - val_categorical_crossentropy: 1.7474\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0552 - categorical_crossentropy: 0.0552 - val_loss: 1.9673 - val_categorical_crossentropy: 1.9673\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0526 - categorical_crossentropy: 0.0526 - val_loss: 2.0805 - val_categorical_crossentropy: 2.0805\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0485 - categorical_crossentropy: 0.0485 - val_loss: 1.9118 - val_categorical_crossentropy: 1.9118\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0337 - categorical_crossentropy: 0.0337 - val_loss: 2.0027 - val_categorical_crossentropy: 2.0027\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0417 - categorical_crossentropy: 0.0417 - val_loss: 2.2039 - val_categorical_crossentropy: 2.2039\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0440 - categorical_crossentropy: 0.0440 - val_loss: 2.0713 - val_categorical_crossentropy: 2.0713\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0321 - categorical_crossentropy: 0.0321 - val_loss: 2.1296 - val_categorical_crossentropy: 2.1296\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0488 - categorical_crossentropy: 0.0488 - val_loss: 2.1534 - val_categorical_crossentropy: 2.1534\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0390 - categorical_crossentropy: 0.0390 - val_loss: 2.1436 - val_categorical_crossentropy: 2.1436\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0268 - categorical_crossentropy: 0.0268 - val_loss: 2.1663 - val_categorical_crossentropy: 2.1663\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0322 - categorical_crossentropy: 0.0322 - val_loss: 2.1756 - val_categorical_crossentropy: 2.1756\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0392 - categorical_crossentropy: 0.0392 - val_loss: 2.2736 - val_categorical_crossentropy: 2.2736\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0405 - categorical_crossentropy: 0.0405 - val_loss: 2.1916 - val_categorical_crossentropy: 2.1916\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0194 - categorical_crossentropy: 0.0194 - val_loss: 2.3104 - val_categorical_crossentropy: 2.3104\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0301 - categorical_crossentropy: 0.0301 - val_loss: 2.2312 - val_categorical_crossentropy: 2.2312\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0357 - categorical_crossentropy: 0.0357 - val_loss: 2.2361 - val_categorical_crossentropy: 2.2361\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0303 - categorical_crossentropy: 0.0303 - val_loss: 2.2948 - val_categorical_crossentropy: 2.2948\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0179 - categorical_crossentropy: 0.0179 - val_loss: 2.4497 - val_categorical_crossentropy: 2.4497\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0378 - categorical_crossentropy: 0.0378 - val_loss: 2.4444 - val_categorical_crossentropy: 2.4444\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0300 - categorical_crossentropy: 0.0300 - val_loss: 2.4322 - val_categorical_crossentropy: 2.4322\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0267 - categorical_crossentropy: 0.0267 - val_loss: 2.5194 - val_categorical_crossentropy: 2.5194\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0171 - categorical_crossentropy: 0.0171 - val_loss: 2.4730 - val_categorical_crossentropy: 2.4730\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0328 - categorical_crossentropy: 0.0328 - val_loss: 2.4296 - val_categorical_crossentropy: 2.4296\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0234 - categorical_crossentropy: 0.0234 - val_loss: 2.5015 - val_categorical_crossentropy: 2.5015\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0227 - categorical_crossentropy: 0.0227 - val_loss: 2.4079 - val_categorical_crossentropy: 2.4079\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0326 - categorical_crossentropy: 0.0326 - val_loss: 2.4737 - val_categorical_crossentropy: 2.4737\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0230 - categorical_crossentropy: 0.0230 - val_loss: 2.6091 - val_categorical_crossentropy: 2.6091\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0275 - categorical_crossentropy: 0.0275 - val_loss: 2.5788 - val_categorical_crossentropy: 2.5788\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0161 - categorical_crossentropy: 0.0161 - val_loss: 2.7495 - val_categorical_crossentropy: 2.7495\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0297 - categorical_crossentropy: 0.0297 - val_loss: 2.7244 - val_categorical_crossentropy: 2.7244\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0227 - categorical_crossentropy: 0.0227 - val_loss: 2.6173 - val_categorical_crossentropy: 2.6173\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0162 - categorical_crossentropy: 0.0162 - val_loss: 2.7992 - val_categorical_crossentropy: 2.7992\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0271 - categorical_crossentropy: 0.0271 - val_loss: 2.7020 - val_categorical_crossentropy: 2.7020\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0230 - categorical_crossentropy: 0.0230 - val_loss: 2.7398 - val_categorical_crossentropy: 2.7398\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0227 - categorical_crossentropy: 0.0227 - val_loss: 2.6983 - val_categorical_crossentropy: 2.6983\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0198 - categorical_crossentropy: 0.0198 - val_loss: 2.7021 - val_categorical_crossentropy: 2.7021\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0129 - categorical_crossentropy: 0.0129 - val_loss: 2.7102 - val_categorical_crossentropy: 2.7102\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0247 - categorical_crossentropy: 0.0247 - val_loss: 2.7117 - val_categorical_crossentropy: 2.7117\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0186 - categorical_crossentropy: 0.0186 - val_loss: 2.9562 - val_categorical_crossentropy: 2.9562\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0285 - categorical_crossentropy: 0.0285 - val_loss: 2.7192 - val_categorical_crossentropy: 2.7192\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0164 - categorical_crossentropy: 0.0164 - val_loss: 2.7874 - val_categorical_crossentropy: 2.7874\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0179 - categorical_crossentropy: 0.0179 - val_loss: 2.8818 - val_categorical_crossentropy: 2.8818\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0214 - categorical_crossentropy: 0.0214 - val_loss: 2.9964 - val_categorical_crossentropy: 2.9964\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0139 - categorical_crossentropy: 0.0139 - val_loss: 2.7997 - val_categorical_crossentropy: 2.7997\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0185 - categorical_crossentropy: 0.0185 - val_loss: 2.6996 - val_categorical_crossentropy: 2.6996\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0185 - categorical_crossentropy: 0.0185 - val_loss: 2.8904 - val_categorical_crossentropy: 2.8904\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0249 - categorical_crossentropy: 0.0249 - val_loss: 2.9120 - val_categorical_crossentropy: 2.9120\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0169 - categorical_crossentropy: 0.0169 - val_loss: 2.9511 - val_categorical_crossentropy: 2.9511\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0104 - categorical_crossentropy: 0.0104 - val_loss: 3.1068 - val_categorical_crossentropy: 3.1068\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0242 - categorical_crossentropy: 0.0242 - val_loss: 3.0526 - val_categorical_crossentropy: 3.0526\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0208 - categorical_crossentropy: 0.0208 - val_loss: 2.8127 - val_categorical_crossentropy: 2.8127\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0178 - categorical_crossentropy: 0.0178 - val_loss: 3.0600 - val_categorical_crossentropy: 3.0600\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0121 - categorical_crossentropy: 0.0121 - val_loss: 3.0293 - val_categorical_crossentropy: 3.0293\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0211 - categorical_crossentropy: 0.0211 - val_loss: 3.0216 - val_categorical_crossentropy: 3.0216\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0158 - categorical_crossentropy: 0.0158 - val_loss: 3.0640 - val_categorical_crossentropy: 3.0640\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0121 - categorical_crossentropy: 0.0121 - val_loss: 3.0958 - val_categorical_crossentropy: 3.0958\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0200 - categorical_crossentropy: 0.0200 - val_loss: 3.0609 - val_categorical_crossentropy: 3.0609\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0158 - categorical_crossentropy: 0.0158 - val_loss: 3.1111 - val_categorical_crossentropy: 3.1111\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0164 - categorical_crossentropy: 0.0164 - val_loss: 2.9952 - val_categorical_crossentropy: 2.9952\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0179 - categorical_crossentropy: 0.0179 - val_loss: 2.9792 - val_categorical_crossentropy: 2.9792\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0143 - categorical_crossentropy: 0.0143 - val_loss: 3.0322 - val_categorical_crossentropy: 3.0322\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0119 - categorical_crossentropy: 0.0119 - val_loss: 3.1241 - val_categorical_crossentropy: 3.1241\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0172 - categorical_crossentropy: 0.0172 - val_loss: 3.0780 - val_categorical_crossentropy: 3.0780\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0163 - categorical_crossentropy: 0.0163 - val_loss: 3.0995 - val_categorical_crossentropy: 3.0995\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0103 - categorical_crossentropy: 0.0103 - val_loss: 3.2250 - val_categorical_crossentropy: 3.2250\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0150 - categorical_crossentropy: 0.0150 - val_loss: 3.2665 - val_categorical_crossentropy: 3.2665\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0126 - categorical_crossentropy: 0.0126 - val_loss: 3.1229 - val_categorical_crossentropy: 3.1229\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0106 - categorical_crossentropy: 0.0106 - val_loss: 3.1050 - val_categorical_crossentropy: 3.1050\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0165 - categorical_crossentropy: 0.0165 - val_loss: 3.3854 - val_categorical_crossentropy: 3.3854\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0139 - categorical_crossentropy: 0.0139 - val_loss: 3.2695 - val_categorical_crossentropy: 3.2695\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0153 - categorical_crossentropy: 0.0153 - val_loss: 3.3456 - val_categorical_crossentropy: 3.3456\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0149 - categorical_crossentropy: 0.0149 - val_loss: 3.2354 - val_categorical_crossentropy: 3.2354\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0127 - categorical_crossentropy: 0.0127 - val_loss: 3.3216 - val_categorical_crossentropy: 3.3216\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0181 - categorical_crossentropy: 0.0181 - val_loss: 3.3263 - val_categorical_crossentropy: 3.3263\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0151 - categorical_crossentropy: 0.0151 - val_loss: 3.4457 - val_categorical_crossentropy: 3.4457\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0163 - categorical_crossentropy: 0.0163 - val_loss: 3.3728 - val_categorical_crossentropy: 3.3728\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0107 - categorical_crossentropy: 0.0107 - val_loss: 3.3159 - val_categorical_crossentropy: 3.3159\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0167 - categorical_crossentropy: 0.0167 - val_loss: 3.3348 - val_categorical_crossentropy: 3.3348\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0143 - categorical_crossentropy: 0.0143 - val_loss: 3.2213 - val_categorical_crossentropy: 3.2213\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modeladmideal=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimadmideal=tf.keras.optimizers.Adam(learning_rate=3e-4)\n",
        "modeladmideal.compile(optimizer=optimadmideal, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "traineradmideal= modeladmideal.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultadmideal= modeladmideal.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMeytUjAUDa1",
        "outputId": "39ed9131-2a3e-4965-c130-7299cce72fcf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 9s 8ms/step - loss: 1.4344 - categorical_crossentropy: 1.4344 - val_loss: 1.0899 - val_categorical_crossentropy: 1.0899\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.9617 - categorical_crossentropy: 0.9617 - val_loss: 0.9347 - val_categorical_crossentropy: 0.9347\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.7768 - categorical_crossentropy: 0.7768 - val_loss: 0.8638 - val_categorical_crossentropy: 0.8638\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.6477 - categorical_crossentropy: 0.6477 - val_loss: 0.7956 - val_categorical_crossentropy: 0.7956\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.5411 - categorical_crossentropy: 0.5411 - val_loss: 0.8119 - val_categorical_crossentropy: 0.8119\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.4391 - categorical_crossentropy: 0.4391 - val_loss: 0.8317 - val_categorical_crossentropy: 0.8317\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.3471 - categorical_crossentropy: 0.3471 - val_loss: 0.8733 - val_categorical_crossentropy: 0.8733\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.2684 - categorical_crossentropy: 0.2684 - val_loss: 0.9656 - val_categorical_crossentropy: 0.9656\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.2176 - categorical_crossentropy: 0.2176 - val_loss: 1.1008 - val_categorical_crossentropy: 1.1008\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.1692 - categorical_crossentropy: 0.1692 - val_loss: 1.1947 - val_categorical_crossentropy: 1.1947\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.1475 - categorical_crossentropy: 0.1475 - val_loss: 1.2697 - val_categorical_crossentropy: 1.2697\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.1185 - categorical_crossentropy: 0.1185 - val_loss: 1.4333 - val_categorical_crossentropy: 1.4333\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.1056 - categorical_crossentropy: 0.1056 - val_loss: 1.5154 - val_categorical_crossentropy: 1.5154\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0963 - categorical_crossentropy: 0.0963 - val_loss: 1.6319 - val_categorical_crossentropy: 1.6319\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0957 - categorical_crossentropy: 0.0957 - val_loss: 1.5896 - val_categorical_crossentropy: 1.5896\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0848 - categorical_crossentropy: 0.0848 - val_loss: 1.6986 - val_categorical_crossentropy: 1.6986\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0947 - categorical_crossentropy: 0.0947 - val_loss: 1.6068 - val_categorical_crossentropy: 1.6068\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0718 - categorical_crossentropy: 0.0718 - val_loss: 1.7329 - val_categorical_crossentropy: 1.7329\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0806 - categorical_crossentropy: 0.0806 - val_loss: 1.8433 - val_categorical_crossentropy: 1.8433\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0673 - categorical_crossentropy: 0.0673 - val_loss: 1.9688 - val_categorical_crossentropy: 1.9688\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0774 - categorical_crossentropy: 0.0774 - val_loss: 1.8644 - val_categorical_crossentropy: 1.8644\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0634 - categorical_crossentropy: 0.0634 - val_loss: 1.9326 - val_categorical_crossentropy: 1.9326\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0643 - categorical_crossentropy: 0.0643 - val_loss: 2.1283 - val_categorical_crossentropy: 2.1283\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0696 - categorical_crossentropy: 0.0696 - val_loss: 1.9415 - val_categorical_crossentropy: 1.9415\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0646 - categorical_crossentropy: 0.0646 - val_loss: 2.0261 - val_categorical_crossentropy: 2.0261\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0556 - categorical_crossentropy: 0.0556 - val_loss: 2.1494 - val_categorical_crossentropy: 2.1494\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0589 - categorical_crossentropy: 0.0589 - val_loss: 2.0890 - val_categorical_crossentropy: 2.0890\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0602 - categorical_crossentropy: 0.0602 - val_loss: 2.1305 - val_categorical_crossentropy: 2.1305\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0574 - categorical_crossentropy: 0.0574 - val_loss: 2.1310 - val_categorical_crossentropy: 2.1310\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0558 - categorical_crossentropy: 0.0558 - val_loss: 2.0849 - val_categorical_crossentropy: 2.0849\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0578 - categorical_crossentropy: 0.0578 - val_loss: 2.2160 - val_categorical_crossentropy: 2.2160\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0508 - categorical_crossentropy: 0.0508 - val_loss: 2.2669 - val_categorical_crossentropy: 2.2669\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0591 - categorical_crossentropy: 0.0591 - val_loss: 2.3708 - val_categorical_crossentropy: 2.3708\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0445 - categorical_crossentropy: 0.0445 - val_loss: 2.3475 - val_categorical_crossentropy: 2.3475\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0644 - categorical_crossentropy: 0.0644 - val_loss: 2.2158 - val_categorical_crossentropy: 2.2158\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0463 - categorical_crossentropy: 0.0463 - val_loss: 2.2132 - val_categorical_crossentropy: 2.2132\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0422 - categorical_crossentropy: 0.0422 - val_loss: 2.3923 - val_categorical_crossentropy: 2.3923\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0504 - categorical_crossentropy: 0.0504 - val_loss: 2.2773 - val_categorical_crossentropy: 2.2773\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0512 - categorical_crossentropy: 0.0512 - val_loss: 2.2576 - val_categorical_crossentropy: 2.2576\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0474 - categorical_crossentropy: 0.0474 - val_loss: 2.3743 - val_categorical_crossentropy: 2.3743\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0551 - categorical_crossentropy: 0.0551 - val_loss: 2.2763 - val_categorical_crossentropy: 2.2763\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0350 - categorical_crossentropy: 0.0350 - val_loss: 2.2830 - val_categorical_crossentropy: 2.2830\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0409 - categorical_crossentropy: 0.0409 - val_loss: 2.3860 - val_categorical_crossentropy: 2.3860\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0460 - categorical_crossentropy: 0.0460 - val_loss: 2.3863 - val_categorical_crossentropy: 2.3863\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0556 - categorical_crossentropy: 0.0556 - val_loss: 2.4690 - val_categorical_crossentropy: 2.4690\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0353 - categorical_crossentropy: 0.0353 - val_loss: 2.6394 - val_categorical_crossentropy: 2.6394\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0421 - categorical_crossentropy: 0.0421 - val_loss: 2.5480 - val_categorical_crossentropy: 2.5480\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0394 - categorical_crossentropy: 0.0394 - val_loss: 2.5426 - val_categorical_crossentropy: 2.5426\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0472 - categorical_crossentropy: 0.0472 - val_loss: 2.4099 - val_categorical_crossentropy: 2.4099\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0349 - categorical_crossentropy: 0.0349 - val_loss: 2.7307 - val_categorical_crossentropy: 2.7307\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0495 - categorical_crossentropy: 0.0495 - val_loss: 2.3984 - val_categorical_crossentropy: 2.3984\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0343 - categorical_crossentropy: 0.0343 - val_loss: 2.6887 - val_categorical_crossentropy: 2.6887\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0488 - categorical_crossentropy: 0.0488 - val_loss: 2.6769 - val_categorical_crossentropy: 2.6769\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0404 - categorical_crossentropy: 0.0404 - val_loss: 2.5904 - val_categorical_crossentropy: 2.5904\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0399 - categorical_crossentropy: 0.0399 - val_loss: 2.5427 - val_categorical_crossentropy: 2.5427\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0375 - categorical_crossentropy: 0.0375 - val_loss: 2.7603 - val_categorical_crossentropy: 2.7603\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0423 - categorical_crossentropy: 0.0423 - val_loss: 2.4954 - val_categorical_crossentropy: 2.4954\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0382 - categorical_crossentropy: 0.0382 - val_loss: 2.6148 - val_categorical_crossentropy: 2.6148\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0363 - categorical_crossentropy: 0.0363 - val_loss: 2.6494 - val_categorical_crossentropy: 2.6494\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0461 - categorical_crossentropy: 0.0461 - val_loss: 2.6988 - val_categorical_crossentropy: 2.6988\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0375 - categorical_crossentropy: 0.0375 - val_loss: 2.6876 - val_categorical_crossentropy: 2.6876\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0326 - categorical_crossentropy: 0.0326 - val_loss: 2.6298 - val_categorical_crossentropy: 2.6298\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0428 - categorical_crossentropy: 0.0428 - val_loss: 2.7015 - val_categorical_crossentropy: 2.7015\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0406 - categorical_crossentropy: 0.0406 - val_loss: 2.7434 - val_categorical_crossentropy: 2.7434\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0374 - categorical_crossentropy: 0.0374 - val_loss: 2.5879 - val_categorical_crossentropy: 2.5879\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0292 - categorical_crossentropy: 0.0292 - val_loss: 2.8265 - val_categorical_crossentropy: 2.8265\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0426 - categorical_crossentropy: 0.0426 - val_loss: 2.8120 - val_categorical_crossentropy: 2.8120\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0322 - categorical_crossentropy: 0.0322 - val_loss: 2.8569 - val_categorical_crossentropy: 2.8569\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0316 - categorical_crossentropy: 0.0316 - val_loss: 2.8822 - val_categorical_crossentropy: 2.8822\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0411 - categorical_crossentropy: 0.0411 - val_loss: 2.8282 - val_categorical_crossentropy: 2.8282\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0393 - categorical_crossentropy: 0.0393 - val_loss: 2.8235 - val_categorical_crossentropy: 2.8235\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0356 - categorical_crossentropy: 0.0356 - val_loss: 2.6997 - val_categorical_crossentropy: 2.6997\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0337 - categorical_crossentropy: 0.0337 - val_loss: 2.8580 - val_categorical_crossentropy: 2.8580\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0366 - categorical_crossentropy: 0.0366 - val_loss: 2.8296 - val_categorical_crossentropy: 2.8296\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0350 - categorical_crossentropy: 0.0350 - val_loss: 2.6658 - val_categorical_crossentropy: 2.6658\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0258 - categorical_crossentropy: 0.0258 - val_loss: 2.9130 - val_categorical_crossentropy: 2.9130\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0416 - categorical_crossentropy: 0.0416 - val_loss: 2.7269 - val_categorical_crossentropy: 2.7269\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0298 - categorical_crossentropy: 0.0298 - val_loss: 2.9885 - val_categorical_crossentropy: 2.9885\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0352 - categorical_crossentropy: 0.0352 - val_loss: 3.0844 - val_categorical_crossentropy: 3.0844\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0295 - categorical_crossentropy: 0.0295 - val_loss: 2.8987 - val_categorical_crossentropy: 2.8987\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0393 - categorical_crossentropy: 0.0393 - val_loss: 2.9734 - val_categorical_crossentropy: 2.9734\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0404 - categorical_crossentropy: 0.0404 - val_loss: 2.9028 - val_categorical_crossentropy: 2.9028\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0240 - categorical_crossentropy: 0.0240 - val_loss: 3.0113 - val_categorical_crossentropy: 3.0113\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0311 - categorical_crossentropy: 0.0311 - val_loss: 2.9353 - val_categorical_crossentropy: 2.9353\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0419 - categorical_crossentropy: 0.0419 - val_loss: 2.8437 - val_categorical_crossentropy: 2.8437\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0213 - categorical_crossentropy: 0.0213 - val_loss: 3.0059 - val_categorical_crossentropy: 3.0059\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0380 - categorical_crossentropy: 0.0380 - val_loss: 2.7930 - val_categorical_crossentropy: 2.7930\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0252 - categorical_crossentropy: 0.0252 - val_loss: 2.8405 - val_categorical_crossentropy: 2.8405\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0346 - categorical_crossentropy: 0.0346 - val_loss: 2.8929 - val_categorical_crossentropy: 2.8929\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0311 - categorical_crossentropy: 0.0311 - val_loss: 2.8984 - val_categorical_crossentropy: 2.8984\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0388 - categorical_crossentropy: 0.0388 - val_loss: 2.8468 - val_categorical_crossentropy: 2.8468\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0231 - categorical_crossentropy: 0.0231 - val_loss: 2.8425 - val_categorical_crossentropy: 2.8425\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0326 - categorical_crossentropy: 0.0326 - val_loss: 3.0291 - val_categorical_crossentropy: 3.0291\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0318 - categorical_crossentropy: 0.0318 - val_loss: 3.1206 - val_categorical_crossentropy: 3.1206\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0303 - categorical_crossentropy: 0.0303 - val_loss: 2.7988 - val_categorical_crossentropy: 2.7988\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0363 - categorical_crossentropy: 0.0363 - val_loss: 2.7606 - val_categorical_crossentropy: 2.7606\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0214 - categorical_crossentropy: 0.0214 - val_loss: 2.9435 - val_categorical_crossentropy: 2.9435\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0339 - categorical_crossentropy: 0.0339 - val_loss: 3.0831 - val_categorical_crossentropy: 3.0831\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0315 - categorical_crossentropy: 0.0315 - val_loss: 2.9818 - val_categorical_crossentropy: 2.9818\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0292 - categorical_crossentropy: 0.0292 - val_loss: 3.1764 - val_categorical_crossentropy: 3.1764\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modeladw=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimadw=tf.keras.optimizers.AdamW(learning_rate=0.001)\n",
        "modeladw.compile(optimizer=optimadw, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "traineradw= modeladw.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultadw = modeladw.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnUSomogUIrN",
