MNIST__ANN_MNIST_digits_recognition_neural_network (1).ipynb

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      "name": "MNIST__ANN_MNIST_digits_recognition_neural_network.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzT6gUT6o4ii"
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      "source": [
        "<h2 style=\"color:blue\" align=\"center\"> MNIST Handwritten digits classification using Artificial neural network (ANN) </h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oebbgthde2KW"
      },
      "source": [
        "**What is Artificial Neural Network?**\n",
        "\n",
        "The term \"Artificial Neural Network\" is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_R_Ipx5afcsE"
      },
      "source": [
        "**The architecture of an artificial neural network:**\n",
        "\n",
        "To understand the concept of the architecture of an artificial neural network, we have to understand what a neural network consists of. In order to define a neural network that consists of a large number of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various types of layers available in an artificial neural network.\n",
        "\n",
        "\n",
        "Artificial Neural Network primarily consists of three layers:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6P0OcPHdfSHC"
      },
      "source": [
        "**Input Layer:**\n",
        "\n",
        "As the name suggests, it accepts inputs in several different formats provided by the programmer.\n",
        "\n",
        "**Hidden Layer:**\n",
        "\n",
        "The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.\n",
        "\n",
        "\n",
        "**Output Layer:** \n",
        "\n",
        "The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer.\n",
        "\n",
        "The artificial neural network takes input and computes the weighted sum of the inputs and includes a bias. This computation is represented in the form of a transfer function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwL9f4Uvo4iu"
      },
      "source": [
        "#!pip install tensorflow"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5JLRDgGo4i4"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQLq7IQYo4i6"
      },
      "source": [
        "In this notebook we will classify handwritten digits using a simple neural network which has only input and output layers. We will than add a hidden layer and see how the performance of the model improves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O846o_J7o4i8"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import cv2"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vu-nandto4i_"
      },
      "source": [
        "(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FqrHBi6o4jB",
        "outputId": "1ad6d684-01e6-4345-9b05-7f53f8e9a74e"
      },
      "source": [
        "len(X_train)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WS3ZNQvjo4jE",
        "outputId": "6c7d8d12-2388-4a56-f5f1-6ee0a7f1ece0"
      },
      "source": [
        "len(X_test)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWFoduGho4jG",
        "outputId": "e1086ab6-f6d7-49b5-87bc-de8ee6ad0d73"
      },
      "source": [
        "X_train[0].shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYRkmbPapNiU",
        "outputId": "4404a02d-d152-4199-d425-60ba30be26c0"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "Ic2pJW3Wo4jH",
        "outputId": "e56544a2-bf0a-4a24-e2fa-f398424b322f"
      },
      "source": [
        "X_train[0]"
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      "execution_count": 23,
      "outputs": [
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvhozl4ubyyN",
        "outputId": "3640cb6f-3161-42dc-fbb7-c1f7f68ec7a1"
