autoencoder-generative-ai-mnist.ipynb

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    "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
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    "<div style=\"text-align:center\">\n",
    "    <img src=\"https://static.vecteezy.com/system/resources/thumbnails/013/083/739/small_2x/stick-man-with-book-shelves-in-library-education-and-learning-concept-3d-illustration-or-3d-rendering-png.png\n",
    "\" alt=\"Image\">\n",
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     "text": [
      "2024-06-27 15:08:14.655400: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-06-27 15:08:14.655543: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-06-27 15:08:14.811684: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
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   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Dense\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
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    "    <b>📂 Libraries:</b> Successfully import the recquired library\n",
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     "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[1m1s\u001b[0m 0us/step\n"
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   "source": [
    "# Load the MNIST dataset\n",
    "(x_train, _), (x_test, _) = mnist.load_data()\n",
    "\n",
    "# Normalize the data to the range [0, 1]\n",
    "x_train = x_train.astype('float32') / 255.0\n",
    "x_test = x_test.astype('float32') / 255.0\n",
    "\n",
    "# Flatten the images for the fully connected neural network\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:])))"
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   "source": [
    "# Define the size of the encoded representation (latent space)\n",
    "encoding_dim = 32  \n",
    "\n",
    "# Input placeholder\n",
    "input_img = Input(shape=(784,))\n",
    "\n",
    "# \"encoded\" is the encoded representation of the input\n",
    "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
    "\n",
    "# \"decoded\" is the lossy reconstruction of the input\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "# This model maps an input to its reconstruction\n",
    "autoencoder = Model(input_img, decoded)"
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   "source": [
    "# Compile the model with a loss function and optimizer\n",
    "autoencoder.compile(optimizer=Adam(), loss='binary_crossentropy')"
   ]
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     "text": [
      "Epoch 1/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - loss: 0.3811 - val_loss: 0.1896\n",
      "Epoch 2/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1804 - val_loss: 0.1539\n",
      "Epoch 3/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1490 - val_loss: 0.1334\n",
      "Epoch 4/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1314 - val_loss: 0.1211\n",
      "Epoch 5/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1198 - val_loss: 0.1128\n",
      "Epoch 6/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1124 - val_loss: 0.1071\n",
      "Epoch 7/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1075 - val_loss: 0.1033\n",
      "Epoch 8/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1037 - val_loss: 0.1003\n",
      "Epoch 9/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.1008 - val_loss: 0.0980\n",
      "Epoch 10/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0986 - val_loss: 0.0963\n",
      "Epoch 11/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0971 - val_loss: 0.0950\n",
      "Epoch 12/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0959 - val_loss: 0.0942\n",
      "Epoch 13/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0953 - val_loss: 0.0936\n",
      "Epoch 14/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0947 - val_loss: 0.0933\n",
      "Epoch 15/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0946 - val_loss: 0.0929\n",
      "Epoch 16/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0940 - val_loss: 0.0928\n",
      "Epoch 17/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0939 - val_loss: 0.0925\n",
      "Epoch 18/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0937 - val_loss: 0.0923\n",
      "Epoch 19/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0937 - val_loss: 0.0923\n",
      "Epoch 20/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0936 - val_loss: 0.0922\n",
      "Epoch 21/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0933 - val_loss: 0.0922\n",
      "Epoch 22/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0934 - val_loss: 0.0922\n",
      "Epoch 23/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0932 - val_loss: 0.0920\n",
      "Epoch 24/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0934 - val_loss: 0.0920\n",
      "Epoch 25/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0919\n",
      "Epoch 26/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0919\n",
      "Epoch 27/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0931 - val_loss: 0.0918\n",
      "Epoch 28/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0919\n",
      "Epoch 29/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0929 - val_loss: 0.0918\n",
      "Epoch 30/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0931 - val_loss: 0.0918\n",
      "Epoch 31/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0917\n",
      "Epoch 32/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0918\n",
      "Epoch 33/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0917\n",
      "Epoch 34/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0916\n",
      "Epoch 35/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0930 - val_loss: 0.0916\n",
      "Epoch 36/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0928 - val_loss: 0.0916\n",
      "Epoch 37/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0916\n",
      "Epoch 38/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0916\n",
      "Epoch 39/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0917\n",
      "Epoch 40/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0916\n",
      "Epoch 41/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 42/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0926 - val_loss: 0.0915\n",
      "Epoch 43/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0928 - val_loss: 0.0916\n",
      "Epoch 44/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0916\n",
      "Epoch 45/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0926 - val_loss: 0.0915\n",
      "Epoch 46/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0916\n",
      "Epoch 47/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 48/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0926 - val_loss: 0.0915\n",
      "Epoch 49/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0923 - val_loss: 0.0915\n",
      "Epoch 50/50\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0924 - val_loss: 0.0915\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7d5f6cdca920>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,  \n",
    "                batch_size=256,  \n",
    "                shuffle=True,  \n",
    "                validation_data=(x_test, x_test))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52746064",
   "metadata": {
    "papermill": {
     "duration": 0.105469,
     "end_time": "2024-06-27T15:09:52.956600",
     "exception": false,
     "start_time": "2024-06-27T15:09:52.851131",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
    "<div style=\"color:black; border: 2px solid #6f42c1; background-color:#f3e8ff; padding: 20px; border-radius: 15px; font-size: 200%; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; text-align:center; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7);\">🔥AutoEncoder Prediction🔥</div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0313d0ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-27T15:09:53.228001Z",
     "iopub.status.busy": "2024-06-27T15:09:53.227105Z",
     "iopub.status.idle": "2024-06-27T15:09:55.476504Z",
     "shell.execute_reply": "2024-06-27T15:09:55.475365Z"
    },
    "papermill": {
     "duration": 2.417869,
     "end_time": "2024-06-27T15:09:55.479035",
     "exception": false,
     "start_time": "2024-06-27T15:09:53.061166",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x400 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Encode and decode some digits\n",
    "encoded_imgs = autoencoder.predict(x_test)\n",
    "decoded_imgs = autoencoder.predict(encoded_imgs)\n",
    "\n",
    "# Plot the original and reconstructed images\n",
    "n = 10  # Number of digits to display\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # Display original\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 reconstruction\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",
    "plt.show()"
   ]
  }
 ],
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