Estudos_de_IA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO1DpXeRz4yjF6Ha87ubDG0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rdisrael/AI/blob/master/Estudos_de_IA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**AI exercise lists.**\\\n",
        "**Comment all lines of code for better explanation and review.**"
      ],
      "metadata": {
        "id": "nrYatt90aVf4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# List 2 Introduction to ML\n"
      ],
      "metadata": {
        "id": "TSSQJe4IZj8H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exercise 1, class 6, tiny ML course for embedded systems"
      ],
      "metadata": {
        "id": "bY0xRTOvvTzU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "#DATASET\n",
        "xs= np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
        "ys= np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)\n",
        "#these are the database of inputs and outputs\n",
        "print(xs)\n",
        "print(ys)\n",
        "#DNN Model\n",
        "model= tf.keras.Sequential([tf.keras.layers.Dense(units=1, input_shape=[1])])\n",
        "model.summary()\n",
        "#by the summary you can check the model especifications\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
        "#training\n",
        "history = model.fit(xs,ys,epochs=500)\n",
        "#this function runs the ML algorithm base on the number of epochs\n",
        "#testing\n",
        "print(model.predict([10.0]))"
      ],
      "metadata": {
        "id": "uWwUzFlfveTM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "history.history.keys()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "QsbNfe-o6ABL",
        "outputId": "2039b5bc-5273-45c0-cb64-4d85647dd05a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#exercise 2- two layers and 2 neurons\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "#DATASET\n",
        "xs= np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
        "ys= np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)\n",
        "#these are the database of inputs and outputs\n",
        "print(xs)\n",
        "print(ys)\n",
        "#DNN Model\n",
        "my_layer1 = tf.keras.layers.Dense(units=2, input_shape=[1])\n",
        "my_layer2 = tf.keras.layers.Dense(units=1)\n",
        "model= tf.keras.Sequential([my_layer1, my_layer2])\n",
        "#by the summary you can check the model especifications\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
        "#training\n",
        "history1 = model.fit(xs,ys,epochs=500)\n",
        "#this function runs the ML algorithm base on the number of epochs\n",
        "#testing\n",
        "print(model.predict([10.0]))"
      ],
      "metadata": {
        "id": "PsUhycbV6gF4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "history1.history.keys()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "03KmApACA1Qr",
        "outputId": "85a4e777-a0b1-4c9a-c8f3-f02927d3fafc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "**-------------------------------------------------------------------------------------------------------------------------------------------------------------**\n",
        "\n"
      ],
      "metadata": {
        "id": "K-gX86a-ohow"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# List 3 Classification\n"
      ],
      "metadata": {
        "id": "aXiiXMgDZ-3Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "exploring DNN learning with TF\n"
      ],
      "metadata": {
        "id": "JutTpVy6L5JV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Load in fashion MNIST\n",
        "mnist = tf.keras.datasets.fashion_mnist #data set name, this dataset is a default of the TF library\n",
        "(training_images, training_labels), (test_images, test_labels) = mnist.load_data() #setting up the labels of training images and testing images, these labels names are arbitrary\n",
        "\n",
        "# Define the base model\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "#the flatten input is a reshape of the original matrix to convert 28*28 square image in a 784 pixel matrix. 784 is the number of input neurons\n",
        "                                    tf.keras.layers.Dense(512, activation=tf.nn.relu), #intermediate layer of neuros, relu apllies non-linearity into the model\n",
        "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)]) #output layer correspondent to the number of outputs you want, in this case 10 is the number of clothes the you want to classify"
      ],
      "metadata": {
        "id": "jBekzUpUQx0d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neural Networks learn the best when the data is scaled / normalized to fall in a constant range. One practitioners often use is the range [0,1]. How might you do this to the training and test images used here?\n",
        "\n",
        "A hint: these images are saved in the standard RGB format"
      ],
      "metadata": {
        "id": "_IRG7kfvaqvR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#The RGB format is divided in 0 to 255 leves of collor\n",
        "training_images  = training_images/255.0\n",
        "test_images = test_images/255.0"
      ],
      "metadata": {
        "id": "YMazdksLayOZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the model\n",
        "model.compile(optimizer = tf.keras.optimizers.Adam(), #Adam is the optimizer that makes the algorithm much more efficient when estimating the hit rate.\n",
