TensorflowRNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO4YvgBa0u9FDiFJ0SEfHvT",
      "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/github/Aravind8281/Tensorflow_Learning/blob/main/TensorflowRNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n"
      ],
      "metadata": {
        "id": "pA-wDyq71J2_"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mnist=tf.keras.datasets.mnist"
      ],
      "metadata": {
        "id": "rDXDBnJw1QoL"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(train_images,train_labels),(test_images,test_labels)=mnist.load_data()\n",
        "train_images,test_images=train_images/255.0,test_images/255.0\n",
        "print(\"Train images :\",len(train_images))\n",
        "print(\"Test Images :\",len(test_images))\n",
        "print(\"Shape\",train_images[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ivx6iWiV1VSq",
        "outputId": "efe316b3-1dff-45d1-8fe5-e1a1ffcb11a4"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train images : 60000\n",
            "Test Images : 10000\n",
            "Shape (28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=tf.keras.models.Sequential([\n",
        "    tf.keras.layers.InputLayer(input_shape=(28,28)),\n",
        "    tf.keras.layers.SimpleRNN(128,activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "metadata": {
        "id": "JN_Da4nQ1hYK"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model,to_file=\"Neura.png\",show_shapes=True,show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "LNtuuI482oOH",
        "outputId": "a4d8f6ac-5046-45e3-a0c3-fbfb7434f2d0"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "8W95B4m73OkU"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images,train_labels,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVLUzraR3l-b",
        "outputId": "f465f225-b4ed-45c2-bcba-61dd33da6ed1"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 20s 10ms/step - loss: 2.2041 - accuracy: 0.2386\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 20s 11ms/step - loss: 2.3053 - accuracy: 0.1010\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 20s 11ms/step - loss: 2.3030 - accuracy: 0.0998\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 18s 10ms/step - loss: 2.3026 - accuracy: 0.1007\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 20s 10ms/step - loss: 2.3028 - accuracy: 0.1017\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7c655a775b40>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_acc,test_loss=model.evaluate(test_images,test_labels,verbose=2)\n",
        "print(test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z5gmJG1-3sy5",
        "outputId": "951fefeb-3f23-4e16-93c5-ec9c36b7fca8"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - loss: 2.3026 - accuracy: 0.1101 - 1s/epoch - 4ms/step\n",
            "2.30259108543396\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NCdQzVxx4Qdw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}