keras-sprint/mnist_convnet.ipynb

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      "source": [
        "# Simple MNIST convnet\n",
        "\n",
        "This example shows how to push your Keras model to the Hugging Face Hub and load the model from Hub.\n",
        "\n",
        "**Original Author of Example:** [fchollet](https://twitter.com/fchollet)<br>\n",
        "**Description:** A simple convnet that achieves ~99% test accuracy on MNIST."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "860CEXn27LP1"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "7Msic2JB7LP1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "🤗 Install Hugging Face Hub"
      ],
      "metadata": {
        "id": "4s6WujK7ILKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "id": "JNzv7-Cg_cgu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import huggingface_hub\n",
        "from huggingface_hub import notebook_login, push_to_hub_keras, from_pretrained_keras"
      ],
      "metadata": {
        "id": "HS4vW65V_-G-"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00LCkZPt7LP3"
      },
      "source": [
        "## Prepare the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6tx7Fkh7LP3",
        "outputId": "48ae4179-4665-4938-9a10-2652cc464bdf"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ]
        }
      ],
      "source": [
        "# Model / data parameters\n",
        "num_classes = 10\n",
        "input_shape = (28, 28, 1)\n",
        "\n",
        "# the data, split between train and test sets\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "\n",
        "# Scale images to the [0, 1] range\n",
        "x_train = x_train.astype(\"float32\") / 255\n",
        "x_test = x_test.astype(\"float32\") / 255\n",
        "# Make sure images have shape (28, 28, 1)\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "print(\"x_train shape:\", x_train.shape)\n",
        "print(x_train.shape[0], \"train samples\")\n",
        "print(x_test.shape[0], \"test samples\")\n",
        "\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5bpZgm6n7LP4"
      },
      "source": [
        "## Build the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "QI34HRui7LP4",
        "outputId": "a0a91950-828e-45a9-d10c-bc7f05cb742e"
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      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                16010     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,826\n",
            "Trainable params: 34,826\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=input_shape),\n",
        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dropout(0.5),\n",
        "        layers.Dense(num_classes, activation=\"softmax\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hwLCbr-7LP5"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard"
      ],
      "metadata": {
        "id": "w_Q7X180AYbB"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log_dir = \"logs/fit/\"\n",
        "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)"
      ],
      "metadata": {
        "id": "vRhyg5W-AbLU"
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      "execution_count": 14,
      "outputs": []
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        "outputId": "45222492-a30b-4a64-e53f-1a7d5ee3652c"
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          "text": [
            "Epoch 1/5\n",
            "422/422 [==============================] - 47s 109ms/step - loss: 0.0274 - accuracy: 0.9909 - val_loss: 0.0292 - val_accuracy: 0.9923\n",
            "Epoch 2/5\n",
            "422/422 [==============================] - 44s 105ms/step - loss: 0.0273 - accuracy: 0.9907 - val_loss: 0.0280 - val_accuracy: 0.9917\n",
            "Epoch 3/5\n",
            "422/422 [==============================] - 43s 102ms/step - loss: 0.0263 - accuracy: 0.9913 - val_loss: 0.0262 - val_accuracy: 0.9937\n",
            "Epoch 4/5\n",
            "422/422 [==============================] - 42s 100ms/step - loss: 0.0242 - accuracy: 0.9916 - val_loss: 0.0260 - val_accuracy: 0.9927\n",
            "Epoch 5/5\n",
            "422/422 [==============================] - 43s 102ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0311 - val_accuracy: 0.9917\n"
          ]
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              "<keras.callbacks.History at 0x7f802978cb10>"
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          "metadata": {},
          "execution_count": 15
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      ],
      "source": [
        "batch_size = 128\n",
        "epochs = 5\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1,\n",
        "          callbacks = [tensorboard_callback])"
      ]
    },
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      "source": [
        "We will push our model to the Hugging Face Hub with tensorboard logs.\n",
        "\n",
        "If you already have access to keras-io organization, you can give \"keras-io/{model-name}\" as the model ID. If not, you can push model to your own account and then carry it to the keras-io organization later. 🥳\n",
        "\n",
        "To push your models to the Hub, you need authentication. To authenticate, you can log using notebook_login. You can get your token from https://huggingface.co/settings/tokens 🙌🏻"
      ],
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          "output_type": "stream",
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          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
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      "source": [
        "Now we can push our model to the Hugging Face Hub 🤩🙌🏻 \n",
        "The below function will:\n",
        "\n",
        "1. create a remote repository on Hugging Face Hub,\n",
        "2. serialize our model,\n",
        "3. create a model card including training hyperparameters, model architecture and couple of fields you can fill about model,\n",
        "4. push the model to the Hub."
      ],
      "metadata": {
        "id": "eLbbU8AnBtqu"
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      "source": [
        "push_to_hub_keras(model, \"merve/mnist\", log_dir = \"logs/fit/\", tags = [\"image-classification\"])"
      ],
      "metadata": {
        "id": "1RNi4hGKAVUv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "See pushed model with TensorBoard and model card [here](https://huggingface.co/merve/mnist)."
      ],
      "metadata": {
        "id": "kW6aHvOeC8oJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's load the model!"
      ],
      "metadata": {
        "id": "XCsztZCtFCdu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = from_pretrained_keras(\"merve/mnist\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Wept7KeFEx7",
        "outputId": "38f10ece-30bc-45b3-fbc8-e7a1b7eb5a3c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "config.json not found in HuggingFace Hub\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_OrylJv7LP6"
      },
      "source": [
        "## Evaluate the trained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVwK3ZHJ7LP6",
        "outputId": "1adb6f61-2532-4475-cf10-55db0e69d552"
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      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Test loss: 0.022792112082242966\n",
            "Test accuracy: 0.9919999837875366\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])"
      ]
    }
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