DeepLearningfor_handwritten_digit_classification.ipynb

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      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": []
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        "(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()"
      ],
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      "outputs": [
        {
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          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
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      "source": [
        "len(X_train)"
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              "60000"
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      "source": [
        "\n",
        "len(X_test)"
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      "outputs": [
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            "text/plain": [
              "10000"
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        "X_train[0].shape"
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            "text/plain": [
              "(28, 28)"
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"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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvameAD7n0Sr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1ccdfbf8-4aea-469b-bd3a-af6463c6bf2f"
      },
      "source": [
        "y_train[0]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzUFA22DoA-b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "93c0c661-2d61-4364-cb7b-3a696ba1a411"
      },
      "source": [
        "y_train[:10]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvifdmX0pljD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6aceb0be-2c3b-453f-e3d6-9aac6564ee3a"
      },
      "source": [
        "#How 're goin' to flatten the 2d Array? Using reshape\n",
        "X_train_flattened= X_train.reshape(len(X_train),28*28)\n",
        "X_test_flattened = X_test.reshape(len(X_test), 28*28)\n",
        "X_train_flattened.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jQfxucLu4uI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train / 255\n",
        "X_test = X_test / 255"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ejn2G-3JqP5O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e23cf799-29fb-43d8-d6c8-d9ba75499bda"
      },
      "source": [
        "\n",
        "len(X_train_flattened[0])\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "784"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxi54dd1rNdr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "edc29d19-e176-428d-da5b-4914fb0a027e"
      },
      "source": [
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train_flattened, y_train, epochs=5)\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 2.2798 - accuracy: 0.1668\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 2.1121 - accuracy: 0.1987\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 2.1048 - accuracy: 0.1990\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 2.0994 - accuracy: 0.2019\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 2.1012 - accuracy: 0.2016\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f86892a8b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yo2tjZegtZ49",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "48c88b9d-351c-4e9e-bc93-045c7e054b3f"
      },
      "source": [
        "model.evaluate(X_test_flattened,y_test)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 3.4946 - accuracy: 0.3134\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.4945597648620605, 0.313400000333786]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9P8JYCS5wAB3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "0eece97c-ee14-4c6c-c6df-e2b9536cce67"
      },
      "source": [
        "plt.matshow(X_test[1])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f86880bf550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQtb2y_Sv5wA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8058d5e9-5eed-47c5-d6f8-1c17b8447bd4"
      },
      "source": [
        "y_predicted = model.predict(X_test_flattened)\n",
        "y_predicted[0]#Probabilty of 10 probablities of each digits and then choose the maximum score among the probabilities\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 1., 0., 1., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HXBvx5DwWSK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3626d1e6-1d4b-4933-f31e-0416cfc2f85a"
      },
