Saroramath/introductionkerasmnist.ipynb

## introductionkerasmnist.ipynb
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      "metadata": {
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        "<a href=\"https://colab.research.google.com/gist/Saroramath/9a9618185545c97d8db76330326ed98b/introductionkerasmnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Hct7P174L4N"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "# Step 1: Load the raw data\n",
        "(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": null,
      "outputs": [
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          "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"
          ],
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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"
        }
      ]
    },
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      "metadata": {
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        },
        "id": "Er3v8Mpq7kB-",
        "outputId": "06e82c53-0af3-4027-96e2-bf1bc798bc14"
      },
      "source": [
        "ds_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_OptionsDataset shapes: ((28, 28, 1), ()), types: (tf.uint8, tf.int64)>"
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            "tags": []
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          "execution_count": 7
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      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "aqq5BdQh7BOv",
        "outputId": "97035244-0f40-4632-b6d7-20a1388c4b95"
      },
      "source": [
        "tf.data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'tensorflow._api.v2.data' from '/usr/local/lib/python3.6/dist-packages/tensorflow/_api/v2/data/__init__.py'>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tu35v2wY4XoQ"
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      "source": [
        "# Step 2: Build the training/testing pipelines\n",
        "def normalize_img(image, label):\n",
        "  return tf.cast(image, tf.float32) / 255., label\n",
        "\n",
        "ds_train = ds_train.map(\n",
        "    normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "ds_train = ds_train.cache()\n",
        "ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)\n",
        "ds_train = ds_train.batch(128)\n",
        "ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "\n",
        "ds_test = ds_test.map(\n",
        "    normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "ds_test = ds_test.cache()\n",
        "ds_test = ds_test.batch(128)\n",
        "ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpt8VhU-4a3q",
        "outputId": "839296cf-bd1b-42a3-c867-ed45d6c49c68"
      },
      "source": [
        "# Step 3: Create the model\n",
        "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",
        "\n",
        "# Compile the model, i.e. associate a loss function and the chosen variant of GD\n",
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "# Summary of the model\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 101,770\n",
            "Trainable params: 101,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyg4nPiI4cud",
        "outputId": "3da96cb1-c6ed-4dce-dbfe-d910962aa536"
      },
      "source": [
        "# Step 4: Train the model\n",
        "hist = model.fit(\n",
        "    ds_train,\n",
        "    epochs=20,\n",
        "    validation_data=ds_test,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "469/469 [==============================] - 10s 7ms/step - loss: 0.6083 - accuracy: 0.8320 - val_loss: 0.2012 - val_accuracy: 0.9405\n",
            "Epoch 2/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1730 - accuracy: 0.9502 - val_loss: 0.1428 - val_accuracy: 0.9573\n",
            "Epoch 3/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1200 - accuracy: 0.9663 - val_loss: 0.1176 - val_accuracy: 0.9644\n",
            "Epoch 4/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0947 - accuracy: 0.9731 - val_loss: 0.0981 - val_accuracy: 0.9703\n",
            "Epoch 5/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0749 - accuracy: 0.9783 - val_loss: 0.0940 - val_accuracy: 0.9725\n",
            "Epoch 6/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0619 - accuracy: 0.9827 - val_loss: 0.0831 - val_accuracy: 0.9735\n",
            "Epoch 7/20\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0547 - accuracy: 0.9845 - val_loss: 0.0803 - val_accuracy: 0.9760\n",
            "Epoch 8/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0432 - accuracy: 0.9875 - val_loss: 0.0833 - val_accuracy: 0.9737\n",
            "Epoch 9/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0361 - accuracy: 0.9901 - val_loss: 0.0758 - val_accuracy: 0.9778\n",
            "Epoch 10/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0317 - accuracy: 0.9916 - val_loss: 0.0739 - val_accuracy: 0.9787\n",
            "Epoch 11/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.0748 - val_accuracy: 0.9774\n",
            "Epoch 12/20\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0230 - accuracy: 0.9941 - val_loss: 0.0763 - val_accuracy: 0.9782\n",
            "Epoch 13/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0178 - accuracy: 0.9958 - val_loss: 0.0720 - val_accuracy: 0.9794\n",
            "Epoch 14/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0149 - accuracy: 0.9967 - val_loss: 0.0696 - val_accuracy: 0.9797\n",
            "Epoch 15/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9976 - val_loss: 0.0743 - val_accuracy: 0.9784\n",
            "Epoch 16/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0104 - accuracy: 0.9980 - val_loss: 0.0744 - val_accuracy: 0.9796\n",
            "Epoch 17/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0090 - accuracy: 0.9982 - val_loss: 0.0777 - val_accuracy: 0.9779\n",
            "Epoch 18/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0075 - accuracy: 0.9988 - val_loss: 0.0828 - val_accuracy: 0.9780\n",
            "Epoch 19/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0082 - accuracy: 0.9980 - val_loss: 0.0820 - val_accuracy: 0.9780\n",
            "Epoch 20/20\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0075 - accuracy: 0.9984 - val_loss: 0.0812 - val_accuracy: 0.9780\n"
          ],
          "name": "stdout"
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    {
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          "height": 1000
        },
        "id": "Km6Wur6-44xs",
        "outputId": "7d5fefd8-2ae9-46e5-899e-435523d32341"
      },
      "source": [
        "# Plot the training progress\n",
        "fig = plt.figure(figsize=(10,10))\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title('Loss function')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "plt.legend(['Training data', 'Test data'])\n",
        "plt.show()\n",
        "\n",
        "fig = plt.figure(figsize=(10,10))\n",
        "plt.plot(hist.history['accuracy'])\n",
        "plt.plot(hist.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "plt.legend(['Training data', 'Test data'])"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6bed1c97b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SXRMvcFH5Wc8",
        "outputId": "4b2557b7-ee63-49ef-ebcc-5ef501d8b46e"
      },
      "source": [
        "# Step 5/6: Evaluate the model (we do no hyperparameter tuning here)\n",
        "loss_accuracy = model.evaluate(ds_test, verbose=0)\n",
        "\n",
        "print(\"Test Loss: {}\".format(loss_accuracy[0]))\n",
        "print(\"Test Accuracy: {}\".format(loss_accuracy[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Loss: 0.08117455244064331\n",
            "Test Accuracy: 0.9779999852180481\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRJVxI3zndGX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "90ba1878-7751-4c6c-e0b3-1ce12d51ccc3"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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