Upload mnist_convnet.ipynb

mnist_convnet.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q5DtHwQEzfyR"
+      },
+      "source": [
+        "# Simple MNIST convnet\n",
+        "\n",
+        "**Author:** [fchollet](https://twitter.com/fchollet)<br>\n",
+        "**Date created:** 2015/06/19<br>\n",
+        "**Last modified:** 2020/04/21<br>\n",
+        "**Description:** A simple convnet that achieves ~99% test accuracy on MNIST."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eZlWB3GpzfyT"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "8utAtD_ozfyU"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbQiYBo1zfyV"
+      },
+      "source": [
+        "## Prepare the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "qJZ6R9iFzfyV",
+        "outputId": "76eaada0-0f90-41e1-fa22-866d75351911",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+            "11493376/11490434 [==============================] - 0s 0us/step\n",
+            "11501568/11490434 [==============================] - 0s 0us/step\n",
+            "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": "kqdKUp6tzfyV"
+      },
+      "source": [
+        "## Build the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "GCmWGoyGzfyW",
+        "outputId": "85fad2b6-4b43-406c-bab6-4097a0741b6d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "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": "lhwcwv48zfyX"
+      },
+      "source": [
+        "## Train the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "lTElNbSEzfyX",
+        "outputId": "22052ddf-9388-4916-84f8-eaecca77d186",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "422/422 [==============================] - 46s 107ms/step - loss: 0.3677 - accuracy: 0.8880 - val_loss: 0.0825 - val_accuracy: 0.9780\n",
+            "Epoch 2/10\n",
+            "422/422 [==============================] - 45s 106ms/step - loss: 0.1108 - accuracy: 0.9664 - val_loss: 0.0628 - val_accuracy: 0.9837\n",
+            "Epoch 3/10\n",
+            "422/422 [==============================] - 45s 106ms/step - loss: 0.0860 - accuracy: 0.9732 - val_loss: 0.0453 - val_accuracy: 0.9877\n",
+            "Epoch 4/10\n",
+            "422/422 [==============================] - 44s 104ms/step - loss: 0.0703 - accuracy: 0.9786 - val_loss: 0.0435 - val_accuracy: 0.9875\n",
+            "Epoch 5/10\n",
+            "422/422 [==============================] - 44s 104ms/step - loss: 0.0599 - accuracy: 0.9810 - val_loss: 0.0398 - val_accuracy: 0.9890\n",
+            "Epoch 6/10\n",
+            "422/422 [==============================] - 44s 104ms/step - loss: 0.0556 - accuracy: 0.9830 - val_loss: 0.0364 - val_accuracy: 0.9898\n",
+            "Epoch 7/10\n",
+            "422/422 [==============================] - 45s 107ms/step - loss: 0.0509 - accuracy: 0.9838 - val_loss: 0.0333 - val_accuracy: 0.9910\n",
+            "Epoch 8/10\n",
+            "422/422 [==============================] - 46s 108ms/step - loss: 0.0477 - accuracy: 0.9847 - val_loss: 0.0314 - val_accuracy: 0.9920\n",
+            "Epoch 9/10\n",
+            "422/422 [==============================] - 44s 104ms/step - loss: 0.0443 - accuracy: 0.9859 - val_loss: 0.0319 - val_accuracy: 0.9930\n",
+            "Epoch 10/10\n",
+            "422/422 [==============================] - 43s 103ms/step - loss: 0.0409 - accuracy: 0.9869 - val_loss: 0.0299 - val_accuracy: 0.9923\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7fd82e27a850>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "batch_size = 128\n",
+        "epochs = 10\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)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YebG6y4izfyY"
+      },
+      "source": [
+        "## Evaluate the trained model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "J5oNREXjzfyY",
+        "outputId": "98337645-eefe-479c-9a2c-9c3cdbf41e2a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test loss: 0.027494722977280617\n",
+            "Test accuracy: 0.9898999929428101\n"
+          ]
+        }
+      ],
+      "source": [
+        "score = model.evaluate(x_test, y_test, verbose=0)\n",
+        "print(\"Test loss:\", score[0])\n",
+        "print(\"Test accuracy:\", score[1])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash"
+      ],
+      "metadata": {
+        "id": "BOyNRT863adC",
+        "outputId": "659ef954-5a83-49ea-926c-d9f367b38d2b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Detected operating system as Ubuntu/bionic.\n",
+            "Checking for curl...\n",
+            "Detected curl...\n",
+            "Checking for gpg...\n",
+            "Detected gpg...\n",
+            "Running apt-get update... done.\n",
+            "Installing apt-transport-https... done.\n",
+            "Installing /etc/apt/sources.list.d/github_git-lfs.list...done.\n",
+            "Importing packagecloud gpg key... done.\n",
+            "Running apt-get update... done.\n",
+            "\n",
+            "The repository is setup! You can now install packages.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install huggingface-hub\n",
+        "!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n",
+        "!sudo apt-get install git-lfs\n",
+        "!git-lfs install"
+      ],
+      "metadata": {
+        "id": "rMkFpfhk0XOk",
+        "outputId": "047a5b9f-c8bd-467d-84d2-a7b4960a26d9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Reading package lists... Done\n",
+            "Building dependency tree       \n",
+            "Reading state information... Done\n",
+            "The following NEW packages will be installed:\n",
+            "  git-lfs\n",
+            "0 upgraded, 1 newly installed, 0 to remove and 40 not upgraded.\n",
+            "Need to get 6,526 kB of archives.\n",
+            "After this operation, 14.7 MB of additional disk space will be used.\n",
+            "Get:1 https://packagecloud.io/github/git-lfs/ubuntu bionic/main amd64 git-lfs amd64 3.0.2 [6,526 kB]\n",
+            "Fetched 6,526 kB in 1s (5,795 kB/s)\n",
+            "debconf: unable to initialize frontend: Dialog\n",
+            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
+            "debconf: falling back to frontend: Readline\n",
+            "debconf: unable to initialize frontend: Readline\n",
+            "debconf: (This frontend requires a controlling tty.)\n",
+            "debconf: falling back to frontend: Teletype\n",
+            "dpkg-preconfigure: unable to re-open stdin: \n",
+            "Selecting previously unselected package git-lfs.\n",
+            "(Reading database ... 155222 files and directories currently installed.)\n",
+            "Preparing to unpack .../git-lfs_3.0.2_amd64.deb ...\n",
+            "Unpacking git-lfs (3.0.2) ...\n",
+            "Setting up git-lfs (3.0.2) ...\n",
+            "Git LFS initialized.\n",
+            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!huggingface-cli login"
+      ],
+      "metadata": {
+        "id": "uZWWvPH82quN",
+        "outputId": "f5c83a3b-62de-4ffd-db6a-25eed36bf9b0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
+            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
+            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
+            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
+            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
+            "\n",
+            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
+            "        (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
+            "        \n",
+            "Token: \n",
+            "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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub.keras_mixin import push_to_hub_keras\n",
+        "push_to_hub_keras(model = model, repo_url = \"https://huggingface.co/keras-io/simple-mnist-convnet\", organization = \"keras-io\")"
+      ],
+      "metadata": {
+        "id": "RhssM1Dy0sl_"
+      },
+      "execution_count": 11,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "mnist_convnet",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}