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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
} |