DressDetection01.ipynb

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  "metadata": {
    "colab": {
      "name": "DressDetection01.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMfkWVBBXkL9BL5HKSZ8pXP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mubinui/DeepLearning/blob/main/DressDetection01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Clothing items** detection "
      ],
      "metadata": {
        "id": "pgvatXexotMH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "E29KYuhHlVS_"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf \n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading dataset from keras "
      ],
      "metadata": {
        "id": "T-YjHVhyp9pi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_set = tf.keras.datasets.fashion_mnist"
      ],
      "metadata": {
        "id": "r7xXafjSp9EQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = data_set.load_data()\n",
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "0aoqOUbxp7EN"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(X_train))\n",
        "print(len(X_test))\n",
        "print(X_train[0].shape)\n",
        "print(len(y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_s0L-sr4qt0R",
        "outputId": "e2971f92-52d7-4de1-a247-21fcb2fd74d8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000\n",
            "10000\n",
            "(28, 28)\n",
            "60000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Looking up the data \n",
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e1mvcC9rlNw",
        "outputId": "07212559-7144-448e-e57f-10e7f543342a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting the image\n",
        "plt.matshow(X_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "amQRrYdDsjtF",
        "outputId": "afa691bd-4e48-4901-abe6-0816eee375e8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9eb0437410>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train[0:30])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9VrfPkSt1Si",
        "outputId": "681c0d91-dfc8-44e4-e311-ea43b39b5392"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9 0 0 3 0 2 7 2 5 5 0 9 5 5 7 9 1 0 6 4 3 1 4 8 4 3 0 2 4 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<big> Lets try a neural network with two layers <big>"
      ],
      "metadata": {
        "id": "su1drj4svgFX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clothing_model = keras.Sequential([\n",
        "                                   keras.layers.Flatten(input_shape=(28,28)),\n",
        "                                   keras.layers.Dense(128,activation='relu'),\n",
        "                                   keras.layers.Dense(10, activation= 'sigmoid')\n",
        "])\n",
        "clothing_model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss ='sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy'] \n",
        ")"
      ],
      "metadata": {
        "id": "vy5qpmtkvX2n"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clothing_model.fit(X_train, y_train, epochs=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WbP7BLexEtL",
        "outputId": "1420c6c0-d602-49d0-abfb-5107de7f0765"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4972 - accuracy: 0.8271\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3716 - accuracy: 0.8665\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3359 - accuracy: 0.8768\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3124 - accuracy: 0.8859\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2932 - accuracy: 0.8910\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2816 - accuracy: 0.8952\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2677 - accuracy: 0.9001\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2583 - accuracy: 0.9035\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2469 - accuracy: 0.9083\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2402 - accuracy: 0.9103\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2318 - accuracy: 0.9139\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2244 - accuracy: 0.9158\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2170 - accuracy: 0.9184\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2125 - accuracy: 0.9199\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2045 - accuracy: 0.9224\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1993 - accuracy: 0.9244\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1925 - accuracy: 0.9274\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1891 - accuracy: 0.9299\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1834 - accuracy: 0.9307\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1800 - accuracy: 0.9321\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9e37a2a9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating the model \n",
        "clothing_model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v28_MHvnxqWa",
        "outputId": "a1ba552d-ea70-4166-bd1f-589bd427098f"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 1.1084 - accuracy: 0.8896\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1083811521530151, 0.8895999789237976]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(X_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "H1_m7o9tyzmw",
        "outputId": "02069076-9998-40a2-9f72-d1027d445564"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9eb02e5ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generating the predicted y values \n",