        "outputId": "aef4e6b9-424b-4900-a0a3-1b2893fac3ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 8s 8ms/step - loss: 1.4455 - categorical_crossentropy: 1.4455 - val_loss: 1.1839 - val_categorical_crossentropy: 1.1839\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0710 - categorical_crossentropy: 1.0710 - val_loss: 1.0300 - val_categorical_crossentropy: 1.0300\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.9168 - categorical_crossentropy: 0.9168 - val_loss: 0.9288 - val_categorical_crossentropy: 0.9288\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7945 - categorical_crossentropy: 0.7945 - val_loss: 0.9062 - val_categorical_crossentropy: 0.9062\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.7026 - categorical_crossentropy: 0.7026 - val_loss: 0.8328 - val_categorical_crossentropy: 0.8328\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.6173 - categorical_crossentropy: 0.6173 - val_loss: 0.8217 - val_categorical_crossentropy: 0.8217\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.5383 - categorical_crossentropy: 0.5383 - val_loss: 0.8164 - val_categorical_crossentropy: 0.8164\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4686 - categorical_crossentropy: 0.4686 - val_loss: 0.9140 - val_categorical_crossentropy: 0.9140\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.3998 - categorical_crossentropy: 0.3998 - val_loss: 0.8396 - val_categorical_crossentropy: 0.8396\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3257 - categorical_crossentropy: 0.3257 - val_loss: 0.8958 - val_categorical_crossentropy: 0.8958\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.2686 - categorical_crossentropy: 0.2686 - val_loss: 0.9271 - val_categorical_crossentropy: 0.9271\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.2156 - categorical_crossentropy: 0.2156 - val_loss: 1.0384 - val_categorical_crossentropy: 1.0384\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.1673 - categorical_crossentropy: 0.1673 - val_loss: 1.1023 - val_categorical_crossentropy: 1.1023\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.1245 - categorical_crossentropy: 0.1245 - val_loss: 1.2280 - val_categorical_crossentropy: 1.2280\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0953 - categorical_crossentropy: 0.0953 - val_loss: 1.3181 - val_categorical_crossentropy: 1.3181\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0732 - categorical_crossentropy: 0.0732 - val_loss: 1.3869 - val_categorical_crossentropy: 1.3869\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0575 - categorical_crossentropy: 0.0575 - val_loss: 1.4755 - val_categorical_crossentropy: 1.4755\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0334 - categorical_crossentropy: 0.0334 - val_loss: 1.6041 - val_categorical_crossentropy: 1.6041\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0277 - categorical_crossentropy: 0.0277 - val_loss: 1.7282 - val_categorical_crossentropy: 1.7282\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0256 - categorical_crossentropy: 0.0256 - val_loss: 1.7878 - val_categorical_crossentropy: 1.7878\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0190 - categorical_crossentropy: 0.0190 - val_loss: 1.7660 - val_categorical_crossentropy: 1.7660\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0145 - categorical_crossentropy: 0.0145 - val_loss: 1.8570 - val_categorical_crossentropy: 1.8570\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0165 - categorical_crossentropy: 0.0165 - val_loss: 1.9167 - val_categorical_crossentropy: 1.9167\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0172 - categorical_crossentropy: 0.0172 - val_loss: 1.9636 - val_categorical_crossentropy: 1.9636\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0098 - categorical_crossentropy: 0.0098 - val_loss: 2.0936 - val_categorical_crossentropy: 2.0936\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0068 - categorical_crossentropy: 0.0068 - val_loss: 2.0942 - val_categorical_crossentropy: 2.0942\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0069 - categorical_crossentropy: 0.0069 - val_loss: 2.1806 - val_categorical_crossentropy: 2.1806\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0107 - categorical_crossentropy: 0.0107 - val_loss: 2.2099 - val_categorical_crossentropy: 2.2099\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0067 - categorical_crossentropy: 0.0067 - val_loss: 2.2190 - val_categorical_crossentropy: 2.2190\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0085 - categorical_crossentropy: 0.0085 - val_loss: 2.2018 - val_categorical_crossentropy: 2.2018\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0095 - categorical_crossentropy: 0.0095 - val_loss: 2.2259 - val_categorical_crossentropy: 2.2259\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0068 - categorical_crossentropy: 0.0068 - val_loss: 2.2746 - val_categorical_crossentropy: 2.2746\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0058 - categorical_crossentropy: 0.0058 - val_loss: 2.2850 - val_categorical_crossentropy: 2.2850\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0072 - categorical_crossentropy: 0.0072 - val_loss: 2.3716 - val_categorical_crossentropy: 2.3716\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0018 - categorical_crossentropy: 0.0018 - val_loss: 2.3204 - val_categorical_crossentropy: 2.3204\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 3.1189e-04 - categorical_crossentropy: 3.1189e-04 - val_loss: 2.4108 - val_categorical_crossentropy: 2.4108\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.0452e-04 - categorical_crossentropy: 2.0452e-04 - val_loss: 2.5115 - val_categorical_crossentropy: 2.5115\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0101 - categorical_crossentropy: 0.0101 - val_loss: 2.4155 - val_categorical_crossentropy: 2.4155\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0015 - categorical_crossentropy: 0.0015 - val_loss: 2.4235 - val_categorical_crossentropy: 2.4235\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.2878e-04 - categorical_crossentropy: 2.2878e-04 - val_loss: 2.4787 - val_categorical_crossentropy: 2.4787\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4127e-04 - categorical_crossentropy: 1.4127e-04 - val_loss: 2.5505 - val_categorical_crossentropy: 2.5505\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0066 - categorical_crossentropy: 0.0066 - val_loss: 2.4553 - val_categorical_crossentropy: 2.4553\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0012 - categorical_crossentropy: 0.0012 - val_loss: 2.5761 - val_categorical_crossentropy: 2.5761\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0080 - categorical_crossentropy: 0.0080 - val_loss: 2.5120 - val_categorical_crossentropy: 2.5120\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0068 - categorical_crossentropy: 0.0068 - val_loss: 2.4690 - val_categorical_crossentropy: 2.4690\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0061 - categorical_crossentropy: 0.0061 - val_loss: 2.4630 - val_categorical_crossentropy: 2.4630\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0055 - categorical_crossentropy: 0.0055 - val_loss: 2.5663 - val_categorical_crossentropy: 2.5663\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0043 - categorical_crossentropy: 0.0043 - val_loss: 2.6094 - val_categorical_crossentropy: 2.6094\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0046 - categorical_crossentropy: 0.0046 - val_loss: 2.4902 - val_categorical_crossentropy: 2.4902\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 3.0631e-04 - categorical_crossentropy: 3.0631e-04 - val_loss: 2.5561 - val_categorical_crossentropy: 2.5561\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2471e-04 - categorical_crossentropy: 1.2471e-04 - val_loss: 2.6251 - val_categorical_crossentropy: 2.6251\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 8.0990e-05 - categorical_crossentropy: 8.0990e-05 - val_loss: 2.7121 - val_categorical_crossentropy: 2.7121\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0013 - categorical_crossentropy: 0.0013 - val_loss: 2.8445 - val_categorical_crossentropy: 2.8445\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0045 - categorical_crossentropy: 0.0045 - val_loss: 2.7012 - val_categorical_crossentropy: 2.7012\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6821e-04 - categorical_crossentropy: 1.6821e-04 - val_loss: 2.7147 - val_categorical_crossentropy: 2.7147\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 7.6923e-05 - categorical_crossentropy: 7.6923e-05 - val_loss: 2.7685 - val_categorical_crossentropy: 2.7685\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 5.2937e-05 - categorical_crossentropy: 5.2937e-05 - val_loss: 2.8296 - val_categorical_crossentropy: 2.8296\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 3.6622e-05 - categorical_crossentropy: 3.6622e-05 - val_loss: 2.9032 - val_categorical_crossentropy: 2.9032\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0044 - categorical_crossentropy: 0.0044 - val_loss: 2.8974 - val_categorical_crossentropy: 2.8974\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0071 - categorical_crossentropy: 0.0071 - val_loss: 2.7307 - val_categorical_crossentropy: 2.7307\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0062 - categorical_crossentropy: 0.0062 - val_loss: 2.7626 - val_categorical_crossentropy: 2.7626\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0042 - categorical_crossentropy: 0.0042 - val_loss: 2.7386 - val_categorical_crossentropy: 2.7386\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0027 - categorical_crossentropy: 0.0027 - val_loss: 2.8778 - val_categorical_crossentropy: 2.8778\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0089 - categorical_crossentropy: 0.0089 - val_loss: 2.6912 - val_categorical_crossentropy: 2.6912\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0020 - categorical_crossentropy: 0.0020 - val_loss: 2.8927 - val_categorical_crossentropy: 2.8927\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0080 - categorical_crossentropy: 0.0080 - val_loss: 2.7175 - val_categorical_crossentropy: 2.7175\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0029 - categorical_crossentropy: 0.0029 - val_loss: 2.6877 - val_categorical_crossentropy: 2.6877\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0087 - categorical_crossentropy: 0.0087 - val_loss: 2.6814 - val_categorical_crossentropy: 2.6814\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0016 - categorical_crossentropy: 0.0016 - val_loss: 2.6793 - val_categorical_crossentropy: 2.6793\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 3.1790e-04 - categorical_crossentropy: 3.1790e-04 - val_loss: 2.7574 - val_categorical_crossentropy: 2.7574\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 9.9770e-05 - categorical_crossentropy: 9.9770e-05 - val_loss: 2.8159 - val_categorical_crossentropy: 2.8159\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 5.2927e-05 - categorical_crossentropy: 5.2927e-05 - val_loss: 2.8789 - val_categorical_crossentropy: 2.8789\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 3.0374e-05 - categorical_crossentropy: 3.0374e-05 - val_loss: 2.9469 - val_categorical_crossentropy: 2.9469\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 8.2194e-04 - categorical_crossentropy: 8.2194e-04 - val_loss: 3.1574 - val_categorical_crossentropy: 3.1574\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0054 - categorical_crossentropy: 0.0054 - val_loss: 2.9014 - val_categorical_crossentropy: 2.9014\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.4132e-04 - categorical_crossentropy: 1.4132e-04 - val_loss: 2.8982 - val_categorical_crossentropy: 2.8982\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 4.9403e-05 - categorical_crossentropy: 4.9403e-05 - val_loss: 2.9329 - val_categorical_crossentropy: 2.9329\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 3.3850e-05 - categorical_crossentropy: 3.3850e-05 - val_loss: 2.9833 - val_categorical_crossentropy: 2.9833\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3342e-05 - categorical_crossentropy: 2.3342e-05 - val_loss: 3.0379 - val_categorical_crossentropy: 3.0379\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.5841e-05 - categorical_crossentropy: 1.5841e-05 - val_loss: 3.1070 - val_categorical_crossentropy: 3.1070\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0508e-05 - categorical_crossentropy: 1.0508e-05 - val_loss: 3.1932 - val_categorical_crossentropy: 3.1932\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 6.7267e-06 - categorical_crossentropy: 6.7267e-06 - val_loss: 3.3023 - val_categorical_crossentropy: 3.3023\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0060 - categorical_crossentropy: 0.0060 - val_loss: 3.1105 - val_categorical_crossentropy: 3.1105\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0032 - categorical_crossentropy: 0.0032 - val_loss: 3.0182 - val_categorical_crossentropy: 3.0182\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0077 - categorical_crossentropy: 0.0077 - val_loss: 3.0265 - val_categorical_crossentropy: 3.0265\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0033 - categorical_crossentropy: 0.0033 - val_loss: 2.9956 - val_categorical_crossentropy: 2.9956\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0050 - categorical_crossentropy: 0.0050 - val_loss: 3.0004 - val_categorical_crossentropy: 3.0004\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0063 - categorical_crossentropy: 0.0063 - val_loss: 2.9209 - val_categorical_crossentropy: 2.9209\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0014 - categorical_crossentropy: 0.0014 - val_loss: 3.0137 - val_categorical_crossentropy: 3.0137\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.0043 - categorical_crossentropy: 0.0043 - val_loss: 2.9700 - val_categorical_crossentropy: 2.9700\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0069 - categorical_crossentropy: 0.0069 - val_loss: 3.0002 - val_categorical_crossentropy: 3.0002\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0032 - categorical_crossentropy: 0.0032 - val_loss: 2.9711 - val_categorical_crossentropy: 2.9711\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 0.0058 - categorical_crossentropy: 0.0058 - val_loss: 2.9212 - val_categorical_crossentropy: 2.9212\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0056 - categorical_crossentropy: 0.0056 - val_loss: 2.9923 - val_categorical_crossentropy: 2.9923\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 0.0016 - categorical_crossentropy: 0.0016 - val_loss: 2.8964 - val_categorical_crossentropy: 2.8964\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.2925e-04 - categorical_crossentropy: 1.2925e-04 - val_loss: 2.9247 - val_categorical_crossentropy: 2.9247\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 4.4720e-05 - categorical_crossentropy: 4.4720e-05 - val_loss: 2.9835 - val_categorical_crossentropy: 2.9835\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.6867e-05 - categorical_crossentropy: 2.6867e-05 - val_loss: 3.0493 - val_categorical_crossentropy: 3.0493\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.7876e-05 - categorical_crossentropy: 1.7876e-05 - val_loss: 3.1174 - val_categorical_crossentropy: 3.1174\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1495e-05 - categorical_crossentropy: 1.1495e-05 - val_loss: 3.2099 - val_categorical_crossentropy: 3.2099\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modeladx=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimadx=tf.keras.optimizers.Adamax(learning_rate=0.001)\n",
        "modeladx.compile(optimizer=optimadx, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "traineradx= modeladx.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultadx = modeladx.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5AIFwc5UMl9",
        "outputId": "0821e520-79d3-4d61-b565-741412f0c585"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 8s 8ms/step - loss: 2.3030 - categorical_crossentropy: 2.3030 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3026 - val_categorical_crossentropy: 2.3026\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 2.3026 - categorical_crossentropy: 2.3026 - val_loss: 2.3027 - val_categorical_crossentropy: 2.3027\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modelftrl=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimftrl=tf.keras.optimizers.Ftrl(learning_rate=0.001)\n",
        "modelftrl.compile(optimizer=optimftrl, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "trainerftrl= modelftrl.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultftrl = modelftrl.evaluate(test_images, test_labels_f, verbose=0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sa9jxNgTURL5",
