MNIST_Train_Task1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Github repo: https://github.com/wenjie-mo/ECE188DeepLearning"
      ],
      "metadata": {
        "id": "nvu4p0wZXRzb"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shreyasrajesh0308/ECE188DeepLearning/blob/main/MNIST_Train_Task1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Project 2- ECE 188, Spring 2022 - Deadline: June 10th, 2022. Please upload a link to your Github on Gradescope.\n",
        "# Train a Deep Learning model on the MNIST dataset and evaluate it on a test set.\n",
        "\n",
        "The first part in this notebook will take you though the training of the [MNIST Dataset](http://yann.lecun.com/exdb/mnist/) which is a collection of handwritten digits. \n",
        "\n",
        "We will build a Deep Learning based system that can recognise handwritten digits using [Keras](https://keras.io/) and [Tensorflow](https://www.tensorflow.org/). \n",
        "\n",
        "Note that the entire project will be done on a Google Colab. A tutorial to some basic colab instructions can be found [here](https://colab.research.google.com/?utm_source=scs-index#scrollTo=GJBs_flRovLc). \n",
        "\n",
        "Note that all the code needed is provided until Task 1, **you will only have to write code after the task 1 header**"
      ],
      "metadata": {
        "id": "a-utSW-VUF2y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Necessary Packages\n",
        "\n",
        "We import Keras and Tensorflow to train our network and numpy to work with our data. \n",
        "\n",
        "We also import matplotlib to view our images. "
      ],
      "metadata": {
        "id": "fgnHUBjoUTcy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aDvD8R1TbbOi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import layers\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (8, 8)\n",
        "mpl.rcParams['axes.grid'] = False\n"
      ],
      "metadata": {
        "id": "OdYVH66MUciD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the Data\n",
        "\n",
        "We perform some data preprocessing. Feel free to skip over this and use the train and test data as x_train and x_test"
      ],
      "metadata": {
        "id": "yuFHL78kUez_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2iKGDDuDbhRM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f0e135f-05ae-4b7d-da17-a3d63c28dedb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n",
            "x_train shape: (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ]
        }
      ],
      "source": [
        "# Model / data parameters\n",
        "num_classes = 10\n",
        "input_shape = (28, 28, 1)\n",
        "\n",
        "# the data, split between train and test sets\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "\n",
        "# Scale images to the [0, 1] range\n",
        "x_train = x_train.astype(\"float32\") / 255\n",
        "x_test = x_test.astype(\"float32\") / 255\n",
        "# Make sure images have shape (28, 28, 1)\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "print(\"x_train shape:\", x_train.shape)\n",
        "print(x_train.shape[0], \"train samples\")\n",
        "print(x_test.shape[0], \"test samples\")\n",
        "\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define a model in keras.\n",
        "\n",
        "\n",
        " We define a simple simple network with 2 hidden layers. One fully connected dense layer with 8 neurons and onr flatten layer that flattens the model into 1D structure which is then fed into a softmax output.\n",
        "\n",
        "This is a barebones keras structure. In task 1 you will be tasked with improving this model. "
      ],
      "metadata": {
        "id": "tEDsgxIcuv0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=input_shape),\n",
        "        layers.Dense(8,activation=\"relu\"),\n",
        "        layers.Flatten(),\n",
        "        layers.Dense(num_classes, activation=\"softmax\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kb3ONTl8Odrq",
        "outputId": "09eb75a2-1142-4572-fb7b-d66beb0df194"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_1 (Dense)             (None, 28, 28, 8)         16        \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 6272)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                62730     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 62,746\n",
            "Trainable params: 62,746\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compile and train the model \n",
        "\n",
        "We know train our model. Before we do that we need to set some parameters to train the model. Hence we define the loss and optimizer to be used as well as batch size. The number of epochs refers to the number of training iterations. "
      ],
      "metadata": {
        "id": "7qepS8u7VJXF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128\n",
        "epochs = 15\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cwCRnA2OgPY",