      },
      "source": [
        "# selecting the first sample\n",
        "x = X_train[1]\n",
        "print(\"The dimension of x is 2D matrix as \", x.shape)\n",
        "# Resizing the image\n",
        "x = cv2.resize(x, (18,18))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The dimension of x is 2D matrix as  (28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hU5hIuiGbsJK",
        "outputId": "614d2e83-7d9d-49ca-cfce-c9d8faf2f94e"
      },
      "source": [
        "print(\"The range of pixel varies between 0 to 255\")\n",
        "print(\"The pixel having black is more close to 0 and pixel which is white is more close to 255\")\n",
        "print(x)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The range of pixel varies between 0 to 255\n",
            "The pixel having black is more close to 0 and pixel which is white is more close to 255\n",
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            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "i4hc7juNo4jJ",
        "outputId": "be0a067a-10a2-4c72-a04a-a272e900bbed"
      },
      "source": [
        "plt.matshow(X_train[1])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f87e641cf50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
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            "tags": [],
            "needs_background": "light"
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        }
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    {
      "cell_type": "code",
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      },
      "source": [
        "y_train[1]"
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      "execution_count": 27,
      "outputs": [
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          "data": {
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          "metadata": {
            "tags": []
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      "source": [
        "X_train"
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      "execution_count": 28,
      "outputs": [
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          "data": {
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      "outputs": [
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              "        ...,\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "3rdon12Qo4jO"
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      "source": [
        "X_train = X_train / 255\n",
        "X_test = X_test / 255"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "X9jm5GKIo4jP",
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      "source": [
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      "outputs": [
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          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AzKFi0Eo4jR"
      },
      "source": [
        "X_train_flattened = X_train.reshape(len(X_train), 28*28)\n",
        "X_test_flattened = X_test.reshape(len(X_test), 28*28)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8uIvVGxo4jS",
        "outputId": "963d38e4-a4f5-4ca5-ae24-d349c900e075"
      },
      "source": [
        "X_train_flattened.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 784)"
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          },
          "metadata": {
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          "execution_count": 33
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    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "c218a4b8-ed4a-45bb-b256-2af09e84e41b"
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      "source": [
        "X_train_flattened[0]"
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      "execution_count": 34,