        "outputId": "b195862a-002e-4e32-db76-5d6c31033b78"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 8s 8ms/step - loss: 1.7200 - categorical_crossentropy: 1.7200 - val_loss: 1.5045 - val_categorical_crossentropy: 1.5045\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3977 - categorical_crossentropy: 1.3977 - val_loss: 1.3667 - val_categorical_crossentropy: 1.3667\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3102 - categorical_crossentropy: 1.3102 - val_loss: 1.3638 - val_categorical_crossentropy: 1.3638\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2728 - categorical_crossentropy: 1.2728 - val_loss: 1.2891 - val_categorical_crossentropy: 1.2891\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.2388 - categorical_crossentropy: 1.2388 - val_loss: 1.3537 - val_categorical_crossentropy: 1.3537\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2168 - categorical_crossentropy: 1.2168 - val_loss: 1.3898 - val_categorical_crossentropy: 1.3898\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1867 - categorical_crossentropy: 1.1867 - val_loss: 1.3325 - val_categorical_crossentropy: 1.3325\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1650 - categorical_crossentropy: 1.1650 - val_loss: 1.2942 - val_categorical_crossentropy: 1.2942\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1532 - categorical_crossentropy: 1.1532 - val_loss: 1.3454 - val_categorical_crossentropy: 1.3454\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1470 - categorical_crossentropy: 1.1470 - val_loss: 1.4269 - val_categorical_crossentropy: 1.4269\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1381 - categorical_crossentropy: 1.1381 - val_loss: 1.2699 - val_categorical_crossentropy: 1.2699\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1414 - categorical_crossentropy: 1.1414 - val_loss: 1.3134 - val_categorical_crossentropy: 1.3134\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1296 - categorical_crossentropy: 1.1296 - val_loss: 1.3144 - val_categorical_crossentropy: 1.3144\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.1121 - categorical_crossentropy: 1.1121 - val_loss: 1.3358 - val_categorical_crossentropy: 1.3358\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0851 - categorical_crossentropy: 1.0851 - val_loss: 1.2687 - val_categorical_crossentropy: 1.2687\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.1203 - categorical_crossentropy: 1.1203 - val_loss: 1.4151 - val_categorical_crossentropy: 1.4151\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0936 - categorical_crossentropy: 1.0936 - val_loss: 1.3420 - val_categorical_crossentropy: 1.3420\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1137 - categorical_crossentropy: 1.1137 - val_loss: 1.4599 - val_categorical_crossentropy: 1.4599\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1281 - categorical_crossentropy: 1.1281 - val_loss: 1.4599 - val_categorical_crossentropy: 1.4599\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1027 - categorical_crossentropy: 1.1027 - val_loss: 1.3805 - val_categorical_crossentropy: 1.3805\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.0873 - categorical_crossentropy: 1.0873 - val_loss: 1.5271 - val_categorical_crossentropy: 1.5271\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0836 - categorical_crossentropy: 1.0836 - val_loss: 1.3952 - val_categorical_crossentropy: 1.3952\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.0735 - categorical_crossentropy: 1.0735 - val_loss: 1.4724 - val_categorical_crossentropy: 1.4724\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0865 - categorical_crossentropy: 1.0865 - val_loss: 1.6783 - val_categorical_crossentropy: 1.6783\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0594 - categorical_crossentropy: 1.0594 - val_loss: 1.4261 - val_categorical_crossentropy: 1.4261\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1015 - categorical_crossentropy: 1.1015 - val_loss: 1.4855 - val_categorical_crossentropy: 1.4855\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0685 - categorical_crossentropy: 1.0685 - val_loss: 1.4252 - val_categorical_crossentropy: 1.4252\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0759 - categorical_crossentropy: 1.0759 - val_loss: 1.4274 - val_categorical_crossentropy: 1.4274\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0462 - categorical_crossentropy: 1.0462 - val_loss: 1.5146 - val_categorical_crossentropy: 1.5146\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0773 - categorical_crossentropy: 1.0773 - val_loss: 1.4565 - val_categorical_crossentropy: 1.4565\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0777 - categorical_crossentropy: 1.0777 - val_loss: 1.4407 - val_categorical_crossentropy: 1.4407\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.0801 - categorical_crossentropy: 1.0801 - val_loss: 1.5981 - val_categorical_crossentropy: 1.5981\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.0680 - categorical_crossentropy: 1.0680 - val_loss: 1.4388 - val_categorical_crossentropy: 1.4388\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0841 - categorical_crossentropy: 1.0841 - val_loss: 1.7211 - val_categorical_crossentropy: 1.7211\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.0762 - categorical_crossentropy: 1.0762 - val_loss: 1.4412 - val_categorical_crossentropy: 1.4412\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1247 - categorical_crossentropy: 1.1247 - val_loss: 1.4572 - val_categorical_crossentropy: 1.4572\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.0952 - categorical_crossentropy: 1.0952 - val_loss: 1.5769 - val_categorical_crossentropy: 1.5769\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1269 - categorical_crossentropy: 1.1269 - val_loss: 1.7345 - val_categorical_crossentropy: 1.7345\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1946 - categorical_crossentropy: 1.1946 - val_loss: 1.6816 - val_categorical_crossentropy: 1.6816\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 1.1736 - categorical_crossentropy: 1.1736 - val_loss: 1.3764 - val_categorical_crossentropy: 1.3764\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1768 - categorical_crossentropy: 1.1768 - val_loss: 1.4856 - val_categorical_crossentropy: 1.4856\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1380 - categorical_crossentropy: 1.1380 - val_loss: 1.6271 - val_categorical_crossentropy: 1.6271\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1688 - categorical_crossentropy: 1.1688 - val_loss: 1.7646 - val_categorical_crossentropy: 1.7646\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1571 - categorical_crossentropy: 1.1571 - val_loss: 1.6823 - val_categorical_crossentropy: 1.6823\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1862 - categorical_crossentropy: 1.1862 - val_loss: 1.3940 - val_categorical_crossentropy: 1.3940\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1573 - categorical_crossentropy: 1.1573 - val_loss: 1.5237 - val_categorical_crossentropy: 1.5237\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1810 - categorical_crossentropy: 1.1810 - val_loss: 1.5889 - val_categorical_crossentropy: 1.5889\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1662 - categorical_crossentropy: 1.1662 - val_loss: 1.5226 - val_categorical_crossentropy: 1.5226\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1871 - categorical_crossentropy: 1.1871 - val_loss: 1.8808 - val_categorical_crossentropy: 1.8808\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.1973 - categorical_crossentropy: 1.1973 - val_loss: 1.5503 - val_categorical_crossentropy: 1.5503\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.1626 - categorical_crossentropy: 1.1626 - val_loss: 1.9413 - val_categorical_crossentropy: 1.9413\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.1916 - categorical_crossentropy: 1.1916 - val_loss: 1.7171 - val_categorical_crossentropy: 1.7171\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2410 - categorical_crossentropy: 1.2410 - val_loss: 1.6004 - val_categorical_crossentropy: 1.6004\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.2717 - categorical_crossentropy: 1.2717 - val_loss: 1.7079 - val_categorical_crossentropy: 1.7079\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.5022 - categorical_crossentropy: 1.5022 - val_loss: 1.5820 - val_categorical_crossentropy: 1.5820\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3060 - categorical_crossentropy: 1.3060 - val_loss: 1.5075 - val_categorical_crossentropy: 1.5075\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.2637 - categorical_crossentropy: 1.2637 - val_loss: 2.1297 - val_categorical_crossentropy: 2.1297\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3186 - categorical_crossentropy: 1.3186 - val_loss: 1.6032 - val_categorical_crossentropy: 1.6032\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3351 - categorical_crossentropy: 1.3351 - val_loss: 1.6397 - val_categorical_crossentropy: 1.6397\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3318 - categorical_crossentropy: 1.3318 - val_loss: 1.6229 - val_categorical_crossentropy: 1.6229\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3310 - categorical_crossentropy: 1.3310 - val_loss: 1.5339 - val_categorical_crossentropy: 1.5339\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3135 - categorical_crossentropy: 1.3135 - val_loss: 1.3819 - val_categorical_crossentropy: 1.3819\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3143 - categorical_crossentropy: 1.3143 - val_loss: 1.4096 - val_categorical_crossentropy: 1.4096\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3367 - categorical_crossentropy: 1.3367 - val_loss: 1.4170 - val_categorical_crossentropy: 1.4170\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3015 - categorical_crossentropy: 1.3015 - val_loss: 1.4457 - val_categorical_crossentropy: 1.4457\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3136 - categorical_crossentropy: 1.3136 - val_loss: 1.4981 - val_categorical_crossentropy: 1.4981\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2848 - categorical_crossentropy: 1.2848 - val_loss: 1.4633 - val_categorical_crossentropy: 1.4633\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3161 - categorical_crossentropy: 1.3161 - val_loss: 1.7162 - val_categorical_crossentropy: 1.7162\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3474 - categorical_crossentropy: 1.3474 - val_loss: 1.5053 - val_categorical_crossentropy: 1.5053\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3383 - categorical_crossentropy: 1.3383 - val_loss: 1.4287 - val_categorical_crossentropy: 1.4287\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 1.3212 - categorical_crossentropy: 1.3212 - val_loss: 1.5970 - val_categorical_crossentropy: 1.5970\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3112 - categorical_crossentropy: 1.3112 - val_loss: 1.4258 - val_categorical_crossentropy: 1.4258\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3048 - categorical_crossentropy: 1.3048 - val_loss: 1.5765 - val_categorical_crossentropy: 1.5765\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3985 - categorical_crossentropy: 1.3985 - val_loss: 1.4897 - val_categorical_crossentropy: 1.4897\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3368 - categorical_crossentropy: 1.3368 - val_loss: 1.5887 - val_categorical_crossentropy: 1.5887\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3125 - categorical_crossentropy: 1.3125 - val_loss: 1.4684 - val_categorical_crossentropy: 1.4684\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3334 - categorical_crossentropy: 1.3334 - val_loss: 1.3399 - val_categorical_crossentropy: 1.3399\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3073 - categorical_crossentropy: 1.3073 - val_loss: 1.3040 - val_categorical_crossentropy: 1.3040\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3451 - categorical_crossentropy: 1.3451 - val_loss: 1.6208 - val_categorical_crossentropy: 1.6208\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3242 - categorical_crossentropy: 1.3242 - val_loss: 1.3769 - val_categorical_crossentropy: 1.3769\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.2708 - categorical_crossentropy: 1.2708 - val_loss: 1.3680 - val_categorical_crossentropy: 1.3680\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3382 - categorical_crossentropy: 1.3382 - val_loss: 1.3952 - val_categorical_crossentropy: 1.3952\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3108 - categorical_crossentropy: 1.3108 - val_loss: 1.5455 - val_categorical_crossentropy: 1.5455\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2986 - categorical_crossentropy: 1.2986 - val_loss: 1.6121 - val_categorical_crossentropy: 1.6121\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3213 - categorical_crossentropy: 1.3213 - val_loss: 1.4094 - val_categorical_crossentropy: 1.4094\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3022 - categorical_crossentropy: 1.3022 - val_loss: 1.6318 - val_categorical_crossentropy: 1.6318\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3183 - categorical_crossentropy: 1.3183 - val_loss: 1.4561 - val_categorical_crossentropy: 1.4561\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3221 - categorical_crossentropy: 1.3221 - val_loss: 1.8060 - val_categorical_crossentropy: 1.8060\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3015 - categorical_crossentropy: 1.3015 - val_loss: 1.5805 - val_categorical_crossentropy: 1.5805\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3319 - categorical_crossentropy: 1.3319 - val_loss: 1.6369 - val_categorical_crossentropy: 1.6369\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2915 - categorical_crossentropy: 1.2915 - val_loss: 1.5042 - val_categorical_crossentropy: 1.5042\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3112 - categorical_crossentropy: 1.3112 - val_loss: 1.5823 - val_categorical_crossentropy: 1.5823\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3078 - categorical_crossentropy: 1.3078 - val_loss: 1.4592 - val_categorical_crossentropy: 1.4592\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.3034 - categorical_crossentropy: 1.3034 - val_loss: 1.3786 - val_categorical_crossentropy: 1.3786\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3299 - categorical_crossentropy: 1.3299 - val_loss: 1.5014 - val_categorical_crossentropy: 1.5014\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2890 - categorical_crossentropy: 1.2890 - val_loss: 1.4553 - val_categorical_crossentropy: 1.4553\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.2850 - categorical_crossentropy: 1.2850 - val_loss: 1.6328 - val_categorical_crossentropy: 1.6328\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.2853 - categorical_crossentropy: 1.2853 - val_loss: 1.4672 - val_categorical_crossentropy: 1.4672\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 6s 8ms/step - loss: 1.3256 - categorical_crossentropy: 1.3256 - val_loss: 1.4666 - val_categorical_crossentropy: 1.4666\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 1.3536 - categorical_crossentropy: 1.3536 - val_loss: 1.3868 - val_categorical_crossentropy: 1.3868\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "modelrms=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimrms=tf.keras.optimizers.RMSprop(learning_rate=0.001,momentum=0.9)\n",
