MNIST_fashion.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMssN3M0ZfmN",
        "outputId": "ef9d5baf-796e-4c77-ddd1-b2f8307afd3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n"
          ]
        }
      ],
      "source": [
        "%tensorflow_version 2.x"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "vfmS8mCOZq2-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Jxs08LK1Z0_J"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images,train_labels),(test_images,test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0VHp-yhVaAFX",
        "outputId": "9f41a24d-e0c4-4d47-b9aa-78572284724b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 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",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 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",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIndhIRMaVBE",
        "outputId": "c0681401-15f0-43b2-f99a-61ddc7f91c95"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjpiEA9ibe3p",
        "outputId": "c39e812f-cca8-4e57-e5a3-52f1bdf10ce8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(train_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "to7APSL3biBF",
        "outputId": "a8515d91-b785-4943-cfb8-216c921ad081"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images[0][23][23]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGVvJyQVbxg7",
        "outputId": "dea08606-7f0d-4ae5-fa13-e588c2255c20"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "194"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0OhHZzGecQB_",
        "outputId": "ea01ce06-34d6-4f48-82a9-92fb6fab5756"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, 3, 0, 2, 7, 2, 5, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "CALICq20eHHJ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[9])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "c3oiBP_ceTqb",
        "outputId": "1d653f8b-06d9-4bf2-fe2e-51a997221f96"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images = train_images/255.0\n",
        "test_images = test_images/255.0"
      ],
      "metadata": {
        "id": "RJWdsiK7ejs9"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(128,activation='relu'),\n",
        "    keras.layers.Dense(10,activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "bMOj-wLFkx3j"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ZbERTswUnl08"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images,train_labels,epochs=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rUC39ZtgogN7",
        "outputId": "810d2cda-af8c-4df8-83bd-5f852a24c9ff"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0961 - accuracy: 0.9635\n",
            "Epoch 2/4\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0933 - accuracy: 0.9653\n",
            "Epoch 3/4\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0934 - accuracy: 0.9649\n",
            "Epoch 4/4\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0904 - accuracy: 0.9656\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5c4da3ced0>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,test_labels,verbose=1)\n",
        "print(\"Test accuracy : \",test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ze7fA6YHpBOz",
        "outputId": "d9cdf27e-4f20-4cc7-b05c-ca1763bbea5a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.5455 - accuracy: 0.8851\n",
            "Test accuracy :  0.8851000070571899\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "metadata": {
        "id": "CEPrt_KOvPAN"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(predictions) # an array of 10 elements for each test image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ti3a14CAvvEY",
        "outputId": "b75982fe-4978-43b9-801f-550588432b83"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tHUjN8rwI8P",
        "outputId": "9471a728-2acd-4020-e27c-9fc8f4697368"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.2712869e-04, 1.4180508e-27, 9.9956948e-01, 6.1282552e-27,\n",
              "       2.5375314e-06, 5.3716956e-18, 8.7912366e-07, 0.0000000e+00,\n",
              "       1.7187292e-21, 1.3712951e-35], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yy8BCDOFwzrV",
        "outputId": "0fe54e35-8f13-4bf6-af44-9617d02b479b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[1])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "WtMQdDAJxXm7",
        "outputId": "a1d20e20-24fd-41f4-e65a-fafd8f99df6e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYNfFRPCxuij",
        "outputId": "c6cfca44-e8fe-4f9c-fba0-f33e547561aa"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "COLOR = 'white'\n",
        "plt.rcParams['text.color']=COLOR\n",
        "plt.rcParams['axes.labelcolor']=COLOR\n",
        "\n",
        "def predict(model, image, correct_label):\n",
        "  class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "  prediction = model.predict(np.array([image]))\n",
        "  # np.array which has 10 diff values/ probability of image belonging to each class\n",
        "  predicted_class = class_names[np.argmax(prediction)]\n",
        "  # highest value of prediction array converted into class_name value\n",
        "  print(\"The image is a: \", predicted_class)\n",
        "  show_image(image, class_names[correct_label], predicted_class)\n",
        "\n",
        "def show_image(image,label,guess):\n",
        "  plt.figure()\n",
        "  plt.imshow(image,cmap=plt.cm.binary)\n",
        "  plt.title(\"Excpected: \" + label)\n",
        "  plt.xlabel(\"Guess: \" + guess)\n",
        "  plt.colorbar()\n",
        "  plt.grid(False)\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "# for user input to select image number from test_images .\n",
        "def get_number(): \n",
        "  while True:\n",
        "    num = input(\"Pick a number: \")\n",
        "    if num.isdigit():\n",
        "      num = int(num)\n",
        "      if 0 <= num <= 1000:\n",
        "        return int(num)\n",
        "    else:\n",
        "      print(\"Try again...\")\n",
        "\n",
        "num = get_number() # get image number\n",
        "image = test_images[num] # search that image in test_images\n",
        "label = test_labels[num] # search for the label in test_labels\n",
        "predict(model, image, label) #predict\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "vCjWi6x6yMsB",
        "outputId": "a77aa2f9-29b6-4857-e39a-bd675bc0844f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pick a number: 65\n",
            "The image is a:  Trouser\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tveRxYr-zuJ7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}