examples/7.OOP_Neural_Network_Adv.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### [PREV](6.OOP_Neural_Network_Simple.ipynb) | [HOME](../README.md)  | [NEXT](8.OPP_Decision_Trees.ipynb)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYysdyb-CaWM"
      },
      "source": [
        "#  Neural Network Classification: Classify Images Of Numbers - Advanced Approach"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbVhjPpzn6BM"
      },
      "source": [
        "This Jupyter Notebook trains a Neural Network model to classify images of numbers for optical character recognition (OCR). Detailed visualisations of the characters and the statistical outputs have been provided to support your understanding of Neural Networks for classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Step 1 - Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "dzLKpmZICaWN"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.18.0\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' \n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yR0EdgrLCaWR"
      },
      "source": [
        "#### Step 2 - Import And Visualise The Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLdCchMdCaWQ"
      },
      "source": [
        "The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.)\n",
        "\n",
        "<img src=\"https://upload.wikimedia.org/wikipedia/commons/b/b1/MNIST_dataset_example.png\"\n",
        "         alt=\"MNIST sprite sample\">\n",
        "\n",
        "We will import the library from an API, then 60,000 images will be used to train the network and 10,000 images to evaluate how accurately the network learned to classify images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "7MqDQO0KCaWS"
      },
      "outputs": [],
      "source": [
        "# Import images from an API to a local variable\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9FDsUlxCaWW"
      },
      "source": [
        "Loading the dataset returns four NumPy arrays:\n",
        "\n",
        "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n",
        "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays.\n",
        "\n",
        "The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The *labels* are an array of integers, ranging from 0 to 9. These correspond to the *class* of number the image represents:\n",
        "\n",
        "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "IjnLH5S2CaWx"
      },
      "outputs": [],
      "source": [
        "class_names = ['Class: 0', 'Class: 1', 'Class: 2', 'Class: 3', 'Class: 4',\n",
        "               'Class: 5', 'Class: 6', 'Class: 7', 'Class: 8', 'Class: 9']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ES6uQoLKCaWr"
      },
      "source": [
        "If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "m4VEw8Ud9Quh"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.xlabel(\"28 Pixels\")\n",
        "plt.ylabel(\"28 Pixels\")\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As you can see the image `train_images[0]` is `class 5` and the pixels that are dark purple (or white in you looked at the image) have a value of `0`. The pixels that are yellow (or black if you looked at the image) have a value of `255`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wz7l27Lz9S1P"
      },
      "source": [
        "#### Step 3: Wrangle The Data\n",
        "The data must be wrangled before training the network to improve the model performance. We will scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It's important that the *training set* and the *testing set* be wrangled in the same way:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "bW5WzIPlCaWv"
      },
      "outputs": [],
      "source": [
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ee638AlnCaWz"
      },
      "source": [
        "To verify that the data is in the correct format and that you're ready to build and train the network, let's visualise the first 25 images from `train_images` and display their class name below each image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "oZTImqg_CaW1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59veuiEZCaW4"