      "outputs": [
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roYX_rzzo4jW"
      },
      "source": [
        "<h3 style='color:purple'>Very simple neural network with no hidden layers</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHbydH85o4jX"
      },
      "source": [
        "<img src=\"digits_nn.jpg\" />"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wzgzpr3no4jY"
      },
      "source": [
        "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"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjW5fRiBdsDt",
        "outputId": "f88270f5-de7b-4767-cac5-9f51d316696e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                7850      \n",
            "=================================================================\n",
            "Total params: 7,850\n",
            "Trainable params: 7,850\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "e-dfLt4rmlBB",
        "outputId": "701e7cbb-2989-4fa8-ee79-c45f6e1919f0"
      },
      "source": [
        "import pydot\n",
        "keras.utils.plot_model(model)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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Ro0cfOk9ECOnp6env7yeEdHV1lZaWPvvss0Kh8NChQ9z1pX9KHZfG6PXUsA1xnujOnTvp6elTpkyZMGFCfHx8dnY2AKjV6gsXLhBCent7jUajVqsNCgriQnzp0qXCwkKWZQFg5syZLS0txcXFXDjCwsKampra2tri4uImTZokFAqnTZuWlZV1//79wboihBw5ckQmk23fvv3B2ioqKubOncuybEhICHcrYe4FeExMzLZt227duuW+sR9K9Y7O1+PU/X5PWVlZUlISbVUFsMTERAA4cOAA34X8x7g8j6OAh7lENMJcIhphLhGNMJeIRphLRCPMJaIR5hLRCHOJaIS5RDTCXCIaYS4RjTCXiEaYS0QjzCWiEeYS0QhziWhE6U8alpWV8V3Co6Kjo0OtVvNdxUCU5jIpKYnvEh4hFP7UM3Xf70EI8PoS0QlziWiEuUQ0wlwiGv0f/lXfXitAvxwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1fNojK6dow4",
        "outputId": "da280bf7-01d2-4782-c23b-b05fd7ebbc1d"
      },
      "source": [
        "model.fit(X_train_flattened, y_train, epochs=5)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.4685 - accuracy: 0.8769\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.3033 - accuracy: 0.9151\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2830 - accuracy: 0.9204\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2729 - accuracy: 0.9242\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2664 - accuracy: 0.9263\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f87e3361790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vephmuro4jZ",
        "scrolled": true,
        "outputId": "90703471-ab71-4f88-8375-a2932e5fff09"
      },
      "source": [
        "model.evaluate(X_test_flattened, y_test)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.2696 - accuracy: 0.9248\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.26961490511894226, 0.9247999787330627]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zxZ0KPjo4ja",
        "outputId": "461e6185-6fb2-4376-d19b-efaf785f8acd"
      },
      "source": [
        "y_predicted = model.predict(X_test_flattened)\n",
        "y_predicted[0]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.6710808e-02, 2.0082869e-07, 4.0452570e-02, 9.5325053e-01,\n",
              "       1.7535686e-03, 9.9020720e-02, 1.4245377e-06, 9.9976116e-01,\n",
              "       8.6834908e-02, 5.9089357e-01], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "T4Ak38jYo4jb",
        "scrolled": true,
        "outputId": "a09eb666-2422-452b-9c10-d611d93eb8d0"
      },
      "source": [