        "y_predicted = clothing_model.predict(X_test)\n",
        "print(y_predicted[0])\n",
        "np.argmax(y_predicted[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zY1AxMplx-dS",
        "outputId": "055d65de-6078-4e25-dce7-5e57122186e4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6.0400220e-13 5.5442985e-07 2.5495942e-11 2.2238456e-08 2.7156962e-11\n",
            " 1.4364042e-02 1.4133061e-08 6.1808670e-01 1.6712578e-07 9.9511069e-01]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets create a confusion matrix\n",
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "8OdmhrlA1TED"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion = tf.math.confusion_matrix(labels=y_test, predictions = y_predicted_labels)\n",
        "confusion "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-jOiSwh1liK",
        "outputId": "7239f0f9-ed4f-4877-c3bd-d06931e01e52"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[802,   2,  22,  34,   4,   0, 127,   0,   9,   0],\n",
              "       [  3, 973,   2,  14,   3,   0,   3,   0,   2,   0],\n",
              "       [ 15,   1, 838,  10,  75,   0,  59,   0,   2,   0],\n",
              "       [ 12,  13,  10, 915,  29,   0,  17,   0,   4,   0],\n",
              "       [  1,   1, 109,  25, 808,   0,  55,   0,   1,   0],\n",
              "       [  0,   0,   0,   1,   0, 970,   0,  16,   1,  12],\n",
              "       [ 71,   1,  88,  37,  62,   0, 727,   0,  14,   0],\n",
              "       [  0,   0,   0,   0,   0,  19,   0, 965,   0,  16],\n",
              "       [  2,   0,   1,   3,   5,   4,   5,   3, 977,   0],\n",
              "       [  0,   0,   0,   0,   0,   8,   0,  52,   0, 940]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#showing the heat map \n",
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(confusion, annot= True, fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Given')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "Sj3IhY9O15_B",
        "outputId": "35a69105-f82a-4fae-d6ff-84679fce5fc7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Given')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3> Lets train the model with <b>3 hidden layer<h3>"
      ],
      "metadata": {
        "id": "04MfaOA23jIb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clothing_model = keras.Sequential([\n",
        "                                   keras.layers.Flatten(input_shape=(28,28)),\n",
        "                                   keras.layers.Dense(400, activation='relu'),\n",
        "                                   keras.layers.Dense(400, activation='relu'),\n",
        "                                   keras.layers.Dense(400, activation='relu'),\n",
        "                                   keras.layers.Dense(400, activation='relu'),\n",
        "                                   keras.layers.Dense(10, activation='sigmoid')\n",
        "])\n",
        "clothing_model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss ='sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy'] \n",
        ")\n",
        "clothing_model.fit(X_train, y_train, epochs=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WSWWF7t3fD1",
        "outputId": "406dff51-ff55-404b-c206-7d8bc162b013"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.4992 - accuracy: 0.8187\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3753 - accuracy: 0.8644\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3406 - accuracy: 0.8744\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3168 - accuracy: 0.8821\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2963 - accuracy: 0.8905\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2828 - accuracy: 0.8954\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2716 - accuracy: 0.8974\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2596 - accuracy: 0.9027\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2485 - accuracy: 0.9064\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2427 - accuracy: 0.9086\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2403 - accuracy: 0.9102\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.2265 - accuracy: 0.9154\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.2196 - accuracy: 0.9166\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.2139 - accuracy: 0.9187\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2080 - accuracy: 0.9215\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2033 - accuracy: 0.9230\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1984 - accuracy: 0.9256\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1933 - accuracy: 0.9266\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1952 - accuracy: 0.9273\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1842 - accuracy: 0.9305\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9e33daef10>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating the model \n",
        "clothing_model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkYucrf67DA2",
        "outputId": "c3ddc79d-d782-44af-e651-99fecd51b610"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3986 - accuracy: 0.8842\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.39858701825141907, 0.8841999769210815]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(X_test[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "104kajuC7MCV",
