Colthes_Model.ipynb

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  "metadata": {
    "colab": {
      "name": "Colthes Model.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6eWZxoYVaUe",
        "colab_type": "code",
        "outputId": "d2774b75-6716-4b55-fe57-68b3aa9d306d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        }
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--LlyUorVvqB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow import keras\n",
        "import numpy as np  \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnX6zsNfWI2s",
        "colab_type": "code",
        "outputId": "8e02e806-b378-48c8-a846-429930e05b18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "ClothData = keras.datasets.fashion_mnist #Mnist has gather data of clothes, we just used that data\n",
        "(TestData, TestLabel) , (TrainData , TrainLabel) = ClothData.load_data() # Test Data nad Train Data has images while Train Labels and Test Labels has labels from 0 to 9"
      ],
      "execution_count": 4,
      "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": {
        "id": "eCfz1atRW-lJ",
        "colab_type": "code",
        "outputId": "8bfd5f5d-51cc-40a4-8ce6-d419c9174dbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "TrainData.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3ifVUt4XcLt",
        "colab_type": "code",
        "outputId": "dfb28e4a-f476-48a4-dc62-da27138aaded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "TestData.shape "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmDuPzjRXm1K",
        "colab_type": "code",
        "outputId": "d129156b-7349-4f88-fee2-faba2c79bbbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "TrainLabel # train label has label from 0 to 9"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTL29R6RXxdA",
        "colab_type": "code",
        "outputId": "267ee736-b774-41cd-87e3-a41217c13a86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# let's check the first image of our testData\n",
        "plt.figure()\n",
        "plt.imshow(TestData[0])\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkyJaN6aYala",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#above image looks like an image of a shoes, and pixels values are ranging from 0 - 255\n",
        "# Now 1 thing to be noted that labels are in integer, so we will assign each label with its class Name in respective order\n",
        "ClothName = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "#now scale pixels values from 0 - 1, for that divided it by 255\n",
        "TrainData = TrainData/255\n",
        "TestData = TestData/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmTy82EHZl3t",
        "colab_type": "code",
        "outputId": "9c527137-8500-4074-f0cb-b08ed951ff4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "#now Let's again plot first 30 images of TrainData and polt them with their Colth's name, to verify that it is in correct order\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(30):\n",
        "    plt.subplot(6,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(TrainData[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(ClothName[TrainLabel[i]])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/SezzsOxvMoBJKA28AYntPgdwZyb/FcC+ZtYRQKcQwjNZ+58B7J/XXutfKdqb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\n",
            "text/plain": [
              "<Figure size 720x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVlvPu7EbQSv",
        "colab_type": "code",
        "outputId": "3356107b-6b4c-491e-d542-6cbf15d0c16c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "source": [
        "# It's Verify that our Data is correct, Now Let's Train pur Model\n",
        "ClothModel = keras.Sequential([\n",
        "                               keras.layers.Flatten(input_shape = (28,28)), # as we have seen our data is in 28,28\n",
        "                               keras.layers.Dense(128,activation='relu'),\n",
        "                               keras.layers.Dense(10,activation = 'softmax')\n",
        "])\n",
        "ClothModel.compile(optimizer = 'adam' , loss = 'sparse_categorical_crossentropy' , metrics = ['accuracy'])\n",
        "ClothModel.fit(TrainData,TrainLabel,epochs = 10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "Train on 10000 samples\n",
            "Epoch 1/10\n",
            "10000/10000 [==============================] - 1s 117us/sample - loss: 0.7321 - acc: 0.7432\n",
            "Epoch 2/10\n",
            "10000/10000 [==============================] - 1s 86us/sample - loss: 0.5133 - acc: 0.8204\n",
            "Epoch 3/10\n",
            "10000/10000 [==============================] - 1s 85us/sample - loss: 0.4579 - acc: 0.8373\n",
            "Epoch 4/10\n",
            "10000/10000 [==============================] - 1s 83us/sample - loss: 0.4235 - acc: 0.8511\n",
            "Epoch 5/10\n",
            "10000/10000 [==============================] - 1s 80us/sample - loss: 0.3997 - acc: 0.8586\n",
            "Epoch 6/10\n",
            "10000/10000 [==============================] - 1s 80us/sample - loss: 0.3811 - acc: 0.8601\n",
            "Epoch 7/10\n",
            "10000/10000 [==============================] - 1s 79us/sample - loss: 0.3516 - acc: 0.8701\n",
            "Epoch 8/10\n",
            "10000/10000 [==============================] - 1s 82us/sample - loss: 0.3387 - acc: 0.8810\n",
            "Epoch 9/10\n",
            "10000/10000 [==============================] - 1s 83us/sample - loss: 0.3229 - acc: 0.8851\n",
            "Epoch 10/10\n",
            "10000/10000 [==============================] - 1s 82us/sample - loss: 0.3118 - acc: 0.8892\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fe6dfad3f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdZmY-MZejEy",
        "colab_type": "code",
        "outputId": "68d29474-b8ab-49ce-b371-ffc589910376",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#accuracy is 88% with TrainData, let's check it with Test Data, and evaluate accuaracy with unseen Data\n",
        "loss , accuracy = ClothModel.evaluate(TestData,TestLabel,verbose=2)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000/60000 - 2s - loss: 0.3896 - acc: 0.8607\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Up13aKgWfI4-",
        "colab_type": "code",
        "outputId": "c0c24223-9a2f-4577-a6fc-e5d789774021",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Test Accuracy is less than the Train Accuracy, It means oue model is less confident for unseen data\n",
        "# Now Let's Test it for a single Image and evaluate our Model.\n",
        "# and use our Train Model for check it on single Image\n",
        "# as I have no other image now, so i am testing it with image randomly pick from test Data\n",
        "index = np.random.randint(0,10000)\n",
        "img = TestData[index]\n",
        "img = (np.expand_dims(img,0)) # we must add image to a list bcz our flatter layers accept data in a list\n",
        "print(img.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1LD4OZ-g5si",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result = ClothModel.predict(img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_I2xU06GhmsK",
        "colab_type": "code",
        "outputId": "d665685d-591b-4656-d84d-81ec8120ecb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(result)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[4.8699771e-04 1.6821406e-04 2.6890519e-01 1.9128018e-03 6.6606486e-01\n",
            "  6.1902319e-09 6.0911901e-02 1.3121648e-07 1.5499266e-03 2.2274559e-10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZ97Fdk9iZMl",
        "colab_type": "code",
        "outputId": "e2bb7c8a-08d1-4abc-88c1-7ce8ba1033b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#It returns list of labels and correct answer would be max value of this list\n",
        "correctResult = np.argmax(result)\n",
        "correctResult"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQ6gQ_9LjNsJ",
        "colab_type": "code",
        "outputId": "d8751dd9-9f80-41de-951e-86fe28a389f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "# let's plot that image in graph and check whether our result is accurate or not\n",
        "plt.figure()\n",
        "plt.grid(False)\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "plt.imshow(TestData[index], cmap = plt.cm.binary) # plot original image with predicted result\n",
        "plt.xlabel(ClothName[correctResult])\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}