        "              loss = 'sparse_categorical_crossentropy', #For classification is used crossentropy to estimate the loss of the algorithm\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# fit the model to the training data\n",
        "model.fit(training_images, training_labels, epochs=5) #here is the training of the algorithm, we only use five epochs because the dataset is very does not demmand to much effort.\n",
        "\n",
        "# test the model on the test data\n",
        "model.evaluate(test_images, test_labels) #test data from the dataset"
      ],
      "metadata": {
        "id": "mcjL6Hgkcbxl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a88766ef-f932-4aee-d597-76da883a1689"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.4708 - accuracy: 0.8319\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.3582 - accuracy: 0.8684\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.3225 - accuracy: 0.8808\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.2984 - accuracy: 0.8892\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.2797 - accuracy: 0.8959\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3430 - accuracy: 0.8747\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3430020213127136, 0.8747000098228455]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#A hint: trying running print(test_labels[0])\n",
        "print(test_labels[0])#print of the label \"ankle boot\" in the dataset\n",
        "classifications = model.predict(test_images) #based on the test images classify all the test data\n",
        "print(classifications[0])#here print the result of the cell 0\n",
        "#when printing it shows the number 9 which corresponds to the \"ankle boot\", printing classifications[0] shows an array and the number closest to one is the ninth number"
      ],
      "metadata": {
        "id": "Dyi_1s8xlQA1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b0a9a4d-e6f8-4432-c4c2-133a141879e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n",
            "313/313 [==============================] - 1s 2ms/step\n",
            "[2.7779930e-07 4.4390735e-08 1.9194226e-09 1.0564680e-08 6.9860775e-09\n",
            " 1.0345438e-03 5.7469009e-08 3.9452888e-02 1.3644522e-07 9.5951200e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test for the hit of the algorith, insert any label of image and see if  the results are ok\n",
        "test_eval = model.evaluate(test_images, test_labels)\n",
        "print (\"Testing data Accuracy: {:.1f}%\".format(test_eval[1]*100))\n",
        "plt.imshow(test_images[3]);\n",
        "print(test_labels[3])\n",
        "classifications = model.predict(test_images)\n",
        "print(classifications[3])\n",
        "# Returns the indices of the maximum values along an axis.\n",
        "np.argmax(classifications[3])\n"
      ],
      "metadata": {
        "id": "LOqodspS47FH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "outputId": "f1aafa37-ed9c-441f-f1ea-c1eefab9746f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3430 - accuracy: 0.8747\n",
            "Testing data Accuracy: 87.5%\n",
            "1\n",
            "313/313 [==============================] - 2s 5ms/step\n",
            "[1.23124515e-08 9.99999344e-01 1.46992374e-09 6.34050707e-07\n",
            " 2.72084044e-09 6.73854044e-16 7.41476491e-10 1.75367124e-19\n",
            " 1.70130544e-12 3.91178721e-14]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now look at the layers in your model. What happens if you double the number of neurons in the dense layer. What different results do you get for loss, training time etc? Why do you think that's the case?\n",
        "\n"
      ],
      "metadata": {
        "id": "7fS0MULiqM3R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NUMBER_OF_NEURONS = 512*2#here i am setting up the number of neuros in the intermediate layer\n",
        "\n",
        "# define the new model\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "                                    tf.keras.layers.Dense(NUMBER_OF_NEURONS, activation=tf.nn.relu),\n",
        "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])\n",
        "\n",
        "# compile fit and evaluate the model again\n",
        "model.compile(optimizer = tf.keras.optimizers.Adam(),\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "model.evaluate(test_images, test_labels)\n"
      ],
      "metadata": {
        "id": "p38WOm8kqN-D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6aebb8d-7afa-4d95-9407-310cb8bedcaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 25s 13ms/step - loss: 0.4684 - accuracy: 0.8305\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 28s 15ms/step - loss: 0.3554 - accuracy: 0.8707\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 23s 12ms/step - loss: 0.3230 - accuracy: 0.8816\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 24s 13ms/step - loss: 0.2961 - accuracy: 0.8910\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 24s 13ms/step - loss: 0.2794 - accuracy: 0.8970\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 0.3365 - accuracy: 0.8807\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3365391790866852, 0.8806999921798706]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Consider the effects of additional layers in the network instead of simply more neurons to the same layer. First update the model to add an additional dense layer into the model between the two existing Dense layers."
