 dl-3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOmFgiYeIa8RcRzDCExdWL6",
      "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/jaswanthBavi/batch1/blob/main/%20dl-3.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/",
          "height": 1000
        },
        "id": "Cxnb7mylHcVK",
        "outputId": "d6a56c06-5deb-46da-9fef-2b50351448fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "(60000, 784)\n",
            "(10000, 784)\n",
            "Epoch 1/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 17ms/step - loss: 0.4213 - val_loss: 0.2298\n",
            "Epoch 2/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 17ms/step - loss: 0.2164 - val_loss: 0.1846\n",
            "Epoch 3/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - loss: 0.1809 - val_loss: 0.1671\n",
            "Epoch 4/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.1650 - val_loss: 0.1548\n",
            "Epoch 5/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - loss: 0.1540 - val_loss: 0.1483\n",
            "Epoch 6/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 17ms/step - loss: 0.1484 - val_loss: 0.1450\n",
            "Epoch 7/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - loss: 0.1458 - val_loss: 0.1428\n",
            "Epoch 8/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - loss: 0.1436 - val_loss: 0.1410\n",
            "Epoch 9/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - loss: 0.1418 - val_loss: 0.1396\n",
            "Epoch 10/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 17ms/step - loss: 0.1403 - val_loss: 0.1383\n",
            "Epoch 11/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.1391 - val_loss: 0.1372\n",
            "Epoch 12/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.1383 - val_loss: 0.1360\n",
            "Epoch 13/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.1371 - val_loss: 0.1350\n",
            "Epoch 14/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.1363 - val_loss: 0.1342\n",
            "Epoch 15/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.1351 - val_loss: 0.1335\n",
            "Epoch 16/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1348 - val_loss: 0.1330\n",
            "Epoch 17/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1342 - val_loss: 0.1324\n",
            "Epoch 18/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 12ms/step - loss: 0.1336 - val_loss: 0.1320\n",
            "Epoch 19/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1331 - val_loss: 0.1317\n",
            "Epoch 20/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.1334 - val_loss: 0.1315\n",
            "Epoch 21/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1330 - val_loss: 0.1313\n",
            "Epoch 22/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - loss: 0.1330 - val_loss: 0.1312\n",
            "Epoch 23/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.1326 - val_loss: 0.1310\n",
            "Epoch 24/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1326 - val_loss: 0.1309\n",
            "Epoch 25/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - loss: 0.1322 - val_loss: 0.1308\n",
            "Epoch 26/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step - loss: 0.1322 - val_loss: 0.1306\n",
            "Epoch 27/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.1324 - val_loss: 0.1305\n",
            "Epoch 28/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 0.1321 - val_loss: 0.1303\n",
            "Epoch 29/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1322 - val_loss: 0.1303\n",
            "Epoch 30/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.1319 - val_loss: 0.1302\n",
            "Epoch 31/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.1323 - val_loss: 0.1301\n",
            "Epoch 32/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1318 - val_loss: 0.1301\n",
            "Epoch 33/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.1317 - val_loss: 0.1300\n",
            "Epoch 34/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 0.1314 - val_loss: 0.1298\n",
            "Epoch 35/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - loss: 0.1313 - val_loss: 0.1299\n",
            "Epoch 36/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 0.1313 - val_loss: 0.1297\n",
            "Epoch 37/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 0.1317 - val_loss: 0.1297\n",
            "Epoch 38/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step - loss: 0.1314 - val_loss: 0.1296\n",
            "Epoch 39/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step - loss: 0.1313 - val_loss: 0.1295\n",
            "Epoch 40/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 0.1315 - val_loss: 0.1295\n",
            "Epoch 41/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 0.1312 - val_loss: 0.1295\n",
            "Epoch 42/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.1310 - val_loss: 0.1295\n",
            "Epoch 43/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1310 - val_loss: 0.1294\n",
            "Epoch 44/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - loss: 0.1310 - val_loss: 0.1293\n",
            "Epoch 45/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 0.1308 - val_loss: 0.1293\n",
            "Epoch 46/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - loss: 0.1308 - val_loss: 0.1293\n",
            "Epoch 47/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step - loss: 0.1307 - val_loss: 0.1293\n",
            "Epoch 48/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1306 - val_loss: 0.1292\n",
            "Epoch 49/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.1309 - val_loss: 0.1292\n",
            "Epoch 50/50\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.1312 - val_loss: 0.1292\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import all the necessary libraries\n",
        "from keras.layers import Dense, Input\n",
        "from keras.models import Model\n",
        "from keras.datasets import mnist\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the encoding dimension that determines how much the input will be compressed\n",
        "encoding_dim = 15\n",
        "\n",
        "# Input layer with shape of 784 (28x28 pixels, flattened)\n",
        "input_img = Input(shape=(784,))\n",
        "\n",
        "# Define the encoded representation of the input (compression layer)\n",
        "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
        "\n",
        "# Define the decoded representation (reconstruction layer)\n",
        "decoded = Dense(784, activation='sigmoid')(encoded)\n",
        "\n",
        "# Create the autoencoder model which takes the input image and returns the decoded image\n",
        "autoencoder = Model(input_img, decoded)\n",
        "\n",
        "# Create the encoder model which takes the input image and returns the encoded (compressed) representation\n",
        "encoder = Model(input_img, encoded)\n",
        "\n",
        "# Create the decoder model which takes the encoded input and returns the reconstructed image\n",
        "encoded_input = Input(shape=(encoding_dim,))\n",
        "decoder_layer = autoencoder.layers[-1]  # Last layer of the autoencoder is the decoder layer\n",
        "decoder = Model(encoded_input, decoder_layer(encoded_input))\n",
        "\n",
        "# Compile the autoencoder model with the Adam optimizer and binary crossentropy loss function\n",
        "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "\n",
        "# Load the MNIST data\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "# Normalize the data by scaling it to values between 0 and 1\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "\n",
        "# Flatten the images (28x28) into vectors of size 784 (28*28)\n",
        "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
        "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
        "\n",
        "# Print the shapes of the training and testing sets\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "\n",
        "# Train the autoencoder model\n",
        "autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))\n",
        "\n",
        "# Encode some test data\n",
        "encoded_imgs = encoder.predict(x_test)\n",
        "\n",
        "# Decode the encoded data\n",
        "decoded_imgs = decoder.predict(encoded_imgs)\n",
        "\n",
        "# Display some of the original and reconstructed images\n",
        "n = 10  # number of images to display\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # Display original images\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.imshow(x_test[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "    # Display reconstructed images\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\n",
        "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()\n"
      ]
    }
  ]
}