classifying_images_of_clothing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "classifying images of clothing.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOe6FjtCb5KfCWJngOuB6UC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Fareed-Ahmad7/tensorflow-learning/blob/main/classifying_images_of_clothing.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": "4vYLhzsdw_OY"
      },
      "outputs": [],
      "source": [
        "!pip install -U tensorflow_datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "# tf.logging.set_verbosity(tf.logging.Error)\n",
        "\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# improve progress bardisplay\n",
        "import tqdm\n",
        "import tqdm.auto\n",
        "tqdm.tqdm = tqdm.auto.tqdm\n",
        "\n",
        "print(tf.__version__)\n",
        "\n",
        "# tf.enable_eager_execution()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pb3xq-NOx-xE",
        "outputId": "11d397aa-adb2-418a-fda3-7068f1911677"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load dataset"
      ],
      "metadata": {
        "id": "m707mHWG92Ou"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "metadata": {
        "id": "zLkun_YF7Ll3"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "QAIPb75j9K5F"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explore the dataset"
      ],
      "metadata": {
        "id": "H6EuBoS499D4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_train_examples = metadata.splits['train'].num_examples\n",
        "num_test_examples = metadata.splits['test'].num_examples\n",
        "\n",
        "print(\"no of training examples: \", num_train_examples)\n",
        "print(\"no of testing examples: \", num_test_examples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9w80QeP9uFw",
        "outputId": "d17b7367-abde-4131-e3c8-83ba594bb1f5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "no of training examples:  60000\n",
            "no of testing examples:  10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Pre-process the data\n",
        "the value of each pixel in image data is an integer in range [0,255]. for the model to work properly we have to normalize to [0,1]"
      ],
      "metadata": {
        "id": "gsXAe8pcAju3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize(images, labels):\n",
        "  images = tf.cast(images, tf.float32)\n",
        "  images /= 255\n",
        "  return images,labels\n",
        "\n",
        "# the map functionapplies the normalize function to each element in the trian and test dataset\n",
        "train_dataset = train_dataset.map(normalize)\n",
        "test_dataset = test_dataset.map(normalize)"
      ],
      "metadata": {
        "id": "zHYp3GrPBOIe"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Explore the processed data"
      ],
      "metadata": {
        "id": "ZI4ik-ahC02u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# take single image and remove the color dimension by reshaping\n",
        "for image, label in test_dataset.take(1):\n",
        "  break\n",
        "image = image.numpy().reshape((28,28))\n",
        "\n",
        "# plot the image\n",
        "plt.figure()\n",
        "plt.imshow(image, cmap=plt.cm.binary)\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.xlabel(class_names[label])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "9Z_Dsj1qC8r1",
        "outputId": "2134b7a7-ea57-4903-920e-00eb01b54add"
      },
      "execution_count": 7,
      "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": [
        "plt.figure(figsize=(10,10))\n",
        "i = 0\n",
        "for (image,label) in train_dataset.take(25):\n",
        "  image = image.numpy().reshape((28,28))\n",
        "  plt.subplot(5,5,i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(image, cmap=plt.cm.binary)\n",
        "  plt.xlabel(class_names[label])\n",
        "  i += 1\n",
        "plt.show()  \n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "Xxb3W7q4FKcA",
        "outputId": "7ed39fbb-cd5f-435b-98fc-89e69e14d6bc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build Model"
      ],
      "metadata": {
        "id": "biCe7SxWGgla"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.Dense(128, activation = tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation = tf.nn.softmax)\n",
        "])"
      ],
      "metadata": {
        "id": "w7wbRpFmGjFB"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compile model"
      ],
      "metadata": {
        "id": "F-htNjDyIN4J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    optimizer= 'adam',\n",
        "    metrics = ['accuracy']\n",
        "    )      "
      ],
      "metadata": {
        "id": "dkw15LjpIRr7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train model"
      ],
      "metadata": {
        "id": "PxC-BcnwJFKE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n",
        "test_dataset = test_dataset.batch(BATCH_SIZE)"
      ],
      "metadata": {
        "id": "g29Ff9hyJEfh"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset, epochs = 5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sw0BEn6jR26w",
        "outputId": "7bf7f866-c8b4-4c0e-f820-6d704421a366"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 14s 5ms/step - loss: 0.4941 - accuracy: 0.8269\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.3704 - accuracy: 0.8661\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.3357 - accuracy: 0.8771\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3104 - accuracy: 0.8856\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2936 - accuracy: 0.8919\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f13d047f550>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate Accuracy"
      ],
      "metadata": {
        "id": "EvULQwysS7nW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_dataset, steps=num_test_examples/32)\n",
        "print(\"Accuracy on test dataset: \", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uIlX_sMS-Vn",
        "outputId": "d66ea09f-c129-4315-fad2-af4cea6cfa1f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "312/312 [==============================] - 1s 4ms/step - loss: 0.3490 - accuracy: 0.8759\n",
            "Accuracy on test dataset:  0.8758999705314636\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Make Predictions"
      ],
      "metadata": {
        "id": "aAi9MpB3T0qF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for test_images, test_labels in test_dataset.take(1):\n",
        "  test_images = test_images.numpy()\n",
        "  test_labels = test_labels.numpy()\n",
        "  predictions = model.predict(test_images)"
      ],
      "metadata": {
        "id": "8pAO_ZX3S7Cm"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D12CgwxTUZ1E",
        "outputId": "7eb5d27c-1d0b-4784-ab35-2751c118c7ea"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHHHMUolUiKu",
        "outputId": "70f4651a-0bd1-41f2-eb13-a85bf1c1c417"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.0114290e-05, 2.2622605e-06, 2.7075937e-02, 1.8971647e-07,\n",
              "       9.6378618e-01, 1.2102426e-08, 9.0543041e-03, 8.5260859e-10,\n",
              "       4.0959654e-05, 2.8843942e-08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYSD9sxQU5AL",
        "outputId": "06e9a3d3-9da3-43bd-b342-7115c5a73284"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRbbVlzmVD3B",
        "outputId": "24bd7642-ba89-45d9-8a20-7e5e729cfdf6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}