      ],
      "metadata": {
        "id": "UfSukUE5rnLs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "YOUR_NEW_LAYER = tf.keras.layers.Dense(512, activation=tf.nn.relu) #add a new layer of neuros i just coppied the dense layer\n",
        "\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "                                    tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "                                    YOUR_NEW_LAYER,\n",
        "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])\n"
      ],
      "metadata": {
        "id": "091UK6rXrsXk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Lets then compile, fit, and evaluate our model. What happens to the error? How does this compare to the original model and the model with double the number of neurons?\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "I9VR5u75sf3_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# compile fit and evaluate the model again\n",
        "model.compile(optimizer = tf.keras.optimizers.Adam(), #running the code again with another layer added\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "model.evaluate(test_images, test_labels)"
      ],
      "metadata": {
        "id": "LDFHsg5jsgmk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08a9c0ee-e3e4-4c63-a439-4e849f0f8d56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 24s 12ms/step - loss: 0.4693 - accuracy: 0.8291\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 27s 14ms/step - loss: 0.3550 - accuracy: 0.8689\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 23s 12ms/step - loss: 0.3212 - accuracy: 0.8812\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 23s 12ms/step - loss: 0.2953 - accuracy: 0.8906\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 23s 13ms/step - loss: 0.2793 - accuracy: 0.8951\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3288 - accuracy: 0.8824\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3288225829601288, 0.8823999762535095]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before you trained, you normalized the data. What would be the impact of removing that? To see it for yourself fill in the following lines of code to get a non-normalized set of data and then re-fit and evaluate the model using this data.\n",
        "\n"
      ],
      "metadata": {
        "id": "kbOhA46Ws2Aj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get new non-normalized mnist data\n",
        "training_images_non = training_images\n",
        "test_images_non = test_images\n",
        "\n",
        "# re-compile, re-fit and re-evaluate\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "                                    tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "                                    YOUR_NEW_LAYER,\n",
        "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])\n",
        "model.compile(optimizer = tf.keras.optimizers.Adam(),\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.fit(training_images_non, training_labels, epochs=5)\n",
        "model.evaluate(test_images_non, test_labels)\n",
        "classifications = model.predict(test_images_non)"
      ],
      "metadata": {
        "id": "6Xg9dxOLs2Yo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ebda25e-7683-4fbb-d33c-c7925cc93d27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 23s 12ms/step - loss: 0.4694 - accuracy: 0.8291\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 22s 12ms/step - loss: 0.3560 - accuracy: 0.8704\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 23s 12ms/step - loss: 0.3193 - accuracy: 0.8824\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 21s 11ms/step - loss: 0.2948 - accuracy: 0.8899\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 23s 12ms/step - loss: 0.2798 - accuracy: 0.8957\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.3614 - accuracy: 0.8703\n",
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusion \\\n",
        "Definely the classification algorithm works. Adding layers and neurons to the program helps with the accuracy. \\\n",
        "First Algorithm: loss=0.3430 and accuracy= 0.8747 \\\n",
        "With additional neurons: loss=0.3365 and accuracy=0.8807 \\\n",
        "with additional layers: loss=0.3288 and accuracy=0.8824 \\\n",
        "Non-normalized data: loss=0.3614 and accuracy=0.8703"
      ],
      "metadata": {
        "id": "xQTUnawB8qyJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# List 4 CNN"
      ],
      "metadata": {