        "outputId": "46d04ea5-99d2-46ce-d08c-a59b65025d83"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9e33dc5d90>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generating the predicted y values \n",
        "y_predicted = clothing_model.predict(X_test)\n",
        "print(y_predicted[1])\n",
        "np.argmax(y_predicted[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n02dNy7m7QC1",
        "outputId": "619ce314-9100-4d17-ce33-e8dc2563c96f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.30500317e-01 1.58380313e-08 9.99111354e-01 3.83380757e-06\n",
            " 1.75890893e-01 1.19886574e-18 1.20258726e-01 5.91649667e-22\n",
            " 8.89221408e-09 3.20129474e-22]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets create a confusion matrix\n",
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "xxb5CwI97TB1"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion = tf.math.confusion_matrix(labels=y_test, predictions = y_predicted_labels)\n",
        "confusion "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0VVpVMar7V-d",
        "outputId": "64412d80-990d-4320-9977-7b373efa1624"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[899,   0,  16,  23,   2,   1,  53,   0,   6,   0],\n",
              "       [  3, 974,   0,  17,   2,   0,   4,   0,   0,   0],\n",
              "       [  9,   0, 870,  16,  42,   0,  63,   0,   0,   0],\n",
              "       [ 28,   1,   8, 930,  15,   0,  14,   0,   4,   0],\n",
              "       [  1,   1, 172,  51, 667,   0, 108,   0,   0,   0],\n",
              "       [  0,   0,   0,   0,   0, 965,   0,  23,   1,  11],\n",
              "       [190,   0,  99,  27,  43,   0, 631,   0,  10,   0],\n",
              "       [  0,   0,   0,   0,   0,   7,   0, 968,   0,  25],\n",
              "       [  5,   0,   1,   3,   2,   1,   7,   7, 974,   0],\n",
              "       [  0,   0,   0,   0,   0,   4,   1,  31,   0, 964]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#showing the heat map \n",
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(confusion, annot= True, fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Given')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "6f8aY31B7ZgN",
        "outputId": "1a2f5e0e-766d-41c7-ba4e-315b9db5471a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Given')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clothing_model = keras.Sequential([\n",
        "                                   keras.layers.Flatten(input_shape=(28,28)),\n",
        "                                   keras.layers.Dense(128, activation='relu'),\n",
        "                                   keras.layers.Dense(256, activation='relu'), \n",
        "                                   keras.layers.Dense(10, activation='sigmoid')\n",
        "])\n",
        "clothing_model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss ='sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy'] \n",
        ")\n",
        "clothing_model.fit(X_train, y_train, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6umpN18aAeMj",
        "outputId": "c86b2031-a5fb-4786-b9f7-8e88701ce639"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4807 - accuracy: 0.8275\n",
            "Epoch 2/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3614 - accuracy: 0.8677\n",
            "Epoch 3/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3265 - accuracy: 0.8796\n",
            "Epoch 4/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3004 - accuracy: 0.8884\n",
            "Epoch 5/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2867 - accuracy: 0.8921\n",
            "Epoch 6/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2689 - accuracy: 0.8997\n",
            "Epoch 7/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2574 - accuracy: 0.9027\n",
            "Epoch 8/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2458 - accuracy: 0.9073\n",
            "Epoch 9/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2351 - accuracy: 0.9109\n",
            "Epoch 10/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2273 - accuracy: 0.9141\n",
            "Epoch 11/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2160 - accuracy: 0.9176\n",
            "Epoch 12/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2105 - accuracy: 0.9194\n",
            "Epoch 13/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2006 - accuracy: 0.9233\n",
            "Epoch 14/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1961 - accuracy: 0.9261\n",
            "Epoch 15/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1885 - accuracy: 0.9280\n",
            "Epoch 16/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1823 - accuracy: 0.9297\n",
            "Epoch 17/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1737 - accuracy: 0.9333\n",
            "Epoch 18/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1686 - accuracy: 0.9354\n",
            "Epoch 19/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1670 - accuracy: 0.9366\n",
            "Epoch 20/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1586 - accuracy: 0.9384\n",
            "Epoch 21/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1546 - accuracy: 0.9402\n",
            "Epoch 22/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1474 - accuracy: 0.9422\n",
            "Epoch 23/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1474 - accuracy: 0.9420\n",