      },
      "source": [
        "#### Step 4: Instantiate a Neural Network Object And Configure The Layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gxg1XGm0eOBy"
      },
      "source": [
        "The basic building block of a neural network is the [*layer*](https://www.tensorflow.org/api_docs/python/tf/keras/layers). Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.\n",
        "\n",
        "Most of deep learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "9ODch-OFCaW4"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(), # Input layer\n",
        "    tf.keras.layers.Dense(128, activation='relu'), # Hidden layer with 128 neurons and ReLU activation\n",
        "    tf.keras.layers.Dense(10) # Output layer with a 10 neurons for categorisation of 0-9\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gut8A_7rCaW6"
      },
      "source": [
        "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
        "\n",
        "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected, or fully connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.\n",
        "\n",
        "##### Compile the model\n",
        "\n",
        "Before the model is ready for training, it needs a few more settings. These are added during the model's [*compile*](https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile) step:\n",
        "\n",
        "* [*Optimizer*](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) —This is how the model is updated based on the data it sees and its loss function.\n",
        "* [*Loss function*](https://www.tensorflow.org/api_docs/python/tf/keras/losses) —This measures how accurate the model is during training. You want to minimize this function to \"steer\" the model in the right direction.\n",
        "* [*Metrics*](https://www.tensorflow.org/api_docs/python/tf/keras/metrics) —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Lhan11blCaW7"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKF6uW-BCaW-"
      },
      "source": [
        "#### Step 5: Fit The Model\n",
        "\n",
        "Fitting (Training) the neural network model requires the following steps:\n",
        "\n",
        "1. Fit the training data to the model. In this example, the training data is in the `train_images` and `train_labels` arrays.\n",
        "2. The model learns to associate images and labels.\n",
        "3. You ask the model to make predictions about a test set—in this example, the `test_images` array.\n",
        "4. Verify that the predictions match the labels from the `test_labels` array.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "xvwvpA64CaW_"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2025-02-12 01:26:18.349203: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 188160000 exceeds 10% of free system memory.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.8761 - loss: 0.4340\n",
            "Epoch 2/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.9637 - loss: 0.1228\n",
            "Epoch 3/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.9764 - loss: 0.0794\n",
            "Epoch 4/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.9835 - loss: 0.0575\n",
            "Epoch 5/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 1ms/step - accuracy: 0.9866 - loss: 0.0434\n",
            "Epoch 6/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9908 - loss: 0.0314\n",
            "Epoch 7/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9926 - loss: 0.0253\n",
            "Epoch 8/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9937 - loss: 0.0219\n",
            "Epoch 9/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9950 - loss: 0.0174\n",
            "Epoch 10/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9954 - loss: 0.0147\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x75b96a8c9190>"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCpr6DGyE28h"
      },
      "source": [