        "id": "xVRU8mVfbmAl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploring CNN learning with TensorFlow**\\\n",
        "Once you run the model at leat once without errors, try change the given hyperparameters, as number of filters, layers and neurons (units). Comment your obsevations.\n"
      ],
      "metadata": {
        "id": "ugd99qOLb47j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "# Load in fashion MNIST\n",
        "mnist = tf.keras.datasets.fashion_mnist\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "x_train.shape\n",
        "plt.imshow(x_train[0]);\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "PJl9BOZKB-1g",
        "outputId": "f2ac7387-346a-4aaf-a726-c74d44cf9e03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train #array of values of training data\n",
        "y_test #array of values of testing data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzq-m6kDGoz6",
        "outputId": "6f6a336d-380b-442d-f67b-9ad789a7778c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical #converts numbers to a matrix\n",
        "y_train.shape #number of data that can be used for training\n",
        "y_cat_test = to_categorical(y_test,10)\n",
        "y_cat_test.shape #number of separate data for testing\n",
        "y_cat_train = to_categorical(y_train,10)\n",
        "y_cat_train.shape #number of separate data for training\n",
        "# verifing one sample label\n",
        "print(y_train[1])\n",
        "print(y_cat_train[1])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sl2O58O2G2dj",
        "outputId": "27b625ac-9af6-423e-c784-cfdc127bc0bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#processing the data and normalizing it\n",
        "x_train.max()\n",
        "x_train.min()\n",
        "VALUE = 255.0\n",
        "x_train = x_train/VALUE\n",
        "x_test = x_test/VALUE\n",
        "plt.imshow(x_train[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "YZz5c9g7KP4T",
        "outputId": "9ce450ed-1e4b-4b81-ee89-ee0919d24332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reshaping the Data"
      ],
      "metadata": {
        "id": "XYJoWvKEKrE7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape\n",
        "x_test.shape\n",
        "x_train = x_train.reshape(60000, 28, 28, 1)\n",
        "# batch_size, widht, height, color_channels\n",
        "x_train.shape\n",
        "x_test = x_test.reshape(10000,28,28,1)\n",
        "x_test.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDWDvlD1Ckdo",
        "outputId": "8874882a-8346-4377-c3c8-c6825bcfffc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create Model Arquitecture and Compile\n"
      ],
      "metadata": {
        "id": "UKZaB1NwVx8b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential #provides training and inference features on this model.\n",
        "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n",
        "LAYER_ACTIVATION = 'relu' #inserts non-linearity into the model\n",
        "LAST_LAYER_ACTIVATION = 'softmax'\n",
        "OUTPUT_LAYER_NEURONS = 10 #\n",
        "\n",
        "LOSS = 'categorical_crossentropy'#If you use one-hot encoding, opt for categorical_crossentropy; if your labels are integers, use sparse_categorical_crossentropy\n",
        "OPT = 'adam' #optimizer\n",
        "model = Sequential()\n",
        "model.add(\n",
        "    Conv2D(\n",
        "        filters=32, #number of cnn filters\n",
        "        kernel_size=(4, 4), #kernel is the convolution matrix\n",
        "        input_shape=(28, 28, 1),\n",
        "        activation=LAYER_ACTIVATION,\n",
        "    ))\n",
        "model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation=LAYER_ACTIVATION))\n",
        "model.add(Dense(OUTPUT_LAYER_NEURONS, activation=LAST_LAYER_ACTIVATION))\n",
        "model.summary()\n",
        "model.compile(loss=LOSS,\n",
        "              optimizer=OPT,\n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IdG9f1eVyWE",
        "outputId": "a2fa1e97-82c3-4dd2-e497-4ff0a81e69f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 25, 25, 32)        544       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 12, 12, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4608)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               589952    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 591,786\n",
            "Trainable params: 591,786\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the Model\\\n",
        "Let's add a callback, \"EarlyStopping\". This one of the techinics to prevent overfitting."