        "outputId": "5b41a78f-11bd-44e5-fc1c-7c1a4896b773"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.6474 - accuracy: 0.8512 - val_loss: 0.2809 - val_accuracy: 0.9212\n",
            "Epoch 2/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.3206 - accuracy: 0.9079 - val_loss: 0.2456 - val_accuracy: 0.9295\n",
            "Epoch 3/15\n",
            "422/422 [==============================] - 1s 4ms/step - loss: 0.2939 - accuracy: 0.9166 - val_loss: 0.2343 - val_accuracy: 0.9345\n",
            "Epoch 4/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2814 - accuracy: 0.9212 - val_loss: 0.2311 - val_accuracy: 0.9353\n",
            "Epoch 5/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2745 - accuracy: 0.9223 - val_loss: 0.2267 - val_accuracy: 0.9365\n",
            "Epoch 6/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2695 - accuracy: 0.9245 - val_loss: 0.2310 - val_accuracy: 0.9368\n",
            "Epoch 7/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2659 - accuracy: 0.9260 - val_loss: 0.2240 - val_accuracy: 0.9383\n",
            "Epoch 8/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2628 - accuracy: 0.9264 - val_loss: 0.2248 - val_accuracy: 0.9385\n",
            "Epoch 9/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2593 - accuracy: 0.9271 - val_loss: 0.2277 - val_accuracy: 0.9367\n",
            "Epoch 10/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2575 - accuracy: 0.9289 - val_loss: 0.2265 - val_accuracy: 0.9378\n",
            "Epoch 11/15\n",
            "422/422 [==============================] - 1s 4ms/step - loss: 0.2557 - accuracy: 0.9288 - val_loss: 0.2249 - val_accuracy: 0.9375\n",
            "Epoch 12/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2535 - accuracy: 0.9293 - val_loss: 0.2269 - val_accuracy: 0.9363\n",
            "Epoch 13/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2520 - accuracy: 0.9297 - val_loss: 0.2300 - val_accuracy: 0.9365\n",
            "Epoch 14/15\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.2519 - accuracy: 0.9300 - val_loss: 0.2285 - val_accuracy: 0.9390\n",
            "Epoch 15/15\n",
            "422/422 [==============================] - 1s 4ms/step - loss: 0.2497 - accuracy: 0.9312 - val_loss: 0.2226 - val_accuracy: 0.9407\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa00ce35190>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate the model "
      ],
      "metadata": {
        "id": "2IscBu-nVLw3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCRqjgb_OiN7",
        "outputId": "646b1fa9-578d-4cef-b51a-1965b7c40b2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.2645205855369568\n",
            "Test accuracy: 0.9261000156402588\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We achieve an accuracy on the test set of 92% but can we do better?"
      ],
      "metadata": {
        "id": "nSdY-BY5vKZb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Looking at a prediction.\n",
        "\n",
        "Lets look at the prediction for a particular image in our model. We will look at one corect and one incorrect prediction and see if we can improve on the incorrect prediction. "
      ],
      "metadata": {
        "id": "y9a6QtAJ4Xby"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_mnist_label(image_probs):\n",
        "\n",
        "  return np.argmax(image_probs), np.max(image_probs)"
      ],
      "metadata": {
        "id": "g3uyTJ6253Jv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = model.predict(x_test)\n",
        "\n",
        "image = x_test[0].reshape(input_shape[0], input_shape[1])\n",
        "label = np.argmax(y_test[0])\n",
        "image_probs = preds[0]"
      ],
      "metadata": {
        "id": "sZP5HsZO5OyY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(image*0.5 + 0.5)  # To change [-1, 1] to [0,1]\n",
        "image_class, class_confidence = get_mnist_label(image_probs)\n",
        "plt.title('{} : {:.2f}% Confidence'.format(image_class, class_confidence*100))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "AdIoM5GX5ygg",
        "outputId": "74331f81-5321-4d84-83b5-5baf8e3583ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Store the incorrect prediction index here\n",
        "incorrect_index = 0\n",
        "\n",
        "for i in range(preds.shape[0]):\n",
        "\n",
        "  if np.argmax(preds[i]) != np.argmax(y_test[i]):\n",
        "\n",
        "    incorrect_index = i\n",
        "\n",
        "image_incorrect = x_test[incorrect_index].reshape(input_shape[0], input_shape[1])"
      ],
      "metadata": {
        "id": "qmmsBM_E6DUc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(image_incorrect*0.5 + 0.5)  # To change [-1, 1] to [0,1]\n",
        "image_class, class_confidence = get_mnist_label(preds[i])\n",
        "plt.title('{} : {:.2f}% Confidence'.format(image_class, class_confidence*100))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "HKsiOalv7Bsj",
        "outputId": "e964cfef-f32b-4a8f-efba-81a16fbec8d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that a 3 has been predicted with high confidence as a 6 does this change with our improved model below?"