      "source": [
        "np.argmax(y_predicted[1])\n",
        "#np.argmax finds a maximum element from an array and returns the index of it"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3YJsWG4Xwbvk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRlpLTOJ_yp7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fd71ee2d-33a2-4ed5-a267-bdf8ecabb264"
      },
      "source": [
        "y_predicted_labels[:5]#The first 5 digits of the corresponding test images"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5, 2, 1, 0, 2]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_KYb9hJAO-W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "801f3fb9-57e1-4acc-9072-3d57e580ddca"
      },
      "source": [
        "#Confusion matrix . y_test and y_predicted To get some understanding \n",
        "#about the accuracy of the model\n",
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 949,    0,   22,    1,    0,    7,    0,    1,    0,    0],\n",
              "       [   0, 1082,   52,    1,    0,    0,    0,    0,    0,    0],\n",
              "       [  23,    0, 1009,    0,    0,    0,    0,    0,    0,    0],\n",
              "       [  22,    0,  935,   52,    0,    1,    0,    0,    0,    0],\n",
              "       [   2,    0,  971,    5,    4,    0,    0,    0,    0,    0],\n",
              "       [  53,    3,  692,  121,    1,   22,    0,    0,    0,    0],\n",
              "       [  80,    3,  875,    0,    0,    0,    0,    0,    0,    0],\n",
              "       [   4,    2,  380,  184,   44,  398,    0,   16,    0,    0],\n",
              "       [   8,    2,  963,    1,    0,    0,    0,    0,    0,    0],\n",
              "       [   7,    6,  842,  111,   40,    3,    0,    0,    0,    0]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOLnXzOCAtv3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "cc2a0b21-10e1-41c2-dd33-386ec5bc5eba"
      },
      "source": [
        "\n",
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WfWOtvbWoz4gskej7YH60P32m0xGKLf60lk5HKLadB/Y6HUFExG+yD6WZQO5v/w+v+ex3banrnwxo9mPllxYda+2dhawrssgRERGRAAjiLidf0b2uREREwlUYdF2FzXV0REREJPyoRUdERCRchUGLjgodERGRcOWHCUnBRl1XIiIiErLUoiMiIhKu1HUlIiIiISsMCh11XYmIiEjIUouOiIhIuNIFA0VERCRkqetKRERExL3UoiMiIhKuwuA6Oip0REREwlUYdF0FbaETFRG00Y6qVMIVTkcotr0LBjgdodhi6/yf0xFERMRF3FVNiIiIiO+oRUdERERCVhhML9esKxEREQlZatEREREJUzZXs65EREQkVIXBGB11XYmIiEjIUouOiIhIuAqDwcgqdERERMJVGIzRUdeViIiIhCy16IiIiISrMBiMrEJHREQkXKnQERERkZAVBncv1xgdERERCVlq0REREQlXYdB1FdItOklJ8UycOJRFi6awcOFkuna9A4Bnn32MefMmMmfOeMaM+ZL4+MoOJy1Yi+aNWb5sBqtWzKJH966O5Xj2o69pdOfTXN/t1fxlu/bspcsLH3LNgy/S5YUP2Z25DwBrLa8N+JarH3iBGx57jRW/b8h/zztfjeL6bq9yfbdXmfjzooB/HUcTLMe4ONyW2W15QZkDwW15wZ2ZC5VrffcIUiFd6GRn5/Dkky9x8cXNaNToOu65pzNnnVWTd97pR716LalfvzUTJiTz1FMPOx31qDweD33fe5lr2nTk/FpNaN/+Os4+u6YjWa5tfAkf97zviGWfj5zCJeefwdj3n+GS88/g85GTAZi1eAV/btzC2Pef4dl72vPSp8MBmLFwOSt/T2XEmz34+pVuDBozlcx9+wP+tRwumI7xsXJbZrflBWUOBLflBXdmlhAvdDIyNpOSsgyAzMy9rFq1loSEKuzZk5m/TUxMDDZIB2PVq3sR69b9wfr1f5GVlcXw4aO4tk0LR7LUOacG5UrHHLFs2vylXNu4HgDXNq7H1HlL85e3aVQPYwy1zqjOnr372bJjF+tSM6h9zulERkQQUzKaM05J4OeUlQH/Wg4XTMf4WLkts9vygjIHgtvygjszF8nm+u4RpPxW6BhjzjLGNDXGlP7X8pb+2mdhTjkliQsvPJf581MAeO657qxZM5ubb76OF19824lIRUpIjGNDanr+69S0jSQkxDmY6Ejbd+3h5ArlAKhUvizbd+0BYPP2XcSdVD5/uyonlWfz9l2cWS2vsNl/8BA7dmcyb/kaMrbtdCT734L9GB+N2zK7LS8ocyC4LS+4M3OR1HV1fIwxDwGjgAeBZcaYtoetfqWQ93UxxiwwxizIzs4saLNii42NYciQT+je/YX81pznnnuTmjUvZejQkdx7720+21e4MsaAKXybBrXO5vKLzqFzz3d44t1B1DqjGh5PEW8SERE5Af5q0bkbqG2tvQ5oDDxjjPl7IEyBv9mstf2ttXWstXUiI0sXtFmxREZGMmTIJwwbNpJRoyb+Z/2wYSO57rpWPtmXr6WnZVA1KSH/dVJiPOnpGQ4mOlLFcmXYsmMXAFt27KJi2TIAVK5Y7oiWmk3bdlK5Yl7LT5cbWjDirSfo/2xXrIVqDg8ED/ZjfDRuy+y2vKDMgeC2vODOzEWxubk+exTFGPOoMWa5MWaZMWaIMaakMaa6MWauMWatMWaYMaaEd9to7+u13vXVjvdr9Feh47HWZgJYa/8gr9hpZYx5myL/7vetTz55g9Wr19K372f5y04/vVr+82uuac5vv60LZKRjNn9BCjVqVKdatapERUXRrl1bxoyd5HSsfI3rnMfo6fMAGD19Hk3qnu9dfj5jfpqHtZYlv62nTExJTq5QjpycXHbu2QvAb3+m8dtf6Vxa6yzH8kPwH+OjcVtmt+UFZQ4Et+UFd2YuUoC6rowxicBDQB1r7XlABHAz8DrwjrW2BrADuNP7ljuBHd7l73i3Oy7+uo7OJmPMhdbaFABrbaYx5hpgAHC+n/b5Hw0a1KFDhxtYunQlc+aMB6B37ze5/fb21Kx5Grm5ufz1VxoPPfR0oCIVS05ODg8/0ovx474hwuNh4KBhrFjxmyNZerw7kAXL17JzTybN7nmG+9u15s7rr+Lxt7/gh6lziD+5Am89mjd9/4qLz2Hm4uVc/eALlCxRghe7dgAgOyeH2595F4DYmJK8+mAnIiMiHPl6/hZMx/hYuS2z2/KCMgeC2/KCOzMHmUiglDEmC4gBNgJXArd61w8CngM+Btp6nwN8C3xgjDH2OGYPGX/MODLGJAHZ1tr/tOkZYy6z1v5c1GeUKnVq8I5sOoqsnGynIxTb3gUDnI5QbLF1/s/pCCIifpN9KC2gvR57X+ros9+1pZ/5+h6gy2GL+ltr+//9wjuE5WVgPzAJeBiY4221wRhTFZhgrT3PGLMMaGmtTfWuWwdcYq3dWtxcfmnR+TtYAeuKLHJEREQkAHw4W8pb1PQ/2jpjTAXyWmmqAzuBEUBAZmGH9HV0REREJCg0A9Zba7