      ],
      "metadata": {
        "id": "vL1iAbuwcD5S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "early_stop = EarlyStopping(monitor='val_loss',patience=2)\n",
        "EPOCHS = 10\n",
        "VAL_SPLIT = 0.1\n",
        "history = model.fit(x_train,\n",
        "                    y_cat_train,\n",
        "                    epochs=EPOCHS,\n",
        "                    validation_split=VAL_SPLIT,\n",
        "                    callbacks=[early_stop])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2ax-cfUcIie",
        "outputId": "9eae3427-8e08-416d-a287-2093e57c51d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1688/1688 [==============================] - 42s 24ms/step - loss: 0.4139 - accuracy: 0.8545 - val_loss: 0.3177 - val_accuracy: 0.8852\n",
            "Epoch 2/10\n",
            "1688/1688 [==============================] - 42s 25ms/step - loss: 0.2841 - accuracy: 0.8978 - val_loss: 0.2760 - val_accuracy: 0.8997\n",
            "Epoch 3/10\n",
            "1688/1688 [==============================] - 39s 23ms/step - loss: 0.2389 - accuracy: 0.9121 - val_loss: 0.2729 - val_accuracy: 0.9007\n",
            "Epoch 4/10\n",
            "1688/1688 [==============================] - 38s 22ms/step - loss: 0.2035 - accuracy: 0.9243 - val_loss: 0.2625 - val_accuracy: 0.9027\n",
            "Epoch 5/10\n",
            "1688/1688 [==============================] - 39s 23ms/step - loss: 0.1772 - accuracy: 0.9343 - val_loss: 0.2500 - val_accuracy: 0.9148\n",
            "Epoch 6/10\n",
            "1688/1688 [==============================] - 39s 23ms/step - loss: 0.1545 - accuracy: 0.9433 - val_loss: 0.2561 - val_accuracy: 0.9097\n",
            "Epoch 7/10\n",
            "1688/1688 [==============================] - 39s 23ms/step - loss: 0.1323 - accuracy: 0.9514 - val_loss: 0.2762 - val_accuracy: 0.9067\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1S72eoE1iOKI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "acc8f88d-2ade-48b3-f609-16565e23476e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate the Model\n"
      ],
      "metadata": {
        "id": "Z_1gHWQeiPYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.metrics_names)\n",
        "print(model.evaluate(x_test,y_cat_test,verbose=0))\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "predictions = np.argmax(model.predict(x_test), axis=-1)\n",
        "predictions.shape\n",
        "y_cat_test.shape\n",
        "y_cat_test[0]\n",
        "predictions[0]\n",
        "y_test\n",
        "print(classification_report(y_test,predictions))\n",
        "confusion_matrix(y_test,predictions)\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.heatmap(confusion_matrix(y_test,predictions), cmap='Blues', annot=True, fmt='g');"
      ],
      "metadata": {
        "id": "q_l67RVgiX4-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "outputId": "a5470ffc-62af-4534-85d6-5d6cfc16003f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['loss', 'accuracy']\n",
            "[0.28456228971481323, 0.9054999947547913]\n",
            "313/313 [==============================] - 2s 7ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.82      0.85      1000\n",
            "           1       0.98      0.99      0.98      1000\n",
            "           2       0.91      0.76      0.83      1000\n",
            "           3       0.91      0.93      0.91      1000\n",
            "           4       0.80      0.90      0.85      1000\n",
            "           5       0.98      0.98      0.98      1000\n",
            "           6       0.72      0.77      0.74      1000\n",
            "           7       0.95      0.97      0.96      1000\n",
            "           8       0.97      0.99      0.98      1000\n",
            "           9       0.97      0.96      0.96      1000\n",
            "\n",
            "    accuracy                           0.91     10000\n",
            "   macro avg       0.91      0.91      0.91     10000\n",
            "weighted avg       0.91      0.91      0.91     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[1]);\n",
        "classifications = model.predict(x_train) #based on the test images classify all the test data\n",
        "print(classifications[1])#here print the result of the cell 0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "oUA6WbFiMhDQ",
        "outputId": "e21f9b54-bc7f-467a-84e8-7da4eab5bc27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 14s 7ms/step\n",
            "[9.9997073e-01 1.1156593e-10 6.0430243e-06 6.9166650e-09 9.2915946e-11\n",
            " 1.7716413e-11 2.3179113e-05 6.4068076e-13 1.5173133e-09 1.1090130e-09]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}