Fashion_MNIST_Image_Classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fashion MNIST Image Classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMMGRP/QEwfJAoDA6lQNzdg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vikranttyagi95/Fashion-MNIST-Classifier/blob/main/Fashion_MNIST_Image_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUXwJQoT8ZoO"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0JJD2uZ9J_v"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7LIVbOj85gK",
        "outputId": "9b094e75-4f50-4240-bc1b-5f97b5bfeef9"
      },
      "source": [
        "fmnist_data = keras.datasets.fashion_mnist\n",
        "(X_train_full, y_train_full), (X_test, y_test) = fmnist_data.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lI5JOcqT9wS9",
        "outputId": "13f273da-55c2-4af9-be96-8f894e4a1c7b"
      },
      "source": [
        "print(X_train_full.shape)\n",
        "print(y_train_full)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "[9 0 0 ... 3 0 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORym5Zjb-_3k"
      },
      "source": [
        "## Creating a Validation Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yag-I71C-Rvd"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, test_size=0.1, random_state=42)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZxgnBPwoH9v4",
        "outputId": "a7c47611-7530-4758-a33a-f27dd45eb403"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(54000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-0bZcfx_-3z"
      },
      "source": [
        "## Normalizing the input features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSxCrxl6_Sf8"
      },
      "source": [
        "X_train = X_train/255.0\n",
        "X_valid = X_valid/255.0\n",
        "X_test = X_test/255.0"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kds2KqtFApeZ"
      },
      "source": [
        "## A peak at an output example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "hCNHNBuM_m-J",
        "outputId": "914cf790-6acc-49f3-d4ff-7618fbb2ac7d"
      },
      "source": [
        "plt.imshow(X_train[4], cmap='binary')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cnYemIbC78v",
        "outputId": "9224644b-e844-4faf-f3da-ba3c44e83a93"
      },
      "source": [
        "y_train[4]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcvZ2MVvBLpp"
      },
      "source": [
        "class_names = [\"T-shirt/Top\",\"Trouser\",\"Pullover\",\"Dress\",\"Coat\",\"Sandal\",\"Shirt\",\"Sneaker\",\"Bag\",\"Ankle boot\"]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "slW0CG1vB1Nm",
        "outputId": "3ec3fc40-54e4-4d60-8430-05936675e247"
      },
      "source": [
        "class_names[y_train[4]]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Sandal'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7tTvouADgLq"
      },
      "source": [
        "## Plotting Some example images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "mid4GPMRDHk7",
        "outputId": "f1688d33-0b2c-4dc8-8007-b8782942a140"
      },
      "source": [
        "n_rows = 2\n",
        "n_cols = 10\n",
        "\n",
        "plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))\n",
        "for row in range(n_rows):\n",
        "  for col in range(n_cols):\n",
        "    index = n_rows * row + col\n",
        "    plt.subplot(n_rows, n_cols, index+1)\n",
        "    plt.imshow(X_train[index], cmap='binary',interpolation='nearest')\n",
        "    plt.title(class_names[y_train[index]], fontsize=14)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x172.8 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YoxKND8rJIe4"
      },
      "source": [
        "## Creating a Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp7HIFugHtUF"
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28,28]))\n",
        "model.add(keras.layers.Dense(200, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(150, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSV841bFJxEk",
        "outputId": "afdaf889-5a1f-484a-cc57-66a7f3ed1761"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_3 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 200)               157000    \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 150)               30150     \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                1510      \n",
            "=================================================================\n",
            "Total params: 188,660\n",
            "Trainable params: 188,660\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00-ecQAAJyiB"
      },
      "source": [
        "hidden1 = model.layers[1]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h57KtZu_KokR"
      },
      "source": [
        "weights, biases = hidden1.get_weights()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNcCRJnvKvIP",
        "outputId": "582f6934-578d-42fc-e7e7-d273d37598a8"
      },
      "source": [
        "weights"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.05960592, -0.07205535, -0.01665654, ...,  0.00579741,\n",
              "        -0.05653534, -0.04993884],\n",
              "       [-0.04411693, -0.03453702, -0.05743594, ..., -0.07148175,\n",
              "         0.01706006,  0.02194314],\n",
              "       [-0.03655287,  0.01445751,  0.03926075, ...,  0.01798882,\n",
              "         0.05224365,  0.01507544],\n",
              "       ...,\n",
              "       [-0.00527821, -0.00618792,  0.01814166, ..., -0.0702503 ,\n",
              "        -0.06052341,  0.03341022],\n",
              "       [ 0.04255588, -0.03320312, -0.03200027, ...,  0.05406391,\n",
              "         0.00303581,  0.02141093],\n",
              "       [-0.03468017, -0.01301967, -0.047933  , ...,  0.0035255 ,\n",
              "        -0.06441922, -0.06285591]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eb8TuoE8KxF-"
      },
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkwy6_gPLFVj",
        "outputId": "36d6656f-62fa-4865-df86-2d23f5e25f69"
      },
      "source": [
        "history = model.fit(X_train, y_train, epochs=30, validation_data=(X_valid, y_valid))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.7332 - accuracy: 0.7622 - val_loss: 0.5506 - val_accuracy: 0.8078\n",
            "Epoch 2/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.4932 - accuracy: 0.8295 - val_loss: 0.4685 - val_accuracy: 0.8367\n",