            "Epoch 24/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1441 - accuracy: 0.9446\n",
            "Epoch 25/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1378 - accuracy: 0.9467\n",
            "Epoch 26/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1344 - accuracy: 0.9482\n",
            "Epoch 27/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1313 - accuracy: 0.9489\n",
            "Epoch 28/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1300 - accuracy: 0.9503\n",
            "Epoch 29/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1256 - accuracy: 0.9507\n",
            "Epoch 30/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1235 - accuracy: 0.9511\n",
            "Epoch 31/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1197 - accuracy: 0.9534\n",
            "Epoch 32/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1189 - accuracy: 0.9534\n",
            "Epoch 33/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1137 - accuracy: 0.9554\n",
            "Epoch 34/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1125 - accuracy: 0.9559\n",
            "Epoch 35/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1087 - accuracy: 0.9580\n",
            "Epoch 36/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1094 - accuracy: 0.9579\n",
            "Epoch 37/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1019 - accuracy: 0.9606\n",
            "Epoch 38/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1072 - accuracy: 0.9588\n",
            "Epoch 39/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1006 - accuracy: 0.9604\n",
            "Epoch 40/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0991 - accuracy: 0.9610\n",
            "Epoch 41/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0997 - accuracy: 0.9618\n",
            "Epoch 42/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0944 - accuracy: 0.9630\n",
            "Epoch 43/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0920 - accuracy: 0.9649\n",
            "Epoch 44/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0925 - accuracy: 0.9649\n",
            "Epoch 45/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0868 - accuracy: 0.9656\n",
            "Epoch 46/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0926 - accuracy: 0.9642\n",
            "Epoch 47/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0861 - accuracy: 0.9674\n",
            "Epoch 48/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0856 - accuracy: 0.9675\n",
            "Epoch 49/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0826 - accuracy: 0.9678\n",
            "Epoch 50/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0830 - accuracy: 0.9687\n",
            "Epoch 51/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0783 - accuracy: 0.9696\n",
            "Epoch 52/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0787 - accuracy: 0.9692\n",
            "Epoch 53/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0804 - accuracy: 0.9699\n",
            "Epoch 54/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0768 - accuracy: 0.9705\n",
            "Epoch 55/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0783 - accuracy: 0.9704\n",
            "Epoch 56/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0732 - accuracy: 0.9717\n",
            "Epoch 57/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0719 - accuracy: 0.9718\n",
            "Epoch 58/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0745 - accuracy: 0.9720\n",
            "Epoch 59/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0736 - accuracy: 0.9722\n",
            "Epoch 60/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0701 - accuracy: 0.9737\n",
            "Epoch 61/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0707 - accuracy: 0.9741\n",
            "Epoch 62/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0686 - accuracy: 0.9742\n",
            "Epoch 63/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0657 - accuracy: 0.9750\n",
            "Epoch 64/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0701 - accuracy: 0.9737\n",
            "Epoch 65/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0680 - accuracy: 0.9743\n",
            "Epoch 66/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0656 - accuracy: 0.9748\n",
            "Epoch 67/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0631 - accuracy: 0.9761\n",
            "Epoch 68/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0639 - accuracy: 0.9758\n",
            "Epoch 69/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0670 - accuracy: 0.9753\n",
            "Epoch 70/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0619 - accuracy: 0.9759\n",
            "Epoch 71/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0590 - accuracy: 0.9779\n",
            "Epoch 72/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0650 - accuracy: 0.9765\n",
            "Epoch 73/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0575 - accuracy: 0.9788\n",
            "Epoch 74/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0642 - accuracy: 0.9761\n",
            "Epoch 75/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0593 - accuracy: 0.9780\n",
            "Epoch 76/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0596 - accuracy: 0.9784\n",