      ],
      "metadata": {
        "id": "VfSIJAF57dhy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " # TASK 1: Improve the accuracy of the model \n",
        "\n",
        "Your goal in task one is to improve the accuracy of the model on the test set. "
      ],
      "metadata": {
        "id": "LifPv5WJvSMu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For image recogniton tasks convolutional neural networks work better than most other type of architectures. Your goal is to push the test accuracy above 99% percent. \n",
        "\n",
        "The following steps will get you there. \n",
        "\n",
        "1. Convert the 1st hidden Dense layer to a conv layer with 32 filters, a filter size of 3x3 and with relu activation. (I've done this step)\n",
        "2. Add a Maxpooling layer of size 2x2. \n",
        "3. Add another Conv layer with 64 filters, with a filter size of 3x3 with relu activation. \n",
        "4. Add a Maxpooling layer of size 2x2. \n",
        "5. Add Dropout to the layer after the Flatten layer. "
      ],
      "metadata": {
        "id": "nq8pen8-wdsh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "improved_model = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=input_shape),\n",
        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        # Add Maxpooling layer here\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        # Add Conv2d layer here\n",
        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Flatten(),\n",
        "        # Add dropout of 0.5 here\n",
        "        layers.Dropout(.5),\n",
        "        layers.Dense(num_classes, activation=\"softmax\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "improved_model.summary()\n"
      ],
      "metadata": {
        "id": "LeDUJgK2wb4l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d49a3123-407e-406f-d11b-c69116dfab5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                16010     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,826\n",
            "Trainable params: 34,826\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Complile and train the improved model. "
      ],
      "metadata": {
        "id": "ffzFzyOZ0UG8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128\n",
        "epochs = 15\n",
        "\n",
        "improved_model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "improved_model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)\n"
      ],
      "metadata": {
        "id": "aPBhYQml1OuH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42cb1315-4eaf-4281-dd6c-d3eca898997d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "422/422 [==============================] - 13s 6ms/step - loss: 0.3552 - accuracy: 0.8923 - val_loss: 0.0778 - val_accuracy: 0.9787\n",
            "Epoch 2/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.1069 - accuracy: 0.9673 - val_loss: 0.0584 - val_accuracy: 0.9855\n",
