HW_MNIST.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Minst handwritten datasını kullanarak bir adet CNN ağ yapısı geliştiriniz.\n",
        "\n",
        "Ağ yapısı:\n",
        "Conv2D(32,(3,3))\n",
        "\n",
        "relu activation\n",
        "\n",
        "Conv2D(64,(3,3))\n",
        "\n",
        "relu activation\n",
        "\n",
        "Maxpooling ((2,2))\n",
        "\n",
        "Conv2D(128,(3,3))\n",
        "\n",
        "relu activation\n",
        "\n",
        "Conv2D(64,(3,3))\n",
        "\n",
        "relu activation\n",
        "\n",
        "Flatten()\n",
        "\n",
        "Dense 256\n",
        "\n",
        "dropout 0.25\n",
        "\n",
        "dense 128\n",
        "\n",
        "dropout 0.3\n",
        "\n",
        "dense layer for last (Buradaki bilgileri sizden bekliyorum.)\n",
        "\n",
        "10 epoch olacak şekilde çalıştırmanızı isitiyorum.\n",
        "\n",
        "daha sonrasında bathc_size 32 64 128 256 512 1024 olacak şekilde deneyiniz. Accuracy değerlerini ve çalışma süreleriniz karşılaştırnız. Bunu plot olarak çizdiriniz.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "R-XO3IJf5cqj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "LWmKwPDK5hnM"
      },
      "outputs": [],
      "source": [
        "from keras.datasets import mnist"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train),(x_test,y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NS7RjbBP5nN_",
        "outputId": "43d171d9-22e8-4bc0-cad8-552cae93e33f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 2s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVbOsEVK5nLE",
        "outputId": "69f879fd-47b9-4342-99e8-4930c6ae60cb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2j_hmnd71nn",
        "outputId": "c6982ce6-c6a4-4058-8fd5-b1179b46cdb2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.data_utils import random\n",
        "import cv2 \n",
        "import numpy as np\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "for i in range(6):\n",
        "  rand_num = random.randint(0,len(x_train))\n",
        "  img = x_train[rand_num]\n",
        "  cv2_imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "id": "W_2cEHJz5nIJ",
        "outputId": "1f368284-16e3-4625-8661-827199a2d1ee"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=28x28 at 0x7F7841FF27D0>"
            ],
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          "metadata": {}
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              "<PIL.Image.Image image mode=L size=28x28 at 0x7F78426C7410>"
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          },
          "metadata": {}
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_rows = x_train[0].shape[0]\n",
        "img_cols = x_train[1].shape[1]"
      ],
      "metadata": {
        "id": "PBgDsTKj5nFj"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.reshape(len(x_train), img_rows, img_cols, 1)\n",
        "x_test = x_test.reshape(len(x_test), img_rows, img_cols, 1)"
      ],
      "metadata": {
        "id": "lyh15Yp15nCw"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.astype(\"float32\")\n",
        "x_test = x_test.astype(\"float32\")"
      ],
      "metadata": {
        "id": "yhDz21045m_3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train /= 255\n",
        "x_test /= 255"
      ],
      "metadata": {
        "id": "kHGcumML5m9B"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhP_mH0q5m6N",
        "outputId": "a06740ec-264f-43eb-83de-aa1beabcfdd4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import np_utils\n",
        "\n",
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test = np_utils.to_categorical(y_test)\n"
      ],
      "metadata": {
        "id": "7xoxGLBVBfyu"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_class = y_test.shape[1]"
      ],
      "metadata": {
        "id": "nXsG6w7WBfvr"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_class"
      ],
      "metadata": {
        "id": "-KdzG8vUBftQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5eb8ec9-d6fb-426f-b2eb-e5a62c303b7a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras \n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD"
      ],
      "metadata": {
        "id": "UMS2T4cSBfqm"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "zq5uu7OH5eDo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size = (3,3), activation = \"relu\", input_shape = (28,28,1)))\n",
        "model.add(Conv2D(64, kernel_size = (3,3), activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(128, kernel_size = (3,3), activation = \"relu\"))\n",
        "model.add(Conv2D(64, kernel_size = (3,3), activation = \"relu\"))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation = \"relu\"))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_class, activation = \"softmax\"))\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer = SGD(0.01), metrics = [\"accuracy\"])\n",
        "\n",
        "print(model.summary())\n"
      ],
      "metadata": {
        "id": "4oms81AkBfns",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94d1d328-a518-4675-f212-78af4a5843c9"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 24, 24, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 12, 12, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 10, 10, 128)       73856     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 8, 8, 64)          73792     \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4096)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               1048832   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,249,482\n",
            "Trainable params: 1,249,482\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32 \n",
        "epochs = 10\n",
        "-\n",
        "  history = model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
        "\n",
        "  score = model.evaluate(x_test, y_test)\n",
        "\n",
        "  print(\"test loss: \", score[0] )\n",
        "  print(\"test loss: \", score[1] )\n",
        "\n",
        "  batch_size = batch_size*2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrrzVtHCWHkm",
        "outputId": "05514992-1876-4761-9be2-b04030360ec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 319s 170ms/step - loss: 0.5705 - accuracy: 0.8225 - val_loss: 0.1311 - val_accuracy: 0.9583\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 319s 170ms/step - loss: 0.1806 - accuracy: 0.9478 - val_loss: 0.0844 - val_accuracy: 0.9742\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 322s 172ms/step - loss: 0.1250 - accuracy: 0.9640 - val_loss: 0.0630 - val_accuracy: 0.9808\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 318s 170ms/step - loss: 0.0947 - accuracy: 0.9729 - val_loss: 0.0566 - val_accuracy: 0.9835\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 317s 169ms/step - loss: 0.0825 - accuracy: 0.9766 - val_loss: 0.0508 - val_accuracy: 0.9848\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 317s 169ms/step - loss: 0.0695 - accuracy: 0.9804 - val_loss: 0.0471 - val_accuracy: 0.9870\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 320s 171ms/step - loss: 0.0615 - accuracy: 0.9821 - val_loss: 0.0426 - val_accuracy: 0.9870\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 324s 173ms/step - loss: 0.0540 - accuracy: 0.9840 - val_loss: 0.0394 - val_accuracy: 0.9890\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 319s 170ms/step - loss: 0.0500 - accuracy: 0.9855 - val_loss: 0.0373 - val_accuracy: 0.9897\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 319s 170ms/step - loss: 0.0449 - accuracy: 0.9870 - val_loss: 0.0354 - val_accuracy: 0.9897\n",
            "313/313 [==============================] - 13s 43ms/step - loss: 0.0354 - accuracy: 0.9897\n",
            "test loss:  0.03536813706159592\n",
            "test loss:  0.9897000193595886\n",
            "Epoch 1/10\n",
