SO_74413904.ipynb

so_74413904.ipynb

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    "colab": {
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      "authorship_tag": "ABX9TyNvGUKCmzqIAQS+pC59f4VM",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/kiransair/9557b4265bc55e2ef07290acc79dee62/so_74413904.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": "w53bBLpoWZxn"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVpvDwAhWaru",
        "outputId": "962fce15-e1a3-4f7b-a48e-be93054186c3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train,y_train),(x_test,y_test)=mnist"
      ],
      "metadata": {
        "id": "57wUoOfsWdPV"
      },
      "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": "yTEfUEY1WgxO"
      },
      "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": "XYdNWi9eWi4p"
      },
      "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": "MX7WjFaPW4w7"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"categorical_crossentropy\", optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "5HF4L1ZBW9YL"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUyEGNxPXGos",
        "outputId": "8ff73714-576c-4de7-f4b1-686f66b4a1b0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 74s 39ms/step - loss: 0.2078 - accuracy: 0.9358\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 58s 31ms/step - loss: 0.0785 - accuracy: 0.9761\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 61s 32ms/step - loss: 0.0621 - accuracy: 0.9808\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 59s 32ms/step - loss: 0.0530 - accuracy: 0.9830\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 58s 31ms/step - loss: 0.0456 - accuracy: 0.9857\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f80cbffc3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1=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": "xiBbh-rKZecD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.compile(loss=\"categorical_crossentropy\", optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "qN8RN0b9bTLG"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.fit(x_test,y_test,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nhnpot1EY660",
        "outputId": "50f8236d-9239-4f82-ae6e-89cedff790c4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "313/313 [==============================] - 11s 32ms/step - loss: 0.5796 - accuracy: 0.8143\n",
            "Epoch 2/5\n",
            "313/313 [==============================] - 10s 31ms/step - loss: 0.1744 - accuracy: 0.9467\n",
            "Epoch 3/5\n",
            "313/313 [==============================] - 10s 31ms/step - loss: 0.1216 - accuracy: 0.9613\n",
            "Epoch 4/5\n",
            "313/313 [==============================] - 10s 31ms/step - loss: 0.0966 - accuracy: 0.9691\n",
            "Epoch 5/5\n",
            "313/313 [==============================] - 10s 31ms/step - loss: 0.0808 - accuracy: 0.9735\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f80c8826ac0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=model1.get_weights()[0]"
      ],
      "metadata": {
        "id": "AlTsKY0KZ6Ju"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y=model.get_weights()[0]"
      ],
      "metadata": {
        "id": "wkrXeIpldcHL"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = tf.constant([[X], [Y]]) "
      ],
      "metadata": {
        "id": "wJSV8H7lpXkW"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.math.reduce_euclidean_norm(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLzVJ5MRdoj6",
        "outputId": "89f2a072-23c2-42e6-8f26-4fb11228c4e5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=4.558021>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yjc69jYTspq9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}