dYa7OA74HLgPLGmL8bXZKANO/zNKAqgHd9OWDb8exYhY6IiEi4ys313aNwfwH1jTExxhgDNAVWANOAG73b3EbepWkARntf410/9XjG54Bu6ikiIhK+AnShP2vtXGPMt8AiIBtYTF431zhgqDHmJe+yz71v+Rz40hizFthO3gyt46JCR0RERPzOWtsb6P2vxb8D9Y6y7QHgJl/sV4WOiIhIuArie1T5igodERGRcBXE96jyFQ1GFhERkZClFh0REZEwdSz3qHI7FToiIiLhSl1XIiIiIu6lFh0REZFwFQYtOip0REREwlUYTC9X15WIiIiELLXohLHcjWucjlBs5UvGOh2hWHYe2Ot0BBGRgqnrSkREREKVDYNCR11XIiIiErLUoiMiIhKuwqBFR4WOiIhIuAqDKyOr60pERERCllp0REREwpW6rkRERCRkhUGho64rERERCVlq0REREQlT1oZ+i44KHRERkXClrisRERER91KLjoiISLgKgxYdFToiIiJhSve6EhEREXGxkC50kpLimThxKIsWTWHhwsl07XoHAK+88jQpKcnMmzeRYcP6Ua5cWYeTFqxF88YsXzaDVStm0aN7V6fj5Pt6ego3vPo1/3vla76algLAh+PmcNNr39Du9SHc++FINu/KBGD+mlQu79GPdq8Pod3rQ+g3YZ6T0QFYtHQqM2aPYdqsUUyZ/h0Az73Yg9kLJvLTL6MZ9PWHlC1XxuGUBQvW86Ign/bvQ3rqElIWJzsd5Zi57RiD+zK7LS+4M3Ohcq3vHkHKBOvUslKlTj3hYHFxlYmLq0xKyjJKl47ll1/G0q5dFxIT45g+/RdycnJ46aUnAejV67UT2ldWTvaJxv0Pj8fDyuUzadn6FlJTNzJn9ng6drqflSvX+OTz94x56rjetzZ9G08MmshXj7UjKiKCrh+Pomf7JlQsHUPpUiUA+OanJfyesZ1e7Zswf00qg6cu5v172pxw5qo39T3hz4C8QqdZoxvYvn1H/rLGV17GzJ/mkJOTw7PPPw7AC73fOqH97Dyw94TefzT+Pi/84YrLLyEzcy9ffPEeF17U1Ok4RXLjMXZbZrflhcBkzj6UZnz2YcdgV6emPisCyn2ZHNDsxyqkW3QyMjaTkrIMgMzMvaxatZaEhCokJ88kJycHgHnzFpOYGO9kzALVq3sR69b9wfr1f5GVlcXw4aO4tk0Lp2Px+6btnH9qHKVKRBEZ4aF2jUSSl6zLL3IA9h/MIijP+EJMn/pz/nmxYP4SEhLjHE50dMF6XhRm5qy5bN+x0+kYx8yNx9htmd2WF9yZWfxY6Bhj6hlj6nqfn2OM6WaMae2v/RXllFOSuPDCc5k/P+WI5Z07t+PHH6c7E6oICYlxbEhNz3+dmraRhATnf/nWiD+JRevS2bl3P/sPZTFrxZ9s2pnXTfX+2Nm0ePYLxi9czX2t6+e/59f1GbR77Ru6fjyKtRu3ORU9n7WWb0cOIPmn7+l8e/v/rO/Q6QaSJ89wIFnRgvW8CCVuPMZuy+y2vODOzEWxudZnj2Dll1lXxpjeQCsg0hgzGbgEmAY8aYy5yFr7cgHv6wJ0AYiMrEhkZGmf5ImNjWHIkE/o3v0F9uzJzF/eo8cD5ORkM3ToDz7ZT7g4La4idzS7mPs+HEWp6CjOTKyEx+S13zx4zaU8eM2lfD5pAUNnLuH+1vU5O6kyE56/jZjoEsxc/gePfjaOMc90dvRruLrFrWRs3ESlShX5dtRA1vy2jtm/LADg0cfvJTs7hxHDRjuaUUTE74K4QPEVf7Xo3AhcBjQEugLXWWtfBFoA//3z2cta299aW8daW8dXRU5kZCRDhnzCsGEjGTVqYv7yjh1vpHXrptx++8M+2Y8/pKdlUDUpIf91UmI86ekZDib6x/WXnsuQHjcz4OEbKBMTzamVyx+xvnWdM0lesg6A0qVKEBOd1611xbnVyM7JZUfm/oBnPlzGxk0AbN26nfFjJ3Nx7QsAuPnW62nesgn33vWYk/EKFcznRahw4zF2W2a35QV3Zhb/FTrZ1toca+0+YJ21djeAtXY/kOunfR7VJ5+8werVa+nb97P8ZVdd1Yhu3e7lxhvvZP/+A4GMUyzzF6RQo0Z1qlWrSlRUFO3atWXM2ElOxwJg+559AGzcvoepS9bRqvaZ/Ln5nzEY05f+TvXKFQDYuntv/v1Ulv6ZgbWW8rElAx/aKyamFKVLx+Y/b3zlZaxcuYYrm13Bg4/cTcf29+q8CHNuPMZuy+y2vODOzEXK9eEjSPnrgoGHjDEx3kKn9t8LjTHlCODhaNCgDh063MDSpSuZM2c8AL17v0mfPs8RHV2CsWO/AvIGJD/0UM9AxTpmOTk5PPxIL8aP+4YIj4eBg4axYsVvTscC4LHPx7Nr7wEiIzw8dVNjysZE8/yQZP7YvAOPMcRXKEPP9k0AmJKyluGzlhHpMURHRfLabS0xxrmhyidXrsSgrz8EIDIygu9GjGHqlJnMS5lMdIkSfDtqIAAL56fw+KO9HctZkGA+Lwry1Zcf0qjhpVSqVJE/fl/A8y+8xRcDhzodq0BuPMZuy+y2vODOzEUJ5rE1vuKX6eXGmGhr7cGjLK8ExFtrlxb1Gb6YXh5I/phe7m/HO73cSb6aXh4o/pheLiKhK9DTy3fc1Nhnv2srjJgelJNt/dKic7Qix7t8K7DVH/sUERGRYgriLidf0b2uREREwlQ4dF2F9AUDRUREJLypRUdERCRcqetKREREQpVVoSMiIiIhKwwKHY3RERERkZClFh0REZEwpa4rERERCV1hUOio60pERERCllp0REREwpS6rkRERCRkhUOho64rERERCVlq0REREQlT4dCiE7SFTlZOttMRQp4pXcHpCMW268BepyOIiIQOa5xO4HfquhIREZGQFbQtOiIiIuJf6roSERGRkGVz1XUlIiIi4lpq0REREQlT6roSERGRkGU160pERETEvdSiIyIiEqbUdSUiIiIhS7OuRERERFxMLToiIiJhylqnE/ifCh0REZEwpa4rERERERdTi46IiEiYUotOCElKSmDKpBH8umQaS1Km8uADdzod6Zi0aN6Y5ctmsGrFLHp07+p0nHxfT57L/575mOt7fcxXk+YA0P3jb2nXux/tevejVff3aNe7HwA7M/dx5xuDqH/fq7zy1QQnYx/Vmt/msHjRFBbMn8Sc2eOdjnNMPB4P8+f9yKgfBjkd5ZgE63lcGGX2P7flBXdmLoy1vnsUxRhT3hjzrTFmlTFmpTHmUmNMRWPMZGPMGu+/FbzbGmNMX2PMWmPMr8aYi4/3awybFp3s7Gy693iexSnLKF06lnlzJzIleQYrV65xOlqBPB4Pfd97mZatbyE1dSNzZo9nzNhJjmdek7qZ72Ys4utedxEVGcH9b39Nw1pn8OZ9N+Zv89bQSZSOiQagRFQkXa9rwtq0zaxN2+JU7EI1u+omtm3b4XSMY/bQg3exatUaypYp43SUIgXreVwYZfY/t+UFd2YOMu8BE621NxpjSgAxwNNAsrX2NWPMk8CTwBNAK6Cm93EJ8LH332ILmxadjIzNLE5ZBkBm5l5WrVPCbOkAACAASURBVFpDYkKcw6kKV6/uRaxb9wfr1/9FVlYWw4eP4to2LZyOxfqNWzm/eiKloqOIjPBQ+8xTSV60Mn+9tZZJ81fQ6pLzAIiJLsHFZ5xCdFTY1NV+lZgYT+tWTRkwYIjTUY5JsJ7HhVFm/3NbXnBn5qLYXOOzR2GMMeWAhsDnANbaQ9banUBb4O+m6UHAdd7nbYHBNs8coLwxJv54vsaAFTrGmMGB2ldRTj01iQtrncfceYudjlKohMQ4NqSm579OTdtIQhAUZzUST2bRmr/YmbmP/QezmLV0DRnbd+evX/TbX5xUNpZTq5zkYMpjZ61lwvghzJ0zgbvu7OB0nCK93ed5nnzqJXJz3XFJ02A9jwujzP7ntrzgzsxFsdb47GGM6WKMWXDYo8thu6oObAG+MMYsNsZ8ZoyJBapYazd6t8kAqnifJwIbDnt/qndZsfnlT2xjzOh/LwKaGGPKA1hrr/XHfo9FbGwMw4d9SrfHe7NnT6ZTMVzttISTuaPVZdzb52tKRUdxZtU4Isw/NfOEucto6W3NcYPGTa4nPT2Dk08+iYkThrJq9VpmzZrrdKyjurp1MzZv3sqixUtp1PBSp+OIiOSz1vYH+hewOhK4GHjQWjvXGPMeed1Uh7/fGmN8fmUff/UlJAErgM8AS16hUwfoU9ibvNVfFwATUQ6PJ9anoSIjIxkx7FOGDPmBkSODb1Dsv6WnZVA1KSH/dVJiPOnpGQ4m+sf/Gl7E/xpeBEDf75KpUqEsANk5uSQvWsXQZ+92Ml6x/H1Mt2zZxshRE6hb98KgLXQaNKhDm2ua06rllZQsGU3ZsmUYNLAvt93+kNPRChTM53FBlNn/3JYX3Jm5KAG811UqkGqt/fuH67fkFTqbjDHx1tqN3q6pzd71aUDVw96f5F1WbP7quqoDLAR6ArustdOB/dban6y1PxX0Jmttf2ttHWttHV8XOQCf9u/DylVrefe9ggrO4DJ/QQo1alSnWrWqREVF0a5dW8aMneR0LAC27d4LwMZtu0heuIpW9c8HYO6K36kedxJVKpZ1Mt4xi4kpRenSsfnPr2rWiOXLVzucqmA9e71GtdPqUOOM+nToeD/Tpv0c1EUOBPd5XBBl9j+35QV3Zi5KrjU+exTGWpsBbDDGnOld1JS8BpHRwG3eZbcBo7zPRwOdvbOv6pNXS2zkOPilRcdamwu8Y4wZ4f13k7/2dawua1CXTh1v5NelK1gwP+/EfOaZ15gwcaqTsQqVk5PDw4/0Yvy4b4jweBg4aBgrVvzmdCwAHvtwOLsy9xMZEcHTHVtRNqYkABPnLT9qt1Wr7u+ReeAgWdk5TFu8ik+6deT0xJMDHfs/qlQ5mW9HfA5ARGQEQ4eOZNKk6c6GCjHBfB4XRJn9z215wZ2Zg8yDwNfeGVe/A3eQ1+Ay3BhzJ/An0M677XigNbAW2Ofd9rgYG4AbXRhjrgYus9Y+fazviSyRGAZ34HBW5rQ3nI5QbGWa9HA6QrHoJBaR4sg+lBbQK/itPquVz35MnblqQlBefTAgrSzW2nHAuEDsS0RERI6NrowsIiIi4mK6gpuIiEiYCsDoFcep0BEREQlT4dB1dUyFjjGmAVDt8O2ttUFzpWMRERGRoymy0DHGfAmcDqQAOd7FFlChIyIi4mJFXf8mFBxLi04d4BwbiHnoIiIiEjA2DAqdY5l1tQxw913LREREJCwV2KJjjBlDXhdVGWCFMWYecPDv9U7emFNEREROXDj01RTWdfVWwFKIiIhIwIX1GJ2/b75pjHndWvvE4euMMa8DBd6cU0RERCQYHMsYnauOsqyVr4OIiIhIYFlrfPYIVoWN0bkPuB843Rjz62GrygC/+DuYiIiI+Fe4j9H5BpgAvAo8edjyPdba7X5NJSIiIuIDhY3R2QXsMsY88a9VpY0xpa21f/kzWPmSsf78eJ/beWCv0xGKbf/bnzododjOqJDkdIRiWb0j1ekIxRYV4b47w2TlZDsdQcSVwnow8mHGkTfN3AAlgerAauBcP+YSERERPwvmsTW+UmShY609//DXxpiLyRu7IyIiIhLUit1Gba1dZIy5xB9hREREJHDUdQUYY7od9tIDXAyk+y2RiIiIBEQYTLo6phadMoc9zyZvzM53/okjIiIigRL2LTrGmAigjLX28QDlEREREfGZwi4YGGmtzTbGXBbIQCIiIhIY4T7rah5543FSjDGjgRFA/sVirLXf+zmbiIiI+FGu0wEC4FjG6JQEtgFX8s/1dCygQkdERESCWmGFTmXvjKtl/FPg/C0cBmqLiIiENEt4d11FAKXhqEdBhY6IiIjL5YbBb/PCCp2N1toXApZERERExMcKK3RCvz1LREQkjOWGwa/6wgqdpgFLISIiIgEXDmN0PAWtsNZuD2QQf1m0dCozZo9h2qxRTJmed0HnJ3s9zE+/jGbarFGMGDmAuLjKDqc8uujoaGb/PJaFCyazJGUqvZ99zOlI+UxMaWIff56yfQdT9r1BRJxxDhGnnk6ZVz6k7NsDiH3qFSgVA0DkBbUp80Y/yr49gDJv9CPyvIsCnvfFd3sxY/kERv70Tf6yx559kDGzhvH9tK9474vXKVO2NADlKpTli+8/Yv7v0+j5SvBdK/PT/n1IT11CyuJkp6MUKCkpnokTh7Jo0RQWLpxM1653APDKK0+TkpLMvHkTGTasH+XKlXU4acFaNG/M8mUzWLViFj26d3U6zjFxW2a35QV3Zg53xtrgHIlUqewZPgm2aOlUmjW6ge3bd+QvK10mlsw9eZcEuvveTpx5Zg0ef7T3Ce1n54G9RW90HGJjY9i7dx+RkZHMmP4Dj3brzdx5i3zy2Vva1Dzu98Y88CTZK5dyKHkcREZiSpSkdO+32D/oY7JXLKHEla3wVI7nwNABRFSvQe7OHdgd2/BUrU6ZZ95gV5ebjmu/l8/af1zvq13/Qvbt3c+rH/Tmuka3AtCg0SXMnbWAnJwcuvXK+4H19ksfUiqmJGefdyY1zjqNmmedzstPv3Vc+wRYvSP1uN9bkCsuv4TMzL188cV7XHiR7xteoyKKfa/f/4iLq0xcXGVSUpZRunQsv/wylnbtupCYGMf06b+Qk5PDSy89CUCvXq+d8P6ycrJP+DMO5/F4WLl8Ji1b30Jq6kbmzB5Px073s3LlGp/ux5fcltlteSEwmbMPpQW0iWVylfY+KwKu2jQsKJuHCmzRCWV/FzkAMTExBGuxB7B37z4AoqIiiYyKCo6sMbFEnlMrr8gByM7G7sskIj6J7BVLAMhasoAS9RsCkLN+LXbHNgByN6yHEtEQGRXQyAvnpLBr5+4jlv3y01xycnIAWLJwGVUS8lr29u87wKJ5Szh08FBAMx6rmbPmsn3HTqdjFCojYzMpKcsAyMzcy6pVa0lIqEJy8sz8Yz5v3mISE+OdjFmgenUvYt26P1i//i+ysrIYPnwU17Zp4XSsQrkts9vygjszF8VifPYIVgEpdIwxlxtjuhljmgdif4ez1vLtyAEk//Q9nW9vn7/86WceZcmKn7ixXRtee/m9QMc6Zh6PhwXzJ7Ex7VeSk2cwb/5ipyMRUTkeu3snMQ88SZk3PyXmvu4QXZKcDX8QVe9yAEo0aIyn0n+7BKPqNyJn/RrIzgp07EL979Y2zEye7XSMkHTKKUlceOG5zJ+fcsTyzp3b8eOP050JVYSExDg2pKbnv05N20hCQpyDiYrmtsxuywvuzCx+KnSMMfMOe3438AF5d0HvbYx50h/7LMjVLW7lyobX0/6Gu/i/uztwaYM6ALzy4jvUOqcR3w4fw133dApkpGLJzc2lTt3mnFq9DnXrXMS5557pdCSIiCDitDM4+OMo9nS/G3twPyWvv5W9H71BdIu2lHmjH6ZkDPZfxYynajVKderCvk/6OBT86Lo8cjvZ2TmM/W6i01FCTmxsDEOGfEL37i+wZ09m/vIePR4gJyeboUN/cDCdiOT68BGs/NWic3i/RBfgKmvt80BzoENBbzLGdDHGLDDGLDhwaJdPgmRs3ATA1q3bGT92MhfXvuCI9d8OH8011wa8oanYdu3azfSffqZF88ZORyF32xZyt20hZ81KALJm/0TkaTXJTfuLzBe7s6fHPRyalUxuxj9/+ZiKJ1O6x4vs7fsquZvSC/rogLuu/dU0uupynrj/WaejhJzIyEiGDPmEYcNGMmrUP0Vkx4430rp1U26//WEH0xUuPS2DqkkJ+a+TEuNJT89wMFHR3JbZbXnBnZmLokLnBD7XGFPBGHMSeQOetwBYa/cCBY4atNb2t9bWsdbWKVmi3AmHiIkpRenSsfnPG195GStXruG000/N36bV1c1Y89vvJ7wvf6hUqWL+rJSSJUvSrGlDVq9e53AqsDu3k7t1M56EqgBEnl+bnNQ/MWXL521gDCVv7MTBSaPzXsaUpnTPV9n/VX9yVi9zKvZ/XN6kPv/XtRMPdH6cA/sPOh0n5HzyyRusXr2Wvn0/y1921VWN6NbtXm688U727z/gYLrCzV+QQo0a1alWrSpRUVG0a9eWMWMnOR2rUG7L7La84M7Mcmw39Twe5YCFeG8AaoyJt9ZuNMYUdEsJvzi5ciUGff0hAJGREXw3YgxTp8zkiy/fp0bN6uTm5pK6IZ3HHjmxGVf+Eh9fhQGfv0tEhAePx8O3345h3PgpTscCYP/nfYl9uBdERZK7aSP7PniNEo1bEN3yOgCy5s7k0NQJAES3up6IuERK3nQbJW+6DYDMFx7H7g7cgNo3P3mRug0upnzF8iQvHsOHb/bn7oduI6pECT4b/j6QNyD5hR6vAzBp/g+ULhNLVIkormzViC7tH2Ldb+sDlrcwX335IY0aXkqlShX54/cFPP/CW3wxcKjTsY7QoEEdOnS4gaVLVzJnzngAevd+kz59niM6ugRjx34F5A1Ifuihnk5GPaqcnBwefqQX48d9Q4THw8BBw1ix4jenYxXKbZndlhfcmbkowTyI2FcCOr3cGBMDVLHWFvkbw1fTywPFX9PL/elEppc75XinlzvFH9PL/c0X08sDzdfTy0WcEujp5WPibvHZ79o2GUOCsmoK6E80a+0+IDj+LBYREZGQ574/3URERMQnwv1eVyIiIhLCXDVG5DiF5ZWRRUREJDyoRUdERCRMBfP1b3xFhY6IiEiYyjWhP0ZHXVciIiISstSiIyIiEqbCYTCyCh0REZEwFQ5jdNR1JSIiIiFLLToiIiJhKjf0xyKr0BEREQlX4XBlZHVdiYiISMhSi46IiEiY0qwrB5WMLOF0hGLa63SAYivZ9lKnIxTb6jGDnY4Q8rJysp2OICIBEg5jdNR1JSIiIiEraFt0RERExL/C4To6KnRERETCVDiM0VHXlYiIiIQsteiIiIiEqXAYjKxCR0REJEyFwxgddV2JiIhIyFKLjoiISJhSi46IiIiELGt89zgWxpgIY8xiY8xY7+vqxpi5xpi1xphhxpgS3uXR3tdrveurHe/XqEJHREREAuVhYOVhr18H3rHW1gB2AHd6l98J7PAuf8e73XFRoSMiIhKmcn34KIoxJgm4GvjM+9oAVwLfejcZBFznfd7W+xrv+qbe7YtNhY6IiEiY8mWhY4zpYoxZcNijy7929y7Qg3/qopOAndbav2+wlwokep8nAhsAvOt3ebcvNg1GFhERkRNmre0P9D/aOmPMNcBma+1CY0zjQOZSoSMiIhKmAngLiMuAa40xrYGSQFngPaC8MSbS22qTBKR5t08DqgKpxphIoByw7Xh2rK4rERGRMJVrfPcojLX2KWttkrW2GnAzMNVa2wGYBtzo3ew2YJT3+Wjva7zrp1prj6suC/lC5677OpH8y0im/PwDH3z6BtHRJah6SiJjJn/DrAXj+ejzt4iKCs6GrejoaGb/PJaFCyazJGUqvZ99zOlI+b6cu4b/9ZvMDf2n8OQP8ziYncMdg3+i3afJtPs0maveG88jI2YDMP/PLVz+1uj8df1mrizi0wOrRfPGLF82g1UrZtGje1en4xwTt2V2W15Q5kBwW15wZ+Yg9wTQzRizlrwxOJ97l38OnORd3g148nh3YI6zQPK7pIrnnXCwuPjKfD9+MFde2pYDBw7y8YC3mDp5JldedQUTxiYz+vsJvNrnWVYsW82XXww7oX1lZO440bhHFRsbw969+4iMjGTG9B94tFtv5s5b5JPP3vNZ5+N636bd+7lj8E98f89VlIyKoPv3c7n89Dja1jo1f5vHvp1D4zPiaXPBqcz/cwuD56zh/fYNTjhzmbsGn/BnHM7j8bBy+Uxatr6F1NSNzJk9no6d7mflyjU+3Y8vuS2z2/KCMgeC2/JCYDJnH0oL6N2n3jmlo8+KgEf/+ioo75zllxYdY8wlxpiy3ueljDHPG2PGGGNeN8aU88c+CxIZGUnJktFERERQqlQpNm/awmVXXMK4UZMAGDF0FC2uvjKQkYpl7959AERFRRIZFUWwFKY5uZaD2Tlk5+ZyICuHk8uUzF+XeTCLeX9uocmZCQ4mPDb16l7EunV/sH79X2RlZTF8+CiubdPC6ViFcltmt+UFZQ4Et+UFd2YuSiCnlzvFX11XA4B93ufvkTeI6HXvsi/8tM//yNi4mX4fDGTur1NYtHIae3bv4deUFezetYecnBwANqZvIi6+cqAiFZvH42HB/ElsTPuV5OQZzJu/2OlIVClbis71a9Ly/Qlc9d54SkdH0eC0Kvnrp61O55JqJ1M6Oip/2a9p22n3aTJdh/zM2i27nYh9VAmJcWxITc9/nZq2kYSEOAcTFc1tmd2WF5Q5ENyWF9yZWfxX6HgOmxdfx1r7iLV2lrX2eeC0gt50+Bz8vQe3n3CIcuXK0rxVEy69qAW1z7mSUjGlaNL08hP+3EDKzc2lTt3mnFq9DnXrXMS5557pdCR27z/E9N82Mq5rSyY91Jr9WdmMW/pX/vqJy1NpeW7V/Ndnx5VnwgMtGX53U26uezqPesfuiIiIs6wPH8HKX4XOMmPMHd7nS4wxdQCMMWcAWQW9yVrb31pbx1pbJza64gmHuLxxfTb8lcb2bTvIzs5mwthk6lxyEWXLlSEiIgKA+IQqZGzcfML78rddu3Yz/aefadG8sdNRmPPHZhLLx1AxNpqoCA9Nz0wgJTVv1t+OfQdZtnEHV9T456+c0tFRxJTIG/B9RY04snMtO/YddCT7v6WnZVA16Z8utqTEeNLTMxxMVDS3ZXZbXlDmQHBbXnBn5qIEataVk/xV6NwFNDLGrAPOAWYbY34HPvWuC4j01I1cVOcCSpbKGz9yecNLWLN6Hb/MmsfVbZsDcNPNbZk0fmqgIhVLpUoVKVeuLAAlS5akWdOGrF69zuFUEF82hl/TtrM/KxtrLXP/2MJplfJyTlmZxhU14oiOjMjffmvmgfyxRUvTtmOtpXypEo5k/7f5C1KoUaM61apVJSoqinbt2jJm7CSnYxXKbZndlheUORDclhfcmbko4TBGxy/zqq21u4DbvQOSq3v3k2qt3eSP/RVk8cKljB89mYnThpOdk8PyX1fx9aARJE+awUefvUmPpx9k2dKVDP3q+0DGOmbx8VUY8Pm7RER48Hg8fPvtGMaNn+J0LM5PrEizsxK55fOpRHg8nFWlHDdcVA2AiStS+b8GZxyx/ZSVaQxf9DuRHg/RkR5eu74ex3nLEp/Lycnh4Ud6MX7cN0R4PAwcNIwVK35zOlah3JbZbXlBmQPBbXnBnZklxKeXB5K/ppf70/FOL3eSr6eXi4gEk0BPL3/1VN9NL3/qz+CcXh6cV8oTERERv8sN6mHEvhHyV0YWERGR8KUWHRERkTAVzIOIfUWFjoiISJgK/Y4rdV2JiIhICFOLjoiISJhS15WIiIiErGC+orGvqOtKREREQpZadERERMJUOFxHR4WOiIhImAr9MkddVyIiIhLC1KIjIiISpjTrSkREREKWxug4yI13A3ebD3ttcDpCsV1R+RynIxRLys71Tkcothcr1Hc6QrE9smma0xGKJcLjvlEDObnh8Le/hKKgLXRERETEv0K/PUeFjoiISNgKh3Y697WfioiIiBwjteiIiIiEKQ1GFhERkZAV+mWOuq5EREQkhKlFR0REJEyFw2BkFToiIiJhyoZB55W6rkRERCRkqUVHREQkTKnrSkREREJWOEwvV9eViIiIhCy16IiIiISp0G/PUaEjIiISttR1JSIiIuJiYVfoeDwe5s/7kVE/DHI6SpGSkhKYMmkEvy6ZxpKUqTz4wJ1ORwIgIjqKDqOfp/PEl7l9yms06PY/AE657Fw6jXuJzhNe5ubvnqH8qVXyti8RyTUfPsCdM/rQYdRzlE2qFPDMPd56jO9ThjNgSv/8Zaefczofju7Lpz9+wifjPuSsC8884j1n1jqDKX9MpOHVVwQ67lF5PB5++nk0Q0f0P2L5a28+w4aMJQ6l+kdEdBQ3jXmem398mVumvEY973mR2OAc2o1/iVumvEqzt+/BROT92ClRphRXD+iWv/3Z7Ro6Gf8/WjRvzPJlM1i1YhY9und1Os5R9ev3Fhv+WsyihVOOWH7/fbfz65JpLF40hVdeftqhdEVzwzH+NzdmLkyuDx/BKuwKnYcevItVq9Y4HeOYZGdn073H81xQqwmXXd6G++67nbPPrul0LHIOZjH85lcY3LIng1v2pHqjC4i/6HSavXw74x7+iMGterJq5GzqP9QWgPPbN+bArr183vAxFnw2kYZP3RzwzBNHTOKJjkf+wL+n590MeudL7m5xL1/0GcQ9Pe/OX+fxeOjy9F3Mn7Ew0FELdO/9t/Pb6rVHLLvwovMoX76cQ4mOlHMwi5HtX2Foi54Ma9mTUxpfQFztmjR75x4mdf2AIc2eYnfaVs66Ma9wPP+2q9i+Jo2hLXryQ7uXueyZW/FERTj8VeTxeDz0fe9lrmnTkfNrNaF9++uC4nvv3778cgRtru10xLJGjS6lTZvm1KnbgosubsY77/ZzKF3h3HKMD+fGzEWxPvwvWPml0DHGPGSMqeqPzz4RiYnxtG7VlAEDhjgd5ZhkZGxmccoyADIz97Jq1RoSE+IcTpUna99BADyREXgiI7EWsFCidCkASpQtReamnQCc3vxiln87E4Dfxs/jlMvODXjeX+cuZffOPUcutJbY0jEAxJaJZdumbfmrrr+jLTPHz2Ln1p2BjFmghIQ4mrdszOBBw/OXeTweXnj5SXr3et3BZEf6z3mRk0tuVjY712cAsGHmMk5vXTdvY2vzz5eo2JIc2LmX3Ozg+LuwXt2LWLfuD9av/4usrCyGDx/FtW1aOB3rP2bNmsuOHUeeo13u7sSbb33EoUOHANiyZdvR3uo4txzjw7kxs/ivRedFYK4xZqYx5n5jzMl+2k+xvN3neZ586iVyc4Pjh2lxnHpqEhfWOo+58xY7HQUA4zF0nvAy9y/+iD9nLSUjZR0/PvEZNwx6nHvm9uXc/13OvI/GAFAmrgJ70rcDYHNyObRnH6UqlHYyPgAfPPcx9/TqwrB5X3PvM1349NXPAagUdxJXtLqcUYPHOJzwH6+80YvevV4nN/efv5ruvrcTE8Yls2nTFgeTHcl4DO0nvsz/pXzEhplL2ZSyDhMRQeULqgNQo3U9yiScBMCvAydToUYCdyz4gFsmv8rM3l+SVzE7LyExjg2p6fmvU9M2khAkf2QUpWbN07jssnrMnDGayZNHULt2LacjHZUbj7EbMxdFXVfH73cgibyCpzawwhgz0RhzmzGmTEFvMsZ0McYsMMYsyM3d69NAV7duxubNW1m0eKlPPzcQYmNjGD7sU7o93ps9ezKdjgOAzbUMbtWTfpc8RFyt06l0RhK172zJd7e9Rb9LHmLZ8Bk0fqaD0zEL1bbzNXz0/Me0r9eBj577mO5vPQZA1+fup98rn2GD5Jdui5ZN2LplG0tSlucvi4urzHXXtaL/J4MdTPZfNtcyrGVPBtZ7iCoXnk7FM5OY1PUDLu/dkZvGPM+hvfvJzcn7kXhKo/PZuuJPvqjzAMNa9qTRi52J8rbwyPGLjIykYoXyXNHwWp566