Item_classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "uRMw4Pvf_mxU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fashion_mnist=keras.datasets.fashion_mnist\n",
        "(train_images,train_labels),(test_images,test_labels)=fashion_mnist.load_data()\n",
        "test_images=test_images/255.0\n",
        "train_images=train_images/255.0"
      ],
      "metadata": {
        "id": "m2q6jN93_nYZ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top','Trouser','Pullover','Dress','Coat',\n",
        "               'Sandal','Shirt','Sneaker','Bag','Ankle boot']"
      ],
      "metadata": {
        "id": "PpGeDyDIAaOa"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "  keras.layers.Flatten(input_shape=(28,28)),\n",
        "  keras.layers.Dense(128, activation='relu'),\n",
        "  keras.layers.Dense (10, activation='softmax')\n",
        "  ])"
      ],
      "metadata": {
        "id": "0QQH68P9B7m9"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='Adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "PI4qRVqLVDRp"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images,train_labels,epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKHJqK2UVEi9",
        "outputId": "0262d436-f846-4934-d368-d587aa98b55b"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1948 - accuracy: 0.9271\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1888 - accuracy: 0.9294\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1848 - accuracy: 0.9309\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1795 - accuracy: 0.9326\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1752 - accuracy: 0.9340\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1695 - accuracy: 0.9362\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1683 - accuracy: 0.9364\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1612 - accuracy: 0.9399\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1598 - accuracy: 0.9397\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1552 - accuracy: 0.9414\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7be903979660>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate (test_images, test_labels, verbose=1)\n",
        "print('Test accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1U6N2KxcVEgk",
        "outputId": "a91a3a5c-3ffc-423f-c5eb-1e09afea6caa"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 5ms/step - loss: 0.3174 - accuracy: 0.8914\n",
            "Test accuracy: 0.8913999795913696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "COLOR = 'white'\n",
        "plt.rcParams['text.color'] = COLOR\n",
        "plt.rcParams['axes.labelcolor'] = COLOR\n",
        "\n",
        "def predict(model, image, correct_label):\n",
        "  class_names = ['T-shirt/top','Trouser','Pullover',\n",
        "                 'Dress','Coat','Sandal','Shirt',\n",
        "                 'Sneaker','Bag','Ankle boot']\n",
        "  prediction = model.predict(np.array([image]))\n",
        "  predicted_class = class_names[np.argmax(prediction)]\n",
        "  show_image(image,class_names[correct_label],predicted_class)\n",
        "\n",
        "def show_image(img,label,guess):\n",
        "  plt.figure()\n",
        "  plt.imshow(img,cmap=plt.cm.binary)\n",
        "  plt.title(label=\"Expexted: \"+label,fontsize=20,color=\"blue\")\n",
        "  plt.xlabel(xlabel=\"Guess: \"+guess,fontsize=20,color=\"blue\")\n",
        "  plt.colorbar()\n",
        "  plt.grid(False)\n",
        "  plt.show()\n",
        "\n",
        "def get_number():\n",
        "  while True:\n",
        "    num = input(\"Pick a number: \")\n",
        "    if num.isdigit():\n",
        "      num = int(num)\n",
        "      if 0 <= num <= 1000:\n",
        "        return int(num)\n",
        "    else:\n",
        "      print(\"Try again...\")\n",
        "\n",
        "num = get_number()\n",
        "image = test_images[num]\n",
        "label = test_labels[num]\n",
        "predict(model,image,label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "V3k3uoBYfVuX",
        "outputId": "e467d0ad-78b9-46c5-c3bd-abc90165b5fc"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pick a number: 78\n",
            "1/1 [==============================] - 0s 26ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GeAR8hOcng1P"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}