            "938/938 [==============================] - 301s 321ms/step - loss: 0.0347 - accuracy: 0.9899 - val_loss: 0.0327 - val_accuracy: 0.9911\n",
            "Epoch 2/10\n",
            "938/938 [==============================] - 312s 332ms/step - loss: 0.0312 - accuracy: 0.9910 - val_loss: 0.0319 - val_accuracy: 0.9902\n",
            "Epoch 3/10\n",
            "938/938 [==============================] - 308s 329ms/step - loss: 0.0302 - accuracy: 0.9913 - val_loss: 0.0344 - val_accuracy: 0.9910\n",
            "Epoch 4/10\n",
            "938/938 [==============================] - 302s 322ms/step - loss: 0.0270 - accuracy: 0.9922 - val_loss: 0.0328 - val_accuracy: 0.9908\n",
            "Epoch 5/10\n",
            "938/938 [==============================] - 303s 323ms/step - loss: 0.0266 - accuracy: 0.9922 - val_loss: 0.0330 - val_accuracy: 0.9912\n",
            "Epoch 6/10\n",
            "938/938 [==============================] - 301s 321ms/step - loss: 0.0249 - accuracy: 0.9928 - val_loss: 0.0335 - val_accuracy: 0.9904\n",
            "Epoch 7/10\n",
            "938/938 [==============================] - 303s 323ms/step - loss: 0.0234 - accuracy: 0.9931 - val_loss: 0.0320 - val_accuracy: 0.9915\n",
            "Epoch 8/10\n",
            "938/938 [==============================] - 301s 321ms/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.0306 - val_accuracy: 0.9921\n",
            "Epoch 9/10\n",
            "938/938 [==============================] - 299s 319ms/step - loss: 0.0213 - accuracy: 0.9934 - val_loss: 0.0327 - val_accuracy: 0.9918\n",
            "Epoch 10/10\n",
            "938/938 [==============================] - 299s 319ms/step - loss: 0.0200 - accuracy: 0.9937 - val_loss: 0.0321 - val_accuracy: 0.9915\n",
            "313/313 [==============================] - 13s 42ms/step - loss: 0.0321 - accuracy: 0.9915\n",
            "test loss:  0.032142817974090576\n",
            "test loss:  0.9915000200271606\n",
            "Epoch 1/10\n",
            "469/469 [==============================] - 292s 622ms/step - loss: 0.0176 - accuracy: 0.9947 - val_loss: 0.0328 - val_accuracy: 0.9926\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 289s 617ms/step - loss: 0.0159 - accuracy: 0.9955 - val_loss: 0.0325 - val_accuracy: 0.9916\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 292s 623ms/step - loss: 0.0150 - accuracy: 0.9954 - val_loss: 0.0339 - val_accuracy: 0.9917\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 301s 642ms/step - loss: 0.0155 - accuracy: 0.9955 - val_loss: 0.0327 - val_accuracy: 0.9924\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 305s 651ms/step - loss: 0.0139 - accuracy: 0.9960 - val_loss: 0.0342 - val_accuracy: 0.9915\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 309s 660ms/step - loss: 0.0139 - accuracy: 0.9958 - val_loss: 0.0347 - val_accuracy: 0.9919\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 316s 673ms/step - loss: 0.0128 - accuracy: 0.9960 - val_loss: 0.0350 - val_accuracy: 0.9917\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 316s 673ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0355 - val_accuracy: 0.9913\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 308s 656ms/step - loss: 0.0127 - accuracy: 0.9964 - val_loss: 0.0374 - val_accuracy: 0.9916\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 310s 661ms/step - loss: 0.0131 - accuracy: 0.9959 - val_loss: 0.0338 - val_accuracy: 0.9924\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.0338 - accuracy: 0.9924\n",
            "test loss:  0.03384467959403992\n",
            "test loss:  0.9923999905586243\n",
            "Epoch 1/10\n",
            "235/235 [==============================] - 300s 1s/step - loss: 0.0108 - accuracy: 0.9966 - val_loss: 0.0341 - val_accuracy: 0.9915\n",
            "Epoch 2/10\n",
            "235/235 [==============================] - 299s 1s/step - loss: 0.0115 - accuracy: 0.9966 - val_loss: 0.0343 - val_accuracy: 0.9917\n",
            "Epoch 3/10\n",
            "235/235 [==============================] - 304s 1s/step - loss: 0.0115 - accuracy: 0.9967 - val_loss: 0.0358 - val_accuracy: 0.9920\n",
            "Epoch 4/10\n",
            "235/235 [==============================] - 304s 1s/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.0351 - val_accuracy: 0.9922\n",
            "Epoch 5/10\n",
            "235/235 [==============================] - 297s 1s/step - loss: 0.0107 - accuracy: 0.9968 - val_loss: 0.0349 - val_accuracy: 0.9916\n",
            "Epoch 6/10\n",
            "235/235 [==============================] - 297s 1s/step - loss: 0.0099 - accuracy: 0.9971 - val_loss: 0.0356 - val_accuracy: 0.9917\n",
            "Epoch 7/10\n",
            "235/235 [==============================] - 294s 1s/step - loss: 0.0104 - accuracy: 0.9969 - val_loss: 0.0348 - val_accuracy: 0.9920\n",
            "Epoch 8/10\n",
            "235/235 [==============================] - 294s 1s/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 0.0361 - val_accuracy: 0.9915\n",
            "Epoch 9/10\n",
            "235/235 [==============================] - 287s 1s/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 0.0360 - val_accuracy: 0.9916\n",
            "Epoch 10/10\n",
            "235/235 [==============================] - 284s 1s/step - loss: 0.0090 - accuracy: 0.9970 - val_loss: 0.0368 - val_accuracy: 0.9919\n",
            "313/313 [==============================] - 14s 44ms/step - loss: 0.0368 - accuracy: 0.9919\n",
            "test loss:  0.03677941858768463\n",
            "test loss:  0.9919000267982483\n",
            "Epoch 1/10\n",
            "118/118 [==============================] - 283s 2s/step - loss: 0.0097 - accuracy: 0.9969 - val_loss: 0.0352 - val_accuracy: 0.9917\n",
            "Epoch 2/10\n",
            "118/118 [==============================] - 282s 2s/step - loss: 0.0088 - accuracy: 0.9975 - val_loss: 0.0355 - val_accuracy: 0.9917\n",
            "Epoch 3/10\n",
            "118/118 [==============================] - 282s 2s/step - loss: 0.0088 - accuracy: 0.9974 - val_loss: 0.0358 - val_accuracy: 0.9920\n",
            "Epoch 4/10\n",
            "118/118 [==============================] - 280s 2s/step - loss: 0.0093 - accuracy: 0.9972 - val_loss: 0.0356 - val_accuracy: 0.9922\n",
            "Epoch 5/10\n",
            "118/118 [==============================] - 282s 2s/step - loss: 0.0080 - accuracy: 0.9977 - val_loss: 0.0357 - val_accuracy: 0.9919\n",
            "Epoch 6/10\n",
            "118/118 [==============================] - 282s 2s/step - loss: 0.0083 - accuracy: 0.9974 - val_loss: 0.0360 - val_accuracy: 0.9919\n",
            "Epoch 7/10\n",
            "118/118 [==============================] - 280s 2s/step - loss: 0.0078 - accuracy: 0.9977 - val_loss: 0.0364 - val_accuracy: 0.9916\n",
            "Epoch 8/10\n",
            "118/118 [==============================] - 282s 2s/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.0366 - val_accuracy: 0.9916\n",
            "Epoch 9/10\n",
            "118/118 [==============================] - 283s 2s/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.0363 - val_accuracy: 0.9915\n",
            "Epoch 10/10\n",
            "118/118 [==============================] - 283s 2s/step - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.0368 - val_accuracy: 0.9916\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.0368 - accuracy: 0.9916\n",
            "test loss:  0.03684386610984802\n",
            "test loss:  0.991599977016449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32 \n",
        "epochs = 10\n",
        "\n",
        "history1 = model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
        "\n",
        "score1 = model.evaluate(x_test, y_test)\n",
        "\n",
        "print(\"test loss: \", score1[0] )\n",
        "print(\"test loss: \", score1[1] )"
      ],
      "metadata": {