        "plt.matshow(X_test[0])"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f87e64062d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOGElEQVR4nO3df6xf9V3H8ddr7e1lvS2uHaPWUqhjbJHNUcwdbAFNF2SyLaSQbbgmNjWZK1FIwCwqIVlook4k/BCdkhSp6xZgwxWEbHWuaaZIxI7SFFpaFMSirZdeoNOWAf359o97ild27+d7e7/f7znf2/fzkTTf7/e8z/ecd09vX/dzzvdzz3VECEBe72i6AQDNIgSA5AgBIDlCAEiOEACSIwSA5BoJAduX2f4X28/bvqGJHkps77K9zfZW25t7oJ81todtbx+1bK7tDbafqx7n9Fh/q2zvqY7hVtufarC/hbZ/YHuH7WdsX1ct74ljWOivlmPouucJ2J4m6V8lXSppt6QnJC2LiB21NlJge5ekwYh4peleJMn2L0l6TdLXI+JD1bJbJO2LiJurIJ0TEb/XQ/2tkvRaRNzaRE+j2Z4vaX5EbLE9W9KTkq6Q9OvqgWNY6O8q1XAMmxgJXCDp+Yh4ISIOSfqmpKUN9DFlRMSjkva9bfFSSWur52s18kXTiHH66xkRMRQRW6rnByTtlLRAPXIMC/3VookQWCDpP0e93q0a/8ITFJK+b/tJ2yubbmYc8yJiqHr+kqR5TTYzjmttP12dLjR2ujKa7UWSzpe0ST14DN/Wn1TDMeTC4NgujohfkPRJSddUw92eFSPndL02//suSWdLWixpSNJtzbYj2Z4laZ2k6yNi/+haLxzDMfqr5Rg2EQJ7JC0c9fqMalnPiIg91eOwpIc0cgrTa/ZW55LHzymHG+7n/4mIvRFxNCKOSbpbDR9D230a+Q92b0Q8WC3umWM4Vn91HcMmQuAJSefY/lnbMyR9XtIjDfQxJtsD1cUZ2R6Q9AlJ28vvasQjklZUz1dIerjBXn7C8f9clSvV4DG0bUn3SNoZEbePKvXEMRyvv7qOYe2fDkhS9VHHn0iaJmlNRPxh7U2Mw/Z7NfLdX5KmS7qv6f5s3y9piaTTJO2VdJOkv5H0gKQzJb0o6aqIaOTi3Dj9LdHIMDYk7ZJ09ajz77r7u1jSP0raJulYtfhGjZx3N34MC/0tUw3HsJEQANA7uDAIJEcIAMkRAkByhACQHCEAJNdoCPTwlFxJ9NeuXu6vl3uT6u2v6ZFAT/9DiP7a1cv99XJvUo39NR0CABrW1mQh25dJulMjM//+MiJuLq0/w/1xigbeen1YB9Wn/knvv9vorz293F8v9yZ1vr839WMdioMeqzbpEJjMzUFO9dy40JdMan8AJm9TbNT+2DdmCLRzOsDNQYCTQDshMBVuDgKghend3kH1UcdKSTpFM7u9OwAnqJ2RwIRuDhIRqyNiMCIGe/lCDJBVOyHQ0zcHATAxkz4diIgjtq+V9Hf6v5uDPNOxzgDUoq1rAhGxXtL6DvUCoAHMGASSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBILnp7bzZ9i5JByQdlXQkIgY70RSA+rQVApWPR8QrHdgOgAZwOgAk124IhKTv237S9spONASgXu2eDlwcEXtsny5pg+1nI+LR0StU4bBSkk7RzDZ3B6DT2hoJRMSe6nFY0kOSLhhjndURMRgRg33qb2d3ALpg0iFge8D27OPPJX1C0vZONQagHu2cDsyT9JDt49u5LyK+15GuANRm0iEQES9IOq+DvQBoAB8RAskRAkByhACQHCEAJEcIAMkRAkBynfgpwjRe/eLHivUzlz9frD87PK9YP3Swr1hfcH+5PnP3a8X6sa07inXkxEgASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkmCdwAn73d+4r1j8z8KPyBs5us4El5fKuI68X63e+/PE2G5jafjh8VrE+cNtPFevTNz7ZyXZ6BiMBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSc0TUtrNTPTcu9CW17a/TfvzZC4v1Vz5cztQ5O8vH+kc/52J9xof/u1i/5UMPFuuXvvONYv27r88q1j89s3y/gna9EYeK9U0HB4r1Jaccbmv/7/vu1cX6+1c+0db2m7QpNmp/7BvzC4yRAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyXE/gRMw8O1NLertbf/U9t6uP/vpJcX6H1y0qLz/fyj/3oRblrzvBDs6MdPfOFasDzw9VKy/+9F1xfrPz2jxext2lesnq5YjAdtrbA/b3j5q2VzbG2w/Vz3O6W6bALplIqcDX5N02duW3SBpY0ScI2lj9RrAFNQyBCLiUUn73rZ4qaS11fO1kq7ocF8AajLZC4PzIuL4CdpLksq/ZA9Az2r704EY+QmkcX8yxvZK25ttbz6sg+3uDkCHTTYE9tqeL0nV4/B4K0bE6ogYjIjBPvVPcncAumWyIfCIpBXV8xWSHu5MOwDq1nKegO37NXLH+9Ns75Z0k6SbJT1g+wuSXpR0VTebxMQceWlvsT6wrlw/2mL7A99+9QQ76qy9v/GxYv2DM8pfzrfu+0CxvuivXijWjxSrU1fLEIiIZeOUpu7dQQC8hWnDQHKEAJAcIQAkRwgAyRECQHKEAJAc9xNAz5h+1sJi/as3frVY7/O0Yv2v7/zlYv3dQ48X6ycrRgJAcoQAkBwhACRHCADJEQJAcoQAkBwhACTHPAH0jGd/e0Gx/pF+F+vPHHqjWJ+74/UT7ikDRgJAcoQAkBwhACRHCADJEQJAcoQAkBwhACTHPAHU5uCnP1Ksb/nsHS22UP4NVr953XXF+jv/6Ycttp8TIwEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJJjngBq8x+fLH/PmeXyPIBl/35psT7ze08V61Gs5tVyJGB7je1h29tHLVtle4/trdWfT3W3TQDdMpHTga9JumyM5XdExOLqz/rOtgWgLi1DICIelbSvhl4ANKCdC4PX2n66Ol2Y07GOANRqsiFwl6SzJS2WNCTptvFWtL3S9mbbmw/r4CR3B6BbJhUCEbE3Io5GxDFJd0u6oLDu6ogYjIjBvhY/BQagfpMKAdvzR728UtL28dYF0NtazhOwfb+kJZJOs71b0k2SltherJGPXndJurqLPWKKeMfs2cX68l98rFjff+zNYn34K+8t1vsPPlGsY2wtQyAilo2x+J4u9AKgAUwbBpIjBIDkCAEgOUIASI4QAJIjBIDkuJ8AOua5VR8s1r9z2l8U60uf+0yx3r+eeQDdwEgASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkmCeACfufX/tosf70r/5psf5vRw4X66/98RnFer+GinVMDiMBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSY54A3jJ9wc8U69d/+VvFer/LX06ff2p5sf6ev+V+AU1gJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHLME0jE08v/3Od9Z3ex/rlZrxbr9x44vVif9+Xy95xjxSq6peVIwPZC2z+wvcP2M7avq5bPtb3B9nPV45zutwug0yZyOnBE0pci4lxJH5V0je1zJd0gaWNEnCNpY/UawBTTMgQiYigitlTPD0jaKWmBpKWS1larrZV0RbeaBNA9J3Rh0PYiSedL2iRpXkQcv+nbS5LmdbQzALWYcAjYniVpnaTrI2L/6FpEhKQY530rbW+2vfmwDrbVLIDOm1AI2O7TSADcGxEPVov32p5f1edLGh7rvRGxOiIGI2KwT/2d6BlAB03k0wFLukfSzoi4fVTpEUkrqucrJD3c+fYAdNtE5glcJGm5pG22t1bLbpR0s6QHbH9B0ouSrupOi+iY8z5QLP/+6d9oa/N//pXPFevveurxtraP7mgZAhHxmCSPU76ks+0AqBvThoHkCAEgOUIASI4QAJIjBIDkCAEgOe4ncBKZdu77i/WV32xvPte5a64p1hd945/b2j6awUgASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkmCdwEnn2t8p3fb985v5ivZUz/v5QeYUY8w5z6HGMBIDkCAEgOUIASI4QAJIjBIDkCAEgOUIASI55AlPIm5dfUKxvvPy2FluY2blmcNJgJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHIt5wnYXijp65LmSQpJqyPiTturJH1R0svVqjdGxPpuNQrpvy6aVqyfOb29eQD3Hji9WO/bX76fAHcTmJomMlnoiKQvRcQW27MlPWl7Q1W7IyJu7V57ALqtZQhExJCkoer5Ads7JS3odmMA6nFC1wRsL5J0vqRN1aJrbT9te43t8r2tAPSkCYeA7VmS1km6PiL2S7pL0tmSFmtkpDDmxHXbK21vtr35sA52oGUAnTShELDdp5EAuDciHpSkiNgbEUcj4pikuyWN+dMtEbE6IgYjYrBP/Z3qG0CHtAwB25Z0j6SdEXH7qOXzR612paTtnW8PQLdN5NOBiyQtl7TN9tZq2Y2SltlerJFPhnZJurorHQLoqol8OvCYJI9RYk7AFPNHr55brD/+K4uK9Rja1sFu0CuYMQgkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKOGn+n/KmeGxf6ktr2B2DEptio/bFvrPk+jASA7AgBIDlCAEiOEACSIwSA5AgBIDlCAEiu1nkCtl+W9OKoRadJeqW2Bk4c/bWnl/vr5d6kzvd3VkS8Z6xCrSHwEzu3N0fEYGMNtEB/7enl/nq5N6ne/jgdAJIjBIDkmg6B1Q3vvxX6a08v99fLvUk19tfoNQEAzWt6JACgYYQAkBwhACRHCADJEQJAcv8LId/VeNhqNOUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KHJ3Xvkzo4jd"
      },
      "source": [
        "**np.argmax finds a maximum element  from an array and returns the index of it**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7C5CFRQHo4je",
        "outputId": "c0d8ab41-44b9-4614-ccd0-925c73eb2308"
      },
      "source": [
        "np.argmax(y_predicted[0])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a83_Oh7Do4jg"
      },
      "source": [
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsLaZf_4o4jh",
        "outputId": "5ed5e53c-35cb-4daa-dec7-8caf83cce275"
      },
      "source": [
        "y_predicted_labels[:5]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 2, 1, 0, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VMjEAOLo4ji",
        "outputId": "eb3f6de9-acca-41b8-e9ac-44936e701915"
      },
      "source": [
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 964,    0,    1,    2,    0,    5,    5,    2,    1,    0],\n",
              "       [   0, 1114,    3,    2,    0,    1,    4,    2,    9,    0],\n",
              "       [   8,    9,  919,   19,    6,    4,   13,    9,   40,    5],\n",
              "       [   4,    0,   15,  918,    0,   31,    3,   11,   20,    8],\n",
              "       [   2,    1,    3,    1,  892,    0,   15,    4,    9,   55],\n",
              "       [  10,    2,    2,   29,    5,  792,   14,    5,   27,    6],\n",
              "       [  11,    3,    3,    1,    7,   16,  914,    2,    1,    0],\n",
              "       [   2,    5,   21,    9,    3,    0,    0,  953,    2,   33],\n",
              "       [   9,    9,    5,   25,    9,   34,    9,   13,  854,    7],\n",
              "       [  11,    7,    1,   11,   16,    7,    0,   24,    4,  928]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "IFgMeQNSo4jj",
        "outputId": "717e35b1-c5fb-4f34-d57d-83a183f68195"
      },
      "source": [
        "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": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "iQ2DwNuyo4jl"
      },
      "source": [
        "<h3 style='color:purple'>Using hidden layer</h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vs8o1M3-o4jl",
        "scrolled": true
      },
      "source": [
        "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"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Y-72_1EeGyr",
        "outputId": "cfb63f51-78ba-41df-fb2d-f180f96fc262"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 100)               78500     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 79,510\n",
            "Trainable params: 79,510\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "t8gjqOeLm1n_",
        "outputId": "b294c8f9-e4e7-425e-ba74-c2607e7c4237"
      },