        "#### Step 6: Evaluate The Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "VflXLEeECaXC"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2025-02-12 01:26:54.947714: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 31360000 exceeds 10% of free system memory.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 - 0s - 1ms/step - accuracy: 0.9775 - loss: 0.0832\n",
            "\n",
            "Test accuracy: 0.9775000214576721\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "\n",
        "print('\\nTest accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-PyD1SYE28q"
      },
      "source": [
        "### Step 7: Make predictions\n",
        "\n",
        "With the model trained, you can use it to make predictions about some images.\n",
        "Attach a softmax layer to convert the model's linear outputs—[logits](https://developers.google.com/machine-learning/glossary#logits)—to probabilities, which should be easier to interpret."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "DnfNA0CrQLSD"
      },
      "outputs": [],
      "source": [
        "probability_model = tf.keras.Sequential([model, \n",
        "                                         tf.keras.layers.Softmax()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Gl91RPhdCaXI"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 920us/step\n"
          ]
        }
      ],
      "source": [
        "predictions = probability_model.predict(test_images)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hw1hgeSCaXN"
      },
      "source": [
        "A prediction is an array of 10 numbers. They represent the model's \"confidence\" that the image corresponds to each of the 10 different articles of clothing. You can see which label has the highest confidence value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "qsqenuPnCaXO"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1.6302166e-09 1.4056048e-10 1.8287403e-06 2.1417580e-05 3.4947993e-14\n",
            " 1.3376670e-08 1.4832569e-16 9.9997658e-01 3.8801517e-08 1.0007248e-07]\n",
            "7\n"
          ]
        }
      ],
      "source": [
        "print(predictions[0])\n",
        "print(np.argmax(predictions[0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zh9yABaME29S"
      },
      "source": [
        "#### Step 8: Plot the Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "HV5jw-5HwSmO"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "    true_label, img = true_label[i], img[i]\n",
        "    plt.grid(False)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "\n",
        "    plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "    predicted_label = np.argmax(predictions_array)\n",
        "    if predicted_label == true_label:\n",
        "        color = 'blue'\n",
        "    else:\n",
        "        color = 'red'\n",
        "\n",
        "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                    100*np.max(predictions_array),\n",
        "                                    class_names[true_label]),\n",
        "                                    color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "    true_label = true_label[i]\n",
        "    plt.grid(False)\n",
        "    plt.xticks(range(10))\n",
        "    plt.yticks([])\n",
        "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "    plt.ylim([0, 1])\n",
        "    predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "    thisplot[predicted_label].set_color('red')\n",
        "    thisplot[true_label].set_color('blue')\n",
        "\n",
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgdvGD52CaXR"
      },
      "source": [
        "Plot several images with their predictions. Note that the model can be wrong even when very confident."