        "id": "TXP6ubyv4m5H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31890c03-ff7e-415b-ccac-f5c549d38414"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 18s 5ms/step - loss: 0.6004 - accuracy: 0.8052 - val_loss: 0.1370 - val_accuracy: 0.9589\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1769 - accuracy: 0.9484 - val_loss: 0.0778 - val_accuracy: 0.9763\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1213 - accuracy: 0.9654 - val_loss: 0.0611 - val_accuracy: 0.9822\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.0971 - accuracy: 0.9725 - val_loss: 0.0533 - val_accuracy: 0.9833\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0812 - accuracy: 0.9768 - val_loss: 0.0437 - val_accuracy: 0.9872\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0696 - accuracy: 0.9805 - val_loss: 0.0441 - val_accuracy: 0.9868\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0617 - accuracy: 0.9822 - val_loss: 0.0428 - val_accuracy: 0.9876\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0562 - accuracy: 0.9839 - val_loss: 0.0379 - val_accuracy: 0.9891\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0512 - accuracy: 0.9851 - val_loss: 0.0369 - val_accuracy: 0.9891\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0447 - accuracy: 0.9866 - val_loss: 0.0354 - val_accuracy: 0.9895\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0354 - accuracy: 0.9895\n",
            "test loss:  0.03542570397257805\n",
            "test loss:  0.9894999861717224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64 \n",
        "epochs = 10\n",
        "\n",
        "history2 = model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
        "\n",
        "score2 = model.evaluate(x_test, y_test)\n",
        "\n",
        "print(\"test loss: \", score2[0] )\n",
        "print(\"test loss: \", score2[1] )"
      ],
      "metadata": {
        "id": "BLyvlK4p4ms4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e18cccf3-8a36-41aa-ce2e-b26ac9971040"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "938/938 [==============================] - 8s 8ms/step - loss: 0.0336 - accuracy: 0.9902 - val_loss: 0.0319 - val_accuracy: 0.9914\n",
            "Epoch 2/10\n",
            "938/938 [==============================] - 7s 7ms/step - loss: 0.0321 - accuracy: 0.9908 - val_loss: 0.0326 - val_accuracy: 0.9907\n",
            "Epoch 3/10\n",
            "938/938 [==============================] - 7s 7ms/step - loss: 0.0305 - accuracy: 0.9910 - val_loss: 0.0319 - val_accuracy: 0.9917\n",
            "Epoch 4/10\n",
            "938/938 [==============================] - 6s 7ms/step - loss: 0.0283 - accuracy: 0.9915 - val_loss: 0.0306 - val_accuracy: 0.9912\n",
            "Epoch 5/10\n",
            "938/938 [==============================] - 7s 7ms/step - loss: 0.0252 - accuracy: 0.9924 - val_loss: 0.0338 - val_accuracy: 0.9907\n",
            "Epoch 6/10\n",
            "938/938 [==============================] - 6s 7ms/step - loss: 0.0254 - accuracy: 0.9927 - val_loss: 0.0346 - val_accuracy: 0.9900\n",
            "Epoch 7/10\n",
            "938/938 [==============================] - 7s 7ms/step - loss: 0.0239 - accuracy: 0.9932 - val_loss: 0.0307 - val_accuracy: 0.9915\n",
            "Epoch 8/10\n",
            "938/938 [==============================] - 7s 7ms/step - loss: 0.0231 - accuracy: 0.9930 - val_loss: 0.0337 - val_accuracy: 0.9903\n",
            "Epoch 9/10\n",
            "938/938 [==============================] - 6s 7ms/step - loss: 0.0220 - accuracy: 0.9934 - val_loss: 0.0348 - val_accuracy: 0.9913\n",
            "Epoch 10/10\n",
            "938/938 [==============================] - 6s 7ms/step - loss: 0.0203 - accuracy: 0.9938 - val_loss: 0.0321 - val_accuracy: 0.9910\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0321 - accuracy: 0.9910\n",
            "test loss:  0.03212399780750275\n",
            "test loss:  0.9909999966621399\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128 \n",
        "epochs = 10\n",
        "\n",
        "history3 = model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
        "\n",
        "score3 = model.evaluate(x_test, y_test)\n",
        "\n",
        "print(\"test loss: \", score3[0] )\n",
        "print(\"test loss: \", score3[1] )"
      ],
      "metadata": {
        "id": "V7N4BNbK4mbM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "928a9652-4d22-4274-d5a0-ad891a47a5a2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 6s 12ms/step - loss: 0.0180 - accuracy: 0.9947 - val_loss: 0.0327 - val_accuracy: 0.9921\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 5s 12ms/step - loss: 0.0162 - accuracy: 0.9950 - val_loss: 0.0327 - val_accuracy: 0.9913\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 5s 11ms/step - loss: 0.0150 - accuracy: 0.9955 - val_loss: 0.0310 - val_accuracy: 0.9913\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 5s 11ms/step - loss: 0.0154 - accuracy: 0.9955 - val_loss: 0.0327 - val_accuracy: 0.9914\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 6s 12ms/step - loss: 0.0149 - accuracy: 0.9956 - val_loss: 0.0326 - val_accuracy: 0.9917\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 5s 11ms/step - loss: 0.0148 - accuracy: 0.9956 - val_loss: 0.0325 - val_accuracy: 0.9908\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 5s 12ms/step - loss: 0.0140 - accuracy: 0.9958 - val_loss: 0.0353 - val_accuracy: 0.9909\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 5s 11ms/step - loss: 0.0131 - accuracy: 0.9963 - val_loss: 0.0347 - val_accuracy: 0.9912\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 5s 12ms/step - loss: 0.0136 - accuracy: 0.9956 - val_loss: 0.0339 - val_accuracy: 0.9915\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 6s 12ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.0364 - val_accuracy: 0.9903\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0364 - accuracy: 0.9903\n",
            "test loss:  0.036369387060403824\n",
            "test loss:  0.9902999997138977\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 256 \n",
        "epochs = 10\n",
        "\n",
        "history4 = model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
        "\n",
        "score4 = model.evaluate(x_test, y_test)\n",
        "\n",
        "print(\"test loss: \", score4[0] )\n",
        "print(\"test loss: \", score4[1] )"
      ],
      "metadata": {
        "id": "MsAJDjkz4mVX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcd998ab-1a3b-4bbf-f569-41e2cbe8006a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "235/235 [==============================] - 5s 19ms/step - loss: 0.0114 - accuracy: 0.9966 - val_loss: 0.0357 - val_accuracy: 0.9912\n",
            "Epoch 2/10\n",
            "235/235 [==============================] - 4s 19ms/step - loss: 0.0115 - accuracy: 0.9966 - val_loss: 0.0348 - val_accuracy: 0.9915\n",
            "Epoch 3/10\n",
            "235/235 [==============================] - 4s 18ms/step - loss: 0.0113 - accuracy: 0.9966 - val_loss: 0.0350 - val_accuracy: 0.9915\n",
            "Epoch 4/10\n",
            "235/235 [==============================] - 4s 18ms/step - loss: 0.0103 - accuracy: 0.9969 - val_loss: 0.0356 - val_accuracy: 0.9916\n",
            "Epoch 5/10\n",
            "235/235 [==============================] - 4s 18ms/step - loss: 0.0102 - accuracy: 0.9970 - val_loss: 0.0355 - val_accuracy: 0.9913\n",
            "Epoch 6/10\n",
            "235/235 [==============================] - 4s 19ms/step - loss: 0.0098 - accuracy: 0.9971 - val_loss: 0.0360 - val_accuracy: 0.9914\n",
            "Epoch 7/10\n",
            "235/235 [==============================] - 4s 19ms/step - loss: 0.0106 - accuracy: 0.9970 - val_loss: 0.0350 - val_accuracy: 0.9911\n",
            "Epoch 8/10\n",
            "235/235 [==============================] - 4s 19ms/step - loss: 0.0099 - accuracy: 0.9969 - val_loss: 0.0357 - val_accuracy: 0.9913\n",
            "Epoch 9/10\n",