mW++fojpyNJEAuHrit/TS+31tpcYBIwyRgTBbQCbgHeAo7awmOt7Q/0B4gskejTo9agQR3aXNOcVi2vpGTJaMqWLcOggX257faHfLkbn4uMjGTEsE8ZMuQHRo6c4HSc/zi4ex8bZq+gepNaVD7nFDJS1gGwaswcbvyyBwB7MnZQJqEimRnbMREeSpSJYf8O5wu25jc25/1n834JTB87g8ff7AbAmRfU5NkP88bzlKtYjkuurEtOdg4///iLIzkvqV+blq2bclXzRkSXjKZMmdLMnj+Bg4cOsejXZABiYkqxcEkytWs1dSTjvx3avY+0X1ZwauMLWNxvPN/f8CIAVRueR/nq8QCc3a4RC72tfrv+2MTuDVuoUCOezSm/O5b7b+lpGVRNSsh/nZQYT3p6hoOJjl1a2kZGjsr7WbFgQQq5uZZKlSqydet2h5MdyY3H2I2ZxX8tOubwF9baLGvtaGvtLcCpftpnoXr2eo1qp9Whxhn16dDxfqZN+znoixyAT/v3YeWqtbz7Xv+iNw6QUhXLEF02b2xLZHQUp15xPtvWplGiTAwVquc14556xXlsW5MGwLrJizjXOwD1jNb12PDLCmeC/8u2TduodekFAFx82UWkrc/Le2uDztxyaSduubQTP42bybs933esyAF44bm3OO/My6l1bmPuvP0RZv40m+pVa3PW6ZdS69zG1Dq3Mfv27Xe8yClZsQwlvOdFRMkoqjY8nx1r0yl1UlkAPCUiufi+Niz7Kq8425O+lare8VqlKpWl/Onx7P5zszPh/2X+ghRq1KhOtWpViYqKol27towZO8npWMdk9OgfadSoAQA1a1QnqkRU0BU54M5j7MbMRQmHrit/tei0L2iFtXafn/YZci5rUJdOHW/k16UrWDA/75vp7bA23QAAGdRJREFUmWdeY8LEqY7miq1cnlZv34MnwoPxGFaPncvvySlMeuJzru33MDY3l4O79jGxe15xtnTYT7R+917unNGHAzszGfvABwHP3OuDp7nw0gsoV7Ecw+d/w8A+g3mrx9s8+Pz9RERGcOjgIfo88W7Ac4WS2MrlafZO3vRx4zGsHTOXP5JTaNDzFqo1vRDj8bDsyymkeQvdBe+NpOnb93DL5FfBwC+vDONAELT0AeTk5PDwI70YP+4bIjweBg4axooVvzkd6z8GD/6AhlfUp1KliqxbO48XX+rDwEHD6N//LRYtnMKhQ4e4665HnY55VG45xodzY+ai5AZJF70/mWAZh/Bvvu66kv96La6J0xGKbVzuJqcjFEvKzvVORyi2FyvUdzpCsT2yaZrTEYolwuO+K3vkuHAShxtlH0ozRW/lO51O/Z/Pftd++ef3Ac1+rHQLCBGR/2/vvsOkqu89jr+/W+hFsMECV7HGLgqCilhAirFFDRpDYoxKilGMSbhJFL16b+6NpiDJ9aoUFUWailhARUAFjDTpTZEisEtTAkjf8r1/zHGDCbuzu8/MnDl7Pi+e8zBzZs6cz8yz5bu/cn4iMRWHFgUVOiIiIjGlta5EREREIkwtOiIiIjGVzde/SRUVOiIiIjEVhyHm6roSERGRWkstOiIiIjEVh8HIKnRERERiKg5jdNR1JSIiIrWWWnRERERiKg6DkVXoiIiIxFS2LgOVSuq6EhERkVpLLToiIiIxpVlXIcrKJVArEcUvlTtHdAs7QrX9utt9YUeo9aK2EngUaSVwyRZx+ErM2kJHRERE0kvTy0VEREQiTIWOiIhITJXhKdsqY2ZtzOxdM1tmZkvNrF+wv7mZvWNmK4P/mwX7zcz+YmafmtkiMzunpu9RhY6IiEhMuXvKtiRKgF+4+6lAJ+BOMzsV+DUwxd1PBKYE9wF6AScGW1/giZq+RxU6IiIiklbuvtHd5wW3vwSWA62Aa4DhwdOGA9cGt68BnvOEmcBhZtayJudWoSMiIhJTZSnczKyvmc09aOt7qHOa2bFAO2AWcLS7bwwe2gQcHdxuBaw/6LANwb5q06wrERGRmErlrCt3HwwMruw5ZtYIeBm4x913mv3jYjLu7maW8mlgatERERGRtDOzfBJFzgvuPi7YvfmrLqng/y3B/kKgzUGHtw72VZsKHRERkZjK4KwrA4YBy939zwc99BpwS3D7FuDVg/Z/P5h91QnYcVAXV7Wo60pERCSmMrio54XA94DFZrYg2Pdb4PfAWDO7DfgM6B08NhG4AvgU2APcWtMTq9ARERGRtHL3GVS8ulPXQzzfgTtTcW4VOiIiIjGlRT1FRESk1tJaVyIiIiIRphYdERGRmCrL3GDk0MSqRaff3XewYMFU5s+fwvPPP07dunXDjlSp1q0LmDzpRRYtfJeFC6Zy189uCztSuRcmz+H6B4dw3QNDGDF5dvn+UVPmcu2Ap7jugSEMfGkqAIvXFNH7oWHl29R5H4cV+18MGfwnijYsZMH8KWFHqZYe3S9h6ZJprFg2g/6/Ssl4vbSKWl5Q5kyIWl6IZubKeAq3bGUZnFpWLfl1WqU0WEFBC9579xXOPOtS9u3bx8iRT/LWm1N57vmxKXn9dHyKLVocRcsWRzF/wRIaNWrI7Flvcf0NP2T58pUpef0vJ/+uRsd9WriVfx88nhG//QH5ebncOWgM9/XpweZtXzJ04t/4613fpk5+Htt27qZ5k4bs3V9Mfl4uebk5bN2+i94PD+OdP9xFXm716+zG3e6rUeaKXNS5I7t27eaZZwZxdrt/GfiflXJycli+dDo9r/gOGzZsZOaHE+nzvZ+m7Osi1aKWF5Q5E6KWFzKTueRAYUUzk9LiolZdU/bra3rhlIxmr6q0tOiYWR0z+76ZdQvu32xm/2tmdwZXRgxFXl4e9evXIzc3lwb161O0cVNYUapk06YtzF+wBIBdu3azYsVKWhW0CDkVrN74OWe0LaB+3XzycnM496Q2TJn3CWPfm8etPTtRJz/RI9q8SUOA8ucBHCguqXB+YRimz5jFtr9vDztGtZzXoR2rVq1lzZp1FBcXM3bsq1x9VY+wY1UoanlBmTMhankhmpmTydQFA8OUrq6rZ4BvAv3M7Hng2yQW7+oADE3TOStVVLSJgQOfZPWq2axfN5+dO3cyefK0MKLUyDHHtObss05n1uz5YUfhhFZHMm/lerbv2sPe/cXMWLyKzdt28tnmbcxbuZ4+//0st/1hBEvWFJUfs3h1Idc9MIQbHhrK/X161qg1RxIKWrVg/YZ/fLYbCjdSkAUFcEWilheUOROilheimTmZOBQ66RqMfIa7n2lmeSTWpihw91IzGwEsrOigYKXTvgA5uU3JyWmYskCHHdaUq67qwYkndWL79p2MHv0UN998HSNHjkt+cMgaNmzA2DFDuPeXD/Lll7vCjsNxLY/g1p7n85OBY6hfN5+T2xxNTo5RWlbGzt37eP43t7Bk7Ub6PzWeCf/zE8yMM45rxbiH72D1xs8Z8PQbXHjG8dTN11h4ERFJr3T9WZ1jZnWAxkADoGmwvy5QYdeVuw929/bu3j6VRQ5A164XsXbtOj7/fBslJSWMH/8m53dqn9JzpENeXh4vjhnCqFGvMH78m2HHKfeti85i1IBbebp/Hxo3qMcxRzfn6GaN6XrOyYnCpm0BOTnG33ft/dpxx7U8ggZ16/Bp4daQkkdfUeEm2rQuKL/fulVLioqytxs2anlBmTMhankhmpmTcfeUbdkqXYXOMGAFsAC4D3jRzIYAc4DRaTpnpdavK+S8judQv349AC67tDMrVmTvoLevDBn8J5av+JTHBg0OO8rXbNu5G4CNX+xg6vyP6dXxNC49+yTmfPwZAJ9t+oLiklKaNapP4dbtlJSWAVD0xQ7WbvqCgsObVvjaUrk5cxdwwgltOfbYNuTn59O79zW8/saksGNVKGp5QZkzIWp5IZqZk1HXVQ25+0AzGxPcLjKz54BuwBB3n1350ekxe858xo2bwOzZb1NSUsLCBUsZMvSFMKJU2YUXdOB7fW5g0eJlzJ2T+GYaMOD3vPnW1JCTwS+eGMeO3XvJy83lNzf3oEmDelzb+SwefHYC1z84hPy8XP7z1isxM+Z/up6n35xJXm4OOTnGb77bg2aNG4T9FgAY8fzjXNzlfI44ojlrV8/loYf/yDPPhlKLV1lpaSn97rmfiRNGkpuTw7PDx7Bs2Sdhx6pQ1PKCMmdC1PJCNDNLjKaXp1ukwgZqOr08TKmeXi4ikk0yPb28Q0GXlP36mlM0LZsm1ZbTaFAREZGYytbGjlTSHF8RERGptdSiIyIiElPZPIg4VVToiIiIxJS6rkREREQiTC06IiIiMaWuKxEREam1PAaFjrquREREpNZSi46IiEhMlcVgMLIKHRERkZhS15WIiIhIhKlFR0REJKbUdSUiIiK1Vhy6rlTopEhWLtmahC+eG3aEajvhsIKwI1TLxj3bwo5QbbsP7As7gohIyqjQERERiSl1XYmIiEitFYeuK826EhERkVpLLToiIiIxpa4rERERqbXUdSUiIiISYWrRERERiSn3srAjpJ0KHRERkZgqU9eViIiISHSpRUdERCSmXLOuREREpLZS15WIiIhIhKlFR0REJKbUdSUiIiK1VhyujKyuKxEREam1YlPonHTS8cydM6l8++LzFdx91+1hx0qqadMmjB49mMWL32fRovfo1PHcsCMBMGL+Z1w/4m/cMOJv/PqtRewvKS1/7JH3V3DBE1PL7z8/7zOue/5v9H7hQ3407iOKdu7NeN7fPTaAD5a+zWvvjy7f1+Oqrrw+bQzLNs3i9LNOKd9/WLOmDB/3BB+teZ8B//OrjGetSE5ODtM/eI0xLw4B4JhjWjPl3ZeZv3Aqzwz/C/n5+SEnPLS6devy4Qdv8NHcd1i4YCoPPvCLsCNVSY/ul7B0yTRWLJtB/1/dGXacKola5qjlhWhmroyn8F+2ik2h88knq2jfoTvtO3TnvI492bNnL+NffTPsWEkN/PPDTHr7Xc4442LOPfdylq9YGXYktuzax6iF63jhpo681OcCysrg7U82A7B08w6+3Ffyted/48jGvHBTR8Z+93y6nnAUgz7I/Ht4ZfQb3HHT3V/bt3LFKu6+tT9zP5z/tf379+9n0CNP8uh/DMpkxKR+8tMf8PHHq8rvP/Sf/fm/x5+h3VmXsX37Dr5/y7dDTFex/fv30617b85tfznntu9Oj+6X0PG8c8KOVamcnBz+Muh3XHlVH84461JuvPFaTjnlxLBjVSpqmaOWF6KZORl3T9mWrdJW6JjZcWb2SzMbZGZ/NrMfm1mTdJ2vOi67rDOrV3/GunWFYUepVJMmjencuSNPPzMKgOLiYnbs2BlyqoTSMmd/SRklZWXsKynlyIZ1KS1zHpuxkn6dv/6N36FNc+rn5wJwZoumbN61L+N5586cz47tX//sVq9cy5pVn/3Lc/fu2ce8WQs5sO9ApuIlVVDQgh49L+W54WPL93W5+HzGv5Io1ke+MI5vXnl5WPGS2r17DwD5+Xnk5edn9Q9FgPM6tGPVqrWsWbOO4uJixo59lauv6hF2rEpFLXPU8kI0MydThqdsy1ZpKXTM7G7gSaAe0AGoC7QBZprZJek4Z3Xc2PsaxowZH3aMpNq2/Tc+//wLhg0dyJzZb/PUk3+gQYP6YcfiqEb1+P45x9LrmelcPnQajermcf4xhzNm0XouPu5IjmxYt8Jjxy8r4sJjjshg2trh94/ezwP3P0JZWWJdmuaHN2PH9i8pLU10GRYVbqJlQYswI1YqJyeHuXMmsbFwEVOmTGP2nPnJDwpRQasWrN9QVH5/Q+FGCrL484XoZY5aXohmZklfi84dQC93/y+gG3Cau98H9AQGVnSQmfU1s7lmNresbHdaguXn53Plld156eU30vL6qZSXm0u7dmfw1FPP0eG8HuzevYf+/X8Wdix27ivmvdVbeOOWzky6rQt7i0t5fXkR76zczE1ntanwuAkrNrJs805uOefYzIWtBXr0vJStW79gwYIlYUepsbKyMtp36M4xbdvToX07Tjvt5LAjiQjx6LpK5/TyPKCURGtOIwB3X2dmFY6YdPfBwGCA/Dqt0vKp9ex5KfPnL2bLls/T8fIptaFwIxs2bCz/6/flcRPo/6vwC51Z67dR0KQ+zRvUAeCy44/iyVmr2F9SxtXDPwBgX3EpVw+fwWu3dAZg5rovGDZnDUOvb0+dvNgMDUuJTp3OpdcVXbm8+yXUq1eXxo0b8cijA2h6WGNyc3MpLS2loFULNhZtCjtqUjt27OS99z9IDOhc+nHYcSpUVLiJNq0Lyu+3btWSoiz/fKOWOWp5IZqZk9H08pobCswxsyHAh8DjAGZ2JLAtTeeskhtvvDYS3VYAmzdvZcOGIk466XggMbZo+fJPQk4FLRrXY/GmHewtLsXdmb1+G33aHcPk2y9m4q0XMfHWi6iXn1te5KzYspPfTV3OwKvOKi+OpOoe+o8/curJnTnztIv54Q/6Me39D7njtnuZPm0m136rFwA3f/c6Jk6YHHLSQzviiOY0bZoYnlevXj26de3ytUHV2WjO3AWccEJbjj22Dfn5+fTufQ2vvzEp7FiVilrmqOWFaGaWNLXouPsgM5sMnAL8yd1XBPu3Al3Scc6qaNCgPt26duGnP/33sCJU2z0/H8Bzw/9KnTr5rF6zjttvvzfsSJzRoindTjiam0fPJNeMbxzZhOtPa13h8wd+sJI9xaX0n7gISBRKg65ql6m4APzpyf+iw4Xn0qz5Yby34A3++uhgdmzfyf3//UuaH96MJ0cOZMWST7j9xsTMrClzX6Vh44bk18mna6+Lua33Xaz6ZE1GMyfz4IBHefrZQdw/4F4WLVrKc8NfDDvSIbVseTRPD3uM3NwccnJyeOml15kwMTuLsq+UlpbS7577mThhJLk5OTw7fAzLloX/R0ZlopY5ankhmpmTyeYup1SxbH2T6eq6kn/YMfBbYUeotnMemhV2hGrZuCfUBswa2X0g87PiRCSh5EChZfJ8TRsdn7LftTt2rcpo9qrSYAkRERGptbTWlYiISExla69OKqnQERERiSnNuhIRERGJMLXoiIiIxFQ2L8aZKip0REREYkpdVyIiIiIRphYdERGRmNKsKxEREam14jBGR11XIiIiUmupRUdERCSm4tB1pRYdERGRmHL3lG3JmFlPM/vYzD41s19n4O0BKnREREQkzcwsF3gc6AWcCnzHzE7NxLlV6IiIiMSUp3BL4jzgU3df7e4HgNHANSl9MxXI2jE6xWlcqt7M+rr74HS9fqpFLS+kL/OKO1P9ign6jDMjapmjlheUOROilrcyJSn8XWtmfYG+B+0afNDn1ApYf9BjG4COqTp3ZeLaotM3+VOyStTyQvQyRy0vKHMmRC0vKHMmRC1vRrj7YHdvf9CWFcVgXAsdERERyZxCoM1B91sH+9JOhY6IiIik2xzgRDNra2Z1gJuA1zJx4qwdo5NmWdGcVg1RywvRyxy1vKDMmRC1vKDMmRC1vKFz9xIz+xnwNpALPO3uSzNxbovDxYJEREQkntR1JSIiIrWWCh0RERGptWJV6IR1+emaMrOnzWyLmS0JO0tVmFkbM3vXzJaZ2VIz6xd2pmTMrJ6ZzTazhUHmh8LOVBVmlmtm883sjbCzVIWZrTWzxWa2wMzmhp2nKszsMDN7ycxWmNlyMzs/7EyVMbOTg8/3q22nmd0Tdq7KmNnPg++7JWY2yszqhZ0pGTPrF+Rdmu2fryTEZoxOcPnpT4DLSVyoaA7wHXdfFmqwSphZF2AX8Jy7nx52nmTMrCXQ0t3nmVlj4CPg2iz/jA1o6O67zCwfmAH0c/eZIUerlJndC7QHmrj7lWHnScbM1gLt3f3zsLNUlZkNB6a7+9BglkgDd98edq6qCH7eFQId3f2zsPMcipm1IvH9dqq77zWzscBEd3823GQVM7PTSVzR9zzgAPAW8GN3/zTUYFKpOLXohHb56Zpy92nAtrBzVJW7b3T3ecHtL4HlJK6GmbU8YVdwNz/Ysrr6N7PWwDeBoWFnqa3MrCnQBRgG4O4HolLkBLoCq7K1yDlIHlDfzPKABkBRyHmSOQWY5e573L0EeB+4LuRMkkScCp1DXX46q38JR5mZHQu0A2aFmyS5oBtoAbAFeMfdsz3zY0B/oCzsINXgwCQz+yi4THy2awtsBZ4JugiHmlnDsENVw03AqLBDVMbdC4E/AuuAjcAOd58UbqqklgAXmdnhZtYAuIKvXwRPslCcCh3JEDNrBLwM3OPuO8POk4y7l7r72SSu1Hle0DydlczsSmCLu38UdpZq6uzu55BYufjOoFs2m+UB5wBPuHs7YDeQ9eP6AIJutquBF8POUhkza0aiVb0tUAA0NLM+4aaqnLsvBx4BJpHotloAlIYaSpKKU6ET2uWn4yQY5/Iy8IK7jws7T3UEXRPvAj3DzlKJC4GrgzEvo4HLzGxEuJGSC/56x923AK+Q6ErOZhuADQe17r1EovCJgl7APHffHHaQJLoBa9x9q7sXA+OAC0LOlJS7D3P3c929C/B3EmM/JYvFqdAJ7fLTcREM7B0GLHf3P4edpyrM7EgzOyy4XZ/EYPUV4aaqmLv/xt1bu/uxJL6Gp7p7Vv8VbGYNg8HpBN0/3Ul0AWQtd98ErDezk4NdXYGsHVT/T75DlndbBdYBncysQfCzoyuJcX1ZzcyOCv7/NxLjc0aGm0iSic0SEGFefrqmzGwUcAlwhJltAB5092HhpqrUhcD3gMXBmBeA37r7xBAzJdMSGB7MUskBxrp7JKZsR8jRwCuJ32XkASPd/a1wI1XJXcALwR9Gq4FbQ86TVFBIXg78KOwsybj7LDN7CZgHlADzicbSCi+b2eFAMXBnxAapx1JsppeLiIhI/MSp60pERERiRoWOiIiI1FoqdERERKTWUqEjIiIitZYKHREREam1VOiIRJSZlQarVC8xsxeDS9LX9LWeNbMbgttDzezUSp57iZlV+8JuwQrmR9Q0o4hITajQEYmuve5+drCy/QHgxwc/GCyUWG3ufnuSFecvIQJXsBURARU6IrXFdOCEoLVlupm9BiwLFiz9g5nNMbNFZvYjSFzF2sz+18w+NrPJwFFfvZCZvWdm7YPbPc1snpktNLMpwWKtPwZ+HrQmXRRcXfrl4BxzzOzC4NjDzWySmS01s6GAZfYjERGJ0ZWRRWqroOWmF4lFBiGxJtPp7r4mWCl8h7t3MLO6wAdmNonEyvInA6eSuHLxMuDpf3rdI4EhQJfgtZq7+zYzexLY5e5/DJ43Ehjo7jOCy+K/DZwCPAjMcPeHzeybwG1p/SBERA5BhY5IdNU/aKmN6STWGbsAmO3ua4L93YEzvxp/AzQFTgS6AKPcvRQoMrOph3j9TsC0r17L3bdVkKMbcGqwxANAk2AF+y4k1gLC3SeY2d9r+D5FRGpMhY5IdO1197MP3hEUG7sP3gXc5e5v/9Pzrkhhjhygk7vvO0QWEZFQaYyOSO32NvATM8sHMLOTgoUfpwE3BmN4WgKXHuLYmUAXM2sbHNs82P8l0Pig500isQAmwfO+Kr6mATcH+3oBzVL2rkREqkiFjkjtNpTE+Jt5ZrYEeIpES+4rwMrgseeAD//5QHffCvQFxpnZQmBM8NDrwLe+GowM3A20DwY7L+Mfs78eIlEoLSXRhbUuTe9RRKRCWr1cREREai216IiIiEitpUJHREREai0VOiIiIlJrqdARERGRWkuFjoiIiNRaKnRERESk1lKhIyIiIrXW/wPHQSRpGOv3PQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rk2xno2jAwDk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "6eb0233c-9a13-41b8-ae48-4bc7ea7b8f0b"
      },
      "source": [
        "#Adding a hidden layer.\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(100, input_shape=(784,), activation='relu'),\n",
        "    keras.layers.Dense(10, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train_flattened, y_train, epochs=5)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 3.4077 - accuracy: 0.2387\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 1.8606 - accuracy: 0.2511\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 1.7704 - accuracy: 0.3586\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 1.6985 - accuracy: 0.3829\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 1.9432 - accuracy: 0.3978\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8686f47860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7HTxGH7BmD2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "27c20da7-607f-47f3-8637-dc260d7ca9d0"
      },
      "source": [
        "model.evaluate(X_test_flattened,y_test)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 2.0680 - accuracy: 0.3283\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.068040370941162, 0.32829999923706055]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rsmf2ZECngr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "3b085759-edcc-4741-d586-d53f0824d314"
      },
      "source": [
        "y_predicted = model.predict(X_test_flattened)\n",
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]\n",
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
        "\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3ZbvD0_CwfE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "6ef415cb-8fc2-4fa3-9cdd-25491da39dc8"
      },
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(100, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train, y_train, epochs=10)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2952 - accuracy: 0.9182\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1371 - accuracy: 0.9599\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0988 - accuracy: 0.9705\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0774 - accuracy: 0.9774\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0614 - accuracy: 0.9812\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0499 - accuracy: 0.9846\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0422 - accuracy: 0.9872\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0355 - accuracy: 0.9888\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0287 - accuracy: 0.9911\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0247 - accuracy: 0.9923\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f86854a47b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vd05Vr2vC6AO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "87d93a54-31f4-4191-ea5c-d5dd4c6a4c34"
      },
      "source": [
        "model.evaluate(X_test,y_test)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.0838 - accuracy: 0.9750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08375390619039536, 0.9750000238418579]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emoJrZtpEtYq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "f3aebf57-b26a-426e-fc8a-16f5b18fb71b"
      },
      "source": [
        "y_predicted = model.predict(X_test_flattened)\n",
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]\n",
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
        "\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input Tensor(\"flatten_input:0\", shape=(None, 28, 28), dtype=float32), but it was called on an input with incompatible shape (None, 784).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHrTvSqUE3N-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}