03_Learning_Tensorflow_CNN_on_Fashion_MNIST.ipynb

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        "<a href=\"https://colab.research.google.com/github/Muhaiminul-Hasan/Learning-TensorFlow/blob/main/03_Learning_Tensorflow_CNN_on_Fashion_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fs7JANZ6XA1z"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)"
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f766a0bea8840a1b61ca90586b340f7"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a7e2d0e46fd47829bb694e80d4c4799"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "813c0ad9ab9643cc8b811f106626a4c6"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a7b6a3484454ecd8aef4c8582ce250e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/60000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d21fe157bb384c2fbf0a0bcdd31e23e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteGJ6DSL/fashion_mnist-train.tfrecord*...:   0…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "a942dba73fc24e28b1f01c1ca1a92ca5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ea6ce68c5674328b79144e752fd08de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteGJ6DSL/fashion_mnist-test.tfrecord*...:   0%…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e24dfb4f163c494e8466428d56966c09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset fashion_mnist downloaded and prepared to /root/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
        "train_dataset  = dataset['train']\n",
        "test_dataset   = dataset['test']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "63tU3SBEbAeT"
      },
      "outputs": [],
      "source": [
        "class_names = metadata.features['label'].names\n",
        "# for index, class_name in enumerate(class_names):\n",
        "#   print(index, \": \",class_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dneYl-V4bAZv"
      },
      "outputs": [],
      "source": [
        "num_train_examples = metadata.splits['train'].num_examples\n",
        "num_test_examples  = metadata.splits['test'].num_examples\n",
        "\n",
        "# print(\"train_examples    test_examples\\n\", num_train_examples, \"            \", num_test_examples)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wkm7JIx_a_1p"
      },
      "outputs": [],
      "source": [
        "def normalize(images, labels):\n",
        "  images = tf.cast(images, tf.float32)\n",
        "  images /= 255\n",
        "  return images, labels\n",
        "\n",
        "# Apply normalize function to each element of the dataset\n",
        "train_dataset = train_dataset.map(normalize)\n",
        "test_dataset  = test_dataset.map(normalize)\n",
        "\n",
        "# Cache the dataset to train faster\n",
        "train_dataset = train_dataset.cache()\n",
        "test_dataset  = test_dataset.cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "txw3Bnjia_sZ",
        "outputId": "64673f66-d286-4344-e01e-6049a393719e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x600 with 30 Axes>"
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Wm5tbWG7evPlhxzKz+P7wySef2Ouvv25Dhgw5bLvFixdb3759U3V9+/YNfgOzd+/eQZnf0qx4mGNR2riPxZFijkVFlSRJ6q9zfP8q6l5g+PDh9umnn1q7du1s1KhRNn78eNu3b5+ZmQ0YMMBat25t7dq1s0suucSefPLJ1P0DKr8v96fly5fb3r17U+NNjRo17OSTT/5KY8mPf/xj+973vmdnnXWW3XPPPYX3vWZV+7u6KvcQ4thjj7Vq1aodcWjXDTfcYOPHj7e7777bpk2bZnPnzrVu3bqVyFI8qDj69+9vc+fOtaVLl9qnn35qjz32mOXk5Fj16tWDG4W9e/eW6HufdNJJ1r59e/v73/9un376qY0fP75wKSazf99gfP/737e5c+cW/jdv3jxbunSptW/fvrCd+nNuVB61atWyAQMG2M9+9jN766237PLLL7fbbrut8N99iFe1atXkEjgHZWdnE5RZBR1JP9q1a5cNHjw4NRYdHDcP/qnz4MGDbdu2bfbwww/bO++8Y++8846ZhUGG55xzTuFSAV+2a9cuu+OOO1LbX7BggS1dutRq1apV2I7xrvxr0aJF6nP8wQ9+YGb/XpZhxowZ1qdPHxs3bpx17NjR3n777cLXfdWxzCy+P7zwwgvWpUuX1Jd6gBlzLEoX97E4UsyxqKgWL15sbdu2LSz7/lXUvUB+fr4tWbLEHnjgAcvOzrarr77aTj/9dNu7d6/l5uba7Nmz7W9/+5s1b97cbr31VuvRo4cVFBSU8k+JsvJV7xFjvu+7/fbbbdGiRXbuuefaa6+9Zl26dLHx48ebWdX+rq7KPYRo0KCBDRw40O6//37bvXt38O8FBQXWuXNnW7t2ra1du7aw/v3337eCggLr0qWLmZlNnz7dLr/8chs2bJh169bNmjVrZqtWrUptKysry/bv35/RnwdlKycnxzp06GDHHHOMHX300YX1jRs3Tv0Gyf79+23hwoXR2+3cubPNmzcv1UenT59u1atXt06dOhXWjRw50p588kn717/+ZdWrV7dzzz238N969epl77//vnXo0CH4Lysrq7g/Miq4Ll26yLHvSDDWVT1fpR/16tXLFi1aZG3atAnGopycHNu6dastWbLEbrnlFjvzzDOtc+fOtn37drmtq666yu655x4bMmSIvf7666n3WLJkiRzvqlevcpc6FdrRRx+d+vwaNGhQ+G89e/a0m2++2d566y3r2rWr/fWvfy3R9z44N/rxbOLEiXb++een6mrUqBG069y5s02fPj1VN3369MJrx4O+/MXOwXLnzp2PaN9RPjDHIpO4j8WRYo5FRfTaa6/ZggUL7IILLjhkm5h7gezsbBs8eLDdd999NnXqVJsxY4YtWLDAzP59bpx11ll277332vz5823VqlX22muvlcrPh/Klffv2lpWVlRpv9u7da7NmzSocbxo3bmw7d+5MzcVz584NttWxY0f70Y9+ZC+//LJ985vftEcffdTMqvZ3dVXyzvz++++3/fv328knn2xPP/20LV261BYvXmz33Xef9e7d28466yzr1q2bjRw50mbPnm0zZ860Sy+91Pr161f45zDHHnusPfPMM4VPrC6++OLgtwHatGljb7zxhn300Ue2ZcuWsvhRUUbOOOMMe+655+y5556zDz74wK666qqv9CR95MiRVqtWLbvsssts4cKFNmXKFBs9erRdcskl1rRp01S72bNn21133WXf+ta3rGbNmoX/9p//+Z/21ltv2bXXXlv4W8cTJ04Mwm5QOW3dutXOOOMMe+KJJ2z+/Pm2cuVKe+qpp+zee+8NLvSPVJs2bWz+/Pm2ZMkS27JlS4n/1Q/KTkn0o2uuuca2bdtmF110kc2aNcuWL19uL730kl1xxRW2f/9+q1+/vjVs2NAeeughW7Zsmb322mv24x//+JDbGz16tP3iF7+w8847z958800zM7v11ltt7Nixdscdd9iiRYts8eLF9ve//91uueWWEjkOKFsrV660m2++2WbMmGGrV6+2l19+2ZYuXVriXyo0adLEsrOzC8PTd+zYYfv27bMXXnghWCaiTZs29uqrr9rGjRsLH5rdeOONNmbMGPvjH/9oS5cutd/85jf2zDPP2A033JB67VNPPWWPPPKIffjhh3bbbbfZzJkzmZsrGOZYlBXuY1HSmGNRnnz++ee2ceNG++ijj2z27Nl299132/nnn2/nnXeeXXrppYd8XVH3AmPGjLG//OUvtnDhQluxYoU98cQTlp2dba1bt7ZJkybZfffdZ3PnzrXVq1fb2LFj7cCBA6lf/kTVkZOTY1dddZXdeOON9uKLL9r7779vo0aNsj179th3v/tdMzM75ZRTrHbt2vZf//Vftnz5cvvrX/+aWo7z008/tWuvvdamTp1qq1evtunTp9usWbMKx9Uq/V1dmSZSlKH169cn11xzTdK6deskKysradmyZTJkyJBkypQpSZIkyerVq5MhQ4YkOTk5SW5ubjJ8+PDCQOAk+XewUv/+/ZPs7OwkPz8/+cMf/hAEMs2YMSPp3r17UrNmzaQKtFTnSgAAA1RJREFUH+pK63DBSV988UVy1VVXJQ0aNEiaNGmS/Pd///dXCqZOkiSZP39+0r9//6RWrVpJgwYNklGjRiU7d+4M3uvkk09OzCx57bXXgn+bOXNmMmDAgKROnTpJTk5O0r179+Suu+465D6g8vjss8+Sm266KenVq1dSt27dpHbt2kmnTp2SW265JdmzZ0+SJDr0rW7dusmjjz6aJIkOzVThdh9//HFhPzOzwnEUFV9J9KMkSZIPP/wwGTZsWFKvXr0kOzs7Oe6445If/vCHyYEDB5IkSZJXXnkl6dy5c1KzZs2ke/fuydSpU1PbVWGGv/71r5Pc3Nxk+vTpSZIkyYsvvpj06dMnyc7OTvLy8pKTTz45eeihhwrbq/1E2YsJzdy4cWMydOjQpHnz5klWVlbSunXr5NZbb03279+fJMm/QzN79OiRes1vf/vbpHXr1oVlFZqp5r+HH344yc/PT6pXr57069cvmTx5ctKqVaug3bPPPpt06NAhOfroo1Pv88ADDyTt2rVLatSokXTs2DEZO3Zs6nVmltx///3JgAEDkpo1ayZt2rRJxo0bd9ifH+UPcyzKEvexiMUcyxxbkVx22WWJmSVmlhx99NFJ48aNk7POOit55JFHCvtjkhz6mv5w9wLjx49PTjnllCQvLy/JyclJTj311GTy5MlJkvw7ELhfv35J/fr1k+zs7KR79+70m0rqUMHUB6/FDvr000+T0aNHJ40aNUpq1qyZ9O3bN5k5c2aqzfjx45MOHTok2dnZyXnnnZc89NBDhfPl559/nnz7299O8vPzk6ysrKRFixbJtddeWxg6nSRV97u6akkSkXAFAAAAVDHXXXed7du3zx544IES2V61atVs/PjxNnTo0BLZHgAAFRVzLABULUcX3QQAAACoerp27Wq9e/cu690AAKDSYY4FgKqFhxAAAACAcOWVV5b1LgAAUCkxxwJA1cJyTAAAAAAAAAAAICOql/UOAAAAAAAAAACAyomHEAAAAAAAAAAAICN4CAEAAAAAAAAAADKChxAAAAAAAAAAACAjeAgBAAAAAAAAAAAygocQAAAAAAAAAAAgI3gIAQAAAAAAAAAAMoKHEAAAAAAAAAAAICN4CAEAAAAAAAAAADLi/wNXGoBYFUJt/QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(20, 6))\n",
        "for i, (image, label) in enumerate(train_dataset.take(30)):\n",
        "  plt.subplot(3, 10, i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  image = image.numpy().reshape(28, 28)\n",
        "  plt.imshow(image, cmap=plt.cm.binary)\n",
        "  plt.xlabel(class_names[label])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UCxlwsi3a_n0"
      },
      "outputs": [],
      "source": [
        "batch_size = 32\n",
        "train_dataset = train_dataset.cache().repeat().shuffle(num_train_examples).batch(batch_size)\n",
        "test_dataset  = test_dataset.cache().batch(batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iQH5j6sbykCT"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), padding='same',\n",
        "                           activation=tf.nn.relu, input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2),\n",
        "\n",
        "    tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding='same',\n",
        "                           activation=tf.nn.relu),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2),\n",