            "Epoch 77/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0577 - accuracy: 0.9787\n",
            "Epoch 78/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0561 - accuracy: 0.9792\n",
            "Epoch 79/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0589 - accuracy: 0.9791\n",
            "Epoch 80/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0524 - accuracy: 0.9801\n",
            "Epoch 81/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0585 - accuracy: 0.9791\n",
            "Epoch 82/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0541 - accuracy: 0.9806\n",
            "Epoch 83/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0512 - accuracy: 0.9806\n",
            "Epoch 84/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0579 - accuracy: 0.9789\n",
            "Epoch 85/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0576 - accuracy: 0.9799\n",
            "Epoch 86/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0489 - accuracy: 0.9817\n",
            "Epoch 87/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0543 - accuracy: 0.9804\n",
            "Epoch 88/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0525 - accuracy: 0.9805\n",
            "Epoch 89/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0495 - accuracy: 0.9817\n",
            "Epoch 90/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0547 - accuracy: 0.9804\n",
            "Epoch 91/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0474 - accuracy: 0.9823\n",
            "Epoch 92/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0576 - accuracy: 0.9804\n",
            "Epoch 93/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0469 - accuracy: 0.9824\n",
            "Epoch 94/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0502 - accuracy: 0.9819\n",
            "Epoch 95/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0497 - accuracy: 0.9826\n",
            "Epoch 96/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0506 - accuracy: 0.9821\n",
            "Epoch 97/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0468 - accuracy: 0.9831\n",
            "Epoch 98/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0488 - accuracy: 0.9833\n",
            "Epoch 99/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0496 - accuracy: 0.9820\n",
            "Epoch 100/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0457 - accuracy: 0.9840\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9e33afe910>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating the model \n",
        "clothing_model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKZV79qCAvPZ",
        "outputId": "9d1f7ce1-8175-4cc4-ebcf-8c026ddb57fc"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 1.1084 - accuracy: 0.8896\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1083811521530151, 0.8895999789237976]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(X_test[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "PR-dSf7gAxtp",
        "outputId": "4e279f55-c8ce-467d-afc7-39d910a0f3f0"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9eb01fd410>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generating the predicted y values \n",
        "y_predicted = clothing_model.predict(X_test)\n",
        "print(y_predicted[1])\n",
        "np.argmax(y_predicted[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FvT0yDhaBEdh",
        "outputId": "bce1ed45-571c-4648-efc3-4895b2fceae1"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2.0124251e-06 0.0000000e+00 9.9999940e-01 3.0962363e-24 1.3534520e-03\n",
            " 2.4459780e-27 1.3111482e-02 0.0000000e+00 1.3812437e-27 0.0000000e+00]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets create a confusion matrix\n",
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "1NO1QFVeBISh"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion = tf.math.confusion_matrix(labels=y_test, predictions = y_predicted_labels)\n",
        "confusion "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJDr1bFbBJgx",
        "outputId": "aa0b9fa0-9662-49e0-ab82-d3c3612c9da6"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[852,   1,  15,  41,   1,   0,  84,   0,   6,   0],\n",
              "       [  2, 971,   2,  16,   4,   0,   4,   0,   1,   0],\n",
              "       [ 20,   0, 811,  12,  88,   0,  68,   0,   1,   0],\n",
              "       [ 14,   7,   6, 923,  23,   0,  25,   0,   2,   0],\n",
              "       [  4,   0,  84,  37, 821,   0,  53,   0,   1,   0],\n",
              "       [  2,   0,   0,   2,   0, 954,   0,  25,   2,  15],\n",
              "       [119,   0,  62,  37, 106,   0, 668,   0,   8,   0],\n",
              "       [  0,   0,   0,   0,   0,   8,   0, 967,   1,  24],\n",
              "       [  6,   0,   3,   6,   9,   1,   2,   3, 970,   0],\n",
              "       [  0,   0,   0,   0,   0,   7,   1,  33,   0, 959]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#showing the heat map \n",
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(confusion, annot= True, fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Given')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "pN-M7E_VBO4G",
        "outputId": "53c2ce73-d84b-49cd-95a3-d9cec6d1db98"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Given')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}