            "Epoch 3/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0819 - accuracy: 0.9743 - val_loss: 0.0464 - val_accuracy: 0.9880\n",
            "Epoch 4/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0690 - accuracy: 0.9783 - val_loss: 0.0416 - val_accuracy: 0.9880\n",
            "Epoch 5/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0592 - accuracy: 0.9817 - val_loss: 0.0362 - val_accuracy: 0.9908\n",
            "Epoch 6/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0538 - accuracy: 0.9837 - val_loss: 0.0342 - val_accuracy: 0.9908\n",
            "Epoch 7/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0486 - accuracy: 0.9841 - val_loss: 0.0368 - val_accuracy: 0.9907\n",
            "Epoch 8/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0458 - accuracy: 0.9856 - val_loss: 0.0320 - val_accuracy: 0.9912\n",
            "Epoch 9/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0424 - accuracy: 0.9865 - val_loss: 0.0303 - val_accuracy: 0.9925\n",
            "Epoch 10/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0398 - accuracy: 0.9874 - val_loss: 0.0330 - val_accuracy: 0.9908\n",
            "Epoch 11/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0380 - accuracy: 0.9878 - val_loss: 0.0332 - val_accuracy: 0.9913\n",
            "Epoch 12/15\n",
            "422/422 [==============================] - 2s 6ms/step - loss: 0.0346 - accuracy: 0.9886 - val_loss: 0.0289 - val_accuracy: 0.9925\n",
            "Epoch 13/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0340 - accuracy: 0.9893 - val_loss: 0.0287 - val_accuracy: 0.9910\n",
            "Epoch 14/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0320 - accuracy: 0.9894 - val_loss: 0.0294 - val_accuracy: 0.9928\n",
            "Epoch 15/15\n",
            "422/422 [==============================] - 2s 5ms/step - loss: 0.0308 - accuracy: 0.9899 - val_loss: 0.0292 - val_accuracy: 0.9917\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa0222e3090>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate the Improved model. \n",
        "\n",
        "You should achieve a test accuracy of greater than 99%!\n",
        "\n",
        "If not the code will throw an assertion error. "
      ],
      "metadata": {
        "id": "7a7EZNcv1r9J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "score = improved_model.evaluate(x_test, y_test, verbose=0)\n",
        "print(\"Improved Test loss:\", score[0])\n",
        "print(\"Improved Test accuracy:\", score[1])\n",
        "\n",
        "assert score[1] > 0.99"
      ],
      "metadata": {
        "id": "lYKx0TKH2P6G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7f2c59f-50ba-4b90-e3e4-28f5a844f3ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Improved Test loss: 0.024597138166427612\n",
            "Improved Test accuracy: 0.9915000200271606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Looking at a prediction.\n",
        "\n",
        "Lets look at the prediction for a particular image in our model. We will look at one corect and one incorrect prediction and see if we can improve on the incorrect prediction. \n",
