Fashion_MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fashion_MNIST.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM0hRTNs3Bg08YhJd/EnpDA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ADITYASHAH-IITP/Fashion-Entity-Classification/blob/main/Fashion_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HTdYNdZXT9Tv"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Z4exm8mjUSSe",
        "outputId": "420e35e0-c6e8-444f-e48a-e30dab2a6c38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.7.0'"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Fashion MNIST\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RHBGOskpvCFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models  import Sequential\n",
        "from tensorflow.keras.layers import Dense,Flatten,Softmax,Conv2D,MaxPooling2D"
      ],
      "metadata": {
        "id": "hcUpRYgCvD6t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preparing Layers "
      ],
      "metadata": {
        "id": "4qXH92R28WrB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential([\n",
        "                  Conv2D(16,(3,3),activation='relu',input_shape=(28,28,1),data_format='channels_last'),\n",
        "                  MaxPooling2D((3,3),data_format='channels_last'),\n",
        "                  Flatten(),       \n",
        "                  Dense(10,activation='relu'),\n",
        "                  \n",
        "])"
      ],
      "metadata": {
        "id": "YnNHM0gNvDBR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preparing Optimizer,Loss and metrics"
      ],
      "metadata": {
        "id": "72MFzuQu8YiJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "opt=tf.keras.optimizers.Adam(learning_rate=0.005)\n",
        "acc=tf.keras.metrics.SparseCategoricalAccuracy()\n",
        "mae=tf.keras.metrics.MeanAbsoluteError()\n",
        "model.compile(optimizer=opt,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=[acc,mae])"
      ],
      "metadata": {
        "id": "cJbk5eGHvPQv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "5xLhxLT_2k_m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing dataset\n"
      ],
      "metadata": {
        "id": "ZiOrsy4EvcaF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "g0yn007zviNf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fashion_mnist_data=tf.keras.datasets.fashion_mnist\n",
        "(train_images,train_labels),(test_images,test_labels)=fashion_mnist_data.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEw4AZlJxU6L",
        "outputId": "de0bb49a-b85f-4c21-ad19-27d5d6feafef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "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": "zPvGJsvlxq80",
        "outputId": "36d6ea9c-f9a2-4d7e-ea62-fa19f0c21129"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Labels Used For Clothes entity"
      ],
      "metadata": {
        "id": "Grb6x_sb8iOI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels=[\n",
        "        'T-shirt/top',\n",
        "        'Trouser',\n",
        "        'Pullover',\n",
        "        'Dress',\n",
        "        'Coat',\n",
        "        'Sandal',\n",
        "        'Shirt',\n",
        "        'Sneaker',\n",
        "        'Bag',\n",
        "        'Ankle boot'\n",
        "]"
      ],
      "metadata": {
        "id": "ZWDdYpVex_Vd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Rescaling Images"
      ],
      "metadata": {
        "id": "83f_3rXP8oLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_images=train_images/255.\n",
        "test_images=test_images/255."
      ],
      "metadata": {
        "id": "d5MY7W_FyqBD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i=0\n",
        "img=train_images[i,:,:]\n",
        "plt.imshow(img)\n",
        "plt.show\n",
        "print(f\"label: {labels[train_labels[i]]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "In0pCfbtyyMm",
        "outputId": "abbeb442-3dad-4776-dbab-f3dc0cf0d665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label: Ankle boot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training"
      ],
      "metadata": {
        "id": "EG8_qLvK8raa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(train_images[...,np.newaxis],train_labels,epochs=8,batch_size=256,verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yypXOPhazme1",
        "outputId": "c445797a-83f0-43cb-e47b-cc95e1be5163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "235/235 [==============================] - 11s 49ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n",
            "Epoch 2/8\n",
            "235/235 [==============================] - 11s 48ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n",
            "Epoch 3/8\n",
            "235/235 [==============================] - 11s 48ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n",
            "Epoch 4/8\n",
            "235/235 [==============================] - 11s 48ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n",
            "Epoch 5/8\n",
            "235/235 [==============================] - 12s 49ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n",
            "Epoch 6/8\n",
            "235/235 [==============================] - 12s 49ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n",
            "Epoch 7/8\n",
            "235/235 [==============================] - 13s 53ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n",
            "Epoch 8/8\n",
            "235/235 [==============================] - 12s 49ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(history.history)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "y5oINxXT0p2m",
        "outputId": "600f86a0-fb44-4620-facf-9ff22af829c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-778b812d-ea61-4d5c-a8cf-6cdb1dec1cbe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>sparse_categorical_accuracy</th>\n",
              "      <th>mean_absolute_error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.302584</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.302584</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.302584</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.302584</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.302584</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-778b812d-ea61-4d5c-a8cf-6cdb1dec1cbe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-778b812d-ea61-4d5c-a8cf-6cdb1dec1cbe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-778b812d-ea61-4d5c-a8cf-6cdb1dec1cbe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       loss  sparse_categorical_accuracy  mean_absolute_error\n",
              "0  2.302584                          0.1                  4.5\n",
              "1  2.302584                          0.1                  4.5\n",
              "2  2.302584                          0.1                  4.5\n",
              "3  2.302584                          0.1                  4.5\n",
              "4  2.302584                          0.1                  4.5"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_plot=df.plot(y=\"loss\",title=\"Loss vs Epochs\",legend=False)\n",
        "loss_plot.set(xlabel=\"Epochs\",ylabel=\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "mD2-7rdz0tvH",
        "outputId": "62965c53-0a85-457d-e267-1f8511f5eaa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Loss'), Text(0.5, 0, 'Epochs')]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Prediction"
      ],
      "metadata": {
        "id": "LiOUqiHI8y8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss,test_accuracy,test_mae=model.evaluate(test_images[...,np.newaxis],test_labels)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GxZBmYMu4zkj",
        "outputId": "0a8c0e75-9cb0-445b-e2f1-e90a28ef185d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 6ms/step - loss: 2.3026 - sparse_categorical_accuracy: 0.1000 - mean_absolute_error: 4.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_inx=np.random.choice(test_images.shape[0])\n",
        "test_image=test_images[random_inx]\n",
        "plt.imshow(test_image)\n",
        "plt.show()\n",
        "print(f\"label: {labels[test_labels[random_inx]]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "ZS8TYXqi5mBl",
        "outputId": "14e4c7df-3e45-4403-94dc-ca6028a97cf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label: T-shirt/top\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction=model.predict(test_image[np.newaxis,...,np.newaxis])\n",
        "print(f\"Model Prediction: {labels[np.argmax(prediction)]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8mcYQ-gg6FYt",
        "outputId": "657680fd-31f3-4a91-bc4d-1d90af4c3d06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Prediction: T-shirt/top\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**So the prediction and the actual labels matches**"
      ],
      "metadata": {
        "id": "XOc73m7I82Qw"
      }
    }
  ]
}