            "235/235 [==============================] - 4s 19ms/step - loss: 0.0092 - accuracy: 0.9970 - val_loss: 0.0355 - val_accuracy: 0.9920\n",
            "Epoch 10/10\n",
            "235/235 [==============================] - 4s 19ms/step - loss: 0.0102 - accuracy: 0.9967 - val_loss: 0.0353 - val_accuracy: 0.9916\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0353 - accuracy: 0.9916\n",
            "test loss:  0.03528863564133644\n",
            "test loss:  0.991599977016449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size =  512\n",
        "epochs = 10\n",
        "\n",
        "history5 = model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
        "\n",
        "score5 = model.evaluate(x_test, y_test)\n",
        "\n",
        "print(\"test loss: \", score5[0] )\n",
        "print(\"test loss: \", score5[1] )"
      ],
      "metadata": {
        "id": "Byf6l3rC4mPo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5818a1f-ef03-49b0-9dac-2a2acd1b6146"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "118/118 [==============================] - 5s 35ms/step - loss: 0.0093 - accuracy: 0.9970 - val_loss: 0.0358 - val_accuracy: 0.9916\n",
            "Epoch 2/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0096 - accuracy: 0.9970 - val_loss: 0.0367 - val_accuracy: 0.9912\n",
            "Epoch 3/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0097 - accuracy: 0.9970 - val_loss: 0.0356 - val_accuracy: 0.9919\n",
            "Epoch 4/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0088 - accuracy: 0.9972 - val_loss: 0.0357 - val_accuracy: 0.9916\n",
            "Epoch 5/10\n",
            "118/118 [==============================] - 4s 34ms/step - loss: 0.0086 - accuracy: 0.9970 - val_loss: 0.0362 - val_accuracy: 0.9916\n",
            "Epoch 6/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0086 - accuracy: 0.9974 - val_loss: 0.0354 - val_accuracy: 0.9911\n",
            "Epoch 7/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0085 - accuracy: 0.9974 - val_loss: 0.0358 - val_accuracy: 0.9916\n",
            "Epoch 8/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0080 - accuracy: 0.9977 - val_loss: 0.0364 - val_accuracy: 0.9914\n",
            "Epoch 9/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0090 - accuracy: 0.9973 - val_loss: 0.0367 - val_accuracy: 0.9912\n",
            "Epoch 10/10\n",
            "118/118 [==============================] - 4s 33ms/step - loss: 0.0085 - accuracy: 0.9976 - val_loss: 0.0363 - val_accuracy: 0.9911\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0363 - accuracy: 0.9911\n",
            "test loss:  0.03626347333192825\n",
            "test loss:  0.991100013256073\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size =  1024\n",
        "epochs = 10\n",
        "\n",
        "history6 = model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
        "\n",
        "score6 = model.evaluate(x_test, y_test)\n",
        "\n",
        "print(\"test loss: \", score6[0] )\n",
        "print(\"test loss: \", score6[1] )"
      ],
      "metadata": {
        "id": "wyxuef0N4mIl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe12562d-7fec-45e8-d41d-9d499705a883"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "59/59 [==============================] - 6s 85ms/step - loss: 0.0083 - accuracy: 0.9977 - val_loss: 0.0364 - val_accuracy: 0.9913\n",
            "Epoch 2/10\n",
            "59/59 [==============================] - 4s 66ms/step - loss: 0.0086 - accuracy: 0.9973 - val_loss: 0.0365 - val_accuracy: 0.9912\n",
            "Epoch 3/10\n",
            "59/59 [==============================] - 4s 64ms/step - loss: 0.0088 - accuracy: 0.9974 - val_loss: 0.0363 - val_accuracy: 0.9916\n",
            "Epoch 4/10\n",
            "59/59 [==============================] - 4s 66ms/step - loss: 0.0083 - accuracy: 0.9975 - val_loss: 0.0367 - val_accuracy: 0.9911\n",
            "Epoch 5/10\n",
            "59/59 [==============================] - 4s 64ms/step - loss: 0.0085 - accuracy: 0.9973 - val_loss: 0.0364 - val_accuracy: 0.9916\n",
            "Epoch 6/10\n",
            "59/59 [==============================] - 4s 64ms/step - loss: 0.0076 - accuracy: 0.9976 - val_loss: 0.0363 - val_accuracy: 0.9917\n",
            "Epoch 7/10\n",
            "59/59 [==============================] - 4s 65ms/step - loss: 0.0080 - accuracy: 0.9976 - val_loss: 0.0363 - val_accuracy: 0.9915\n",
            "Epoch 8/10\n",
            "59/59 [==============================] - 4s 66ms/step - loss: 0.0078 - accuracy: 0.9977 - val_loss: 0.0365 - val_accuracy: 0.9912\n",
            "Epoch 9/10\n",
            "59/59 [==============================] - 4s 64ms/step - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.0361 - val_accuracy: 0.9914\n",
            "Epoch 10/10\n",
            "59/59 [==============================] - 4s 64ms/step - loss: 0.0074 - accuracy: 0.9976 - val_loss: 0.0362 - val_accuracy: 0.9915\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9915\n",
            "test loss:  0.036237169057130814\n",
            "test loss:  0.9915000200271606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "jXpzWl0D3hfq"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict1 = history1.history\n",
        "loss_values = history_dict1[\"loss\"]\n",
        "val_loss_values = history_dict1[\"val_loss\"]\n",
        "epochs = range(1,len(val_loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs,val_loss_values,label=\"Validation/Test Loss\")\n",
        "line2 = plt.plot(epochs,loss_values,label=\"Train Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "VZdliwN03hdn",
        "outputId": "8ded0e9b-9559-4d1c-d10d-6b57babcbaa1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict1 = history1.history\n",
        "acc_values = history_dict1[\"accuracy\"]\n",
        "val_acc_values = history_dict1[\"val_accuracy\"]\n",
        "epochs = range(1,len(val_acc_values)+1)\n",
        "line1 = plt.plot(epochs,val_acc_values,label=\"Validation/Test Acc\")\n",
        "line2 = plt.plot(epochs,acc_values,label=\"Train Acc\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Acc\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Qc3Dg5Gl3hbI",
        "outputId": "56089cfb-98f9-4a70-e521-128682e43a8a"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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mwv5iOPE8+PJzcOI50S6ZMSbGWOBopY/2VPDg4s0s37KXnN4p/GnqWL445gTiukPH97YV8OYvnNFRA0bAtLkwbJJ1chtjWmSB4zgC342R6r4b45bzhpCc0A1e7bl7A7x5PxS/4byIaPJjcMY0e22pMeaYLHAcw5/f+pjHln9Cg9/P9PPzuPOSU+mTmhjtYnXcwR2w/H9g3UvOS4sm/grO/pa90tQY0yoWOI5hX2Udl4wYyE8mBb8bo8sKHVp7wQ/gwrsgxR7jboxpPQscx3Df1SO7Rx+GDa01xoSRBY5j6PJBI3Ro7fCrnKG1A0dEu2TGmC7MAkd3FDq0dvC5NrTWGBM2Fji6Gxtaa4yJMAsc3YUNrTXGdJKIBg4RmQT8CefVsU+q6kMh60/Cec/4AOAA8FVV9brrfMB6d9NPVfUaNz0PmAv0A1YBN6tqXSSPI6bZ0FpjTCeLWOAQEQ/wGDAR8AKFIrJIVTcFbPYI8KyqPiMilwAPAje766pVdWwLWT8M/EFV54rIX4FvAI9H6jhilg2tNcZESSSvOM4GilV1K4CIzAUmA4GBYyTwQ3d+ObDgWBmK8yakS4CvuEnPAPfTkwKHDa01xkRZJANHDlASsOwFQof1fABcj9OcdR2QLiL9VHU/kCwiRUAD8JCqLsBpnjqkqg0BefaIM6b4G6DoKRtaa4yJumh3jv8Y+LOITAf+DZQCPnfdSapaKiInA2+JyHqgrLUZi8htwG0AWVlZFBQUhLPcnarPgbWM3/JXqN1FWcZpfDLubsozT4NNu51PD1NZWdmlf57hZvURzOqjWaTqIpKBoxQYHLCc66Y1UdWdOFcciEgaMEVVD7nrSt3pVhEpAMYBrwC9RSTeveo4Is+AvGcBswDy8/N1woQJYTuwTuX3w29u5XBcCkybS+awSZzZw4fWFhQU0GV/nhFg9RHM6qNZpOoiLuw5NisEhopInogkAlOBRYEbiEh/EWkswz04I6wQkT4iktS4DXABsElVFacv5AZ3n1uBhRE8hujb9xHUHGLHSV+G4VfY/RjGmKiLWOBwrwi+B7wGbAbmqepGEZkpIte4m00AtojIR0AW8ICbfhpQJCIf4ASKhwJGY/0U+KGIFOP0efwtUscQE7wrASjPGBblghhjjCOifRyquhhYHJJ2X8D8y8DLLez3LjD6KHluxRmx1TN4CyG5N9UpPWIMgDGmC4hkU5UJB28R5J5lTVTGmJhhgSOW1ZTBZ5udwGGMMTHCAkcsK10NKAy2wGGMiR0WOGKZtxAQyBkf7ZIYY0wTCxyxzFvoPBo9OTPaJTHGmCYWOGKVqhM4cvOjXRJjjAligSNW7f8Eqg9ax7gxJuZY4IhV3kJnOrjn3LJijOkaLHDEKm8hJGVA/+HRLokxxgSxwBGrvCsh50yIsx+RMSa22FkpFtVVwZ6NkGvNVMaY2GOBIxaVrgb1W8e4MSYmWeCIRY0d4zYU1xgTgyxwxCJvEfQ7FXr1jXZJjDHmCBY4Yo2q0zFuzVTGmBhlgSPWHNoBVXstcBhjYpYFjljjLXKmFjiMMTHKAkesKVkJCakwcGS0S2KMMS2KaOAQkUkiskVEikVkRgvrTxKRZSKyTkQKRCTXTR8rIu+JyEZ33Y0B+zwtIttEZK37GRvJY+h03kLnxj9PRN/qa4wx7RaxwCEiHuAx4ApgJDBNREL/jX4EeFZVxwAzgQfd9MPALap6OjAJ+KOI9A7Y779Udaz7WRupY+h09dWwe50NwzXGxLRIXnGcDRSr6lZVrQPmApNDthkJvOXOL29cr6ofqerH7vxO4DNgQATLGht2fQD+Brtj3BgT0yLZHpIDlAQse4FzQrb5ALge+BNwHZAuIv1UdX/jBiJyNpAIfBKw3wMich+wDJihqrWhXy4itwG3AWRlZVFQUNDhA4q03JIFnAq882kd9bsLgtZVVlZ2iWPoDFYXwaw+gll9NItYXahqRD7ADcCTAcs3A38O2eYE4B/AGpzg4QV6B6zPBrYA54akCZAEPAPcd7yyjB8/XruEuV9V/cPoFlctX768c8sSw6wugll9BLP6aNbRugCKtIVzaiSvOEqBwQHLuW5aE3Waoa4HEJE0YIqqHnKXM4BXgZ+r6n8C9tnlztaKyGzgxxE7gs7mLYIhF0S7FMYYc0yR7OMoBIaKSJ6IJAJTgUWBG4hIfxFpLMM9wFNueiIwH6fj/OWQfbLdqQDXAhsieAydp6wUKnba/RvGmJgXscChqg3A94DXgM3APFXdKCIzReQad7MJwBYR+QjIAh5w078MfB6Y3sKw2xdEZD2wHugP/DpSx9CpvCudqY2oMsbEuIjeLKCqi4HFIWn3Bcy/DLzcwn7PA88fJc9LwlzM2OAtgvhkyBod7ZIYY8wx2Z3jsaJkJWSPhfjEaJfEGGOOyQJHLGiode7hGGz9G8aY2GeBIxbs3gC+WusYN8Z0CRY4YkFTx7gFDmNM7LPAEQu8hZCRCxknRLskxhhzXBY4YoG30IbhGmO6DAsc0VaxBw59as1UxpguwwJHtHkLnelgeyKuMaZrsMARbd5CiEuAQWOiXRJjjGkVCxzR5i2E7DGQkBztkhhjTKtY4IgmXwOUrrYXNxljuhQLHNG0ZwM0VNuIKmNMl2KBI5qsY9wY0wVZ4IgmbxGkZUHm4ONva4wxMcICRzR5Vzr3b4hEuyTGGNNqFjiipWo/HNhqN/4ZY7ocCxzRUlrkTC1wGGO6GAsc0VKyEsQDJ4w9/rbGGBNDjhs4RCRVROICluNEpFdrMheRSSKyRUSKRWRGC+tPEpFlIrJORApEJDdg3a0i8rH7uTUgfbyIrHfzfFSki3YQeAth0ChITI12SYwxpk1ac8WxDAgMFL2AN4+3k4h4gMeAK4CRwDQRGRmy2SPAs6o6BpgJPOju2xf4BXAOcDbwCxHp4+7zOPAtYKj7mdSKY4gtfh+UrrJmKmNMl9SawJGsqpWNC+58a644zgaKVXWrqtYBc4HJIduMBN5y55cHrL8ceENVD6jqQeANYJKIZAMZqvofVVXgWeDaVpQltuz9EOoq7Y5xY0yXFN+KbapE5ExVXQ1OUxFQ3Yr9coCSgGUvzhVEoA+A64E/AdcB6SLS7yj75rgfbwvpRxCR24DbALKysigoKGhFkTtH9s7XGQ68X+qn+mBBq/aprKyMqWOIJquLYFYfwaw+mkWqLloTOO4C/i4iOwEBBgE3hun7fwz8WUSmA/8GSgFfODJW1VnALID8/HydMGFCOLINjwV/h179OOeKqa2+h6OgoICYOoYosroIZvURzOqjWaTq4riBQ1ULRWQEMNxN2qKq9a3IuxQIvCU6100LzHsnzhUHIpIGTFHVQyJSCkwI2bfA3T83JD0ozy7BW2g3/hljuqzWjKr6LpCqqhtUdQOQJiLfaUXehcBQEckTkURgKrAoJO/+ASO27gGecudfA74gIn3cTvEvAK+p6i6gXETOdUdT3QIsbEVZYkf1Idi3xR5saIzpslrTOf4tVT3UuOB2Vn/reDupagPwPZwgsBmYp6obRWSmiFzjbjYB2CIiHwFZwAPuvgeAX+EEn0JgppsG8B3gSaAY+ARY0opjiB12458xpotrTR+HR0TEHcXUOMw2sTWZq+piYHFI2n0B8y8DLx9l36dovgIJTC8CRrXm+2OStwgQyBkf7ZIYY0y7tCZwLAVeEpH/5y5/m672X34sKVkJA0dCUnq0S2KMMe3SmsDxU5xhrbe7y+twRlaZtvL7naaq06+LdkmMMabdjtvHoap+4H1gO85NfZfg9FmYttpfDDVl1r9hjOnSjnrFISLDgGnuZx/wEoCqXtw5ReuGvCudqQUOY0wXdqymqg+BFcDVqloMICJ3d0qpuitvISRnQr+h0S6JMca027Gaqq4HdgHLReQJEbkU585x017eIsjJhzh7mr0xpus66hlMVReo6lRgBM4DCO8CBorI4yLyhc4qYLdRWwGfbbJmKmNMl9eazvEqVX1RVb+I84iPNTgjrUxblK4G9cNgCxzGmK6tTW0mqnpQVWep6qWRKlC31dgxbjf+GWO6OGts7yzeIug/HFL6HH9bY4yJYRY4OoNq8xNxjTGmi7PA0RkObIXD++2JuMaYbsECR2fwuk/EHWyvijXGdH0WODqDtxAS02DAiGiXxBhjOswCR2fwroScMyHOE+2SGGNMh1ngiLS6w7B7A+RaM5UxpnuwwBFpO9eA+mxElTGm27DAEWneQmdqgcMY001ENHCIyCQR2SIixSIyo4X1J4rIchFZIyLrRORKN/0mEVkb8PGLyFh3XYGbZ+O6gZE8hg7zFkLfkyG1X7RLYowxYdGaNwC2i/tu8seAiYAXKBSRRaq6KWCze4F5qvq4iIzEeT/5EFV9AXjBzWc0sEBV1wbsd5P77vHY1njj38kTol0SY4wJm0hecZwNFKvqVlWtA+YCk0O2USDDnc8EdraQzzR3366nrAQq91gzlTGmW4nYFQeQA5QELHuBc0K2uR94XUTuBFKBy1rI50aODDizRcQHvAL8WlU1dCcRuQ3nXelkZWVRUFDQjkPomAGfreB0oGhPHJUd/P7KysqoHEMssroIZvURzOqjWaTqIpKBozWmAU+r6u9E5DzgOREZ5b7nHBE5BzisqhsC9rlJVUtFJB0ncNwMPBuasarOAmYB5Ofn64QJEyJ8KC1YshTiU8i/8hbwJHQoq4KCAqJyDDHI6iKY1Ucwq49mkaqLSDZVlQKDA5Zz3bRA3wDmAajqe0Ay0D9g/VRgTuAOqlrqTiuAF3GaxGKTt9C58a+DQcMYY2JJJANHITBURPJEJBEnCCwK2eZT4FIAETkNJ3DsdZfjgC8T0L8hIvEi0t+dTwCuBjYQi+prYNcH9mBDY0y3E7GmKlVtEJHvAa8BHuApVd0oIjOBIlVdBPwIeEJE7sbpKJ8e0F/xeaBEVbcGZJsEvOYGDQ/wJvBEpI6hQ3avA3+9dYwbY7qdiPZxqOpinCG2gWn3BcxvAi44yr4FwLkhaVVA13iFnt34Z4zppuzO8UgpWQmZJ0L6oGiXxBhjwsoCR6R4i2CwXW0YY7ofCxyRUL4Tyr3WTGWM6ZYscESC9W8YY7oxCxyR4C0ETxIMGhPtkhhjTNhZ4IgEbxFknwHxidEuiTHGhJ0FjnBrqHNe3mTNVMaYbsoCR7jt2QANNTaiyhjTbVngCDfrGDfGdHMWOMLNWwjpJ0BmbrRLYowxEWGBI9y8hfZgQ2NMt2aBI5wq98LB7dZMZYzp1ixwhFNj/8bg2H1FiDHGdJQFjnDyFkJcvHMPhzHGdFMWOMLJWwiDRkNCSrRLYowxEWOBI1x8DVC6GnKtmcoY071Z4AiXzzZBfZV1jBtjuj0LHOHS1DFugcMY071FNHCIyCQR2SIixSIyo4X1J4rIchFZIyLrRORKN32IiFSLyFr389eAfcaLyHo3z0dFRCJ5DK3mLYLUAdD7pGiXxBhjIipigUNEPMBjwBXASGCaiIwM2exeYJ6qjgOmAn8JWPeJqo51P7cHpD8OfAsY6n4mReoY2sS70mmmipE4ZowxkRLJK46zgWJV3aqqdcBcYHLINgpkuPOZwM5jZSgi2UCGqv5HVRV4Frg2vMVuh8MHYH+x9W8YY3qE+AjmnQOUBCx7gXNCtrkfeF1E7gRSgcsC1uWJyBqgHLhXVVe4eXpD8sxp6ctF5DbgNoCsrCwKCgrafSDH03f/KsYAa/clcChC31NZWRnRY+hKrC6CWX0Es/poFqm6iGTgaI1pwNOq+jsROQ94TkRGAbuAE1V1v4iMBxaIyOltyVhVZwGzAPLz83XChAlhLnqAt94BiWPslV+DpLSIfEVBQQERPYYuxOoimNVHMKuPZpGqi0gGjlJgcMByrpsW6Bu4fRSq+p6IJAP9VfUzoNZNXyUinwDD3P0DHzvbUp6dz1sIWadHLGgYY0wsiWQfRyEwVETyRCQRp/N7Ucg2nwKXAojIaUAysFdEBrid64jIyTid4FtVdRdQLiLnuqOpbgEWRvAYjs/vh9JV1r9hjOkxInbFoaoNIvI94DXAAzylqhtFZCZQpKqLgB8BT4jI3Tgd5dNVVUXk88BMEakH/MDtqnrAzfo7wNNACrDE/UTPvi1QW912yZMAABUmSURBVG53jBtjeoyI9nGo6mJgcUjafQHzm4ALWtjvFeCVo+RZBIwKb0k7wN74Z4zpYezO8Y4qWQkpfaDfKdEuiTHGdAoLHB3lLbIb/4wxPYoFjo6oKYO9H1ozlTGmR7HA0RGlqwC1d4wbY3oUCxwd4S0CBHLGR7skxhjTaSxwdETJShgwApIzo10SY4zpNBY42kvVGYpr798wxvQwFjjaa/8nUHPIOsaNMT2OBY728q50phY4jDE9jAWO9vIWQlIG9B8e7ZIYY0ynssDRXt5CZzRVnFWhMaZnsbNee9RWwp6N1kxljOmRLHC0x841oH4YbE/ENcb0PBY42qOxY9xu/DPG9EDRfnVs1+Qtgn6nQq++0S6JMR1WX1+P1+ulpqYm2kUJi8zMTDZv3hztYsSE1tZFcnIyubm5JCQktCpfCxxt1Xjj36kTo10SY8LC6/WSnp7OkCFDkG7wlOeKigrS09OjXYyY0Jq6UFX279+P1+slLy+vVflaU1VbHdwOVXvtwYam26ipqaFfv37dImiYthMR+vXr16YrzogGDhGZJCJbRKRYRGa0sP5EEVkuImtEZJ2IXOmmTxSRVSKy3p1eErBPgZvnWvczMJLHcARvkTO1jnHTjVjQ6Nna+vOPWFOViHiAx4CJgBcoFJFF7utiG90LzFPVx0VkJM5rZocA+4AvqupOERmF897ynID9bnJfIdv5vIWQkAoDTovK1xtjTLRF8orjbKBYVbeqah0wF5gcso0CGe58JrATQFXXqOpON30jkCIiSREsa+t5V0LOmeCx7iFjwuHiiy/mtddeC0r74x//yB133HHUfSZMmEBRkfO/45VXXsmhQ4eO2Ob+++/nkUceOeZ3L1iwgE2bmv+Xve+++3jzzTfbUvwg9fX15OXlMXbsWMaOHcugQYPIyclpWq6rq2tVPgUFBbz77rvH3Obaa6/l3HPPbXdZOyKSgSMHKAlY9hJ81QBwP/BVEfHiXG3c2UI+U4DVqlobkDbbbab6b+nMa+z6ati93m78MyaMpk2bxty5c4PS5s6dy7Rp01q1/+LFi+ndu3e7vjs0cMycOZPLLrusXXkBvP3221x99dWsXbuWtWvXcvvtt3P33Xc3LScmJrYqn+MFjkOHDrFq1SrKysrYunVru8vbXtH+t3ka8LSq/k5EzgOeE5FRquoHEJHTgYeBLwTsc5OqlopIOvAKcDPwbGjGInIbcBtAVlYWBQUFHS5s5qFNjPM3sP5QMvvDkF9bVFZWhuUYugOri2AdrY/MzEwqKioAePj1T/hwT2WYSuYYkZXGT79wylHXX3755fz85z9n//79JCYmsmPHDkpLSxk7dizf/OY3Wb16NdXV1UyePJmf//znAPh8PqqqqqioqGDUqFH861//ol+/fvz2t7/lxRdfZMCAAeTk5DBu3DgqKip4+umnmT17NvX19Zx88snMmjWL9evXs3DhQgoKCpg5cybPPfccv/nNb5g0aRLXXnstBQUF3HvvvTQ0NHDmmWfyhz/8gaSkJEaNGsW0adNYunQp9fX1PPvsswwbNgyARYsWcdFFFzXVZ21tLQkJCVRUVLBmzRp+9rOfUVVVRd++ffnrX//KoEGDePzxx3nqqaeIj49n+PDh/PKXv+Txxx/H4/Hw7LPP8tvf/pbzzz8/qM5efPFFLr/8cgYOHMgzzzzDj3/8YwA++eQT7r77bvbt24fH42H27Nmceuqp/OEPf+Cll14iLi6OiRMn8stf/vKIn0NNTU2rf48iGThKgcEBy7luWqBvAJMAVPU9EUkG+gOfiUguMB+4RVU/adxBVUvdaYWIvIjTJHZE4FDVWcAsgPz8fJ0wYULHj+iddQCMnvR1SBvQ8fzaoKCggLAcQzdgdRGso/WxefPmpiGbCYkJeDyeMJWMpjyPNSQ0PT2dc845h7fffpvJkyfzz3/+kxtvvJGMjAx+85vf0LdvX3w+H5deeinbtm1jzJgxeDweUlNTSU9PR0RIS0vjo48+Yv78+bzzzjukpKRw5plncu6555Kens5XvvIV7rzTadC49957mTdvHnfeeSeTJ0/m6quv5oYbbnDKmpBASkoKCQkJfOc732HZsmUMGzaMW265heeff5677roLESEnJ4e1a9fyl7/8hccff5wnn3wSgHfeeYcHHniAXr16AZCUlERSUhLJycnMmDGDhQsXMmDAAF566SUefPBBnnrqKf74xz+ybds2kpKSOHToEL179+aOO+4gLS2tKSCEmj9/Pvfddx9ZWVlMmTKlKRB8+9vfZsaMGVx33XXU1NRQVlbG22+/zdKlSyksLKRXr14cOHCgxZ9HcnIy48aNa9XPNJKBoxAYKiJ5OAFjKvCVkG0+BS4FnhaR04BkYK+I9AZeBWao6juNG4tIPNBbVfeJSAJwNdD+Bsm28hZCnyGdHjSM6Sy/+OLpUfnexuaqyZMnM3fuXP72t78BMG/ePGbNmkVDQwO7du1i06ZNjBkzpsU8VqxYwXXXXUevXr1IT0/nmmuuaVq3YcMG7r33Xg4dOkRlZSWXX375McuzZcsW8vLymq4kbr31Vh577DHuuusuAK6//noAxo8fzz/+8Q8ASktL6du3b1PQCM1vw4YNTJzo3P/l8/nIzs4GYMyYMdx0001ce+21XHvttcetqz179vDxxx9z4YUXIiIkJCSwYcMGTjrpJEpLS7nuuusAJxDU19fz5ptv8rWvfa2pXH37dvzG5Yj1cahqA/A9nBFRm3FGT20UkZki0vgT/RHwLRH5AJgDTFdVdfc7FbgvZNhtEvCaiKwD1uIEpCcidQwhB+QEDuvfMCbsJk+ezLJly1i9ejWHDx9m/PjxbNu2jUceeYRly5axbt06rrrqqnbf3T59+nT+/Oc/s379en7xi190+C75pCRnrI7H46GhoQGApUuXHjUgqSqnn356U1/H+vXref311wF49dVX+e53v8vq1as566yzmvI7mnnz5nHw4EHy8vIYMmQI27dvZ86cOR06nraK6H0cqrpYVYep6imq+oCbdp+qLnLnN6nqBap6hqqOVdXX3fRfq2qqm9b4+UxVq1R1vKqOUdXTVfUHquqL5DE0KS+Fil2Qa/dvGBNuaWlpXHzxxXz9619v6hQvLy8nNTWVzMxM9uzZw5IlS46Zx+c//3kWLFhAdXU1FRUV/N///V/TuoqKCrKzs6mvr+eFF15oSk9PT2/qjwg0fPhwtm/fTnFxMQDPPfccF1100TG/f+nSpVxxxRUtrhs+fDh79+7lvffeA5zRVxs3bsTv91NSUsLFF1/Mww8/TFlZGZWVlUctF8CcOXNYunQp27dvZ/v27axatYq5c+eSnp5Obm4uCxYsAJz+lcOHDzNx4kRmz57N4cOHAThw4MAxj6M17M7x1vIWOlO7Y9yYiJg2bRoffPBBU+A444wzGDduHCNGjOArX/kKF1xwwTH3P/PMM7nxxhs5//zzueKKKzjrrObWgV/96lecc845XHDBBYwYMaIpferUqfz2t79l3LhxfPJJU1cqycnJzJ49my996UuMHj2auLg4br/99qN+t8/no7i4OCjvQImJibz88sv89Kc/5YwzzmDs2LG8++67+Hw+vvrVrzJ69GjGjRvH97//fXr37s0Xv/hF5s+fz9ixY1mxYkVTPtu3b2fHjh1Bw3Dz8vLIzMzk/fff57nnnuPRRx9lzJgxnH/++ezZs4dJkyZxzTXXkJ+fz9ixY487RLlVVLXbf8aPH68dtuQe1V8NVK2v7Xhe7bB8+fKofG8ssroI1tH62LRpU3gKEiPKy8s7/TtXrFih3/72tzv9e4+nLXXR0u8BUKQtnFOjPRy36/AWwgnjIL5147CNMT3HhRdeyIUXXhjtYnQaa6pqjYZa2LXWmqmMMQYLHK2zez346mxElTHGYIGjdZo6xm1ElTHGWOBojZKVkJELGdnRLokxxkSdBY7W8BbBYGumMsYYsMBxfBW7oexT698wJkL279/f7seQFxUV8f3vf7/N37l27VpEhKVLl7a32D2aDcc9nqb+DQscxkRCv379WLt2LeC8QyP04X4NDQ3Ex7d8qsrPzyc/v+2jHefMmcOFF17InDlzmDRpUvsK3oNZ4DgebyF4EiH7jGiXxJjIWzLDGUUYToNGwxUPtWmX6dOnk5yczJo1a7jggguYOnUqP/jBD6ipqSElJYXZs2czfPhwCgoKeOSRR/jnP//J/fffz6effsrHH39MaWkpd911V4tXI6rK3//+d9544w0+97nPUVNTQ3JyMgAPP/wwzz//PHFxcVxxxRU89NBDFBcXc/vtt7N37148Hg9///vfOeWUoz8mviewwHE83iIYNAbiY+MFhMb0FF6vl3fffRePx0N5eTkrVqwgPj6eN998k5/97Ge88sorR+zz4YcfsmjRIsB5PtQdd9xBQkJC0DbvvvsueXl5nHLKKUyYMIFXX32VKVOmsGTJEhYuXMj777/f9PhxgJtuuinoUeV+vz/yBx/jLHAci68eSlfD+OnRLokxnaONVwaR9KUvfanp3SBlZWXceuutfPzxx4gI9fX1Le5z1VVXkZSURHp6OgMHDmTPnj3k5uYGbTNnzhymTp0KOM+qevbZZ5kyZUqLjx+vqKg44lHlxgLHse3ZCA3VNqLKmChITU1tmv/v//5vLr74YubPn8/27duP+uKqxsedQ/Ajzxv5fD5eeeUVFi5cyAMPPICqsn///qM+ida0zEZVHYt1jBsTE8rKysjJyQHg6aefbnc+y5YtY8yYMZSUlDQ9aXbKlCnMnz+/xcePH+1R5T2dBY5j8RZC2iDIHHz8bY0xEfOTn/yEe+65h3Hjxh33RUfHMmfOnKZmp0ZTpkxpGl3V0uPHQx9Vvnv37g4dS3cgzpNzu7f8/HwtKipq+44rfg+15XDZ/eEuUpvZe7abWV0EC8c7x0877bTwFSjKKioqjvmO856kLXXR0u+BiKxS1SPGO1sfx7F87ofRLoExxsSciDZVicgkEdkiIsUiMqOF9SeKyHIRWSMi60TkyoB197j7bRGRy1ubpzHGmMiKWOAQEQ/wGHAFMBKYJiIjQza7F5inquOAqcBf3H1HusunA5OAv4iIp5V5GmPaqCc0WZuja+vPP5JXHGcDxaq6VVXrgLnA5JBtFMhw5zOBne78ZGCuqtaq6jag2M2vNXkaY9ogOTmZ/fv3W/DooRqHJLflHpVI9nHkACUBy17gnJBt7gdeF5E7gVTgsoB9/xOyb447f7w8ARCR24DbALKysigoKGjzAcSSysrKLn8M4WJ1Eayj9SEipKamUlJScvyNuwBVRUSiXYyY0Nq68Pl8VFVVsWPHjlblG+3O8WnA06r6OxE5D3hOREaFI2NVnQXMAmdUVVcfhWMjiZpZXQSz+ghm9dEsUnURycBRCgTeAJHrpgX6Bk4fBqr6nogkA/2Ps+/x8jTGGBNBkezjKASGikieiCTidHYvCtnmU+BSABE5DUgG9rrbTRWRJBHJA4YCK1uZpzHGmAiK2BWHqjaIyPeA1wAP8JSqbhSRmUCRqi4CfgQ8ISJ343SUT1enh26jiMwDNgENwHdV1QfQUp6ROgZjjDFH6hF3jovIXqB1vT6xqz+wL9qFiBFWF8GsPoJZfTTraF2cpKoDQhN7RODoDkSkqKVb/3siq4tgVh/BrD6aRaou7CGHxhhj2sQChzHGmDaxwNF1zIp2AWKI1UUwq49gVh/NIlIX1sdhjDGmTeyKwxhjTJtY4DDGGNMmFjhimIgMdt9XsklENorID6JdpljgPmJ/jYj8M9pliTYR6S0iL4vIhyKy2X3mW48kIne7fycbRGSO+wijHkNEnhKRz0RkQ0BaXxF5Q0Q+dqd9wvFdFjhiWwPwI1UdCZwLfNfePwLAD4DN0S5EjPgTsFRVRwBn0EPrRURygO8D+ao6CufJElOjW6pO9zTus/8CzACWqepQYJm73GEWOGKYqu5S1dXufAXOSSHn2Ht1byKSC1wFPBntskSbiGQCnwf+BqCqdap6KLqliqp4IEVE4oFeNL/fp0dQ1X8DB0KSJwPPuPPPANeG47sscHQRIjIEGAe8H92SRN0fgZ8A/mgXJAbk4TwUdLbbdPekiKRGu1DRoKqlwCM4D07dBZSp6uvRLVVMyFLVXe78biArHJla4OgCRCQNeAW4S1XLo12eaBGRq4HPVHVVtMsSI+KBM4HH3dcvVxGmpoiuxm27n4wTTE8AUkXkq9EtVWxxHyAblvsvLHDEOBFJwAkaL6jqP6Jdnii7ALhGRLbjvDb4EhF5PrpFiiov4FXVxqvQl3ECSU90GbBNVfeqaj3wD+D8KJcpFuwRkWwAd/pZODK1wBHDxHnn49+Azar6+2iXJ9pU9R5VzVXVITgdn2+pao/9r1JVdwMlIjLcTboU51UEPdGnwLki0sv9u7mUHjpQIMQi4FZ3/lZgYTgytcAR2y4Absb5z3qt+7ky2oUyMeVO4AURWQeMBf4nyuWJCveq62VgNbAe59zWox49IiJzgPeA4SLiFZFvAA8BE0XkY5yrsofC8l32yBFjjDFtYVccxhhj2sQChzHGmDaxwGGMMaZNLHAYY4xpEwscxhhj2sQChzHtJCK+gGHSa0UkbHdti8iQwKecGhNL4qNdAGO6sGpVHRvtQhjT2eyKw5gwE5HtIvIbEVkvIitF5FQ3fYiIvCUi60RkmYic6KZnich8EfnA/TQ+KsMjIk+475h4XURS3O2/776jZZ2IzI3SYZoezAKHMe2XEtJUdWPAujJVHQ38GeeJvgD/CzyjqmOAF4BH3fRHgX+p6hk4z5ra6KYPBR5T1dOBQ8AUN30GMM7N5/ZIHZwxR2N3jhvTTiJSqappLaRvBy5R1a3uQyp3q2o/EdkHZKtqvZu+S1X7i8heIFdVawPyGAK84b6ABxH5KZCgqr8WkaVAJbAAWKCqlRE+VGOC2BWHMZGhR5lvi9qAeR/NfZJXAY/hXJ0Uui8uMqbTWOAwJjJuDJi+586/S/PrTG8CVrjzy4A7oOl96plHy1RE4oDBqroc+CmQCRxx1WNMJNl/Ksa0X4qIrA1YXqqqjUNy+7hPrK0Fprlpd+K8re+/cN7c9zU3/QfALPdppj6cILKLlnmA593gIsCjPfx1sSYKrI/DmDBz+zjyVXVftMtiTCRYU5Uxxpg2sSsOY4wxbWJXHMYYY9rEAocxxpg2scBhjDGmTSxwGGOMaRMLHMYYY9rk/wNj/pMCPvPUrAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AVE3s4UV3hYm",
        "outputId": "d1947bcd-e7f3-4e1d-bd8a-a66bec0b9bc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1024"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict2 = history2.history\n",
        "loss_values = history_dict2[\"loss\"]\n",
        "val_loss_values = history_dict2[\"val_loss\"]\n",
        "epochs = range(1,len(val_loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs,val_loss_values,label=\"Validation/Test Loss\")\n",
        "line2 = plt.plot(epochs,loss_values,label=\"Train Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "qAbwTBIBvmwY",
        "outputId": "bb87cee8-d6c6-4e2f-da35-b49975811b51"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict2 = history2.history\n",
        "acc_values = history_dict2[\"accuracy\"]\n",
        "val_acc_values = history_dict2[\"val_accuracy\"]\n",
        "epochs = range(1,len(val_acc_values)+1)\n",
        "line1 = plt.plot(epochs,val_acc_values,label=\"Validation/Test Acc\")\n",
        "line2 = plt.plot(epochs,acc_values,label=\"Train Acc\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Acc\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "xm0z0iYVvmwa",
        "outputId": "b31c2d2e-b040-4dc1-c480-8a4ba21c4f89"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8K6IuafFvnaA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict3 = history3.history\n",
        "loss_values = history_dict3[\"loss\"]\n",
        "val_loss_values = history_dict3[\"val_loss\"]\n",
        "epochs = range(1,len(val_loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs,val_loss_values,label=\"Validation/Test Loss\")\n",
        "line2 = plt.plot(epochs,loss_values,label=\"Train Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "bAO92AQWvngu",
        "outputId": "9d055b51-1d8b-4cd7-a2a0-47c43447f4e4"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict3 = history3.history\n",
        "acc_values = history_dict3[\"accuracy\"]\n",
        "val_acc_values = history_dict3[\"val_accuracy\"]\n",
        "epochs = range(1,len(val_acc_values)+1)\n",
        "line1 = plt.plot(epochs,val_acc_values,label=\"Validation/Test Acc\")\n",
        "line2 = plt.plot(epochs,acc_values,label=\"Train Acc\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Acc\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "IDuz5VO7vngv",
        "outputId": "b516ddc3-fc97-4013-b082-2ce1537fff72"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cVeQLT9yvn7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict4 = history4.history\n",
        "loss_values = history_dict4[\"loss\"]\n",
        "val_loss_values = history_dict4[\"val_loss\"]\n",
        "epochs = range(1,len(val_loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs,val_loss_values,label=\"Validation/Test Loss\")\n",
        "line2 = plt.plot(epochs,loss_values,label=\"Train Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "_eEF2J_HvoCF",
        "outputId": "8fa9b99c-e6f0-4c7c-e897-8d977ac1e1df"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict4 = history4.history\n",
        "acc_values = history_dict4[\"accuracy\"]\n",
        "val_acc_values = history_dict4[\"val_accuracy\"]\n",
        "epochs = range(1,len(val_acc_values)+1)\n",
        "line1 = plt.plot(epochs,val_acc_values,label=\"Validation/Test Acc\")\n",
        "line2 = plt.plot(epochs,acc_values,label=\"Train Acc\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Acc\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "6J1rcvmOvoCH",
        "outputId": "cc526024-aa05-4e51-95e1-b1e8e2dfd71d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iy7O4LAGvocK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict5 = history5.history\n",
        "loss_values = history_dict5[\"loss\"]\n",
        "val_loss_values = history_dict5[\"val_loss\"]\n",
        "epochs = range(1,len(val_loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs,val_loss_values,label=\"Validation/Test Loss\")\n",
        "line2 = plt.plot(epochs,loss_values,label=\"Train Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "jYNjyNjhvohg",