        "modelrms.compile(optimizer=optimrms, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "trainerrms= modelrms.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultrms = modelrms.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n-j6612kVBlx",
        "outputId": "31024ba2-daed-4b03-e82d-851589ecd52d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 11s 10ms/step - loss: 1.3124 - categorical_crossentropy: 1.3124 - val_loss: 1.6554 - val_categorical_crossentropy: 1.6554\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.8865 - categorical_crossentropy: 0.8865 - val_loss: 0.9217 - val_categorical_crossentropy: 0.9217\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.7025 - categorical_crossentropy: 0.7025 - val_loss: 0.7947 - val_categorical_crossentropy: 0.7947\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.5680 - categorical_crossentropy: 0.5680 - val_loss: 0.7933 - val_categorical_crossentropy: 0.7933\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.4574 - categorical_crossentropy: 0.4574 - val_loss: 0.7874 - val_categorical_crossentropy: 0.7874\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.3599 - categorical_crossentropy: 0.3599 - val_loss: 0.8263 - val_categorical_crossentropy: 0.8263\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.2739 - categorical_crossentropy: 0.2739 - val_loss: 1.1299 - val_categorical_crossentropy: 1.1299\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.2117 - categorical_crossentropy: 0.2117 - val_loss: 1.0264 - val_categorical_crossentropy: 1.0264\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.1637 - categorical_crossentropy: 0.1637 - val_loss: 1.1552 - val_categorical_crossentropy: 1.1552\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.1366 - categorical_crossentropy: 0.1366 - val_loss: 1.2722 - val_categorical_crossentropy: 1.2722\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.1164 - categorical_crossentropy: 0.1164 - val_loss: 1.3452 - val_categorical_crossentropy: 1.3452\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.1041 - categorical_crossentropy: 0.1041 - val_loss: 1.2990 - val_categorical_crossentropy: 1.2990\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0965 - categorical_crossentropy: 0.0965 - val_loss: 1.4423 - val_categorical_crossentropy: 1.4423\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0971 - categorical_crossentropy: 0.0971 - val_loss: 1.4880 - val_categorical_crossentropy: 1.4880\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0860 - categorical_crossentropy: 0.0860 - val_loss: 1.4878 - val_categorical_crossentropy: 1.4878\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0778 - categorical_crossentropy: 0.0778 - val_loss: 1.6113 - val_categorical_crossentropy: 1.6113\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0754 - categorical_crossentropy: 0.0754 - val_loss: 1.6956 - val_categorical_crossentropy: 1.6956\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0724 - categorical_crossentropy: 0.0724 - val_loss: 1.6833 - val_categorical_crossentropy: 1.6833\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0712 - categorical_crossentropy: 0.0712 - val_loss: 1.9261 - val_categorical_crossentropy: 1.9261\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0639 - categorical_crossentropy: 0.0639 - val_loss: 1.8414 - val_categorical_crossentropy: 1.8414\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0611 - categorical_crossentropy: 0.0611 - val_loss: 2.0200 - val_categorical_crossentropy: 2.0200\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0712 - categorical_crossentropy: 0.0712 - val_loss: 1.7486 - val_categorical_crossentropy: 1.7486\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0602 - categorical_crossentropy: 0.0602 - val_loss: 1.8918 - val_categorical_crossentropy: 1.8918\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0665 - categorical_crossentropy: 0.0665 - val_loss: 1.9767 - val_categorical_crossentropy: 1.9767\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0572 - categorical_crossentropy: 0.0572 - val_loss: 1.9134 - val_categorical_crossentropy: 1.9134\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0607 - categorical_crossentropy: 0.0607 - val_loss: 1.9843 - val_categorical_crossentropy: 1.9843\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0568 - categorical_crossentropy: 0.0568 - val_loss: 2.0321 - val_categorical_crossentropy: 2.0321\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0553 - categorical_crossentropy: 0.0553 - val_loss: 2.2339 - val_categorical_crossentropy: 2.2339\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0508 - categorical_crossentropy: 0.0508 - val_loss: 2.2220 - val_categorical_crossentropy: 2.2220\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0623 - categorical_crossentropy: 0.0623 - val_loss: 2.1230 - val_categorical_crossentropy: 2.1230\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0464 - categorical_crossentropy: 0.0464 - val_loss: 2.2948 - val_categorical_crossentropy: 2.2948\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0546 - categorical_crossentropy: 0.0546 - val_loss: 2.1579 - val_categorical_crossentropy: 2.1579\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0546 - categorical_crossentropy: 0.0546 - val_loss: 2.3236 - val_categorical_crossentropy: 2.3236\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0465 - categorical_crossentropy: 0.0465 - val_loss: 2.3465 - val_categorical_crossentropy: 2.3465\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0540 - categorical_crossentropy: 0.0540 - val_loss: 2.4023 - val_categorical_crossentropy: 2.4023\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0459 - categorical_crossentropy: 0.0459 - val_loss: 2.2160 - val_categorical_crossentropy: 2.2160\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0450 - categorical_crossentropy: 0.0450 - val_loss: 2.4752 - val_categorical_crossentropy: 2.4752\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0540 - categorical_crossentropy: 0.0540 - val_loss: 2.4315 - val_categorical_crossentropy: 2.4315\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0454 - categorical_crossentropy: 0.0454 - val_loss: 2.3987 - val_categorical_crossentropy: 2.3987\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0508 - categorical_crossentropy: 0.0508 - val_loss: 2.3544 - val_categorical_crossentropy: 2.3544\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0461 - categorical_crossentropy: 0.0461 - val_loss: 2.4296 - val_categorical_crossentropy: 2.4296\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0483 - categorical_crossentropy: 0.0483 - val_loss: 2.3127 - val_categorical_crossentropy: 2.3127\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0426 - categorical_crossentropy: 0.0426 - val_loss: 2.2829 - val_categorical_crossentropy: 2.2829\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0366 - categorical_crossentropy: 0.0366 - val_loss: 2.6409 - val_categorical_crossentropy: 2.6409\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0498 - categorical_crossentropy: 0.0498 - val_loss: 2.4600 - val_categorical_crossentropy: 2.4600\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0406 - categorical_crossentropy: 0.0406 - val_loss: 2.4595 - val_categorical_crossentropy: 2.4595\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0453 - categorical_crossentropy: 0.0453 - val_loss: 2.5931 - val_categorical_crossentropy: 2.5931\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0493 - categorical_crossentropy: 0.0493 - val_loss: 2.4861 - val_categorical_crossentropy: 2.4861\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0380 - categorical_crossentropy: 0.0380 - val_loss: 2.5768 - val_categorical_crossentropy: 2.5768\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0440 - categorical_crossentropy: 0.0440 - val_loss: 2.5475 - val_categorical_crossentropy: 2.5475\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0436 - categorical_crossentropy: 0.0436 - val_loss: 2.5496 - val_categorical_crossentropy: 2.5496\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0485 - categorical_crossentropy: 0.0485 - val_loss: 2.4949 - val_categorical_crossentropy: 2.4949\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0352 - categorical_crossentropy: 0.0352 - val_loss: 2.7131 - val_categorical_crossentropy: 2.7131\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0377 - categorical_crossentropy: 0.0377 - val_loss: 2.7267 - val_categorical_crossentropy: 2.7267\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0400 - categorical_crossentropy: 0.0400 - val_loss: 2.7313 - val_categorical_crossentropy: 2.7313\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0439 - categorical_crossentropy: 0.0439 - val_loss: 2.7738 - val_categorical_crossentropy: 2.7738\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0426 - categorical_crossentropy: 0.0426 - val_loss: 2.8195 - val_categorical_crossentropy: 2.8195\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0463 - categorical_crossentropy: 0.0463 - val_loss: 2.9170 - val_categorical_crossentropy: 2.9170\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0322 - categorical_crossentropy: 0.0322 - val_loss: 3.0359 - val_categorical_crossentropy: 3.0359\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0475 - categorical_crossentropy: 0.0475 - val_loss: 2.8549 - val_categorical_crossentropy: 2.8549\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0330 - categorical_crossentropy: 0.0330 - val_loss: 2.9331 - val_categorical_crossentropy: 2.9331\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0370 - categorical_crossentropy: 0.0370 - val_loss: 2.7268 - val_categorical_crossentropy: 2.7268\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0385 - categorical_crossentropy: 0.0385 - val_loss: 2.8445 - val_categorical_crossentropy: 2.8445\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0279 - categorical_crossentropy: 0.0279 - val_loss: 2.9987 - val_categorical_crossentropy: 2.9987\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0480 - categorical_crossentropy: 0.0480 - val_loss: 3.0641 - val_categorical_crossentropy: 3.0641\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0427 - categorical_crossentropy: 0.0427 - val_loss: 3.0016 - val_categorical_crossentropy: 3.0016\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0356 - categorical_crossentropy: 0.0356 - val_loss: 2.9185 - val_categorical_crossentropy: 2.9185\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0244 - categorical_crossentropy: 0.0244 - val_loss: 3.1140 - val_categorical_crossentropy: 3.1140\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0418 - categorical_crossentropy: 0.0418 - val_loss: 3.1586 - val_categorical_crossentropy: 3.1586\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0491 - categorical_crossentropy: 0.0491 - val_loss: 3.1514 - val_categorical_crossentropy: 3.1514\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0326 - categorical_crossentropy: 0.0326 - val_loss: 3.0235 - val_categorical_crossentropy: 3.0235\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0312 - categorical_crossentropy: 0.0312 - val_loss: 3.1343 - val_categorical_crossentropy: 3.1343\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0428 - categorical_crossentropy: 0.0428 - val_loss: 3.0162 - val_categorical_crossentropy: 3.0162\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0422 - categorical_crossentropy: 0.0422 - val_loss: 3.1281 - val_categorical_crossentropy: 3.1281\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0325 - categorical_crossentropy: 0.0325 - val_loss: 3.3197 - val_categorical_crossentropy: 3.3197\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0328 - categorical_crossentropy: 0.0328 - val_loss: 3.1392 - val_categorical_crossentropy: 3.1392\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0384 - categorical_crossentropy: 0.0384 - val_loss: 3.1290 - val_categorical_crossentropy: 3.1290\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0357 - categorical_crossentropy: 0.0357 - val_loss: 3.3297 - val_categorical_crossentropy: 3.3297\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0342 - categorical_crossentropy: 0.0342 - val_loss: 3.2682 - val_categorical_crossentropy: 3.2682\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0291 - categorical_crossentropy: 0.0291 - val_loss: 3.4692 - val_categorical_crossentropy: 3.4692\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0435 - categorical_crossentropy: 0.0435 - val_loss: 3.3075 - val_categorical_crossentropy: 3.3075\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 7s 10ms/step - loss: 0.0378 - categorical_crossentropy: 0.0378 - val_loss: 3.4939 - val_categorical_crossentropy: 3.4939\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0352 - categorical_crossentropy: 0.0352 - val_loss: 3.2256 - val_categorical_crossentropy: 3.2256\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0384 - categorical_crossentropy: 0.0384 - val_loss: 3.2998 - val_categorical_crossentropy: 3.2998\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0388 - categorical_crossentropy: 0.0388 - val_loss: 3.1543 - val_categorical_crossentropy: 3.1543\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0385 - categorical_crossentropy: 0.0385 - val_loss: 3.3711 - val_categorical_crossentropy: 3.3711\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0281 - categorical_crossentropy: 0.0281 - val_loss: 3.4707 - val_categorical_crossentropy: 3.4707\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0343 - categorical_crossentropy: 0.0343 - val_loss: 3.4143 - val_categorical_crossentropy: 3.4143\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0415 - categorical_crossentropy: 0.0415 - val_loss: 3.4636 - val_categorical_crossentropy: 3.4636\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0398 - categorical_crossentropy: 0.0398 - val_loss: 3.5181 - val_categorical_crossentropy: 3.5181\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0297 - categorical_crossentropy: 0.0297 - val_loss: 3.5568 - val_categorical_crossentropy: 3.5568\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0330 - categorical_crossentropy: 0.0330 - val_loss: 3.4995 - val_categorical_crossentropy: 3.4995\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0395 - categorical_crossentropy: 0.0395 - val_loss: 3.4874 - val_categorical_crossentropy: 3.4874\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0342 - categorical_crossentropy: 0.0342 - val_loss: 3.3781 - val_categorical_crossentropy: 3.3781\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0273 - categorical_crossentropy: 0.0273 - val_loss: 3.7454 - val_categorical_crossentropy: 3.7454\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0441 - categorical_crossentropy: 0.0441 - val_loss: 3.4616 - val_categorical_crossentropy: 3.4616\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0298 - categorical_crossentropy: 0.0298 - val_loss: 3.8572 - val_categorical_crossentropy: 3.8572\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0410 - categorical_crossentropy: 0.0410 - val_loss: 3.5655 - val_categorical_crossentropy: 3.5655\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 6s 9ms/step - loss: 0.0265 - categorical_crossentropy: 0.0265 - val_loss: 3.9553 - val_categorical_crossentropy: 3.9553\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 7s 9ms/step - loss: 0.0340 - categorical_crossentropy: 0.0340 - val_loss: 3.6554 - val_categorical_crossentropy: 3.6554\n"