        "\n",
        "Check the predictions on the improved model, do you see improvements on the incorrect sample. "
      ],
      "metadata": {
        "id": "aD885MO57xoV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "improved_preds = improved_model.predict(x_test)\n",
        "\n",
        "image = x_test[0].reshape(input_shape[0], input_shape[1])\n",
        "label = np.argmax(y_test[0])\n"
      ],
      "metadata": {
        "id": "0ozaLxNg7xoW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(image*0.5 + 0.5)  # To change [-1, 1] to [0,1]\n",
        "image_class, class_confidence = get_mnist_label(improved_preds[0])\n",
        "plt.title('{} : {:.2f}% Confidence'.format(image_class, class_confidence*100))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "y0qsS5jY7xoW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "5bc9fef3-efe0-41a6-cd77-222068870afc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(image_incorrect*0.5 + 0.5)  # To change [-1, 1] to [0,1]\n",
        "image_class, class_confidence = get_mnist_label(improved_preds[i])\n",
        "plt.title('{} : {:.2f}% Confidence'.format(image_class, class_confidence*100))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Dyj5CbQD7xoW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "7633a5a4-11a7-4a54-8712-ef52ba8b6279"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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eMXIGa0aOh/nmSBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOjEpd92cHffdtuo8QefePnMY1/8n583attrr/3mzGOPuPJTo7a9adTocXY54HtnHnvCr3101LYftmblLt12yR13jRp/4Dt27zQTmJ0jVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATkQVADoRVQDoRFQBoBNRBYBOXE+VbWbXv7pw1PhxV9dcOe34Y0eNf9j/umTmsS/Z/wujtr2SnvX+Xxo1/sgLPt1pJjA7R6oA0ImoAkAnogoAnYgqAHQiqgDQiagCQCeiCgCdiCoAdCKqANCJqAJAJ6IKAJ2IKgB0IqoA0ImoAkAnLv0GnX3leePGv//+n+kzkTlz8F+v9AxgPEeqANCJqAJAJ6IKAJ2IKgB0IqoA0ImoAkAnogoAnYgqAHQiqgDQiagCQCeiCgCdiCoAdCKqANCJqAJAJ6IKAJ24niqwKrz6dX88avxrNzx95rGbrv7qqG3DZo5UAaATUQWATkQVADrZalSr6uyqurGqLp1a9sqqur6qLh4+nrZtpwkAq99yjlTfmuQpCyz/vdbascPHB/pOCwDmz1aj2lr7eJJvbIe5AMBcG/Oc6vOr6pLh9PB+3WYEAHNq1qi+OckRSY5NsjHJ6xe7YVWtr6oNVbXhznx3xs0BwOo3U1Rbaze01u5qrd2d5C1Jjlvitme21ta11tatydpZ5wkAq95MUa2qg6a+PTHJpYvdFgB2Flv9M4VVdV6SJyQ5oKquS/KKJE+oqmOTtCRXJzltG84RAObCVqPaWjtpgcVnbYO5AMBc8xeVAKATUQWATkQVADpxPVXo7EFvbKPGX/LYu2Yee8xuu4za9kp67NrZf+4k+cILD5557ENe7Hqq9OFIFQA6EVUA6ERUAaATUQWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6MSl36Cz+sTnRo3/tWf+zMxjbzpun1Hb/pNffcOo8Q9ds9uo8WPscv9/XrFtw2aOVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6cT1VWGXaZy+beewBnx237f/2PS8dNf6zL/iDcRMY4fzH/vHMY3/1AT8+atubrrt+1Hh2HI5UAaATUQWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATlz6jW3mX370uFHjv3XE7A/PQz5006ht33XFlaPGz6s9bmorPYWZPWzNmtkHr/FPIX04UgWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATkQVADoRVQDoxEUEWdIuDz1i5rFvfdMbRm37AbvuMfPYC184atM56YO/MPPY7/vkLqO2/T1v++So8WN845j5vZ7qmbccPvPYdvt3+k2EnZojVQDoRFQBoBNRBYBOthrVqjq0qj5WVZdX1WVV9cJh+f5V9eGqunL4vN+2ny4ArF7LOVLdlOSlrbWjkjwmyfOq6qgkL0/y0dbakUk+OnwPADutrUa1tbaxtXbR8PVtSa5IckiSE5KcM9zsnCTP2FaTBIB5cK/eUlNVhyd5RJJPJzmwtbZxWPW1JAcuMmZ9kvVJsnv2nHWeALDqLfuFSlV13yTvTPKi1tqt0+taay3Jgm9wa62d2Vpb11pbtyZrR00WAFazZUW1qtZkEtRzW2vvGhbfUFUHDesPSnLjtpkiAMyH5bz6t5KcleSK1tr0n8h5b5JThq9PSfKe/tMDgPmxnOdUj09ycpLPV9XFw7LTk7wmyTuq6tQk1yR59raZIgDMh61GtbX2d0lqkdVP7DsdAJhf/qISAHQiqgDQSU3eDbN97FP7t0eXM8bb0y0/+ZhR48941ZtmHvuonfQdVLfc/S+jxl96x96dZnLvPWLtt0eN37N26zSTe+/oj//szGMfdNLnOs6EHd1H2vkXttbWLbTOkSoAdCKqANCJqAJAJ6IKAJ2IKgB0IqoA0ImoAkAnogoAnYgqAHQiqgDQiagCQCeiCgCdiCoAdCKqANCJqAJAJ7uu9ATYtjbtUaPGv+yXf3HmsXt/+bZR2z7h7X8z89hT9/3qqG2Pse99dh81/vjd7+w0k1ms3PVQx1p70V4rPQVwpAoAvYgqAHQiqgDQiagCQCeiCgCdiCoAdCKqANCJqAJAJ6IKAJ2IKgB0IqoA0ImoAkAnogoAnYgqAHRSrbXttrF9av/26Hridtse8+0+e81+Ka/rT3v4qG2/bP07Zh570t43jNr2zurhf/hLo8Yf+jufnn3w3XeN2jY7l4+08y9sra1baJ0jVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATkQVADoRVQDoRFQBoBNRBYBOXE8VFlBrdpt97NEPGbXt614x+9iLjnvbqG2fecvho8a//+kLXmJyWe768tWjtp3t+G8ZOzfXUwWA7UBUAaATUQWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaCTXVd6ArAatTvvmH3sxZeP2vbBJ84+9kfzqFHbHu8rK7x9WFmOVAGgE1EFgE5EFQA62WpUq+rQqvpYVV1eVZdV1QuH5a+squur6uLh42nbfroAsHot54VKm5K8tLV2UVXtneTCqvrwsO73Wmu/u+2mBwDzY6tRba1tTLJx+Pq2qroiySHbemIAMG/u1XOqVXV4kkck+fSw6PlVdUlVnV1V+3WeGwDMlWVHtarum+SdSV7UWrs1yZuTHJHk2EyOZF+/yLj1VbWhqjbcme92mDIArE7LimpVrckkqOe21t6VJK21G1prd7XW7k7yliTHLTS2tXZma21da23dmqztNW8AWHWW8+rfSnJWkitaa2+YWn7Q1M1OTHJp/+kBwPxYzqt/j09ycpLPV9XFw7LTk5xUVccmaUmuTnLaNpkhAMyJ5bz69++S1AKrPtB/OgAwv/xFJQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaATUQWATkQVADoRVQDoRFQBoBNRBYBORBUAOhFVAOhEVAGgE1EFgE5EFQA6EVUA6ERUAaCTaq1tv41V3ZTkmiVuckCSm7fTdHYU9tls7LfZ2G/3nn02m9W83w5rrd1voRXbNapbU1UbWmvrVnoe88Q+m439Nhv77d6zz2Yzr/vN6V8A6ERUAaCT1RbVM1d6AnPIPpuN/TYb++3es89mM5f7bVU9pwoA82y1HakCwNxaFVGtqqdU1T9W1Zeq6uUrPZ95UVVXV9Xnq+riqtqw0vNZrarq7Kq6saounVq2f1V9uKquHD7vt5JzXG0W2WevrKrrh8fbxVX1tJWc42pUVYdW1ceq6vKquqyqXjgs93hbxBL7bC4fbyt++reqdknyxSRPSnJdks8kOam1dvmKTmwOVNXVSda11lbre7lWhar6j0luT/InrbUfGJa9Lsk3WmuvGX6R26+19isrOc/VZJF99sokt7fWfncl57aaVdVBSQ5qrV1UVXsnuTDJM5L8dDzeFrTEPnt25vDxthqOVI9L8qXW2lWttTuS/GmSE1Z4TuxAWmsfT/KNLRafkOSc4etzMvmfmMEi+4ytaK1tbK1dNHx9W5IrkhwSj7dFLbHP5tJqiOohSa6d+v66zPEO3c5akg9V1YVVtX6lJzNnDmytbRy+/lqSA1dyMnPk+VV1yXB62CnMJVTV4UkekeTT8Xhbli32WTKHj7fVEFVm97jW2iOTPDXJ84ZTdtxLbfIciJfBb92bkxyR5NgkG5O8fmWns3pV1X2TvDPJi1prt06v83hb2AL7bC4fb6shqtcnOXTq+wcMy9iK1tr1w+cbk1yQyal0lueG4bmczc/p3LjC81n1Wms3tNbuaq3dneQt8XhbUFWtySQO57bW3jUs9nhbwkL7bF4fb6shqp9JcmRVPaiqdkvynCTvXeE5rXpVtdfwpH6qaq8kT05y6dKjmPLeJKcMX5+S5D0rOJe5sDkKgxPj8fZvVFUlOSvJFa21N0yt8nhbxGL7bF4fbyv+6t8kGV4qfUaSXZKc3Vr77RWe0qpXVQ/O5Og0SXZN8nb7bWFVdV6SJ2Ry1YsbkrwiybuTvCPJAzO5ctKzW2temDNYZJ89IZNTcS3J1UlOm3qekCRV9bgkf5vk80nuHhafnslzhB5vC1hin52UOXy8rYqoAsCOYDWc/gWAHYKoAkAnogoAnYgqAHQiqgDQiagCQCeiCgCdiCoAdPL/AfhMoG78PEwqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "MNIST_Train_Task1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}