      "source": [
        "import pydot\n",
        "keras.utils.plot_model(model)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6BbSHF_eDFh",
        "outputId": "027dbecf-7979-4ec6-8e0d-d2080e0242ad"
      },
      "source": [
        "model.fit(X_train_flattened, y_train, epochs=5)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2763 - accuracy: 0.9221\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1264 - accuracy: 0.9633\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0863 - accuracy: 0.9741\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0676 - accuracy: 0.9791\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0529 - accuracy: 0.9837\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f87d1691b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o14pqZjgo4jn",
        "scrolled": true,
        "outputId": "0be43d87-061b-4b9a-de3c-cb4baac0aa7d"
      },
      "source": [
        "model.evaluate(X_test_flattened,y_test)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.0786 - accuracy: 0.9762\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07855760306119919, 0.9761999845504761]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "XezzQXk_o4jo",
        "outputId": "907606bf-c5be-4a49-940a-746a41015a6a"
      },
      "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": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "XeFNj6Ggo4jp"
      },
      "source": [
        "<h3 style='color:purple'>Using Flatten layer so that we don't have to call .reshape on input dataset</h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PguD57gPo4jq",
        "scrolled": true
      },
      "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"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmelWFDtea8t",
        "outputId": "8f69d427-4715-4712-9aae-f129c480d9a6"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 100)               78500     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 79,510\n",
            "Trainable params: 79,510\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "MLdlycjZnAqY",
        "outputId": "6438a1a8-2011-41b9-99d3-932d334d8782"
      },
      "source": [
        "import pydot\n",
        "keras.utils.plot_model(model)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRpQwFTFeZbr",
        "outputId": "bfe65530-05f7-42fb-b891-5dd9ea5edeab"
      },
      "source": [
        "\n",
        "model.fit(X_train, y_train, epochs=10)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2678 - accuracy: 0.9236\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1213 - accuracy: 0.9645\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0849 - accuracy: 0.9743\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0662 - accuracy: 0.9795\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0518 - accuracy: 0.9840\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0424 - accuracy: 0.9874\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0335 - accuracy: 0.9897\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0292 - accuracy: 0.9907\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0231 - accuracy: 0.9926\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0201 - accuracy: 0.9937\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f87cfc23dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePgh_dPDo4jr",
        "outputId": "b1d3101d-6ca6-4f3d-ee32-e4680dd8b2a8"
      },
      "source": [
        "model.evaluate(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 1ms/step - loss: 0.0854 - accuracy: 0.9762\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08542901277542114, 0.9761999845504761]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfkQqATqdX7A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}