colab-issue.ipynb

colab-issue.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/gist/SupreethRao99/e53e122883149326e82553a0cf7e6811/colab-issue.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfQEgmDcPqEI"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": 1,
      "outputs": []
    },
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        "outputId": "2037d156-66aa-429b-da91-745cb4814f6e"
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      "source": [
        "(ds_train, ds_test), ds_info = tfds.load(\n",
        "    'mnist',\n",
        "    split=['train', 'test'],\n",
        "    shuffle_files=True,\n",
        "    as_supervised=True,\n",
        "    with_info=True,\n",
        ")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
            "local data directory. If you'd instead prefer to read directly from our public\n",
            "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
            "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
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              "HBox(children=(FloatProgress(value=0.0, description='Dl Completed...', max=4.0, style=ProgressStyle(descriptio…"
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          "text": [
            "\n",
            "\n",
            "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNW4irM-QS2-"
      },
      "source": [
        "def normalize_img(image, label):\n",
        "  \"\"\"Normalizes images: `uint8` -> `float32`.\"\"\"\n",
        "  return tf.cast(image, tf.float32) / 255., label\n",
        "\n",
        "ds_train = ds_train.map(normalize_img)\n",
        "ds_train = ds_train.batch(128)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REFE87jhQTZ5"
      },
      "source": [
        "ds_test = ds_test.map(normalize_img)\n",
        "ds_test = ds_test.batch(128)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfQu-SABQW5W"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "  tf.keras.layers.Dense(128,activation='relu'),\n",
        "  tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRhT_AU2QbWN",
        "outputId": "0aa49946-0316-4a32-dc74-3cb55d1b8e50"
      },
      "source": [
        "model.fit(ds_train,epochs=10,validation_data=ds_test)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 10s 21ms/step - loss: 0.3597 - accuracy: 0.8998 - val_loss: 0.1909 - val_accuracy: 0.9456\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 4s 8ms/step - loss: 0.1650 - accuracy: 0.9535 - val_loss: 0.1381 - val_accuracy: 0.9596\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 4s 8ms/step - loss: 0.1184 - accuracy: 0.9664 - val_loss: 0.1133 - val_accuracy: 0.9667\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 3s 7ms/step - loss: 0.0912 - accuracy: 0.9742 - val_loss: 0.1003 - val_accuracy: 0.9714\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 4s 8ms/step - loss: 0.0734 - accuracy: 0.9792 - val_loss: 0.0923 - val_accuracy: 0.9733\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 4s 8ms/step - loss: 0.0603 - accuracy: 0.9831 - val_loss: 0.0877 - val_accuracy: 0.9745\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 4s 8ms/step - loss: 0.0503 - accuracy: 0.9865 - val_loss: 0.0842 - val_accuracy: 0.9757\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 4s 8ms/step - loss: 0.0422 - accuracy: 0.9891 - val_loss: 0.0816 - val_accuracy: 0.9756\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.0353 - accuracy: 0.9913 - val_loss: 0.0803 - val_accuracy: 0.9758\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 4s 9ms/step - loss: 0.0295 - accuracy: 0.9930 - val_loss: 0.0786 - val_accuracy: 0.9763\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1769bb0c50>"
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          "metadata": {
            "tags": []
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          "execution_count": 6
        }
      ]
    },
    {
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Td_as7ZCQif8",
        "outputId": "c65ce3fe-9745-429b-a45e-323af1475d35"
      },
      "source": [
        "model.save('MNIST-Model')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: MNIST-Model/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: MNIST-Model/assets\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAMyWSpsQn2G",
        "outputId": "c594ef3f-b904-4c2c-aa12-38fd10e34ac3"
      },
      "source": [
        "!zip -r /content/MNIST.zip /content/MNIST-Model"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/MNIST-Model/ (stored 0%)\n",
            "  adding: content/MNIST-Model/assets/ (stored 0%)\n",
            "  adding: content/MNIST-Model/variables/ (stored 0%)\n",
            "  adding: content/MNIST-Model/variables/variables.data-00000-of-00001 (deflated 12%)\n",
            "  adding: content/MNIST-Model/variables/variables.index (deflated 59%)\n",
            "  adding: content/MNIST-Model/saved_model.pb (deflated 87%)\n",
            "  adding: content/MNIST-Model/keras_metadata.pb (deflated 85%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "ka7I7rE9Qv-o",
        "outputId": "a4e7a9f4-2489-4f85-ed8a-1237c4036094"
      },
      "source": [
        "from google.colab import files\n",
        "files.download('/content/MNIST.zip')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
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              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
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              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
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              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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