TF_Forum_7381.ipynb

tf_forum_7381.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO3Oe77J6T9XOGJHviR8171",
      "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/kiransair/d630702b1363f5053487034fe76da73a/tf_forum_7381.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": {
        "id": "oNXut5uYwtFw"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mnist=keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "id": "jVrOH5Nowyhx"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train,y_train),(x_test,y_test)=mnist"
      ],
      "metadata": {
        "id": "TtdvZDYqw0Ke"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=x_train.astype('float32')/255\n",
        "x_test=x_test.astype('float32')/255"
      ],
      "metadata": {
        "id": "99EIKooAw1_H"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train=keras.utils.to_categorical(y_train,10)\n",
        "y_test=keras.utils.to_categorical(y_test,10)"
      ],
      "metadata": {
        "id": "LcBwx_Oqw4AY"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "                        keras.Input(shape=(28,28,1),),\n",
        "                        keras.layers.Conv2D(32,kernel_size=(3,3),activation='relu',),\n",
        "                        keras.layers.MaxPooling2D(pool_size=(2,2)),\n",
        "                        keras.layers.Conv2D(64,kernel_size=(3,3),activation='relu'),\n",
        "                        keras.layers.MaxPooling2D(pool_size=(2,2)),\n",
        "                        keras.layers.Flatten(),\n",
        "                        keras.layers.Dropout(0.5),\n",
        "                        keras.layers.Dense(10,activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "6jSvd5QUw6DK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"categorical_crossentropy\", optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "pTGkDkr4w8Dy"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import Callback\n",
        "\n",
        "class Callback(Callback):\n",
        "    def __init__(self, x_train):\n",
        "        self.x_train = x_train\n",
        "    def on_train_begin(self, logs=None):\n",
        "        shapeofxtrain = self.x_train.shape\n",
        "        print(shapeofxtrain)\n"
      ],
      "metadata": {
        "id": "AYFcRAhlw-rY"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train,validation_data=(x_test, y_test),epochs=1,callbacks=[Callback(x_train)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixLnTQa6xFUZ",
        "outputId": "5370c1df-a2d9-4bff-e7de-a24a21bb1bbd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "1875/1875 [==============================] - 71s 37ms/step - loss: 0.2074 - accuracy: 0.9364 - val_loss: 0.0586 - val_accuracy: 0.9823\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa37c6c5610>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rZqe1wivxKq-"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}