          ]
        }
      ],
      "source": [
        "\n",
        "modelndm=Sequential([\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same',input_shape=(32,32,3)),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu', kernel_initializer='he_uniform',padding='same'),\n",
        "    tf.keras.layers.MaxPooling2D((2,2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128,activation='relu',kernel_initializer='he_uniform'),\n",
        "    tf.keras.layers.Dense(10,activation='linear')\n",
        "])\n",
        "optimndm=tf.keras.optimizers.Nadam(learning_rate=0.001)\n",
        "modelndm.compile(optimizer=optimndm, loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalCrossentropy(from_logits=True)])\n",
        "trainerndm= modelndm.fit(train_images, final_labels_encoded, epochs=100, batch_size=64, validation_data=(valid_images,valid_lfinal), verbose=1)\n",
        "_, resultndm = modelndm.evaluate(test_images, test_labels_f, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "4u9AgyoOVKCc"
      },
      "outputs": [],
      "source": [
        "def plotteropt(trainerid,resultid):\n",
        "  xaxis=np.arange(1,101)\n",
        "  fig,ax=plt.subplots(1,1,figsize=(12,4))\n",
        "  ax.set_title('Cross Entropy Loss')\n",
        "  ax.plot(xaxis,trainerid.history['loss'], color='blue', label='train')\n",
        "  ax.plot(xaxis,trainerid.history['val_loss'], color='orange', label='validation')\n",
        "  ax.axhline(y=0.278, color='green', linestyle='-')\n",
        "  ax.legend()\n",
        "  plt.show()\n",
        "  print(f\"Final model performance- Categorical Cross Entropy Loss on test dataset= {resultid:.3f}\")\n",
        "  print(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BitZUq-MVNKv",
        "outputId": "acefb4f0-a335-43cb-aa0e-aa5b7ab60cef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SGD\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 2.903\n",
            "\n",
            "\n",
            "Adam- with high learning rate (0.1)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 2.308\n",
            "\n",
            "\n",
            "Adam- with lower initial learning rate (3e-4)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 3.302\n",
            "\n",
            "\n",
            "Adamax\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 3.388\n",
            "\n",
            "\n",
            "AdamW\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 3.373\n",
            "\n",
            "\n",
            "Adagrad\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 0.963\n",
            "\n",
            "\n",
            "Adadelta\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 1.320\n",
            "\n",
            "\n",
            "Adafactor\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= nan\n",
            "\n",
            "\n",
            "Ftrl\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 2.303\n",
            "\n",
            "\n",
            "Nadam\n"
          ]
        },
        {
          "data": {
            "image/png": 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RERERkWJzvi7IGqhMREREREREpICUVIuIiIiIiIgUkJJqERERERERkQIqFX2qnWG320lJSXF3GFJIfHx88h22XkREREREpCS4oKT61VdfZfTo0YwcOZK33347z+0mTZrE008/za5du6hfvz6vvfYa/fr1u5BTZ5OSksLOnTux2+2FdkxxLw8PD6Kjo/Hx8XF3KCIiIiIiInkqcFK9dOlSPv74Y1q0aJHvdgsXLuTGG29kzJgxXH755UyYMIFBgwaxYsUKmjVrVtDTZ7Asi4MHD+Lp6UlkZKRqN8sAu93OgQMHOHjwILVq1TrvaHsiIiIiIiLuYrMsy3J1p8TERNq0acOHH37ISy+9RKtWrfKsqb7++us5deoUU6dOzVjXqVMnWrVqxbhx45w6X0JCAiEhIcTHx+eYUis1NZVt27ZRvXp1QkJCXL0UKaHi4+M5cOAA9erVw9vb293hiIiIiIhIOZNfHppVgap1hw8fTv/+/enZs+d5t120aFGO7Xr37s2iRYvy3Cc5OZmEhIRsS17S09MB1Ey4jHH8PB0/XxERERERkZLI5ebfEydOZMWKFSxdutSp7WNjYwkPD8+2Ljw8nNjY2Dz3GTNmDM8//7xLcamJcNmin6eIiIiIiJQGLtVU7927l5EjR/Ldd9/h5+dXVDExevRo4uPjM5a9e/cW2blERERERERECsqlpHr58uUcOnSINm3a4OXlhZeXF3PnzuXdd9/Fy8sr16a6ERERxMXFZVsXFxdHREREnufx9fUlODg42yJ5i4qKynf0dRERERERkSJn2WHFw7DpbXdHUqxcav7do0cP1q5dm23drbfeSqNGjXjsscfw9PTMsU9MTAyzZs3igQceyFg3c+ZMYmJiChZxGdG9e/d8B3hzxdKlS6lQocKFByUiIiIiIlJQh+bBpjcBG9QcCIHR7o6oWLiUVAcFBeWYBqtChQpUrlw5Y/2QIUOoUaMGY8aMAWDkyJF069aNN998k/79+zNx4kSWLVvGJ598UkiXUDZZlkV6ejpeXuf/EVWtWrUYIhIREREREcnHvl/PFizY/jm0fMmt4RSXQp/Uec+ePRw8eDDjfefOnZkwYQKffPIJLVu2ZPLkyfzyyy+FMkd1aTVs2DDmzp3LO++8g81mw2azMX78eGw2G9OnT6dt27b4+voyf/58tm/fzsCBAwkPDycwMJD27dvz999/Zzveuc2/bTYbn332GVdeeSUBAQHUr1+f3377rZivUkREREREyg3LypJUY5Jqe6r74ilGLo/+fa45c+bk+x7g2muv5dprr73QUznFsuD06WI5VQ4BAeDMoNXvvPMOW7ZsoVmzZrzwwgsArF+/HoDHH3+cN954gzp16lCxYkX27t1Lv379ePnll/H19eXrr79mwIABbN68mVq1auV5jueff56xY8fy+uuv89577zF48GB2795NpUqVCuVaRUREREREMsSvg1M7wcMXfEIgKRb2T4XIK90dWZG74KS6pDl9GgID3XPuxERwpmtzSEgIPj4+BAQEZAzYtmnTJgBeeOEFLrvssoxtK1WqRMuWLTPev/jii0yZMoXffvuNESNG5HmOYcOGceONNwLwyiuv8O6777JkyRL69OlTkEsTERERERHJm6OWOqInhDaHDa/Cto/LRVJd6M2/5cK0a9cu2/vExEQefvhhGjduTGhoKIGBgWzcuJE9e/bke5wWLVpklCtUqEBwcDCHDh0qkphFRERERKSc23e2u2nNgVDvTlM++Bck7nRfTMWkzNVUBwSYGmN3nftCnTuK98MPP8zMmTN54403qFevHv7+/lxzzTWkpKTkexxvb+9s7202G3a7/cIDFBERERGRsic9GZbdD5XbQb27XNv39AE4thSwQY0B4B8BEZdB7MxyMWBZmUuqbTbnmmC7m4+PT67zep9rwYIFDBs2jCuvNM0mEhMT2bVrVxFHJyIiIiIi5cq+X2H7p7DzK6h5Jfi5MMPQ/rO11JU7moQaTGLuSKqbPwse3nnvX8qp+bebREVFsXjxYnbt2sWRI0fyrEWuX78+P//8M6tWrWL16tXcdNNNqnEWEREREZHCtf9382pPgR1fuLavoz91zSsy19W4AvzCMgcsK8OUVLvJww8/jKenJ02aNKFq1ap59pF+6623qFixIp07d2bAgAH07t2bNm3aFHO0IiIiIiJSZtnT4MC0zPdbPwbLyYq81JMQN9uUaw7MXO/pA3VuM+VtnxROnCWUzbIsy91BnE9CQgIhISHEx8cTHByc7bOkpCR27txJdHQ0fn5+bopQCpt+riIiIiIixeTQPPi7G/hUMsl06gno/gdU73v+ffdMhvnXQmA9GLAl+xzDJ7fD7/UAG1yxAwKjiugCikZ+eWhWqqkWEREREREpzxwjd1fvD3WGmfLWj5zc19H0e2D2hBogqK6ZYgsLtn9WGJGWSEqqRUREREREyjNHf+qaV0D9e86umwqndue/nz01s9l41qbfWdW727zu+MJsXwYpqRYRERERESmvEjbDyS3g4QPVekNwQwi/FLDO3xf68HxIOQ6+laFKTO7bOAYsO3OwzA5YpqRaRERERESkNEuJh3+vgR1fub6vo5Y6rDt4B5ly/fvM6/bPID0l730zmo1fDh55zNbs6QN1bjXlMjpgmZJqERERERGR0mzHl7D3J1g2AlITXNvXkRhnnQ6r5hXgXw2SDsG+KbnvZ1nZ+1Pnp+6d5vXgn5C4y7X4SgEl1SIiIiIiIqXZ3snmNS0Rdn7j/H7JR+HIAlOucXnmeg/vzEQ4rwHL4tfBqZ3g6QfVeuV/njI+YJmSahERERERkdLq9AE4vDDz/Zb3TS2yMw78YabQCm0JFWpn/6zenWDzhENz4cT6nPs6aqnDe4JXhfOfq95d5rUMDlimpFpERERERKS02jcFsCC0BXgFQsImiJvt5L5nm37XGJDzs4CaZpAxgG3jctnX0fT7ipyf5abGwDI7YJmSahERERERkdJqz9mm39FDIHqoKW95//z7pSebPs6Qd2Jc/17zuvNrSE3MXH96PxxbBthyT8hzU4YHLFNSXYpFRUXx9ttvZ7y32Wz88ssveW6/a9cubDYbq1atuqDzFtZxRERERETkAiQdgsPzTDnyKmgw3JT3/3b+OaYPzYW0k2ZAskptc98mogcE1jODn+3+PnO9Y8Twyh3BP8L5eOveYV7L2IBlSqrLkIMHD9K3b99CPeawYcMYNGhQtnWRkZEcPHiQZs2aFeq5RERERETEBft+MX2iK7WFwGgIaQzhPcy6rbk02c7KkRhXvxxseaSFNg+of48pb/0ws6+2s6N+nyuonomvjA1YpqS6DImIiMDX17fIz+Pp6UlERAReXnnMRSciIiIiIkVvz0/mNfLqzHUNRpjX7Z9C2pnc97Os3KfSyk2dYWaE7+Or4OhiSD2Z2Wfb1aQaTJIefomp5S4jlFS7ySeffEL16tWx2+3Z1g8cOJDbbruN7du3M3DgQMLDwwkMDKR9+/b8/fff+R7z3ObfS5YsoXXr1vj5+dGuXTtWrlyZbfv09HRuv/12oqOj8ff3p2HDhrzzzjsZnz/33HN89dVX/Prrr9hsNmw2G3PmzMm1+ffcuXPp0KEDvr6+VKtWjccff5y0tLSMz7t3787//vc/Hn30USpVqkRERATPPfec61+ciIiIiIhA8rHM5DZrUl3jcgioZabL2vND7vueWAun94Cn/9ma43z4VoZa15vy1o9M0217imkWHtzI9bhrXQM9ZkNNJ/tilwJlL6m2LEg75Z7F2aHrgWuvvZajR4/yzz//ZKw7duwYM2bMYPDgwSQmJtKvXz9mzZrFypUr6dOnDwMGDGDPnj1OHT8xMZHLL7+cJk2asHz5cp577jkefvjhbNvY7XZq1qzJpEmT2LBhA8888wxPPPEEP/74IwAPP/ww1113HX369OHgwYMcPHiQzp075zjX/v376devH+3bt2f16tV89NFHfP7557z00kvZtvvqq6+oUKECixcvZuzYsbzwwgvMnDnT6e9MRERERETO2v8bWGlm1O/gBpnrPbygwX2mvPm93HOU/WdrqSMuAy//85/LMWDZ7h9gx5emXHMg2GwFj78MKXvtd9NPw4+B7jn3dYnOzdEGVKxYkb59+zJhwgR69DBPhyZPnkyVKlW45JJL8PDwoGXLlhnbv/jii0yZMoXffvuNESNGnPf4EyZMwG638/nnn+Pn50fTpk3Zt28f9957b8Y23t7ePP/88xnvo6OjWbRoET/++CPXXXcdgYGB+Pv7k5ycTERE3gMQfPjhh0RGRvL+++9js9lo1KgRBw4c4LHHHuOZZ57Bw8M8u2nRogXPPvssAPXr1+f9999n1qxZXHbZZU59ZyIiIiIicpZj1O+stdQOdW6HNc/C8RWmyXaVTtk/z28qrdxU7gAVW8PxlWZuayhY0+8yquzVVJcigwcP5qeffiI5ORmA7777jhtuuAEPDw8SExN5+OGHady4MaGhoQQGBrJx40ana6o3btxIixYt8PPzy1gXExOTY7sPPviAtm3bUrVqVQIDA/nkk0+cPkfWc8XExGDL8qSqS5cuJCYmsm/fvox1LVq0yLZftWrVOHTokEvnEhEREREp91LiIfYvU651Tc7P/apA1I2mfO70WmcOwrGlplzjcufOZ7NB/fsy3/tWgSo5W7CWV2WvptozwNQYu+vcLhgwYACWZTFt2jTat2/Pv//+y//93/8Bpun1zJkzeeONN6hXrx7+/v5cc801pKSkFFq4EydO5OGHH+bNN98kJiaGoKAgXn/9dRYvXlxo58jK29s723ubzZajT7mIiIiISKlxJhaOLoXK7V2bWupC7Z8K9lTTpzmkSe7bNBgBO8bDnh+h9ZvgH565L7g+HVbUjbDyYUiNN8m4h+cFXUJZ4lJN9UcffUSLFi0IDg4mODiYmJgYpk+fnuf248ePzxjgyrFkrTktEjabaYLtjsXFPgV+fn5cddVVfPfdd3z//fc0bNiQNm3aALBgwQKGDRvGlVdeSfPmzYmIiGDXrl1OH7tx48asWbOGpKSkjHX//fdftm0WLFhA586due+++2jdujX16tVj+/bt2bbx8fEhPT39vOdatGgRVpb+GgsWLCAoKIiaNWs6HbOIiIiISIl3ajdsehtmXgxTqsO8K+DXKFg6/PxzQxeWvY6m37nUUjtUaguVO5nke/unmesdU2k52/TbwasCNHkMPHyg7l2u7VvGuZRU16xZk1dffZXly5ezbNkyLr30UgYOHMj69evz3Cc4ODhjkKuDBw+ye3cx3WilxODBg5k2bRpffPEFgwcPzlhfv359fv75Z1atWsXq1au56aabXKrVvemmm7DZbNx5551s2LCBP/74gzfeeCPbNvXr12fZsmX8+eefbNmyhaeffpqlS5dm2yYqKoo1a9awefNmjhw5Qmpqao5z3Xfffezdu5f777+fTZs28euvv/Lss88yatSojP7UIiIiIiKlVsIWWD8GZrQ3CfSKB+HwfMCCgEiwJ5t5nH+rB4uGQfymooslNREOzjDl3Jp+Z+WYXmvrOJNcp52G2LMDBZ9vKq3cNHkcrk+Cqjm7lZZnLmU8AwYMoF+/ftSvX58GDRrw8ssvExgYmKMGNCubzUZERETGEh4efsFBlyWXXnoplSpVYvPmzdx0000Z69966y0qVqxI586dGTBgAL17986oxXZGYGAgv//+O2vXrqV169Y8+eSTvPbaa9m2ufvuu7nqqqu4/vrr6dixI0ePHuW+++7Lts2dd95Jw4YNadeuHVWrVmXBggU5zlWjRg3++OMPlixZQsuWLbnnnnu4/fbbeeqpp1z8NkRERERESojkoyaRntYMpjaE1U/AsWVg84CwbtD2HRi4Bwbuhh7/QERPMxr3zq9gWhP491o4tvL853HVgT8gPQkC65qRv/NT61rwC4cz+2HfLxA7y+xboTaENHP93DabRvzOhc2yXJgHKov09HQmTZrE0KFDWblyJU2a5GzLP378eO644w5q1KiB3W6nTZs2vPLKKzRt2tSlcyUkJBASEkJ8fDzBwcHZPktKSmLnzp1ER0cXfdNyKTb6uYqIiIiIWyTuhE1vwfYvzMxCADYviOhhRtquORD8wnLf98gS2DDGJLAO1fqaKakC60BAdfAOvbDEdP51sGeSaYrd6tXzb7/mGVj3IoR1haAGsP0zaHA/tHu34DGUE/nloVm5PFDZ2rVriYmJISkpicDAQKZMmZJrQg3QsGFDvvjiC1q0aEF8fDxvvPEGnTt3Zv369fn2tU1OTs4YEdtxMSIiIiIiUoQSd8KCm6D2DdBopLujKX5Hl8LGN0x/Zetst8uKraDhSKg5CHxCz3+MKh2g6xQ4sQ42vAq7v4eD083i4OkHftVMgu1/dgmoab73gPOMR5R2OnNKq9ym0spNvbth/StwaB4cW2HWudqfWvLlck11SkoKe/bsIT4+nsmTJ/PZZ58xd+7cPBPrrFJTU2ncuDE33ngjL774Yp7bPffcc9nmT3ZQTXX5oZ+riIiIiItSjhe8FtSyYHZPiJsNnv4waB/4Vir0EAuNPQ3iN0BoM9Mcu6Asu0lSN75ukk6Han2g8cMQfumF1Sqf3G4S9cPzzFRWKcfz3tYvDLpPh0r5dPncOwX+vQoCasHAXc7H5qjdBvAKgquPgKeP05dRXhVZTbWPjw/16tUDoG3btixdupR33nmHjz/++Lz7ent707p1a7Zt25bvdqNHj2bUqFEZ7xMSEoiMjHQ1VBERERGR8mHf7/DvIIi6BTp96XoiuONLk1ADpJ+BbeOg6ROFHmahSDoE/15tBgoLbgiNH4Wom11LElMTYOe3Zg7nhI1mnYc31L4JGj8Eoc0LJ9agutDho8z3aWcgKRbOHDi7HDSv+6dC/Hr4uzt0+xXCL8n9eHt/Mq+1rnHtZ9xgRGZSXb2vEupCdsFDM9vt9mxNtfOTnp7O2rVrqVatWr7b+fr6Zkzb5VhERERERCQXaadg2XBT67rzK9j+uWv7nzkIKx4y5bCu5nXze5Du3N/4xerEWvizw9mRt4GEzbD4dvitDmz6PzMydn6Or4Yl98CUGuY7S9gI3sEmMb9iJ8SML7yEOjde/hAYDVW7mEHEGv7P9Iu+bAGEdYe0k/BPH9jzU85905Mzp8Nytum3Q9WLIbSlKde88oIuQXJyKakePXo08+bNY9euXaxdu5bRo0czZ86cjKmghgwZwujRozO2f+GFF/jrr7/YsWMHK1as4Oabb2b37t3ccccdhXsVIiIiIiLl1fpX4PRe8PA175ffb/r0OmvZ/ZB6wsxr3H0G+Ncwtam7JxZJuAW27zf4q7OZCzqwHvReCq3fMH2Sz+yHFaPg11qw5llIOpK5X3qSqZX+qwtMbwXbPoa0RAhuZEbwHrQXWr8GATXcdmn4hMAl0yHyKrCnwPxrYes5LYFj/zY17P7VoUon145vs0HXXyDmG6h9faGFLYZLzb8PHTrEkCFDOHjwICEhIbRo0YI///yTyy67DIA9e/Zkm5f4+PHj3HnnncTGxlKxYkXatm3LwoULnep/7aoCDmIuJZR+niIiIiJOSNhq+uwCdJkA2z41cxgvuB56LwGvCvnvv3eKaVJs84SOn5ma1Ib3w6rHzQjY0UPcP4WSZcGG18yUVlgQ3gMu+tH0+a7czjRt3vkNbBwLJ7fCuhfMd1LvTvOgYcfnZnosMKN4R15lRuMO6+b+a8vK0w+6/Ghq0Ld9DEvvgaQ4aPa0iXPvZLNd5FUF60ceGGUWKXQFnlKrOOXXQTw9PZ2tW7cSEBBA1apVsZWkfxhSIJZlcfjwYU6fPk39+vXx9PR0d0giIiIiJY9lwZz+ZmTpar3NIFfJR2B6S9Oku85t0CmfpuApJ8x8ymcOQpPR0OqVs+uPwy+Rpln5pX+bqaTcJT0JFt8Fu74x7+vfB23fNv2fz2VPh31TzNzSx1dk/ywgEurdBXVvB//8u6K6nWXB2ufMwwGA+sOhzZswpZr52fSYA+Hd3Bhg+eHsQGWlPqkGSExMZN++fardLENsNhs1a9YkMDDQ3aGIiIiIlEz7foV5g0yC2W8dBDcw6+PmwOwepo91zDcQfXPu+y++C7Z/auYu7rfa1JQ6LPsfbHnPzLF8yR9FfSW5OxML866Eo/+ZmvS270KD+86/n2WZptJb3jfv694O1fuBh8tjNLvXlg9M03wsqNjGPCjwC4NBB8BDlU7FoVwl1WBqrFNTU4s5Mikq3t7eqqEWERERyUvaGVPLfGpX9lpmh7XPm9pOrwrQZ0Vmwu0QNwdmnR1huufczAHKHE5uh9/rAxb0Xw8hhd99M1/HVsK8gaavuE9FuGgyRFxavDGUBLt/hEU3g/1snlPvbugwzr0xlSNFNqVWSeXp6akkTERERETKhw2vmYQ6oCY0ezLn502fMonzoTmmf3WvRZk10WlnYPGdplzv7pwJNZipoCKvhL0/m1G1O35aRBeSi1O74e+uZwcTawhdf4fg+sV3/pKk9nWm7/i8K833UfsGd0ckubjgKbVERERERKQYJe6ADa+acpu3ch+MzMMTOn8HvlXh+CpY8XDmZ+ueh8RtZhTpVq/lfZ5Go8zrzm/M3NDFZfWTJoGs3AF6/Vd+E2qHiJ6mtUHX3yC8u7ujkVwoqRYRERERcbdTe8Ce5ty2yx8Ae7IZBTvymry3C6hu+lQDbP3AzH18bGXmaOHtPzRTOeWlSmeT2NqTYetHzsV2oY4th13fnY3vI/AJLZ7zlnTB9aHmAHdHIXlQUi0iIiIikpf900wz6qK05UP4tTZMawq7vjcDjOUXz/7fzdRQ7d47/5RQ1XtDk8dMefHtsGgIWOlQ61qoOTD/fW02aPTQ2Rg/MM3Gi5JlZdaoR90CldoU7flEComSahERERGR3MT9A3Mvh9mXmRreonDmoJkTGuDkFlh4E/zR0swffe54wulJsHykKTd6AEIaO3eOFi9ClRhIjYf4dWbgr7bvObdv5FVQoTYkH86sQT4fe3rO2J2xf6rpA+7hCy1fcn1/ETdRUi0iIiIicq70JFhyjylbafDfrZCeUvjnWfkIpJ00zaxbvATeISbx/fcqmNHO1Ew7EtSNb0DidtMXutkzzp/Dwxu6fG+SaYDWb4J/uJP7ekHDs4n8prfyT5btqSbGyRVhTj/Xvi97Gqx61JQbPQgVajm/r4ibKakWERERETnX+ldNzbFfBPhWhhOrYcOYwj3HoXlna39t0O4DM4r3wJ1m5G6vQDMv8dzL4a/OsPM7WH922qzWb4B3kGvnqlAbev4LF/8EdYa5tm/d28ErCBI2wsEZuW9zeJF5COB4SHBwBiwb4XyN9fbPIGET+FaBJo+7Fp+ImympFhERERHJKn5TZgLd7j2T8AKsewmOry6cc9jTTNIJUO8uqNzOlH0qQssX4Yqd0PgR8PSHo/+ZuYrTz0BYt4JPqxTa1DTnPl8/7HN5B0O9s1NwbXor+2cpx2HJ3TCzM5xYYx5ANH4EsMH2T2HL++c/fupJWPusKTd7Nv/B00RKICXVIiIiIiIOlgVL7wZ7ClTvD5FXQ63rTDJqpcF/w0wz5wu15QM4sRZ8KkHLl3N+7lcFWo+FK3ZAg/+Bh4+ZZ9qZwcmKQsP/gc0TYv82DxYsy9SeT20E2z4x29QZBv03mbhbjzXrVjxo9snPhrFmyq6g+lD/7iK9DJGiYLOsgowiULwSEhIICQkhPj6e4OBgd4cjIiIiImXV9i9h8W3gGQD910NglFl/Jg6mNYGUY9D8BWj+dMHPcSYWpjaE1ARoP865RDLpEKQnQ4XIgp/3Qs2/Afb8YB42pCdB3CyzPrixmf4qvFvmtpZlHkDs/NrUvvdanPt806f3w+/1TS38xT9D5JXFcikiznA2D1VNtYiIiIgIQNJhWHl2SqcWz2cm1GAG9mp3tinz+hfh+JqCn2fVYyahrtQW6t7h3D5+Ye5NqAEajTKvB6aZhNrTzwyu1ndV9oQaTG16h4+hcifTRHzeFZASn/OYa542CXXVLlBzUFFfgUiRUFItIiIiIgImoU45BqEtM0e8zqr2DWZuZ3uqGQ28IM3ADy8wtbdg+mp7eF5YzMWpSgeIuMyUq/WGfuvM4GqePrlv7+kHXadAQE0zCNmCG810Ww7HV8OO8abc+k33NGsXKQRKqkVEREREYmefTXbP1rB6eOfcxmYzzZx9KpqRuTeMde0c9jRYOtyU694OVTpecNjF7uKfod8a6D4dguqef3v/COj6ixlw7eB0WJ1lZO+VjwIW1Lq+dH4XImcpqRYRERGR8i09CZaenZO6/n35J3j+1aDtu6a87nk4sc7582wdZ6bm8g6FloU8PVdx8Q6E0Oau1SpXagudvjTljW/Ajq/gwJ8Q+5d5eNHqlaKJVaSYKKkWERERkcJlWc7PT1wSrB8DJ7eahDm3kbjPFTUYagw42wx8mKmBPp+kQ7DmKVNu+TL4Vb2gkEud2teb+bcBltwFS+815Qb3Q2Ad98UlUgi83B2AiIiIiJQB9nSImw07v4K9U8BKNTWy3iHgk8trcCMzBVNuzayLU9Y5qdu+69wcyTabGbX70L9wbDlsfB2ajs5/n1WPQ2o8VGwN9crptFEtnof49bBvCpzaae6Ppk+6OyqRC6akWkREREQKLmGzac676xs4vS/7Z8mHzZKXQ/Mg5iuwFaDxZNoZU7vsalPkrDLmpE6F6pebOamdFVAd2r4D/w2Ftc+Zmmi/cDNKt29V8+oon1gLO842f273fukanKww2Twg5muY2QVOrIHmz4BvJXdHJXLBlFSLiIiIiGtSjsPuH8zIzUcXZ673DoWoGyF6CPhXNzWzKfGQeiLzNTXezPm89QPY9a2puW73nmuJ8en98E8viN8ADR+ANm8VLLHe8p5J7D0DoP37rh8j+hbY86OZYmrz2/lsePa40UOhamfX4yxLvAOh51w4sgiq9XF3NCKFQkm1iIiIiDhv3UtmsSeb9zZPkxzVGWr6GXv6OXecKh1h4c0mufYJhZYvObdfwlb45zI4tdu83/w2WOmm1tiVpHjjW7DyIVNu8SJUqO38vg42G1z0o6mFPrXb1FYnHTK180mHIPmQGQQNy9RYt3rN9XOURT6hUL2vu6MQKTRKqkVERETEOYf+hTVPm3Joc4geBlE3mWmTXBV1k6m1XnofrH/Z1Fg3eST/fY6vgn96m4Q1sB7UvRVWP2VqnK0007T6fE3JLQvWPAPrzybxjR+GRg+6Hr+DVwA0GJ73udJOQVIc+FZxrr+2iJQ6SqpFRERE5PzsqZkjNte908zlXNC+zA717zXNwlePhlWPmhrMenfmvu2hf2Hu5ZCaABVbQfcZ4B8OftVg8e2w9SNTY93+o7wTa8sOy/5nascBWr4CTR6/8OvIi81mmjt7BxbN8UWkRNCUWiIiIiJlgT0NDi8yA3gVhU3/Z0Zu9q0KrV4tvES06ePQ5DFTXnK36at9rv1TTR/q1ASoejH0mGMSajC11Z3GAzbY9omZrsmy5zyGPRUWDT2bUNug/YdmxO6iSqhFpNxQUi0iIiJSmlkW7P0F/mgGMzvD7/Vg68cmiSwsp3bD2udNufXrhT9ic8sxZ6eZskw/6/1/ZH6281uYN8j0Ta5+OVzyZ85m1HWGQMw3poZ6++em5tqenvl52hn492ozMJrNCzp/Z2rJRUQKgUtJ9UcffUSLFi0IDg4mODiYmJgYpk+fnu8+kyZNolGjRvj5+dG8eXP++OOPfLcXERERKTbpKWbgq9Lq8EKYeRH8e6WZ2grgzAFYeg9MbQQ7v8meXBbU8pGQfhrCupqRvQubzQbtPoDaN5q+0fOvNqNyb34XFt1imnVH3QxdfwYv/9yPET0YOk8wA6ftGA//DTPXnpoAc/rC/t/NIGpdfzEjlIuIFBKXkuqaNWvy6quvsnz5cpYtW8all17KwIEDWb9+fa7bL1y4kBtvvJHbb7+dlStXMmjQIAYNGsS6desKJXgRERGRAks6DH+2g6kNTBJWmiRsMTWvM7vAkYXg6Q9Nn4SrDkHbd838yIk7YNEQmN4C9v5sarQLYt/vsO9XU8Pb7sOiay7t4WnmrK7e39RKz+5lknmAhiPNZx7e+R+j9vXQZaKJdde3sPAmmNUDDs0FryDTD7tG/6KJX0TKLZtlFfQ3rFGpUiVef/11br/99hyfXX/99Zw6dYqpU6dmrOvUqROtWrVi3LhxTp8jISGBkJAQ4uPjCQ4OvpBwRURERMzo0bMuNX2EAbyDof96CKjp3rjO50wcrHve9B220k1z5zq3QfPnIKBG5nZpp2Dze7DhNTM3NECldtDiJajWy/nEOO0UTGtqmn83ecz0pS5qaWdMzfKhueZ98xeg2VOuJfN7p8D860ytN5iRty+ZAZXaFn68IlJmOZuHFrhPdXp6OhMnTuTUqVPExMTkus2iRYvo2bNntnW9e/dm0aJF+R47OTmZhISEbIuIiIhIoTgTB7MuMQm1fzUzknRqghkk68LqGorWxrfO9pc+O8p1jQHQdw10/DR7Qg3gVcEMADZwJzR9yrw/tgzm9IHZPUwttjPWvWQS6gq1odnThX9NufHyh26/QaNRpjl386ddrx2PvBIu/gk8fCEgEnr+q4RaRIqMy0n12rVrCQwMxNfXl3vuuYcpU6bQpEmTXLeNjY0lPDw827rw8HBiY2PzPceYMWMICQnJWCIjI10NU0RERCSnM7FnE+oN4F8Desw1iZuHLxz4A3Z+7e4Ic7f9S1j5EKQlQqX2ZvTrbr9BaNP89/MJhZYvwhU7oOGD5jrj/oE/WsC2z/J/iBC/ATa+Ycpt3zWJeXHxDoY2b15Y3+eaV8CgvTBgC4Q0KrzYRETO4XJS3bBhQ1atWsXixYu59957GTp0KBs2bCjUoEaPHk18fHzGsnfv3kI9voiIiJRDZw6ahDpho2nm3XMOBNeHkMam+TTA8gfg9AE3BpmLYyth2X2m3PQp6L0Ywru5dgy/MGj7Fly+yQw2lnYKltwJ8waamvtzWRYsvc80n65xhUlQSyO/qmZwMhGRIuRyUu3j40O9evVo27YtY8aMoWXLlrzzzju5bhsREUFcXPZf1HFxcUREROR7Dl9f34wRxh2LiIiISIGdPgB/d4eETaY5cI85EFQv8/PGD5s+x6knzMjZJaUZeMpxMyBZepIZwKvF8xc2UFhgFFw620yL5eFjRsT+o5mZkiurnd+YPs2eAdDu3Qu5AhGRMu+C56m22+0kJyfn+llMTAyzZs3Ktm7mzJl59sEWERERKXSn98Os7nByCwTUMjXUQXWzb+PhBZ2+NKNL7/8ddk1wR6TZWXYzZ/OpnVAhGjqfnYf5Qnl4mocIfZZBaAtIPmKm5PrvNtO3PPkYrHzYbNv8GdOfWkRE8uTSb+bRo0czb948du3axdq1axk9ejRz5sxh8ODBAAwZMoTRo0dnbD9y5EhmzJjBm2++yaZNm3juuedYtmwZI0aMKNyrEBEREcnN6X2mhvrkVpMc9pwLgXVy3za0GTR71pSX32/6X59P6klY9j8zJ/SR/wotbADWvWz6eXv6mUG3fCoW7vFDm0PvJWZUb2yw40v4o6WZhiv5MIQ0Mf2wRUQkXy4l1YcOHWLIkCE0bNiQHj16sHTpUv78808uu+wyAPbs2cPBgwcztu/cuTMTJkzgk08+oWXLlkyePJlffvmFZs2aFe5ViIiIiJwrYatJqBO3QYWoswl1VP77NHkUKrYxza6X3pt/M/CDf8G0ZrDlPUjYDIvvBHta4cR+4E9YezbBb/8RVGpdOMc9l6evmSar51zzHZ3aBQemZZ7X06dozisiUoZc8DzVxUHzVIuIiIhLDvwJC24wfaQrRJsm3xVqObfv8TXwZzuwp0Ln7yHqhuyfpxyHFaNgx3jzvkKUaTadcgzavQ8Nhl9Y7Kd2w/Q25nj17oIOH1/Y8ZyVehJWPAjbP4f690H7D4rnvCIiJVSRz1MtIiIiUuJYlpkGam4/k1BXiYFeC5xPqAEqtjCjbAMsH5F9dOy9U2Bqk7MJtQ0ajoT+66DFi+bzNU9D8tGCx5+eBP9eYxLqSu2gbe6DwRYJ7yDo+Blcc/bhgIiIOEVJtYiIiJQNaWdg0S2w8hEzyFed26DHP+BfzfVjNR0NoS1NgrzsbGI9/zr49ypIioXghnDZfGj7tpm/ud5dpo9yynFY80zBr2H5SDi2DHwqwcWT3TMdlE/FCxthXESknFFSLSIiIqXf6X3wd1fY9R3YPKHte6bW1dO3YMfz8IaY8WDzgr2T4fd6sGeSOXbTJ6DvKqjaOcv2Xpm1ytvGwYm1rp9z+5ew7RPABl2+16jbIiKlhJJqERERKd0OL4QZ7UwNr29luOQvaDjiwmtbK7YyCTRAWqJ533sptHw59xrk8Esg8mpTS758pGtzXR9bDsvuM+Xmz0O1XhcWu4iIFBsvdwcgIiIiUmDbPzejdNtTTfPrrr9CYHThHb/pk2BPAb8waDDC1GDnp/UbZvTsuH9g789Q6+rzn+PYCph9melPXb0/NHuycGIXEZFioZpqERERKV3s6XBgBsy7ChbfYRLqyKvgsoWFm1CDmVKq1Rho9OD5E2owU3Y1fsSUVz5k+nnn5+hSmNXD9MWu3Ak6fwc2/XkmIlKa6Le2iIiIlA4nt8Hqp+C3KJjTF/ZNMeubPw8XTQLvQLeGl6HJYxBQ00yNtenNvLc7vAhm9zSjlFftApf+CT4hxRamiIgUDjX/FhERkZIrNdEMFLbjSzg0L3O9TyWIGgx1b4eKLd0XX268KkCrsbDwJlg/BuoMM0l2VofmmwcDaYkQ1hW6TSs5DwVERMQlSqpFRESkZLHsJoHe+Q3s+dEknmCaRUf0grq3QY0rCj6yd3GofQNs/RAOz4eVj0KXCZmfxc2Fuf0h7RSEXwrdfjOJuIiIlEpKqkVERMT9LAtOrDZTYu36Hs7sz/wssB7UvRWih+Ss8S2pbDYzxdaMdrD7e6h/H4RdBLGzYe7lkH4GIi6Drr+AV4C7oxURkQugpFpERETcJ3En7JpgkumEjZnrvUOh1jUmka560YVPj+UOldqY5unbP4Pl/4OWY+DfQWaU72p9oOuU3KfmEhGRUkVJtYiIiBSe9GRTy3x6v2m2nXYa0k/n/np0MRxZlLmvhy/UGABRN0H1fiW7ebezWr4MeybB8ZWmDzUWVL8cLp5cNq5PRESUVIuIiIiLTu8zA22d3mvKp/dmLklxLh7MZvoVRw0202KVtdGv/cKg+bOwYhRgQc1B0OUHM1WXiIiUCUqqRURExHkp8TCjLSQdynsbTz/wrwnewaa/sGdA7q/+NUwiHVC9+OJ3hwYjIH6jeWDQ8hXn5rsWEZFSQ0m1iIiIOG/Dqyah9oswNcwVIiHgnMW3cunsA11UPLyh4yfujkJERIqIkmoRERFxzqm9sPltU+7wMdS8wq3hiIiIlAQe7g5ARERESok1T5uRq8O6mgHFREREREm1iIiUc/Z0SE10dxQl3/HVsPNrU271upp3i4iInKWkWkREyrcF18OUanBkibsjKdlWPgJYUOt6qNLB3dGIiIiUGEqqRUSk/DpzEPb+bOZTXnyradosOR38C2JnmgG3Wr3i7mhERERKFCXVIiJSfu35CbBMOX4DrH3BreGUSPb0s7XUQP0REFjHvfGIiIiUMEqqRUSk/Nrzo3mN6GVeN46Fo8sK/zypibB0BGz5ACyr8I9flHZ9AyfWgHcINHvS3dGIiIiUOEqqRUSkfDp9AA7PN+WOn0HtG8BKh/9uhfTkwjtPegr8ezVs/QCWjYAld4M9zfXj2NNg++ew4XU48h/YUwsvxryknYHVT5ly0yfN/NMiIiKSjeapFhGR8mnv2abfVTpDhUho+x7EzoL4dbD+ZWhRCE3BLTssvh1i/wJPP7CnwPZPISkWukwErwDnjnN6Pyy4EQ7/m7nOMwCqdoawbmaKq8odzDkK0+a34cx+CKgFDe8v3GOLiIiUES4l1WPGjOHnn39m06ZN+Pv707lzZ1577TUaNmyY5z7jx4/n1ltvzbbO19eXpCQNBiMiIm7kaPpd6zrz6lcF2n8I86+F9a9AzSuhUusLO8eq0bDrW7B5wsU/m4HQFt4E+3+HWT2g2+/mvPk5OBMWDobkw+AVBOHd4fACSDkGsX+bBcDDF6p0hCox4OFjaratVFOjnVFOM++D6kO9u8A/PO/zJh2G9WNMueXLhZ+wi4iIlBEuJdVz585l+PDhtG/fnrS0NJ544gl69erFhg0bqFChQp77BQcHs3nz5oz3Ns1tKSIi7nR6f2bT71pXZ66vdQ1EXgN7J5tm4L2XgKdPwc6x6W3TRxug4+dQva8pX/o3zB0AR/+DmV3gkhkQGJ1zf3s6rHsB1r0IWFCxFVw0CYLqmRrw+PVwaB4cmmuWpENn389zLr71L0OdodBoFATn8nB83YuQdhIqtoGomwrwBYiIiJQPNssq+Igphw8fJiwsjLlz59K1a9dctxk/fjwPPPAAJ06cKOhpSEhIICQkhPj4eIKDgwt8HBEREQA2vwvLR0LVLnDZ/OyfJR2CaU0g+Sg0fw6aP+v68Xf/AAtuMOWWY6Dp49k/j98I//SB03vALwK6/5G9VvxMnKnRjptt3te7C9q8DV7+uZ/PsuDkFpNcH18N2MDDy0yBZTvnFWDfL3DUMS+3DWpeAY0fMd8HQMJW8x1YaXDpLIi41PXvQEREpJRzNg+9oD7V8fHxAFSqVCnf7RITE6lduzZ2u502bdrwyiuv0LRp0ws5tYiISMGd2/Q7K78waPeBSYrXvQQ1B0HFls4fO3Y2LBpiyg3uhyaP5dwmpDH0WgRz+pqRtf/uapqHV7sM4uaY/tNJseBVAdp/DNGD8z+nzWZqm3Orcc5Nk8dNTf3G101T9H2/mqVKDDR+GHZ9ZxLq6v2UUIuIiJxHgWuq7XY7V1xxBSdOnGD+/Pl5brdo0SK2bt1KixYtiI+P54033mDevHmsX7+emjVr5rpPcnIyycmZI68mJCQQGRmpmmoREblwp/fBL5GADQbthYAaObexLDNi974pULE19F6cWcubn+OrYGZX02y61rXQ+Xvw8Mx7+5R4+PdKiPvH1CRH3WT6YFt2CGlqmnuHNC7olTonfiNsegt2fm0GUnOweUDf1RDarGjPLyIiUkI5W1Nd4Cm1hg8fzrp165g4cWK+28XExDBkyBBatWpFt27d+Pnnn6latSoff/xxnvuMGTOGkJCQjCUyMrKgYYqIiKvSk01ta2qCuyMpGnsmm9eqXXJPqMHU/Lb/EHwqwfGVsOG18x83cRf809ck1GHdIObr/BNqAJ8Q6D4dat9oaoZ3fm0S6uihJpEv6oQazDk6fgoDd5tps3wqmvV171BCLSIi4oQC1VSPGDGCX3/9lXnz5hEdncvgKudx7bXX4uXlxffff5/r56qpFhFxo8V3mWmfPP1M0+eoW6BaL9NHtyz4qwscWQht3z3/NFE7v4NFN5ta6s7fn52n2WaSbmymNhebmd968R2mX3Noc+g5D3xCnY/Jspv5oHd9Z/pw172t4Nd3oVITTX/rsIudq50XEREpo5ytqXYpqbYsi/vvv58pU6YwZ84c6tev73Jg6enpNG3alH79+vHWW285tY8GKhMRKSan98Fvdcy0S1n5hZva1OhbTHPo0jqLw6m98GstTNPvfRBQPf/tLQvmDYL9vzl3/IBa0Gth3jXgIiIiUmoUyUBlw4cPZ8KECfz6668EBQURGxsLQEhICP7+ZkTSIUOGUKNGDcaMMXNbvvDCC3Tq1Il69epx4sQJXn/9dXbv3s0dd9xR0GsTEZGisultk1CHdYXWb8DOb2D395AUB5vfNktIE4geArWuh8Cooo0nPaXgU1rlZu/Zpt9hF58/oQbz8KDDx7DwJJyJBSyzWOe8YkFATTOomBJqERGRcsWlpPqjjz4CoHv37tnWf/nllwwbNgyAPXv24OGR2VX7+PHj3HnnncTGxlKxYkXatm3LwoULadKkyYVFLiIihSvlBGw7O95F48egcnuztHkTDsyAXd/Avt8gfgOsetwsQQ2gWm/TPDysO3gHFlIsx2HVaNj+menb2+6D8/dPdsbus6N+R17r/D7+EdBj9oWfW0RERMqkC5qnurio+beIiJMsO6SeNANguWr9GFj9BIQ0g35rcm/inXLCDPS161szJZOVnvmZhzdU6WwS7IheUKnN2T7HrsRvmemulo80teMOUTdDpy8vrF/3qT3wa23ABlfuB/9qBT+WiIiIlHlFPvq3iIiUIOnJsO0zmNoYfqoMe6e4uH8SbH7HlJs8mnefaZ9QqHcH9JwDVx8xcyvXuwcqRJtm44fmwuon4c/28HMYLLkHDi8620z6PBJ3wpx+Zn7opDgz53KLF8HmaZL4hTfn7OvtCseo32FdlVCLiIhIoSkjQ7mKiJRTqQmwdZzp63zmYOb6pfeY5NG3snPH2fm1SWQDIqH2Dc7t4xMKkVeaBeDkdoj9Cw7+BXGzIfmoaU6+7WMIqm/6YUfdnLMftj0VNv0frH0O0s+Ahw80fQKaPA6evma+5gXXw54fwEo1o3AXpJ/1nrNNv2u50PRbRERE5DzU/FtEpDQ6E2tqlrd+mDmftH8NaDQKdnxu+j3XGWaaTJ+PPR2mNYaTW6HN/0GjBy48PnsqHJpnkvW9P0HaqczPwrqfHejsGhPnkrvgxJrMzzqMM7XUWe2fCv9eDfYUqDEALppkEm5nndoNv0Zhmn4fMP2kRURERPJRJFNquYuSahERTBPqhI0mmd7xFdiTzfrgxqbJdu2bTA3u4UUwswtgwSV/QbXL8j/unp9g/jXgUxEG7im8wcYcUhNh788mwY6bbeIC8PQ3zc6xTI166zdNsp1X0/ODf8G8gWafan1M03Mvf+di2PgmrHzYJO09/ymEixIREZGyrkim1BIRkWJiT4X4jXB8JRxflfmaGp+5TZUYaPKYqbnNOiBY1RhoMAK2vGdqgfuvA68KuZ/HsmDDa6Zcf3jhJ9RgjllniFlO7YFd38HOryBhs/k8eqiZvsuvSv7HqdYLuk2DuQPg4Azz2u3XvK8tK0fT79rXXdi1iIiIiJxDNdUiIiXFiXWmFvr4SlN21ERn5eFtRtZu8hhUvSjvWt3UkzCtKZzea5qEt3kz9+3i5sKs7uDhC4P2gF9YoV1OviwLTqw25w1p7Nq+h/41A5qlJZp+492mgndQ3tsn7oLfos2Dh0EHwD/8gkIXERGR8kE11SIipcmZgzDrEkg+krnOOxgqtoLQVlCpNVRsbZp6OzNIl3cQdPjYJJ+b3zaDj1Vun3M7Ry11nVuLL6EG8zCgYquC7Rt2sWnWPqeP6bc982KIGgzh3c13dO60W3sdo353U0ItIiIihU5JtYhIYUjYbGqXI68swNzMdlg0xCTUoc2h2bMm4QyMdv1YWVXva5LNXd/B4tuh97LsCfnxNXBwujlH44cKfh53qBoDl86C2ZeZGu9Vq816ryBTgx/e3STRldrCbseo32r6LSIiIoVPSbWIyIU6vQ/+7ASpJ8w0UK3GuLb/xjcg9m/wDIAuP0JIo8KLrc3/mf7HJ9bCxteh2ZNZzvu6eY28GoLqFd45i0vldtBvjamJjptjaq1TT5gHBQenm228Ak0zcZsHRF7lzmhFRESkjLqAKhARETG1zMNMMgew4VUzb7Szji6F1WcT3bbvFG5CDeBXFdq8Y8rrXoD4TaZ8ajfs/t6UGz9auOcsThUiodGDZsCyq49A35XmQULNgWY087REs114z+Jt3i4iIiLlhpJqEZELsfldiJtlapnr3W3WLRtu5lU+n9STsOBGsNKg1rVQ9/aiiTHqJqjW18zxvORO8yBg0/+BlQ7hl5oa37LAw9M0m2/0AHT95WySvQo6jXduvm4RERGRAlBSLSJl35mDkHLcjDhdmE6sh1WPm3KbN6H9R1DnNpO0zr8eji7Lf/+lwyFxOwTUgg6f5D2S94Wy2aDDR2bqqcPzYf0rsO1T81mTx4rmnCWBzQMqtoQ6QyGgurujERERkTJKfapFpGzb/iUsvs2UPf3Arxr4Z12qm9dKbc0gYc5KT4FFN5tpr6r3M7XUNht0GGf6WMf+BXP7Q6//zIBj59r5Hez6xiR+XSaAT2ihXG6eKtSGlmNg+f9gzdNmXWhLiLisaM8rIiIiUsYpqRaRsiv5KKx8OPN9ehKc2mmWHGzQ8mUz0JgzNcZrn4Hjq8C3CnT8PHMfD2+4eBLM7GpGpZ7TDy5bAL6VMvdN3AFL7zXlZs9A1S4FvULX1L/P9KM+ssi8b/Jo0dWOi4iIiJQTSqpFpOxa/SSkHIPQFnDZfEg+bJqCZ1sOmCT38L+w+gkzLVbHz8DLP+/jHvoXNow15Q6fgH9E9s+9g6H7NPirEyRsgn+vNPMqe/qCPdX0o047aaZ+avpkzuMXFQ9Pc21/doQKtTTFlIiIiEghUFItImXTseWw7RNTbvc+eAeZJbBO7ttv/QiW3Q+7J8DJrWagq9z64aYmwKJbAAvq3Grmpc5NQA3o/gfMvMhM9bRoqGnmveZZOLoEvEOh83fgUcy/hkOawBU7TFP44j63iIiISBmkgcpEpOyx7LB0BGBB1GAIu/j8+9S/Fy6dCT6V4NhS+LN97gONLfufmY6qQrSZAis/oc3h4immSfieH+Dfq8yUWwAdPzW1xe7gV9U8YBARERGRC6akWkTKnp1fw9H/wCsQWo11fr/wS6D3EghubJqF/30x7P4h8/M9P8HOr8zgYjFfO5eYRlxq+lwD7PsVsKDunVDrGpcuSURERERKJiXVIlK2pJyAVWeniWr+rOtTKQXVhV6LzIje6Umw4AZY/TSc3g9L7jLbNH4Mwi5y/pjRt0CLF005uDG0/T/XYhIRERGREstmWYU9cWvhS0hIICQkhPj4eIKDg90djoiUZMtGwpZ3TfLab7Vpel0Q9nRY/ThsfMO89w6F1BNQsbWZJsvTx7XjWRYcWwbBjdT0WkRERKQUcDYPVU21iJQdx9fA1vdNud27BU+owYyU3fp16DQePHxMQu3pB52/dT2hBjN1VeX2SqhFREREyhgN/SoiZYNlwfL7zSBlkddARM/COW6doRBUH9Y+D/XuMKNni4iIiIicpaRaRMqG3RPN1FWe/tDmzcI9dtXOcOmfhXtMERERESkT1PxbREq/1JOw8mFTbvqk+6aqEhEREZFyR0m1iJR+614yU2AF1oXGD7k7GhEREREpR1xKqseMGUP79u0JCgoiLCyMQYMGsXnz5vPuN2nSJBo1aoSfnx/Nmzfnjz/+KHDAIiLZxG+CzWenqGr7jhlMTERERESkmLiUVM+dO5fhw4fz33//MXPmTFJTU+nVqxenTp3Kc5+FCxdy4403cvvtt7Ny5UoGDRrEoEGDWLdu3QUHLyLlmGVB3FxYNATsqVD9cqjR391RiYiIiEg5c0HzVB8+fJiwsDDmzp1L165dc93m+uuv59SpU0ydOjVjXadOnWjVqhXjxo1z6jyap1qkEJ3eD/Ovg4jLoMVz7o7GdUlHYOdXsO0TOLnFrPP0h35rIaiue2MTERERkTKjWOapjo+PB6BSpUp5brNo0SJ69sw+tU3v3r1ZtGjRhZxaRArCsmDxnXBkIax/CU7tcXdEzrEsiPsHFtwIv9Qwg5Kd3AJegVDvLui9VAm1iIiIiLhFgafUstvtPPDAA3Tp0oVmzZrluV1sbCzh4eHZ1oWHhxMbG5vnPsnJySQnJ2e8T0hIKGiYIpLVji/h4HRTttJhy/vQeqx7Y8pPaoKpkd72CZzcmrm+UjuTTNe+AbyD3BefiIiIiJR7Ba6pHj58OOvWrWPixImFGQ9gBkQLCQnJWCIjIwv9HCLlzqk9sOJBU67W17xu+wRSEwv/XJYFW8fBzK6w7VOwp7m+/85v4PeGsPIRk1B7BUG9e6DPcuizFOrdqYRaRERERNyuQEn1iBEjmDp1Kv/88w81a9bMd9uIiAji4uKyrYuLiyMiIiLPfUaPHk18fHzGsnfv3oKEKSIOlgWL7zA1v1VioNuvEFQfUuNhx/jCPdeZOJg7AJbeC4f/hSV3wbQmsPtHsOzn3//YSph5kRmALCnWxNnhU7jyAHT4CCq1Kdx4RUREREQugEtJtWVZjBgxgilTpjB79myio6PPu09MTAyzZs3Ktm7mzJnExMTkuY+vry/BwcHZFhG5ANs/hdiZZrqpTuPBwxsajjSfbX7HuWTXGfunwR/N4cA08PA1Ncu+VUxN84LrYUY7ODDDJPnnSj4KS+6FGW1Nn2+vCtDqVTMAWb07wDuwcGIUERERESlELiXVw4cP59tvv2XChAkEBQURGxtLbGwsZ86cydhmyJAhjB49OuP9yJEjmTFjBm+++SabNm3iueeeY9myZYwYMaLwrkKkPDl9ANJTnN/+1G5Y8ZApt3wFghuYcp1h4FMRErfB/ql57u6UtNOwdDjMvRySD0NoC+izzNQsX7EDmj9vmm8fXwlz+sKs7nB4odnXnm6aiv/eALaNAyyofSNcvhmaPAaevhcWm4iIiIhIEXJpSi2bzZbr+i+//JJhw4YB0L17d6Kiohg/fnzG55MmTeKpp55i165d1K9fn7Fjx9KvXz+ng9SUWiJnxc6Cf/pAhdrQ+Tuo0jH/7S07zL4M4mZD1Yugxxzw8Mz8fNXjsOE1COsOPf8pWEzHVsLCmyBhk3nfaBS0fNnUimeVdAQ2vGoGR7OfHYiwen84c8Ak2wChzaHtexDerWCxiIiIiIgUEmfz0Auap7q4KKkWwTSZ/qsTHF1i3ts8odmz0HQ0eOQxkP/Wj2DpfWfncV4DQfWyf356H/waDVYa9FkBlVq7EI8dNr4Ba54Ceyr4V4NOX0G1y/Lf7/Q+WPsC7PjCjEAO4B0KLV6A+vfmfS0iIiIiIsWoWOapFpFidPAvk1B7+kPkNSYhXfsM/N0NEnfk3D5xpxk5G6DVazkTaoCAmlDrWlPe9H/Ox5KaCLN7warHTEJd80rT9/l8CbXjnB0/gf4boM6t0GAEDNgCDe9XQi0iIiIipY6SapHSwLJg3fOmXP9euOhHiPkGvIPNoF5/tIIdX2UOAGbZ4b/bIO0UhHWDBsPzPnajs9Ns7ZkIZw46EYsdFt0CcbPMYGIdP4OLfwLfyq5dU3AD6PQFtHsP/Kq6tq+IiIiISAmhpFqkNIibBUcWmX7KjR8Gmw2ib4a+q01f6bST8N8wM8J28jHY8iEcmmOS3k5fgC2ff+qV20PVLqbGecsH549l9ZOw7xfw8IFL/oK6t5t4RERERETKISXVIiWdZcHas7XUde8yfZcdAqPM4GMtXwabF+yZBH+0MM2yAVqNhcA65z9Hw7O11dvGQdqZvLfb8bUZbAyg4+dQtbOLFyMiIiIiUrYoqRYp6Q7NgcPzzbzPTR7L+bmHJzR9AnothKAGcGY/pJ+G8Euh/j3OnaPmIKgQZeaK3vVNHnHMhyV3mnLTJ0xNuYiIiIhIOaekWqSky6ilvgMCque9XeX20HcFNPgfhHU9f7PvrDw8oeFIU970dmbfbIfEXfDvlWBPgciroMWLrl6FiIiIiEiZpKRapCSLmwuH5pr+y00fP//2XhWg3TvQc66Zy9oVdW8DryBI2AgH/8xcn5oAcwdA8hGo2BpivnY+WRcRERERKeP0l7FISbbubI1wndvMVFRFyTvY1IZD5vRa9nRYcBPErzN9ubv9ZhJ3EREREREBlFSLlFyHF5hRvz28naulLgwN/2dqoWP/ghPrYNWjcGCaGXW8669Fn9iLiIiIiJQySqpFSqq1L5jX6GGuN+UuqMAoqHmlKc+/Fja9ZcqdvjJ9tkVEREREJBsl1SIl0ZH/TG2xzQuaji7eczc6O71Wwibz2vx5qH1d8cYgIiIiIlJKKKkWKYkyaqmHQGB08Z67Smeo3MGUa98AzZ4u3vOLiIiIiJQiXu4OQETOcWQJHJwOtrPzTxc3mw26TITYmSapt9mKPwYRERERkVJCSbVISeMY8TvqZgiq654YAqOh3l3uObeIiIiISCmipFqkuMT9A+tfMWX/GuBf3SwBWcpnDsCBqWYEbnfUUouIiIiIiEuUVIsUtfRkWPMUbHwTsJzbp/aNENygSMMSEREREZELp6S6sKUnmTl9RcDM9bxwMJxYY97XvQOqXgxn9pta6TMH4LSjfBCsNPCqoMHBRERERERKCSXVheX0PvirC6Qch2vjNbhTeWfZYfN7sOoxsCeDbxXo+DnUvCL/fZIOg6cP+FQsvlhFRERERKTAlFQXFr9wU9topZkEu0KkuyMSdzl9AP4bZkbPBqjezyTU/hH572fzAP/wIg9PREREREQKj+apLiwe3pl9YOM3uDcWcZ89P8EfzU1C7ekP7T+EblPPn1CLiIiIiEippKS6MAU3Ma8JSqrLHXs6LLkX5l8DKcegYhvoswLq36uuACIiIiIiZZiS6sIUcjapVk11+WLZYendsG0cYDNTYfVaBCGN3B2ZiIiIiIgUMfWpLkxKqssfy4JlI2D756ZPdOfvofZ17o5KRERERESKiWqqC1NwY/Mav8EkW1K2WRasGAVbPwJs0OkrJdQiIiIiIuWMkurCFNzA1FamnoCkOHdHI0XJsmD1aNj8tnnf8VOIvtmtIYmIiIiISPFzOameN28eAwYMoHr16thsNn755Zd8t58zZw42my3HEhsbW9CYSy5PPwisa8pqAl62rX0eNrxmyu0+gLq3uzceERERERFxC5eT6lOnTtGyZUs++OADl/bbvHkzBw8ezFjCwsJcPXXpoH7VZd/6MbDueVNu83/Q4D73xiMiIiIiIm7j8kBlffv2pW/fvi6fKCwsjNDQUJf3K3WCmwC/alqtsmrT/8HqJ0y51avQ6AG3hiMiIiIiIu5VbH2qW7VqRbVq1bjssstYsGBBvtsmJyeTkJCQbSk1VFNdOlkW2FOzLGlZlnSzbPnADEwG0Pw5aPKYW0MWERERERH3K/IptapVq8a4ceNo164dycnJfPbZZ3Tv3p3FixfTpk2bXPcZM2YMzz//fFGHVjRCsowALiXfyW2wYzzs/BpO73VunyajodkzRRqWiIiIiIiUDjbLKvjcTzabjSlTpjBo0CCX9uvWrRu1atXim2++yfXz5ORkkpOTM94nJCQQGRlJfHw8wcHBBQ23eKSdgh8DTfmqw+BXxb3xSE6pibB3Mmz/Ag7/6/x+Nk9o/Ai0fAVstqKLT0RERERE3C4hIYGQkJDz5qFFXlOdmw4dOjB//vw8P/f19cXX17cYIypEXhWgQhSc2gUJG8HvYndHJGCadx9eADu+gD0/mocfYKZAi+gNdW+F8EtN4oyVfT8HDx/wDizWsEVEREREpGRzS1K9atUqqlWr5o5TF4+QJiapjt8AYUqq3e7QfFh8O5zckrkusB7UvQ2ih0BADffFJiIiIiIipZrLSXViYiLbtm3LeL9z505WrVpFpUqVqFWrFqNHj2b//v18/fXXALz99ttER0fTtGlTkpKS+Oyzz5g9ezZ//fVX4V1FSRPSBA78oX7VJUHCFph3BaQcN60Ial0HdW6Dql3UhFtERERERC6Yy0n1smXLuOSSSzLejxplRkMeOnQo48eP5+DBg+zZsyfj85SUFB566CH2799PQEAALVq04O+//852jDIn+OwI4JpWy71SjsPcAea1cie49C/wDnJ3VCIiIiIiUoZc0EBlxcXZDuIlxpH/4K8Y8K8OV+53dzTlkz0V/ukLcbMgIBJ6LwX/cHdHJSIiIiIipYSzeWixzVNdrgSfnVbrzAFIiXdvLOXV8gdMQu1VAbr9roRaRERERESKhJLqouATAv5nB79K2OjeWMqjLR/A1g8BG3SeABVbujsiEREREREpo5RUF5WQs/2qNVhZ8Tr4FywfacqtXoWaV7g3HhERERERKdOUVBcVJdUXLj0Z1r0Msy6FNc/Bqd35bx+/EeZfB1Y6RA+Fxo8UR5QiIiIiIlKOuWWe6nLB0a9aSXXBHJwJy4bDya3mfdw/sO4FiOhppsSKHASefpnbJx