        "outputId": "7c1c7197-a6fa-4d66-ccde-e1213e1e8602"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict5 = history5.history\n",
        "acc_values = history_dict5[\"accuracy\"]\n",
        "val_acc_values = history_dict5[\"val_accuracy\"]\n",
        "epochs = range(1,len(val_acc_values)+1)\n",
        "line1 = plt.plot(epochs,val_acc_values,label=\"Validation/Test Acc\")\n",
        "line2 = plt.plot(epochs,acc_values,label=\"Train Acc\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Acc\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "QVEU-oFXvohh",
        "outputId": "fece6576-fc16-4918-9e25-b4ecae3f360b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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8O3+oiESISAIwBOcsA6A9cLuILBeR2SLS0WdHYIwx5hQ+a6qqo8eAl0RkDLAQ2AmUqeo8EekNLAH2A0uBMnebUKBIVXuJyC3AJGBQzR2LyFhgLEBSUhLZ2dk+PhTfKiwsbPTHUF+sLqqz+qjO6qOKz+pCVX3yAvoBcz3ejwPGnWH9KCDvNMveBYa50xuAVHdagKNnK0tmZqY2dgsWLPB3ERoMq4vqrD6qs/qocr51ASzXWr5TfdlUtQzoKCKpIhICjAZmeq4gIgkiUlGGcThnD4hIoNtkhYikA+nAPHe96ThNVwBXAd/68BiMMcbU4LOmKlUtFZGHgblAIDBJVdeKyHicFJsJDAaeFRHFaap6yN08GFgkIgD5wF2qWuouew54R0QeBQqB+311DMYYY07l0z4OVZ0FzKox72mP6anA1Fq2K8K5sqq2fR4BhtdvSY0xxtSV3TlujDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxis+DQ4RGSoiG0UkR0SerGV5GxGZLyKrRCRbRFI8lj0vImvc1+0e898Qka0issJ9ZfjyGIwxxlTns+AQkUDgZeAGIA24Q0TSaqw2EXhLVdOB8cCz7rbDgZ5ABtAXeExEYjy2+4WqZrivFb46BmOMMafy5RlHHyBHVbeoajEwBciqsU4a8C93eoHH8jRgoaqWquoxYBUw1IdlNcYYU0e+DI5kYIfH+zx3nqeVwC3u9EggWkTi3flDRSRCRBKAIUArj+0muM1bfxSRUN8U3xhjTG2C/Pz5jwEvicgYYCGwEyhT1Xki0htYAuwHlgJl7jbjgD1ACPA34AmcZq5qRGQsMBYgKSmJ7Oxsnx6IrxUWFjb6Y6gvVhfVWX1UZ/VRxWd1oao+eQH9gLke78cB486wfhSQd5pl7wLDapk/GPjkbGXJzMzUxm7BggX+LkKDYXVRndVHdVYfVc63LoDlWst3qi/POJYBHUUkFedMYjRwp+cKbjPUIVUtd4Nlkjs/EGiiqgdFJB1IB+a5y1qo6m4REeBmYI0Pj8GYi15JSQl5eXkUFRX5uyj1IjY2lvXr1/u7GA1CXesiLCyMlJQUgoOD67RfnwWHqpaKyMPAXCAQmKSqa0VkPE6KzcQ5Y3hWRBSnqeohd/NgYJGTDeQDd6lqqbvsHRFJBARYATzgq2Mw5lKQl5dHdHQ0bdu2xf0/16gVFBQQHR3t72I0CHWpC1Xl4MGD5OXlkZqaWqf9+rSPQ1VnAbNqzHvaY3oqMLWW7YpwrqyqbZ9X13MxjbmkFRUVXTShYbwnIsTHx7N///46b2N3jhtjLDQucd7+/i04jDF+NWTIEObOnVtt3p/+9CcefPDB024zePBgli9fDsCwYcM4cuTIKev8+te/ZuLEiWf87OnTp7Nu3brK908//TSfffaZN8WvpqSkhNTUVDIyMsjIyKB58+YkJydXvi8uLq7TfrKzs1myZMkZ17n55pu54oorzrms58OCwxjjV3fccQdTpkypNm/KlCnccccdddp+1qxZNGnS5Jw+u2ZwjB8/nmuvvfac9gWwePFibrzxRlasWMGKFSt44IEHePTRRyvfh4SE1Gk/ZwuOI0eO8NVXX3H06FG2bNlyzuU9VxYcxhi/uvXWW/n0008r/xrPzc1l165dDBo0iAcffJBevXrRtWtXnnnmmVq3b9u2LQcOHABgwoQJ9OjRg4EDB7Jx48bKdf7+97/Tu3dvunfvzqhRozh+/DhLlixh5syZ/OIXvyAjI4PNmzczZswYpk51ul3nz59Pjx496NatG/fddx8nT56s/LxnnnmGnj170q1bNzZs2FD5OXPmzOGGG26otZxfffUVV111FZmZmVx//fXs3r0bgBdffJG0tDTS09MZPXo0ubm5vPLKK/zxj38kIyODRYsWnbKvjz76iJtuuonRo0dXC92cnByuvfZaunfvTs+ePStD5fnnn6dbt250796dJ588ZdhAr/n7BkBjTAPym4/Xsm5Xfr3uM61lDM/c1PW0y+Pi4ujTpw+zZ88mKyuLKVOm8N3vfhcRYcKECcTFxVFWVsY111zDqlWrSE9Pr3U/X331FVOmTOHzzz8nPDycnj17kpmZCcAtt9zCD3/4QwCeeuopXnvtNX784x8zYsQIbrzxRm699dZq+yoqKmLMmDHMnz+fTp068f3vf5+//vWv/PSnPwUgISGBr7/+mv/93/9l4sSJvPrqqwAsWLCg1oArKSnhxz/+MTNmzCAxMZH33nuPX/3qV0yaNInnnnuOrVu3EhoaypEjR2jSpAkPPPAAUVFRPPbYY7Ue6+TJk3n66adJSkpi1KhR/PKXvwTge9/7Hk8++SQjR46kqKiIo0ePMnv2bGbMmMEXX3xBREQEhw4dOtOvq07sjMMY43eezVWezVTvv/8+PXv2pEePHqxdu7Zas1JNixYtYuTIkURERBATE8OIESMql61Zs4ZBgwbRrVs33nnnHdauXXvG8mzcuJHU1FQ6deoEwD333MPChQsrl99yizNSUmZmJrm5uQDs3LmTuLg4IiIiat3fmjVruO6668jIyOB3v/sdeXl5AKSnp/O9732Pt99+m6Cgs/8tv3fvXjZt2sTAgQPp1KkTwcHBrFmzhoKCAnbu3MnIkSMB596MiIgIPvvsM+69997KcsXFxZ31M87mrKUUkUjghHuTHiISAISp6vHz/nRjTINypjMDX8rKyuLRRx/l66+/5vjx42RmZrJ161YmTpzIsmXLaNq0KWPGjDnnmxTHjBnD9OnT6d69O2+88cZ5D8MRGuoMkRcYGEhpqXOL2Zw5c7j++utrXV9V6dq1K0uXLj1l2aeffsrChQv5+OOPmTBhAqtXrz7jZ7///vscPny48p6L/Px8Jk+eXC9NUHVVlzOO+YBnhEYA537ZgTHG1BAVFcWQIUO47777Ks828vPziYyMJDY2lr179zJ79uwz7uPKK69k+vTpnDhxgoKCAj7++OPKZQUFBbRo0YKSkhLeeeedyvnR0dEUFBScsq/OnTuTm5tLTk4OAP/4xz+46qqrzvj5Z+rf6Ny5M/v3768MjpKSEtauXUt5eTk7duxgyJAhPP/88xw9epTCwsLTlgucZqo5c+aQm5tLbm5uZRNddHQ0KSkpTJ8+HYCTJ09y/PhxrrvuOl5//XWOH3f+1r9QTVVhqlpY8cadPvVczBhjzsMdd9zBypUrK4Oje/fu9OjRgy5dunDnnXcyYMCAM27fs2dPbr/9dvr3788NN9xA7969K5f99re/pW/fvgwYMIAuXbpUzh89ejQvvPACPXr0YPPmzZXzw8LCeP3117ntttvo1q0bAQEBPPDA6QepKCsrIycnp9q+PYWEhDB16lSeeOIJunfvTkZGBkuWLKGsrIy77rqLbt260aNHDx555BGaNGnCTTfdxLRp007pHM/NzWXbtm3VLsNNTU0lNjaWL774gn/84x+8+OKLpKen079/f/bu3cvQoUMZMWIEvXr1IiMj46yXKNdJbQNYafWBBD8Henq8zwSWnm27hvSyQQ4vLlYX1Z1vfaxbt65+CtJA5OfnX/DPXLRokf7oRz+64J97Nt7URW3/DjiPQQ5/CnwgIrtwxodqDtx+5k2MMebSMXDgQAYOHOjvYlwwZw0OVV0mIl2Azu6sjapa4ttiGWOMaajO2schIg8Bkaq6RlXXAFEi8v98XzRjjDENUV06x3+oqpUDwajqYeCHviuSMcaYhqwuwREoHkMnug9ZqtuAK8YYYy46dekcnwO8JyL/577/EXDmC6qNMcZctOpyxvEE8C+cJ+09AKwGwn1ZKGPMpePgwYPnPAz58uXLeeSRR7z+zBUrViAizJkz51yLfUmry1VV5SLyBdAe+C6QAHzo64IZYy4N8fHxrFixAnCeoVFzcL/S0tLTjuHUq1cvevXq5fVnTp48mYEDBzJ58mSGDh16bgW/hJ32jENEOonIMyKyAfgLsB1AVYeo6ksXqoDGmEvPmDFjeOCBB+jbty+PP/44X375Jf369aNHjx7079+/csj07OxsbrzxRsAJnfvuu49hw4bRrl07XnzxxVr3rap88MEHvPHGG/zzn/+sNv5VbcOP1xyq3PMO80vVmc44NgCLgBtVNQdARB69IKUyxvjH7Cdhz5kH2fNa825ww3Neb5aXl8eSJUsIDAwkPz+fRYsWERQUxGeffcYvf/lLPvzw1IaPDRs2MHPmTMAZH+rBBx8kODi42jpLliwhNTWV9u3bM3jwYD799FNGjRp12uHHaw5VXl5efg6VcHE5U3DcAowGFojIHGAKzp3jxhjjc7fddhuBgYEAHD16lHvuuYdNmzYhIpSU1H4P8vDhwwkNDSU6OppmzZqxd+9eUlJSqq0zefJkRo8eDThjVb311luMGjWq1uHHaxuq3JwhOFR1OjDdHVY9C2fokWYi8ldgmqrOO9vORWQo8GcgEHhVVZ+rsbwNMAlIBA4Bd6lqnrvseWC4u+pvVfW9Gtu+CNynqlF1OlJjzNmdw5mBr0RGRlZO/9d//RdDhgxh2rRp5ObmMnjw4Fq3qRjuHKoPeV6hrKyMDz/8kBkzZjBhwgRUlYMHD552JFpTu7NeVaWqx1T1XVW9CUgBvsG50uqM3Ps9XgZuANKAO0QkrcZqE4G3VDUdGA886247HOgJZAB9gcdEJMZj372Apmc/PGPMxeDo0aMkJycD8MYbb5zzfubPn096ejo7duyoHGl21KhRTJs2rdbhx083VPmlzqsnAKrqYVX9m6peU4fV+wA5qrpFVYtxmrqyaqyThnOpL8ACj+VpwEJVLVXVY8AqYChUBtILwOPelN0Y03g9/vjjjBs3jh49epxyFuGNyZMnVzY7VRg1alTl1VW1DT9ec6jyPXv2nNexXBRqGzK3Pl7ArTjNUxXv7wZeqrHOu8BP3OlbAAXige/gDOcegXP57xbg5+56PwEedacL61IWG1b94mJ1UZ0Nq16dP4ZVb6j8Oay6Lz0GvCQiY4CFwE6gTFXniUhvYAmwH1gKlIlIS+A2YPDZdiwiY4GxAElJSef9qEh/KywsbPTHUF+sLqo73/qIjY29qNr4y8rKLqrjOR/e1EVRUVGd/x35Mjh2Aq083qe48yqp6i6cMw1EJAoYpe6Aiqo6AZjgLnsX+BboAXQActzhsyJEJEdVO9T8cFX9G/A3gF69eunpOtMai+zs7NN2CF5qrC6qO9/6WL9+PdHR0fVXID8rKCi4qI7nfHhTF2FhYfTo0aNO6/oyOJYBHUUkFScwRgN3eq4gIgnAIVUtB8bhXGFV0Y/RRFUPikg6kA7MU9VSnAdJVWxfWFtoGGOM8R2fBYeqlorIw8BcnMtxJ6nqWhEZj9NuNhOnyelZEVGcpqqH3M2DgUXuWUU+zmW6594jZow5I1XFYxBsc4lxujPqzqd9HKo6C5hVY97THtNTgam1bFeEc2XV2fZv93AYc57CwsI4ePAg8fHxFh6XIHXvZfHm5kZ/d44bY/wsJSWFvLw89u/f7++i1IuioiK7w9tV17oICws75Q77M7HgMOYSFxwcTGpqqr+LUW+ys7Pr3Ml7sfNVXXh1A6AxxhhjwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7zi0+AQkaEislFEckTkyVqWtxGR+SKySkSyRSTFY9nzIrLGfd3uMf81EVnpbjNVRKJ8eQzGGGOq81lwiEgg8DJwA5AG3CEiaTVWmwi8parpwHjgWXfb4UBPIAPoCzwmIjHuNo+qand3m+3Aw746BmOMMafy5RlHHyBHVbeoajEwBciqsU4a8C93eoHH8jRgoaqWquoxYBUwFEBV8wFERIBwQH14DMYYY2rwZXAkAzs83ue58zytBG5xp0cC0Roy34YAABf7SURBVCIS784fKiIRIpIADAFaVWwkIq8De4AuwF98U3xjjDG1CfLz5z8GvCQiY4CFwE6gTFXniUhvYAmwH1gKlFVspKr3uk1hfwFuB16vuWMRGQuMBUhKSiI7O9u3R+JjhYWFjf4Y6ovVRXVWH9VZfVTxWV2oqk9eQD9grsf7ccC4M6wfBeSdZtm7wLBa5l8JfHK2smRmZmpjt2DBAn8XocGwuqjO6qM6q48q51sXwHKt5TvVl01Vy4COIpIqIiHAaGCm5woikiAiFWUYB0xy5we6TVaISDqQDswTRwd3vgAjgA0+PAZjjDE1+KypSlVLReRhYC4QCExS1bUiMh4nxWYCg4FnRURxmqoecjcPBhY52UA+cJe7vwDgTfcKK8HpC3nQV8dgjDHmVD7t41DVWcCsGvOe9pieCkytZbsinCuras4vBwbUf0mNMcbUld05bowxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrFhzGGGO8YsFhjDHGKxYcxhhjvGLBYYwxxisWHMYYY7xiwWGMMcYrPg0OERkqIhtFJEdEnqxleRsRmS8iq0QkW0RSPJY9LyJr3NftHvPfcfe5RkQmiUiwL4/BGGNMdT4LDhEJBF4GbgDSgDtEJK3GahOBt1Q1HRgPPOtuOxzoCWQAfYHHRCTG3eYdoAvQDQgH7vfVMRhjjDmVL884+gA5qrpFVYuBKUBWjXXSgH+50ws8lqcBC1W1VFWPAauAoQCqOktdwJdACsYYYy4YXwZHMrDD432eO8/TSuAWd3okEC0i8e78oSISISIJwBCgleeGbhPV3cAcH5TdGGPMaQT5+fMfA14SkTHAQmAnUKaq80SkN7AE2A8sBcpqbPu/OGcli2rbsYiMBcYCJCUlkZ2d7ZMDuFAKCwsb/THUF6uL6qw+qrP6qOKzulBVn7yAfsBcj/fjgHFnWD8KyDvNsneBYR7vnwGmAwF1KUtmZqY2dgsWLPB3ERoMq4vqrD6qs/qocr51ASzXWr5TfXnGsQzoKCKpOGcSo4E7PVdwm6EOqWq5GyyT3PmBQBNVPSgi6UA6MM9ddj9wPXCNu525AJbkHOBP8zeRGBXKwI4JDOyQQKu4CH8XyxjjBz4LDlUtFZGHgblAIDBJVdeKyHicFJsJDAaeFRHFaap6yN08GFgkIgD5wF2qWuouewXYBix1l3+kquN9dRyXugOFJ5nw6XqmfbOT5CbhbD94nE9X7wagdVwEAzo4IdK/fTxNI0P8XFpjzIXg0z4OVZ0FzKox72mP6anA1Fq2K8K5sqq2ffq7X+aSUF6uvLd8B8/N3sDx4lIeuboD/29IB0KDAti8/xif5xxgcc4BPlm5i8lfbkcELm8ZWxkkvdo2JSw40N+HYYzxAfsSNqfYuKeAX01bzfJth+mbGseEkZfToVl05fIOzaLo0CyKe/q3pbSsnFU7j7J4kxMkry3ewiv/3kxoUAC928ZVBklayxgCA8SPR2WMqS8WHKbSieIy/jx/E68u2kJ0WBAv3JrOrZkpuE2CtQoKDKBn66b0bN2UR67pyLGTpXyZe4jP3SB5fs4GngeaRATTv308AzskMrBDAq3jrX/EmMbKgsMAsGDDPv5rxhryDp/gtswUxg27jLhz6LOIDA1iSOdmDOncDIB9BUUs3Xyw8oxk1uo9ALSKC2dghwQGdEigf/uEc/osY4x/WHBc4vbmF/Gbj9cya/Ue2idGMmXsFVzRLr7e9t8sOoysjGSyMpJRVbYccPtHNh3gk5W7mfzlDkSga8uYymat3m3jrH/EmAbMguMSVVauvP2fbbwwdyPFZeX8/LpOjL2qHaFBvvvCFhHaJ0bRPjGK7/er6h+paNaatHgr//fvLYQEBdC7bdPKIOnaMtb6R4xpQCw4zqC8XAm4CL+w1uw8yq+mrWZl3lEGdUzgt1mX0zYh8oKXw7N/5MfXdOR4cSlfbj1U2az1P3M28j9sJDbc7R9x7x9x7ksyxviLBccZ/Gr6arYfOk5WRjJDL29OTFjjHsG98GQpf5j3LW8s2UpcZCh/Hp3BiO4tz9j5fSFFhAQxuHMzBrv9I/sLTrJk84HKpq3Za5z+kSahQtzX2X4sqSMmLJjbe7diZI/kS7ZpTVVZtOkAby7J5eiJErq0iKZz8xguax5Np+bRjf7/jKmdBccZtI2P5POcgzw+dRVPTV/DtZc1IysjmcGdE33apOMLc9fu4dcz17Inv4g7+7Tm8aFdiA1v2P+pE6NDq/WPbHX7R2Yv20hcQszZd+BjOfsKGffRal6Yu5G7rmjD3Ve0ITE61N/FuiCKSsqYsWInry3eyrd7C0mMDqVNXAQzvtlFwcntleslNwmnS/PoaoGSmhBJUKA9Q64xs+A4gx9d1Z6xV7bjmx1HmPHNTj5ZtZtZq/cQExbE8PQWZGUk06dtXINuztp55ATPzFjLZ+v30qV5NC9/ryc9Wzf1d7G8JiK0S4yiXWIUrU7mMnhwT38XCVXlP1sO8driLbw4fxOvZG8mK6MlPxiUSpfm/g82XzhQeJJ/LN3G2//ZxsFjxVzWIobf39adG7u3IDQoEFVl19EiNuzOZ8OeAjbsKWDjnnyyv91PWbnTxBgSGECHZlGnBEpidGiDOfs1Z2bBcRYiUtkO/183prE45wAzVuxixopdTP5yBy1iwxjRvSU390jmshYN58uitKyc1z/P5Y+ffYsq/HJYF+4dkEqw/aVXb0SEfu3j6dc+ni37C3n981w++GoHH3yVx8AOCfxgUCpXdUxs0H9Y1NXGPQVMWryVaSt2UlxazjVdmvGDQan0axdf7cteREhuEk5yk3CuuSypcv7J0jI27zvGhj35bNxTwPo9BXy++QAffbOzcp2mEcF0bh5Nl+YxbqjE0CkpiogQ+5pqaOw34oWgwIDKNvjjxaX8c91eZqzYxWuLt/J/C7fQOSmaERktycpoSUpT/93g9s32w/xy2hrW787nmi7N+E1WV7+W51LQLjGK3958OT+7rhPvfrmdt5bmcu/ry+jQLIofDExtlP0gqsq/v93Pa4u3smjTAcKCA7gtM4X7BqbSPjHKq32FBgWS1jKGtJbV/7g6fKy48qyk4gzlvWU7OFHiPEVBBNrERZwSKK3jIuxKOz+y4DhHESFBle3vh44V8+mqXUxfsYsX5m7khbkb6d22KVkZyQzv1uKCDf539EQJL8zdwDtfbCcpOoxX7urJ9V2b2+n/BdQ0MoSHhnTgh4Pa8enqXby6aGtVP0jf1tzVrw3NosP8XcwzKiopY/o3Tv/Fpn2FNIsO5RfXd+bOPq3r/d9y08iQyrO2CuXlyo7Dx1m/u4CNewrYuDefDbsL+Oe6vbitXYQFB9A5KbpaoHRuHk181KXRx+RvFhz1IC4yhLv7teXufm3Zceg4M1bsZPqKXTw1fQ2/+XgtV3VKJCsjmWsvSyI8pP7/6lRVPl61m99+so6DhScZ078tP/9OZ6JC7dfrLyFBAYzskcLNGcl8sfUQry7ayl8W5PDKv7cwIqMlPxiY2qCaNsG5iu0f/3H6Lw4dKyatRQx/+G53bkxvSUjQhWviDAgQ2sRH0iY+kqGXN6+cf6K4jE37nLOSDbudQJm/fh/vL8+rXCcxOpRmISV8fmwdnd1A6dAsqtGd7TV09s1Sz1rFRfDw1R15aEgH1u7KZ+bKXcxcsYvP1u8jMiSQ6y9vTlZGMgPax9fLlSXbDh7jqelrWLTpAN2SY5l0T2+6pcTWw5GY+iAiXNEunivaxbP1wDFe/3wrHyzPY2pFP8jAVK7q5N9+kA178nlt0VZmrNhFSbnbfzGwHVe0i2tQZ6vhIYGkpzQhPaVJtfn7C06ycU8BG/bks353ActzdvHm0m0UlzqP6wkMEFITIuncPJrLmkdXBkpK0/AGdXyNiQWHj4gIlyfHcnlyLE8M7cIXWw8y45tdzFqzm4++3klCVCg3prfg5h7JdE+J9fofcHFpOX9f5FzNExwYwK9vSuPufm2t3bcBS02IZHxWVT/Im0tyufeNZbRPjOS+ganc0iPFJ2ektSkvV/69aT+TPPovbu/dinsHtKWdl/0X/pYYHUpitPOAMYDs7MMMHHQluQePVwbKhj0FrMo7wqerdlduFxUaRGe3iasiUDo3j27wl6k3BBYcF0BggNC/vTOY32+yupK9cR/Tv9nFu19s540luaQmRFZemZVahzu4v9x6iF9NW82mfYUM69acp2/sSvPYht1ubqo0iQjh/w3uwP0D2zFr9W5eXbyFX01bw8SK+0F82A9SVFLGNLf/ImdfIUkxoTw+1Om/aBJx8Qw0GeRe8tuhWRTD01tUzi88Wer0m3gEyicrd/HuF6WV67SMDXP6Tlq4nfHNY2iXGGlXJHqw4LjAwoIDGXp5C4Ze3oKjJ0qYs2Y307/ZxYv/2sSf52+ie0osWRnJ3Ni9xSlfHoePFfPs7PW8vzyP5CbhTBrTi6u7JJ3mk0xDFxIUwM09ksnKaMmXWw/x2uKtvLQgh1f+vZkR3ZP5wcDUU65COlf7Cop4e+k23v5iO4eOFXN5cgx/vL07w7td2P4Lf4sKDSKzTVMy21Tdy6Sq7Mkvquo7cQNlcc4BSsqc3vjgQGectYqrupyzlBiSYi7Ne08sOPwoNjyY23u35vberdl99AQfr9zF9G92Mf6Tdfzu03UM6JBAVkYy13dNYvHOEn626N/knyjhR1e14yfXdLTr2y8SIkLfdvH0bRdPrtsP8v7yPD78Oo/+7eO5f1Aqgzs1O6d+kPW783lt8VZmuv0X116WxA8GptI3tWH1X/iTiNAiNpwWseGVjwMApzl4y4FC574TN1C+2HqI6St2Va4TGx5cve+kRTSdk6KJvMgvTLm4j64RaREbztgr2zP2yvZs2lvAjBW7mL5iJ499sJInPhTKypWerZvw37d0u2jvSjbQNiGS32Rdzs+u68zkZdt54/Nc7ntjOe0SI7lvQCqjep69H6S8vOr+i8U5BwgPDmR0n1bcOyC1Tk2hxhESFOBe6htDVkbV/KPHS5wbGfdWBcrUr/I4VlxWuU6ruHBaxIQTHxVCQlQoCVGhldOJ0SHu+1AiQwIbZYBbcDRAHZOieez6zvz8O534evth5qzZQ9nhnTx1Z/+L4i5kc3axEcE8cFV7fjAwlVmrd/Pa4q08NX0NE+dt5K6+bfh+vzY0i6nelHmiuIyPvslj0uKtbN5/jOYxYTwxtAt39Gl1UfVf+FtsRHDlGWKF8nJl55ETbnNXPt/uK2RffhHf7i1g6ZaDHDleUuu+woIDKkMksZaQcV7OdGx4cIP5/2/B0YCJCJlt4shsE0d29r4G84/GXDjBgQFkZSQzontLlm87zKuLtvBydg7/t3AzN3V37gc5crKc38/byNv/2cbh4yV0S47lz6MzGNathXXoXiABAUKruAhaxUVwXdqp/Y7FpeUcOlbMgcKT7suZPugxvfNIESvzjnLoWHHluF6eggKEuEg3UKKrAqXiZ7w7nRgVStPIEJ/+7n0aHCIyFPgzEAi8qqrP1VjeBpgEJAKHgLtUNc9d9jww3F31t6r6njv/YeCnQHsgUVUP+PIYjGkIRITebePo3TaObQeP8frnuby/fAcffb2TQIFycrjusiTuH9SO3m2bNsrmj4tZSFAAzWPD6nT1Y3m5cvh4MQePFXOg4CT73XA56BE6BwtPsnlfIfsLT1ber1JT04hgwgNKeefyY/XeROmz4BCRQOBl4DogD1gmIjNVdZ3HahOBt1T1TRG5GngWuFtEhgM9gQwgFMgWkdmqmg98DnwCZPuq7MY0ZG3iI/n1iK48el0n3l+2g2/W5/D4qAF+eRiXqX8BAUK8ewbRKSn6jOuqKgUnSznonrUcKDjJATdwDhSeZEPuTqLD6v9r3pdnHH2AHFXdAiAiU4AswDM40oCfudMLgOke8xeqailQKiKrgKHA+6r6jbs/HxbdmIYvNjyYH17Zjuzy7RYalygRISYsmJiw4FrPKrKzD5Lgg/G7fNkAmgzs8Hif587ztBK4xZ0eCUSLSLw7f6iIRIhIAjAEaOXDshpjjKkjf3eOPwa8JCJjgIXATqBMVeeJSG9gCbAfWAqUnXYvtRCRscBYgKSkJLKzs+ux2BdeYWFhoz+G+mJ1UZ3VR3VWH1V8VRe+DI6dVD9LSHHnVVLVXbhnHCISBYxS1SPusgnABHfZu8C33ny4qv4N+BtAr169dPDgwed0EA1FdnY2jf0Y6ovVRXVWH9VZfVTxVV34sqlqGdBRRFJFJAQYDcz0XEFEEkSkogzjcK6wQkQC3SYrRCQdSAfm+bCsxhhj6shnweF2bD8MzAXW43RsrxWR8SIywl1tMLBRRL4FknDPMIBgYJGIrMM5a7jL3R8i8oiI5OGcwawSkVd9dQzGGGNO5dM+DlWdBcyqMe9pj+mpwNRativCubKqtn2+CLxYvyU1xhhTV3ZbqTHGGK9YcBhjjPGKqJ46JsrFRkT2A9v8XY7zlADY8CoOq4vqrD6qs/qocr510UZVE2vOvCSC42IgIstVtZe/y9EQWF1UZ/VRndVHFV/VhTVVGWOM8YoFhzHGGK9YcDQef/N3ARoQq4vqrD6qs/qo4pO6sD4OY4wxXrEzDmOMMV6x4GjARKSViCwQkXUislZEfuLvMjUE7lhm34jIJ/4ui7+JSBMRmSoiG0RkvYj083eZ/EVEHnX/n6wRkckicvbH7V1ERGSSiOwTkTUe8+JE5J8issn92bQ+PsuCo2ErBX6uqmnAFcBDIlLrUCyXmJ/gjH9mnEczz1HVLkB3LtF6EZFk4BGgl6pejvO46tH+LdUF9wbOA+88PQnMV9WOwHz3/Xmz4GjAVHW3qn7tThfgfCnUfBjWJUVEUnCeRX/JD24pIrHAlcBrAKpaXPFYgktUEBAuIkFABLDLz+W5oFR1IXCoxuws4E13+k3g5vr4LAuORkJE2gI9gC/8WxK/+xPwOFDu74I0AKk4Dzp73W26e1VELslnyKrqTmAisB3YDRxVVXsUAySp6m53eg/OKOTnzYKjEXAfcvUh8FNVzfd3efxFRG4E9qnqV/4uSwMRBPQE/qqqPYBj1FNTRGPjtt1n4YRpSyBSRO7yb6kaFnUuoa2Xy2gtOBo4EQnGCY13VPUjf5fHzwYAI0QkF5gCXC0ib/u3SH6VB+SpasVZ6FScILkUXQtsVdX9qloCfAT093OZGoK9ItICwP25rz52asHRgImI4LRfr1fVP/i7PP6mquNUNUVV2+J0fP5LVS/ZvypVdQ+wQ0Q6u7OuAdb5sUj+tB24QkQi3P8313CJXihQw0zgHnf6HmBGfezUgqNhGwDcjfOX9Qr3NczfhTINyo+Bd0RkFZAB/Lefy+MX7lnXVOBrYDXOd9sldQe5iEwGlgKdRSRPRH4APAdcJyKbcM7KnquXz7I7x40xxnjDzjiMMcZ4xYLDGGOMVyw4jDHGeMWCwxhjjFcsOIwxxnjFgsOYcyQiZR6XSa8QkXq7a1tE2nqOcmpMQxLk7wIY04idUNUMfxfCmAvNzjiMqWcikisi/yMiq0XkSxHp4M5vKyL/EpFVIjJfRFq785NEZJqIrHRfFUNlBIrI391nTMwTkXB3/UfcZ7SsEpEpfjpMcwmz4DDm3IXXaKq63WPZUVXtBryEM6IvwF+AN1U1HXgHeNGd/yLwb1XtjjPW1Fp3fkfgZVXtChwBRrnznwR6uPt5wFcHZ8zp2J3jxpwjESlU1aha5ucCV6vqFneQyj2qGi8iB4AWqlrizt+tqgkish9IUdWTHvtoC/zTfQAPIvIEEKyqvxOROUAhMB2YrqqFPj5UY6qxMw5jfENPM+2Nkx7TZVT1SQ4HXsY5O1nmPrjImAvGgsMY37jd4+dSd3oJVY8z/R6wyJ2eDzwIlc9Tjz3dTkUkAGilqguAJ4BY4JSzHmN8yf5SMebchYvICo/3c1S14pLcpu6ItSeBO9x5P8Z5Wt8vcJ7cd687/yfA39zRTMtwQmQ3tQsE3nbDRYAXL/HHxRo/sD4OY+qZ28fRS1UP+LssxviCNVUZY4zxip1xGGOM8YqdcRhjjPGKBYcxxhivWHAYY4zxigWHMcYYr1hwGGOM8YoFhzHGGK/8fyGtpNaJnsrTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Eet6fbekvply"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict6 = history6.history\n",
        "loss_values = history_dict6[\"loss\"]\n",
        "val_loss_values = history_dict6[\"val_loss\"]\n",
        "epochs = range(1,len(val_loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs,val_loss_values,label=\"Validation/Test Loss\")\n",
        "line2 = plt.plot(epochs,loss_values,label=\"Train Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "AyRswZxnvpvD",
        "outputId": "351c4999-ec1b-4335-df13-72d3bd40ccfe"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict6 = history6.history\n",
        "acc_values = history_dict6[\"accuracy\"]\n",
        "val_acc_values = history_dict6[\"val_accuracy\"]\n",
        "epochs = range(1,len(val_acc_values)+1)\n",
        "line1 = plt.plot(epochs,val_acc_values,label=\"Validation/Test Acc\")\n",
        "line2 = plt.plot(epochs,acc_values,label=\"Train Acc\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Acc\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "dKtQaV35vpvE",
        "outputId": "6791987a-d57d-40de-fceb-f97878cd22f8"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}