tf-prac.ipynb

tf-prac.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tf-prac.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMmAqCgNoL4uFv6/BOFPNut",
      "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/gist/Yaswanth820/cc343a9b0bb41e8dcdc4f3641df0be6f/tf-prac.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCLHseqR8Dk3",
        "outputId": "961ec4fb-2e36-49f1-bdf1-c9a2b2863290"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BUtn7Hf82xB",
        "outputId": "ed65cc77-6bf5-483d-c645-65e48acef856"
      },
      "execution_count": 2,
      "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": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLx8IFxL9IxH",
        "outputId": "3a0cefc7-632a-4084-b55b-320c597702b9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "metadata": {
        "id": "LYl_cJmE9nff"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "BnsGDGvi-HIV"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5onfochE-owh",
        "outputId": "950b7f0a-ef17-4ac7-f9f1-0716056d49ea"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 3.3322 - accuracy: 0.7030\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.6537 - accuracy: 0.7706\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5677 - accuracy: 0.7989\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.5170 - accuracy: 0.8206\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4889 - accuracy: 0.8331\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4769 - accuracy: 0.8378\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4714 - accuracy: 0.8399\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4599 - accuracy: 0.8425\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4582 - accuracy: 0.8450\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4464 - accuracy: 0.8470\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0195aa1790>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])\n",
        "predictions = probability_model.predict(x_test)\n",
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QC_R-Bg9_Hby",
        "outputId": "82f38fa7-2f2a-4cf8-c531-dff5066cc742"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.7452521e-14, 1.1022565e-07, 1.3565481e-38, 3.2323789e-11,\n",
              "       6.0320506e-34, 2.8959396e-03, 2.4874879e-17, 5.2263577e-02,\n",
              "       2.8615256e-13, 9.4484031e-01], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}

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