81I32nxpvpsjp8rCmzRERERESkyCmpLiQnTsCECbB9O7z5Jpk11ZpWyzWnD8CKUbDnB/PeLwIa3g+xsyBuNsTONItPRYi6Gerebh5g/Hs1JG6HClFw8RTw9HXrZYiIiIiISPmgKbUKyYEDUKOGqRw9dAiqBB2Fn6qYD69LNKNQS97sabDlfVjzDKSdBJsH1B8BLV4wA78BJO6AHeNhx5dwel/mvv7V4MxB8AqEXosgtJlbLkFERERERMoOTalVzKpXhxYtwLJg5kzAtzL4hZkPEza5NbYS7/BCmNEWVjxoEurKHaH3Mmj3TmZCDRBYxyTZV+yC7tMh8hrw8DYJNTboMlEJtYiIiIiIFCsl1YWoTx/zOmPG2RXBGqwsX+nJsORumNkFTqwxTbo7fAK9FkKl1nnv5+EJ1fvAxZNg0AFo/xF0nwY1+hdf7CIiIiIiIiipLlSOpPrPP8FuRyOA5yftDMwbBNs+Me/r3AaXb4Z6d5qm387yqwL174HqfYskTBERERERkfxooLJC1KULBAZCXBysXg2tNQJ47tJOw7yBEPs3eAbAxT9D9d7ujkpERERERMRlqqkuRD4+0KOHKc+YgWqqc5OaCHP6m4TaKxAuma6EWkRERERESi0l1YUsW79qR1J9agekJ7ktphIjNQHm9IFDc8A7GC75E8K6ujsqERERERGRAlNSXch6n610XbAA4pPDzeBblh0Strg3MHdLOQGze8HhBeAdCpfMhKqd3R2ViIiIiIjIBVFSXciio6FhQ0hPh1mzbWoCDpB8DGb3hKOLwacS9JgFVTq4OyoREREREZELpqS6CGRrAu6YViuhnCbVSYdh1qVwbDn4VoUe/0ClNu6OSkREREREpFAoqS4CWZNqqzyPAH4mDmZdAidWg1849JwDFVu4OyoREREREZFCo6S6CHTrBn5+sHcv7Ikvp82/HU2+49eDf3XoOTezKbyIiIiIiEgZoaS6CPj7m8Qa4M//ziaSJ7eCPdV9Qbnq+BrY9knBYk5NhDn9IH5dZkId3LDwYxQREREREXEzJdVFxNEEfPIfNc18zFYanNzm3qCcZdlh3iBYcjf809eM3O2s9GSzr2NQskv+gqB6RRSoiIiIiIiIeympLiKOpHruXBvpgaWsCXjs33BqpynHzYKZXeDU7vPvZ0+DBTeafbwCoft0CG1atLGKiIiIiIi4kZLqItKwIdSuDSkpcPB0KUuqt31qXqv3N8234zfAnx3h6NK897HssORO2DcFPHyg66+aNktERERERMo8JdVFxGbLrK1evvXsCOClYVqtpEOw/1dTbvkK9F4MoS0gKQ7+7gZ7f8m5j2XBiodhx3iweUKXHyDi0uKMWkRERERExC2UVBchR1I9bUEpqqne8ZUZnKxyBzP9VUBNuGw+VOsD6Wfg36tg09smkXZY/zJs/j9T7vg5RA5yR+QiIiIiIiLFzuWket68eQwYMIDq1atjs9n45ZdfzrvPnDlzaNOmDb6+vtSrV4/x48cXINTS59JLwcsL/l5yNqlO2Az2dPcGlR/Lgu2fmXLdOzPXewdBt9+h3j2ABSsehOX/M32oN78Pa54227V5G+oMLe6oRURERERE3MblpPrUqVO0bNmSDz74wKntd+7cSf/+/bnkkktYtWoVDzzwAHfccQd//vmny8GWNsHB0KUL7D5Sm1TLH+zJmQOAlUSH5sHJLWaQsdo3ZP/MwwvafwitXzfvt7wPf8XA8vvN+2bPQKORxRuviIiIiIiIm3m5ukPfvn3p27ev09uPGzeO6Oho3nzzTQAaN27M/Pnz+b//+z969+7t6ulLnT59YO5cT/Ycb0TdSitNE/CSOsXU9rMDlNW+EbwDc35us0Hjh6FCFCy6BY4tM+sb3A/NnyuuKEVEREREREqMIu9TvWjRInr27JltXe/evVm0aFFRn7pEcPSrXrqlhPerTj4Geyabcr0789+21jXQ4x8zgFmD/0Hbt03CLSIiIiIiUs64XFPtqtjYWMLDw7OtCw8PJyEhgTNnzuDv759jn+TkZJKTkzPeJyQkFHWYRaZlS4iIgLV7GnNDJ0puUr3rW9M8PbQlVGp3/u2rdIJ+q4s+LhERERERkRKsRI7+PWbMGEJCQjKWyMhId4dUYDYb9O4NG/Y7BisrgUm1ZWXOTV3vTtU6i4iIiIiIOKnIk+qIiAji4uKyrYuLiyM4ODjXWmqA0aNHEx8fn7Hs3bu3qMMsUn36ZE2qN4Fld29A5zryH8SvA09/iBrs7mhERERERERKjSJv/h0TE8Mff/yRbd3MmTOJiYnJcx9fX198fX2LOrRic9llMORwXVLSvPHhFJzeCxVquzusTI4BympdCz6hbg1FRERERESkNHG5pjoxMZFVq1axatUqwEyZtWrVKvbs2QOYWuYhQ4ZkbH/PPfewY8cOHn30UTZt2sSHH37Ijz/+yIMPPlg4V1AKVK4Mbdt5sflgQ7OiJPWrTk2A3T+Yct3zDFAmIiIiIiIi2bicVC9btozWrVvTunVrAEaNGkXr1q155plnADh48GBGgg0QHR3NtGnTmDlzJi1btuTNN9/ks88+KxfTaWWVrQn4iXXuDSarXRMg/TQEN4aqXdwdjYiIiIiISKlisyzLcncQ55OQkEBISAjx8fEEBwe7O5wCWbwYfnppLGNvfAzLpyK2nnMhtLm7w4LpbeH4CmjzFjQqP60HRERERERE8uNsHloiR/8ui9q1g4nL7mPhlhhsKcdh9mWQsNW9QR1bYRJqDx+IusW9sYiIiIiIiJRCSqqLiacndOkeSP/Xp7HvVEtIioPZPeGUG0c2d0yjFXkV+FVxXxwiIiIiIiKllJLqYjRsGJw4XZH2j/7JGe8GcHqPSayTDhV/MGmnYNd3pqwBykRERERERApESXUx6t0bbroJYk+Ec8Xbf2MF1IKTW2B2L0g5XrzB7P4R0k5CYF0I71685xYRERERESkjlFQXs3fegapV4e+Fkby/4W/wC4cTq2FOf0hNLL5AHHNT170DbLoNRERERERECkLZVDGrUgXee8+UH3quPlsjZ4JPRTiyCOYNgvSkoju5ZYfDC2D5A+Z8Ni+oM6zoziciIiIiIlLGKal2g+uugyuugNRUGDy8OWkXTwevChA3CxbcAPbUwjuZPQ1iZ8PS4fBLTZh5EWx+x3xWZyj4RxTeuURERERERMoZzVPtJgcOQJMmEB8Pr78OD9/yD/zTF+zJUPViCKoHNs+zi0eWsqepYfYKAK8g8A48+xoEXoFnX4MgcQfs/Qn2/QLJRzJP7B0CNQZA5NVQ43Lw8HLbdyAiIiIiIlJSOZuHKql2o88/hzvuAD8/WLsW6vlPhXlXgpVWuCfyrQw1B5lEOrwHePoU7vFFRERERETKGCXVpYBlQa9e8Pff0K0bzJ4NHidWQOwssNKzLPZz3qdB2mkzenfqybOviVneJ5pa65pXmEQ6rJtqpEVERERERFygpLqU2LkTmjWD06fho4/gnnvcHZGIiIiIiIg4m4dqoDI3i46GV14x5Ucfhb173RuPiIiIiIiIOE9JdQkwYgTExMDJk3D33aZZuIiIiIiIiJR8SqpLAE9PM2iZjw9Mnw7ffefuiERERERERMQZSqpLiMaN4dlnTfl//4Nt29wbj4iIiIiIiJyfkuoS5JFHoH17OH4c+vSBQ4fcHZGIiIiIiIjkR0l1CeLtDb/9BlFRsH079O8PiYnujkpERERERETyoqS6hImIgBkzoHJlWLYMrrsOUlPdHZWIiIiIiIjkRkl1CdSwIUydCv7+ZuAyjQguIiIiIiJSMimpLqE6dYIffgAPD/jyy8xBzERERERERKTkUFJdgg0YAB99ZMovvggff+zeeERERERERCQ7JdUl3F13wTPPmPJ995mBzERERERERKRkUFJdCjz3HNx2G9jtcMMNsGiRuyMSERERERERUFJdKthsMG4c9OsHZ86YZuGbNrk7KhEREREREVFSXUp4e8OPP0L79nD0KHTrBitXujsqERERERGR8q1ASfUHH3xAVFQUfn5+dOzYkSVLluS57fjx47HZbNkWPz+/AgdcnlWoANOmQatWcOgQdO8O8+a5OyoREREREZHyy+Wk+ocffmDUqFE8++yzrFixgpYtW9K7d28OHTqU5z7BwcEcPHgwY9m9e/cFBV2eVa0Kc+bAxRdDQgL07g2//+7uqERERERERMonl5Pqt956izvvvJNbb72VJk2aMG7cOAICAvjiiy/y3MdmsxEREZGxhIeHX1DQ5V1ICPz5p+lbnZQEV14J33zj7qhERERERETKH5eS6pSUFJYvX07Pnj0zD+DhQc+ePVmUz5DUiYmJ1K5dm8jISAYOHMj69evzPU9ycjIJCQnZFsnO3x9++gluuQXS02HIEHjnHXdHJSIiIiIiUr64lFQfOXKE9PT0HDXN4eHhxMbG5rpPw4YN+eKLL/j111/59ttvsdvtdO7cmX379uV5njFjxhASEpKxREZGuhJmueHtDePHwwMPmPcPPGDmtLYsNwYlIiIiIiJSjhT56N8xMTEMGTKEVq1a0a1bN37++WeqVq3Kxx9/nOc+o0ePJj4+PmPZu3dvUYdZanl4wFtvwUsvmfcvvgjDh5vaaxERERERESlaXq5sXKVKFTw9PYmLi8u2Pi4ujoiICKeO4e3tTevWrdm2bVue2/j6+uLr6+tKaOWazQZPPgmVK8N998FHH8GxY6YWWwOti4iIiIiIFB2Xaqp9fHxo27Yts2bNylhnt9uZNWsWMTExTh0jPT2dtWvXUq1aNdcilfO65x6YONE0C//hBzOX9YED7o5KRERERESk7HK5+feoUaP49NNP+eqrr9i4cSP33nsvp06d4tZbbwVgyJAhjB49OmP7F154gb/++osdO3awYsUKbr75Znbv3s0dd9xReFchGa67DmbMgIoVYckSaNcO/vvP3VGJiIiIiIiUTS41/wa4/vrrOXz4MM888wyxsbG0atWKGTNmZAxetmfPHjw8MnP148ePc+eddxIbG0vFihVp27YtCxcupEmTJoV3FZLNpZfC0qUwcCCsX29qrD/+GIYNc3dkIiIiIiIiZYvNskr+WNEJCQmEhIQQHx9PcHCwu8MpNU6ehKFDYcoU837kSHjjDfBy+VGKiIiIiIhI+eJsHlrko3+L+wQFweTJ8Nxz5v0770CfPnD0qFvDEhERERERKTOUVJdxHh7w7LPw889QoQLMmgUdOsC6de6OTEREREREpPRTUl1OXHklLFoE0dGwYwd06gSTJrk7KhERERERkdJNSXU50ry5GcDs0kvh1CkzUvgdd0BiorsjExERERERKZ2UVJczlSvDn3/C6NFgs8Hnn0ObNrB8ubsjExERERERKX2UVJdDXl7wyiswezbUqAFbt0JMDIwdC3a7u6MTEREREREpPZRUl2Pdu8OaNXDVVZCaCo89Br16wf797o5MRERERESkdChV81QfOHxA81QXAcuCr76CRx6FM6ehUiX48EO4/HJ3RyYiIiIiIuIeCQkJVK9a/bzzVJeqpJrHAT93RyMiIiIiIiJlXhLwKudNqtX8W0RERERERKSASlVNtZp/F585c+Hxx2HdWvO+YkUYNQruvhsCAtwbm4iIiIiISFErk82/z3cxUrjsdpg8GZ55BjZvNusiIuDJJ+HOO8HX173xiYiIiIiIFBVn81A1/5Y8eXjAddfBunXw5ZcQFQWxsXD//dCgAXzxBaSluTtKERERERER91FSLefl5QXDhpna6g8/hGrVYM8euP12iI6G55+HffvcHaWIiIiIiEjxU1ItTvPxgXvvhe3b4Y03oGpVk0w/9xzUrg1XXAFTp0J6ursjFRERERERKR5KqsVl/v7w0EOmtnrCBOjWzfS//v13GDBAtdciIiIiIlJ+aKAyKRSbN8Mnn8D48XDsmFnn4QF9+kCPHhATA23aaHAzEREREREpHZzNQ5VUS6FKSoKff4aPP4Z587J/5uMD7dpB584myY6JMf2zRUREREREShol1eJ2GzfCb7/BokWwcCEcPpxzm6goaNsWmjWDpk3NUr8+eHsXe7giIiIiIiIZlFRLiWJZZoCzhQvNsmgRrF1r1p/L2xsaNsxMsps0gchIqF4dwsOVcIuIiIiISNFTUi0lXkICLF4Ma9bA+vVm2bABEhPz3sdmg7Awk2DXqGFeq1c3I5GHhkLFiuY1a9nPr3iuR0REREREyg4l1VIq2e1mVHFHkr1+vRkE7cABOHgQ0tJcP6avLwQHm4Tcssw5LCtnOSAAIiJMbXhERPYlPNwslSubZF0DromIiIiIlG1KqqXMsdtNv+wDB7Iv+/fD0aNw/DicOJF9Kaq7OyAAKlUyCXalSpnloCDTPN2x+PjkfJ918fXNWfb2Bk9PM3p6bq+enhAYaBaPIpoU7/RpOHXKXJenZ9GcQ0RERESkJHM2D/UqxphELoiHR2aNcevW59/eboeTJ01ynZBg1tlsmYuHR/b3p05BbGzmEheX870jUT992izunIvbw8PUwIeGQkhI9tegIJOk55bIOxL3hATzkOLQocxXR/nUqcxzVKmSvcY+66vNZr7jhITcX5OSzMOGKlXMUrlyzrK3NyQnZy5JSdnLqanmWvz88l58fDIfOHh6gpdX5oMIm83179ayzNRwBw9mLocPm24GdeqYJSKi6B5qiIiIiEjpoaRayiwPD5NkhoQU3jHT003CeOyYqRk/9zUx0SSBKSnm9dzFsT452ZRTUnKWU1PNA4H09MzXrOW0NFO22zNr5IuK3Z6ZbK9ZU3TnKUqORNvf37QwcCznvk9NzUygY2PN+/z4+UF0dGaSXaeOqdk/cSL3++PYMfPZ+boweHmZByNZW0Jkfa1Y0cR2bquMEycyW2skJZn7/tz9sh7Lz8/8G3EsjgdNjsVuNzEfPWqWI0cyy47FZstsqVG5cvbXSpVMDMnJ5t/FqVPZXx1luz3zZ1ChQvafiePnZLfn/u/JsXh4ZLbeCAw0D5Vye+9stw3HvbBvn1n27jWtYnx9c38wVKWKuVbHA5z0dDhzxlyf4wGcYzlzxvx8HK9Zy2fOmGutWtU8tKlWLfOBlqMLS17S0syDrJMnzXksK/OBIWR/gGizmWt0tEhxxJa1nJxs4oiMhJo1zRIU5Nz3V1CWZeI693uB7A/Nzm25A5kP4xz7Zl2Sk839WKuWGYvDx8e5eNLSzM9/507Yvdv8/GvXNrNWlLSHapaV2XVq+3bzeyQw0PybqlAhs5z1NTCwYA8dnWW3m/8v4+PN76XcXtPTze/RunXNEh5e+DElJpr7IOvD9KwP1T08zPfl7H1R2jl+Jo57QIO/lh+OCoPjx814RAEB7o6obClQUv3BBx/w+uuvExsbS8uWLXnvvffo0KFDnttPmjSJp59+ml27dlG/fn1ee+01+vXrV+CgRdzF0zMzQXEXyzJ/IOT1h8qJE+YPa0cSn9cSFGT+aA4LM8u55QoVTCKVW829owzmOMHBub/6+pp4jhzJTMocZceSlpZZE5311VH29jbxnvuHdtb3+TXzdzyUSEkx35ErKlfOTG6qVjUPF3bsMH+8JiWZaeM2bizQjzFfcXGFf0wx91JQUPbFkXRblulKsm+fubftdteO7eiW4UjiCpufX2aC7emZmUA7lqI457lCQkxy7Ui0Q0JyPjDM76FiXg8as/67dvV7d5XNZv49R0aaJNuxhISYf9c7d8KuXeZ1717zuyM3Pj7mGLVrZy7VqplrOvfByZkzmeWUlMzfSWlpmWXHezC/dxytsrIuYWHmNTEx+7gjzgzymZusD55zW8LCsj/YcbRSypp82u3m38zWrbBli3l1lHfuPP/DyXNVqJCZYDuWkJCcD1WyLmlp5t+s44Fo1hZGsbGZLa/OJzDQ/J6vUsW8nlsOCMgcg8WxQPYxWnJ7eJj1IWJaWu4tx7K+P/f6HC2vHIuPT+YDSMdDk6zv/fzM/yG7d2cue/Zkls/9f9DHJ/tDSMeStXta1hgdZQ+PzMoAx3Lu+6zj1uT2vWWV9QFg1tegoMx73/E3StZyxYrmQaDjd6GjpVzWJT4++99K5/7ddPJk5sOVrN+5l1dmuUKF7H8rnftapYq53rxa3iUnm98DCQnZF0fMjrLj55vXQ7HAQPM3VkhI5mvWcoUK5m+VbdvMsn17ZnnbtuwVMVWrmt9/tWtnf61VK/PfVV4tOE+eNIl51t+BWZewMPOdnq8S4OKLISbGuX+jJZ3Lfap/+OEHhgwZwrhx4+jYsSNvv/02kyZNYvPmzYSFheXYfuHChXTt2pUxY8Zw+eWXM2HCBF577TVWrFhBs2bNnDqn+lSLSF4sK/sfpucuaWlmSUrKrCk8t+bw9Gnzy79atcw/IsPD867ZTE01f3Dv2GH+w9qxwywnTuRdw1ypkqmBPl9tSGqq+c8ma033ubXe3t7ZR7rPulSsaP6oio/P/xjJyZl/CDqWrO9tNnOsypUzF0ftrGMB86DEUaOd9fXYMRODn1/utWSOV5st588ka63pmTOZf9xkHZ/A2ztznd2e8w/YxETzn76jlspV3t6mVtNRS1ujhvnZnPtw6OjR/JOZ3FpHOLot+PvnLNtspquBIyGIjc3svuIMHx9zDscfpLn98Q/mu8vaMiDrH+UBAeb64+LMfb5vn+sPpAqD48EaZP837Wi1k/WvF0/P/LuHHDliEgtXHz74+Jia6agocx/t3m2+j7ySbXfy9oYGDcyUlJZl/g1lTfKyli/k4UWlSpk19du2nf/fl59fzi5KjjKY353btpl7ragfqojh7e36Aw8pOwICzP+vRcnX1/xePt95XnwRnnqqaGO5UEU2UFnHjh1p374977//PgB2u53IyEjuv/9+Hn/88RzbX3/99Zw6dYqpU6dmrOvUqROtWrVi3LhxhXoxIiIi50pLy0yyc1sSE80f844EumbNzKfszkhKykyus3YrcDSxv1CnT2evIbCs7LXsWctF1YT15MnM5vCOJvGJiXkPypjXktt2/v45HzA4asLy43igBuYhwflYlnlgsXevSbCzLidOmNqZqCjTHDk62pSrVcsZR1qa6Q6wa1f22sC4OBN31us599VRE5lbjZinp4nxyBFzrNyWhASzbYMG0LQpNGliXps2hfr1nWvKa1nmYZWjxu7cGjzH+0OHstcAx8bm3n3Fy8vUKNevb5YGDTLLYWHOT2uZkmK+U0ftmmM5fTr/B6eenpnjfDgejDoejjpes9YyZ32A6HhNTTX/hg8fNsuRI5llx+J4IHNuVwrHOg+P7LWJ59YuBgaaWPNqReboGpbbg+Gs75OTsz94dDwwcZTPnDEPQM+tOcxaExkYaM7neNDi+D2Y9YFk1rjOLaemmlgcg6w6arXPLXt65t8NJevDv7xeExLMvRgXl9kdLWs5JcVse24rpKyLY/yZ3MagCQnJ7NrieAif9YG8o3zyZN7j0Bw6ZO4fT8/M7yC3xd8/s1Y5a8s+x+L4uZzbZSpr2VGzHR+f89Xx79PDw/yc69UzS926meU6dUwc8fE5WzJkffXxydlKJev7wEDTuivr70DHsn9/zpYIQUG5VwZceSUMGuTc7wh3KZKkOiUlhYCAACZPnsygLN/A0KFDOXHiBL/++muOfWrVqsWoUaN44IEHMtY9++yz/PLLL6xevTrX8yQnJ5Oc5XFyQkICkZGRSqpFRESk3EpKMn8wu6P/r91uWrpkTbDr1TOJmjMPNUQKm+MBUWE9wCzNHF0DExJMsurOqV9TU83DV8sysQQHl+7fEUUy+veRI0dIT08nPDw82/rw8HA2bdqU6z6xsbG5bh/r6JCZizFjxvD888+7EpqIiIhImeZsrW9R8PDI7PrRtKn74hBxsNk02JaDzZbZSsbdvL1Na5/ypkQ+1xk9ejTx8fEZy969e90dkoiIiIiIiEgOLtVUV6lSBU9PT+LOGZo2Li6OiIiIXPeJiIhwaXsAX19ffN3ZbkFERERERETECS7VVPv4+NC2bVtmzZqVsc5utzNr1ixi8hgPPSYmJtv2ADNnzsxzexEREREREZHSwuVu46NGjWLo0KG0a9eODh068Pbbb3Pq1CluvfVWAIYMGUKNGjUYM2YMACNHjqRbt268+eab9O/fn4kTJ7Js2TI++eSTwr0SERERERERkWLmclJ9/fXXc/jwYZ555hliY2Np1aoVM2bMyBiMbM+ePXhkGYKvc+fOTJgwgaeeeoonnniC+vXr88svvzg9R7WIiIiIiIhISeXyPNXuoHmqRUREREREpDg5m4eWyNG/RUREREREREoDJdUiIiIiIiIiBaSkWkRERERERKSAlFSLiIiIiIiIFJDLo3+7g2MstYSEBDdHIiIiIiIiIuWBI/8839jepSKpPnnyJACRkZFujkRERERERETKk5MnTxISEpLn56ViSi273c6BAwcICgrCZrO5JYaEhAQiIyPZu3evpvWSEk33qpQWuleltNC9KqWF7lUpDUrTfWpZFidPnqR69ep4eOTdc7pU1FR7eHhQs2ZNd4cBQHBwcIn/4YuA7lUpPXSvSmmhe1VKC92rUhqUlvs0vxpqBw1UJiIiIiIiIlJASqpFRERERERECkhJtZN8fX159tln8fX1dXcoIvnSvSqlhe5VKS10r0ppoXtVSoOyeJ+WioHKREREREREREoi1VSLiIiIiIiIFJCSahEREREREZECUlItIiIiIiIiUkBKqkVEREREREQKSEm1kz744AOioqLw8/OjY8eOLFmyxN0hSTk3ZswY2rdvT1BQEGFhYQwaNIjNmzdn2yYpKYnhw4dTuXJlAgMDufrqq4mLi3NTxCLw6quvYrPZeOCBBzLW6T6VkmL//v3cfPPNVK5cGX9/f5o3b86yZcsyPrcsi2eeeYZq1arh7+9Pz5492bp1qxsjlvIoPT2dp59+mujoaPz9/albty4vvvgiWcce1r0q7jBv3jwGDBhA9erVsdls/PLLL9k+d+a+PHbsGIMHDyY4OJjQ0FBuv/12EhMTi/EqCkZJtRN++OEHRo0axbPPPsuKFSto2bIlvXv35tChQ+4OTcqxuXPnMnz4cP777z9mzpxJamoqvXr14tSpUxnbPPjgg/z+++9MmjSJuXPncuDAAa666io3Ri3l2dKlS/n4449p0aJFtvW6T6UkOH78OF26dMHb25vp06ezYcMG3nzzTSpWrJixzdixY3n33XcZN24cixcvpkKFCvTu3ZukpCQ3Ri7lzWuvvcZHH33E+++/z8aNG3nttdcYO3Ys7733XsY2ulfFHU6dOkXLli354IMPcv3cmfty8ODBrF+/npkzZzJ16lTmzZvHXXfdVVyXUHCWnFeHDh2s4cOHZ7xPT0+3qlevbo0ZM8aNUYlkd+jQIQuw5s6da1mWZZ04ccLy9va2Jk2alLHNxo0bLcBatGiRu8KUcurkyZNW/fr1rZkzZ1rdunWzRo4caVmW7lMpOR577DHroosuyvNzu91uRUREWK+//nrGuhMnTli+vr7W999/XxwhiliWZVn9+/e3brvttmzrrrrqKmvw4MGWZelelZIBsKZMmZLx3pn7csOGDRZgLV26NGOb6dOnWzabzdq/f3+xxV4Qqqk+j5SUFJYvX07Pnj0z1nl4eNCzZ08WLVrkxshEsouPjwegUqVKACxfvpzU1NRs926jRo2oVauW7l0pdsOHD6d///7Z7kfQfSolx2+//Ua7du249tprCQsLo3Xr1nz66acZn+/cuZPY2Nhs92pISAgdO3bUvSrFqnPnzsyaNYstW7YAsHr1aubPn0/fvn0B3atSMjlzXy5atIjQ0FDatWuXsU3Pnj3x8PBg8eLFxR6zK7zcHUBJd+TIEdLT0wkPD8+2Pjw8nE2bNrkpKpHs7HY7DzzwAF26dKFZs2YAxMbG4uPjQ2hoaLZtw8PDiY2NdUOUUl5NnDiRFStWsHTp0hyf6T6VkmLHjh189NFHjBo1iieeeIKlS5fyv//9Dx8fH4YOHZpxP+b294DuVSlOjz/+OAkJCTRq1AhPT0/S09N5+eWXGTx4MIDuVSmRnLkvY2NjCQsLy/a5l5cXlSpVKvH3rpJqkTJg+PDhrFu3jvnz57s7FJFs9u7dy8iRI5k5cyZ+fn7uDkckT3a7nXbt2vHKK68A0Lp1a9atW8e4ceMYOnSom6MTyfTjjz/y3XffMWHCBJo2bcqqVat44IEHqF69uu5VETdR8+/zqFKlCp6enjlGoo2LiyMiIsJNUYlkGjFiBFOnTuWff/6hZs2aGesjIiJISUnhxIkT2bbXvSvFafny5Rw6dIg2bdrg5eWFl5cXc+fO5d1338XLy4vw8HDdp1IiVKtWjSZNmmRb17hxY/bs2QOQcT/q7wFxt0ceeYTHH3+cG264gebNm3PLLbfw4IMPMmbMGED3qpRMztyXEREROQaCTktL49ixYyX+3lVSfR4+Pj60bduWWbNmZayz2+3MmjWLmJgYN0Ym5Z1lWYwYMYIpU6Ywe/ZsoqOjs33etm1bvL29s927mzdvZs+ePbp3pdj06NGDtWvXsmrVqoylXbt2DB48OKOs+1RKgi5duuSYlnDLli3Url0bgOjoaCIiIrLdqwkJCSxevFj3qhSr06dP4+GR/U94T09P7HY7oHtVSiZn7suYmBhOnDjB8uXLM7aZPXs2drudjh07FnvMLnH3SGmlwcSJEy1fX19r/Pjx1oYNG6y77rrLCg0NtWJjY90dmpRj9957rxUSEmLNmTPHOnjwYMZy+vTpjG3uueceq1atWtbs2bOtZcuWWTExMVZMTIwboxaxso3+bVm6T6VkWLJkieXl5WW9/PLL1tatW63vvvvOCggIsL799tuMbV599VUrNDTU+vXXX601a9ZYAwcOtKKjo60zZ864MXIpb4YOHWrVqFHDmjp1qrVz507r559/tqpUqWI9+uijGdvoXhV3OHnypLVy5Upr5cqVFmC99dZb1sqVK63du3dbluXcfdmnTx+rdevW1uLFi6358+db9evXt2688UZ3XZLTlFQ76b333rNq1apl+fj4WB06dLD+++8/d4ck5RyQ6/Lll19mbHPmzBnrvvvusypWrGgFBARYV155pXXw4EH3BS1i5UyqdZ9KSfH7779bzZo1s3x9fa1GjRpZn3zySbbP7Xa79fTTT1vh4eGWr6+v1aNHD2vz5s1uilbKq4SEBGvkyJFWrVq1LD8/P6tOnTrWk08+aSUnJ2dso3tV3OGff/7J9W/ToUOHWpbl3H159OhR68Ybb7QCAwOt4OBg69Zbb7VOnjzphqtxjc2yLMs9deQiIiIiIiIipZv6VIuIiIiIiIgUkJJqERERERERkQJSUi0iIiIiIiJSQEqqRURERERERApISbWIiIiIiIhIASmpFhERERERESkgJdUiIiIiIiIiBaSkWkRERERERKSAlFSLiIiIiIiIFJCSahEREREREZECUlItIiIiIiIiUkBKqkVEREREREQK6P8Bq2dCtVY9gLQAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 3.609\n",
            "\n",
            "\n",
            "RMSprop\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final model performance- Categorical Cross Entropy Loss on test dataset= 1.387\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"SGD\")\n",
        "plotteropt(trainersgd,resultsgd)\n",
        "print(\"Adam- with high learning rate (0.1)\")\n",
        "plotteropt(traineradm,resultadm)\n",
        "print(\"Adam- with lower initial learning rate (3e-4)\")\n",
        "plotteropt(traineradmideal,resultadmideal)\n",
        "print(\"Adamax\")\n",
        "plotteropt(traineradx,resultadx)\n",
        "print(\"AdamW\")\n",
        "plotteropt(traineradw,resultadw)\n",
        "print(\"Adagrad\")\n",
        "plotteropt(traineradd,resultadd)\n",
        "print(\"Adadelta\")\n",
        "plotteropt(trainerada,resultada)\n",
        "print(\"Adafactor\")\n",
        "plotteropt(traineradf,resultadf)\n",
        "print(\"Ftrl\")\n",
        "plotteropt(trainerftrl,resultftrl)\n",
        "print(\"Nadam\")\n",
        "plotteropt(trainerndm,resultndm)\n",
        "print(\"RMSprop\")\n",
        "plotteropt(trainerrms,resultrms)"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
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      "display_name": "Python 3",
      "name": "python3"
    },
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      "name": "python"
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