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "hQlnbqaw2Qu_"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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dIqKzfQciKKKffvpJRUSXLl3qqp6aNWvq66+/HtY2mZmZ2qxZM12wYIF27dpVBw0aFNb2Y8aM0bbhDnqQz4gRI/SCCy5wVUd+gwYN0qZNm2pubm5I6/fq1Uv79+/vV3bNNddo3759Q37NrKwsjYyM1Dlz5viVt2/fXkeNGhVyPSWpvMaK0aMDx9bYudPauGZN4dsHG+shlHr/+EfV/F+LN9+08YWc/Pe/NmbF6tWq69ap1qnjXdahg4076GTrVnvdLVu8ZafC8YqJUX3//dDrPlmIFf6IFYZYEagsnXt8FeeYTFxXlPx1RUFjMqlyXREOYgWxoigc+/fUqmXd8198UeTo0cDlTlNxitijaklJNt1lhw7WbdWpK2Lz5iL/+Idlsa+5RmTyZO+yhg2tJ9KsWSJDh9qz1+HyeKwbZGys3fFt2NCmiy6q5GTbZ99xnRYtsmeWO3f2rrNsmT0LnWfBAuum6pTofOMNe5+vusoe7RPxbnv8uLcsv6gom35z82ZvWUSEyI032rTXe/cGbpN35ye/1attHyZMsOk8mzd33r6gY1Knjsitt9oMSM8+6z+zRTjq1rVnsmfOtLspl1ziv3zjRutCW5YcO3ZMVq9eLSk+A6xERERISkqKrMgbYKUU/Pq/KT9q1apVpO1zcnJkxowZcvToUUlOTg5r29TUVOnVq5ffexKubdu2SUJCgjRp0kT69u0r6enpYW3/0UcfSYcOHaRPnz4SHx8v7dq1k9eKciL5n2PHjsnbb78t/fv3F4/HE9I2559/vixcuFC2bt0qIiLr1q2T5cuXy2WXXRby6544cUJycnIkJt9Ac7GxsbLcd9CyUlReY0Vyso0F8dNP3rIFC+zueMuWoddTlHqTkwPvni9YYOVOHnnEZilq397igu+5vKBY0bSpva5vrKjox2vHDrtjTqwIDbGCWHGylNdzT0nhuqLkrysKw3VF6IgVxIoiCZZ92rFDtV49u4P6wQd292DzZtXnnlNt0cK7nu8dh3/9S7VSJdXp01W3b7d1a9XyZpKzsixrvHixzS6wfLlq06Y2I4Kq3d2dN8/uUK5erdq5s82sk+fii1VfeKHgrNmTT9oMZRs32t2OypX9Z6lwsm+falqa6muv2f4sW2b/P3DAu86ll6q2a2cz/yxfbjNd/PnP3uWHD9uMQP362WvPmGGzJkyaFPh6P/6o2rix/4wNZ5+tOnas3bWpVq3gnldDhqhee61/2YEDdlwSE1Xfekt10yY7Zm+8YbMz5N1B8L3jsHat7e+zz9rxnjrVZv7wveNQ0DEZPVr1ww9tJriNG222iE6dvG3q10915Mjg+6Fqx3P1arvbMXGizR7y3HP+6+zaZXdZdu8uuK6Tbc+ePSoi+tVXX/mVDxs2TDv5vhEhkmK445CTk6O9evXSLl26hL3t+vXrtWrVqhoZGak1atTQT8KcgmX69OnaunVr/e1/U50U5Y7Dp59+qu+9956uW7dO582bp8nJydqoUSPNCGV6yP+Jjo7W6Ohove+++3TNmjU6adIkjYmJ0SlTpoTVljwzZ87UyMhI3eM0xUoQOTk5OmLECPV4PFqpUiX1eDz6WCi3cPNJTk7Wrl276p49e/TEiRM6bdo0jYiI0ObNm4ddV0kpj7HixAmb/aZHDzsPzptnPYTuu6/gff3+e4sNDz1k5+m0NPuTN3NOKPXu3GmxYdgw1e++U33xRbt7PW9e4Ott2mSxJm82pqws1dNPV339ddU5c+yO8X/+E7y911wTeBe4Ih+vyZNVmzQJXk9pIVb4I1Z4ESvK9rlH1X7jpqWp/u1vqs2be8/7Bc1EynVF6V1XFBanVbmuCAexglhRVEGTTKqqe/fayTspybqfNmhg0ycvXuxTQb6pRocNsx/B1arZtMzPPOMNBtnZqjfeqNqwodWXkKB6993e6TfvvtuCQ3S0/YDs10/1l1+8dSclWXfVglx0kb1eTIyduD79tPA3YcwY24/8fyZP9q5z4ICd/KtVs8cabrvN/4Slao8yXHCBtb9BA9Xx451f78YbA4PaN9/YybxWLTsxFmTTJkvGHD7sX374sJ18mzWz97duXdWUFDs+eb3x8k81+vTTqvXrW309e1pA8A0GBR2TceMsiMXGWrt797agkadrV9Vbby14X/r1s22jolTbtLHXz++xx7zTe5clZTEYDBw4UJOSkvSHH34Ie9vs7Gzdtm2brlq1SkeOHKm1a9fWTZs2hbRtenq6xsfH67p1634vK0owyO/QoUMaFxcXVvfaypUra3Jysl/ZPffco+edd16R2tCjRw+94oorwtpm+vTpmpiYqNOnT9f169fr1KlTtVatWmEHpO3bt+uFF16oIqKRkZHasWNH7du3r7bw/UVeBpTHWLF7t02pHRtr0yUPHeqdkjmYW291jhW++xlKvYsXq55zju1bkyb+sSZPbq5qly6qH3/sX/7xx6qNGtn5/bXXCm7vp5/ascjJ8S+vqMerRw/VIjxRUOKIFV7ECn/EirJ/7una1fm8v2tX8G24rii964pQ4jTXFaEjVhAriqrAJBPKruuuC+3Z9vIuO9suqJYvL+2WBMrOztbIyMiAE/gtt9yiV111Vdj1uQ0GqampmpiYqDt9I7IL3bt31wGhPJCvqrNnz/79hJX3R0TU4/FoZGSknjhxosjt6NChg44s7NaVj0aNGuntt9/uV/bSSy9pQkJC2K+9e/dujYiI0A8//DCs7RITE3XixIl+ZePGjdOzzjor7Daoqh45ckT37t2rqqrXX3+9Xn755UWqB6eW3FzVjh1V3323tFtS8jZuVI2PD7xIKguIFV7ECn/ECpQVXFeUPmKFF7HCX3mMFWVgzjUUxVNP2RhGFV16usj999t0p2VNVFSUnHvuubLQZ4CV3NxcWbhwYdjPHLuhqnL33XfL7NmzZdGiRXLGGWcUS725ubmSnZ0d0rrdu3eXDRs2yNq1a3//06FDB+nbt6+sXbtWIiMji9SGI0eOyI4dO6R+GHPWdunSJWCq1a1bt0pSUlLYrz958mSJj4+XXr16hbVdVlaWROSbIjEyMlJyc3PDboOISNWqVaV+/fpy6NAhmT9/vvTu3btI9eDU4vHYeBZOY2dUNPv22cxDNWqUdksCESu8iBX+iBUoK7iuKH3ECi9ihb9yGSuKPW0FnEJmzJih0dHROmXKFN28ebMOGDBATzvtNN2/f39I22dmZmpaWpqmpaWpiOjTTz+taWlp+v3334fchjvvvFNr1KihS5Ys0X379v3+JysrK+Q6Ro4cqUuXLtVdu3bp+vXrdeTIkerxePSzzz4LuY78itKtdejQobpkyRLdtWuXfvnll5qSkqK1a9fWn376KeQ6vv32W61UqZI++uijum3bNn3nnXe0SpUq+vbbb4fVlpycHG3UqJGOGDEirO1UVW+99VZt0KCBzpkzR3ft2qWzZs3S2rVr6/C8gSJCNG/ePJ07d67u3LlTP/vsM23btq127txZjx07FnabAJQeYkVwxApiBQBDrAiOWFG+YgVJJsClF154QRs1aqRRUVHaqVMn/frrr0PedvHixSoiAX9uLeyhcx9O24uITnYa6CWI/v37a1JSkkZFRWmdOnW0e/furgKBatGCwQ033KD169fXqKgobdCggd5www26ffv2sF/7448/1tatW2t0dLS2aNFCX3311bDrmD9/voqIbvGdAz5EGRkZOmjQIG3UqJHGxMRokyZNdNSoUZpd0EihDmbOnKlNmjTRqKgorVevnqampurhsvg8EIBCESucESuIFQC8iBXOiBXlK1Z4VFWLv38UAAAAAAAATiWMyQQAAAAAAADXSDIBAAAAAADANZJMAAAAAAAAcI0kEwAAAAAAAFwjyQQAAAAAAADXSDIBAAAAAADANZJMAAAAAAAAcI0kEwAAAAAAAFwjyQQAAAAAAADXSDIBAAAAAADANZJMAAAAAAAAcO3/AQOMTbtDabgYAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1200x1000 with 30 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the first X test images, their predicted labels, and the true labels.\n",
        "# Color correct predictions in blue and incorrect predictions in red.\n",
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions[i], test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions[i], test_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "classification.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}