        "\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Py-h3UMfbwOU"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              optimizer='adam',\n",
        "              # optimizer=tf.keras.optimizers.Adam(0.1),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8TNlFr7dAtG",
        "outputId": "8bde9664-f80c-49d5-e461-f64ea13049e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 22s 4ms/step - loss: 0.3931 - accuracy: 0.8588\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f882655de70>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "model.fit(train_dataset, epochs=10, steps_per_epoch=math.ceil(num_train_examples/batch_size))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abI-oXlFdDx_",
        "outputId": "b5a23e15-1297-4290-da56-ccd1e3bf9fb1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.2785 - accuracy: 0.8943\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_accuracy = model.evaluate(train_dataset, steps=math.ceil(num_test_examples/batch_size))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZnFvvJGgBXz",
        "outputId": "8854e026-7929-48c3-b053-54aca3e28306"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 139ms/step\n",
            "(32, 28, 28, 1)\n",
            "(32,)\n",
            "(32, 10)\n"
          ]
        }
      ],
      "source": [
        "for test_images, test_labels in test_dataset.take(1):\n",
        "  test_images = test_images.numpy()\n",
        "  test_labels = test_labels.numpy()\n",
        "  predictions = model.predict(test_images)\n",
        "\n",
        "print(test_images.shape)\n",
        "print(test_labels.shape)\n",
        "print(predictions.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GJnbr-zehbGo"
      },
      "outputs": [],
      "source": [
        "def plot_image(i, predictions_array, true_labels, images):\n",
        "  predictions_array, true_label, image = predictions_array[i], true_labels[i], images[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.imshow(image[...,0], cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  color = 'blue' if predicted_label == true_label else 'red'\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                       100 * np.max(predictions_array),\n",
        "                                       class_names[true_label]),\n",
        "                                       color = color)\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"gray\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 936
        },
        "id": "Ip8bv9EyheHT",
        "outputId": "82346046-4703-47b2-ae48-60bb6ba28f17"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 60 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "num_rows = 6\n",
        "num_cols = 5\n",
        "num_images = num_rows * num_cols\n",
        "\n",
        "plt.figure(figsize=(20, 2*num_rows))\n",
        "for i in range (num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions, test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions, test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AMYjpoSIing0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "8ad92cba-b0fb-498a-d102-5618fc29a72a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 120ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Grab an image from the test dataset\n",
        "img = test_images[0]\n",
        "img = np.array([img])   # Add the image to a batch where it's the only member.\n",
        "\n",
        "predictions_single = model.predict(img)\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_image(0, predictions_single, test_labels, test_images)\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_value_array(0, predictions_single, test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPPRWPUClymGiQaLnJD7U1a",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
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    },
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      "name": "python"
    },
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