            "Epoch 3/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.4481 - accuracy: 0.8433 - val_loss: 0.4283 - val_accuracy: 0.8487\n",
            "Epoch 4/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.4208 - accuracy: 0.8520 - val_loss: 0.4253 - val_accuracy: 0.8453\n",
            "Epoch 5/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.4028 - accuracy: 0.8596 - val_loss: 0.4077 - val_accuracy: 0.8542\n",
            "Epoch 6/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3856 - accuracy: 0.8643 - val_loss: 0.3877 - val_accuracy: 0.8608\n",
            "Epoch 7/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3726 - accuracy: 0.8683 - val_loss: 0.4172 - val_accuracy: 0.8482\n",
            "Epoch 8/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3626 - accuracy: 0.8718 - val_loss: 0.3866 - val_accuracy: 0.8590\n",
            "Epoch 9/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3506 - accuracy: 0.8769 - val_loss: 0.3608 - val_accuracy: 0.8698\n",
            "Epoch 10/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3413 - accuracy: 0.8779 - val_loss: 0.3547 - val_accuracy: 0.8707\n",
            "Epoch 11/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3325 - accuracy: 0.8809 - val_loss: 0.3556 - val_accuracy: 0.8727\n",
            "Epoch 12/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3244 - accuracy: 0.8834 - val_loss: 0.3500 - val_accuracy: 0.8740\n",
            "Epoch 13/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3176 - accuracy: 0.8860 - val_loss: 0.3431 - val_accuracy: 0.8767\n",
            "Epoch 14/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3103 - accuracy: 0.8883 - val_loss: 0.3490 - val_accuracy: 0.8722\n",
            "Epoch 15/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.3040 - accuracy: 0.8901 - val_loss: 0.3531 - val_accuracy: 0.8737\n",
            "Epoch 16/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2983 - accuracy: 0.8928 - val_loss: 0.3328 - val_accuracy: 0.8852\n",
            "Epoch 17/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2922 - accuracy: 0.8942 - val_loss: 0.3289 - val_accuracy: 0.8820\n",
            "Epoch 18/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2869 - accuracy: 0.8960 - val_loss: 0.3569 - val_accuracy: 0.8717\n",
            "Epoch 19/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2817 - accuracy: 0.8977 - val_loss: 0.3374 - val_accuracy: 0.8780\n",
            "Epoch 20/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2777 - accuracy: 0.9007 - val_loss: 0.3327 - val_accuracy: 0.8793\n",
            "Epoch 21/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2721 - accuracy: 0.9017 - val_loss: 0.3301 - val_accuracy: 0.8778\n",
            "Epoch 22/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2676 - accuracy: 0.9039 - val_loss: 0.3202 - val_accuracy: 0.8865\n",
            "Epoch 23/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2621 - accuracy: 0.9047 - val_loss: 0.3652 - val_accuracy: 0.8668\n",
            "Epoch 24/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2588 - accuracy: 0.9076 - val_loss: 0.4326 - val_accuracy: 0.8420\n",
            "Epoch 25/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2556 - accuracy: 0.9071 - val_loss: 0.3188 - val_accuracy: 0.8873\n",
            "Epoch 26/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2510 - accuracy: 0.9098 - val_loss: 0.3201 - val_accuracy: 0.8828\n",
            "Epoch 27/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2464 - accuracy: 0.9109 - val_loss: 0.3198 - val_accuracy: 0.8838\n",
            "Epoch 28/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2429 - accuracy: 0.9117 - val_loss: 0.3145 - val_accuracy: 0.8847\n",
            "Epoch 29/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2386 - accuracy: 0.9149 - val_loss: 0.3301 - val_accuracy: 0.8813\n",
            "Epoch 30/30\n",
            "1688/1688 [==============================] - 4s 2ms/step - loss: 0.2357 - accuracy: 0.9157 - val_loss: 0.3168 - val_accuracy: 0.8870\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dw44p-vsNtwa"
      },
      "source": [
        "## Plotting loss per iteration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "5hngUGQTLcJP",
        "outputId": "0a9dfecb-163b-4012-e535-569db5af8f79"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(history.history['loss'], label='loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osVhfhB1P9U4"
      },
      "source": [
        "## Plotting accuracy per iteration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1yyWv4ROJEG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "outputId": "72b02119-29e3-4427-eae8-5fbb4ef34ae7"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-uILWxWQWW1"
      },
      "source": [
        "# Evaluating the model on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O19E4fUAQUBE",
        "outputId": "7a103480-97c5-403b-def4-d02654cbed2d"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.3439 - accuracy: 0.8785\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.34392815828323364, 0.8784999847412109]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWkbFYVCQkHi",
        "outputId": "74438d19-1d1f-41cb-ed3f-c6538ec2948e"
      },
      "source": [
        "X_new = X_test[:3]\n",
        "y_prob = model.predict(X_new)\n",
        "y_prob.round(2)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.99],\n",
              "       [0.  , 0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NpV1tbiRKux",
        "outputId": "46bf27b4-1444-48c6-ddf3-63bf4fc2b52b"
      },
      "source": [
        "y_pred = model.predict_classes(X_new)\n",
        "y_pred"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MU1XGMG2SNw4",
        "outputId": "e979823c-3511-4aef-fc4e-98b85d708d04"
      },
      "source": [
        "y_new = y_test[:3]\n",
        "y_new"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fSFsrbHR3h1"
      },
      "source": [
        "# Plotting the predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "75XJ6lGtRbMf",
        "outputId": "f0639af7-2fc9-4e9f-dbeb-0915e06b1491"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "for index, image in enumerate(X_new):\n",
        "    plt.subplot(1, 3, index+1)\n",
        "    plt.imshow(image, cmap='binary', interpolation='nearest')\n",
        "    plt.title(class_names[y_new[index]], fontsize=12)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmY5C4HVSVPI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}