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Daniel Tomlinson
2019-08-03 22:32:03 +01:00
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 开发 AI 应用\n",
"\n",
"未来，AI 算法在日常生活中的应用将越来越广泛。例如，你可能想要在智能手机应用中包含图像分类器。为此，在整个应用架构中，你将使用一个用成百上千个图像训练过的深度学习模型。未来的软件开发很大一部分将是使用这些模型作为应用的常用部分。\n",
"\n",
"在此项目中，你将训练一个图像分类器来识别不同的花卉品种。可以想象有这么一款手机应用，当你对着花卉拍摄时，它能够告诉你这朵花的名称。在实际操作中，你会训练此分类器，然后导出它以用在你的应用中。我们将使用[此数据集](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html)，其中包含 102 个花卉类别。你可以在下面查看几个示例。 \n",
"\n",
"<img src='assets/Flowers.png' width=500px>\n",
"\n",
"该项目分为多个步骤：\n",
"\n",
"* 加载和预处理图像数据集\n",
"* 用数据集训练图像分类器\n",
"* 使用训练的分类器预测图像内容\n",
"\n",
"我们将指导你完成每一步，你将用 Python 实现这些步骤。\n",
"\n",
"完成此项目后，你将拥有一个可以用任何带标签图像的数据集进行训练的应用。你的网络将学习花卉，并成为一个命令行应用。但是，你对新技能的应用取决于你的想象力和构建数据集的精力。例如，想象有一款应用能够拍摄汽车，告诉你汽车的制造商和型号，然后查询关于该汽车的信息。构建你自己的数据集并开发一款新型应用吧。\n",
"\n",
"首先，导入你所需的软件包。建议在代码开头导入所有软件包。当你创建此 notebook 时，如果发现你需要导入某个软件包，确保在开头导入该软件包。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Imports here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 加载数据\n",
"\n",
"在此项目中，你将使用 `torchvision` 加载数据（[文档](http://pytorch.org/docs/master/torchvision/transforms.html#)）。数据应该和此 notebook 一起包含在内，否则你可以[在此处下载数据](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz)。数据集分成了三部分：训练集、验证集和测试集。对于训练集，你需要变换数据，例如随机缩放、剪裁和翻转。这样有助于网络泛化，并带来更好的效果。你还需要确保将输入数据的大小调整为 224x224 像素，因为预训练的网络需要这么做。\n",
"\n",
"验证集和测试集用于衡量模型对尚未见过的数据的预测效果。对此步骤，你不需要进行任何缩放或旋转变换，但是需要将图像剪裁到合适的大小。\n",
"\n",
"对于所有三个数据集，你都需要将均值和标准差标准化到网络期望的结果。均值为 `[0.485, 0.456, 0.406]`，标准差为 `[0.229, 0.224, 0.225]`。这样使得每个颜色通道的值位于 -1 到 1 之间，而不是 0 到 1 之间。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_dir = 'train'\n",
"valid_dir = 'valid'\n",
"test_dir = 'test'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Define your transforms for the training, validation, and testing sets\n",
"data_transforms = \n",
"\n",
"# TODO: Load the datasets with ImageFolder\n",
"image_datasets = \n",
"\n",
"# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
"dataloaders = "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 标签映射\n",
"\n",
"你还需要加载从类别标签到类别名称的映射。你可以在文件 `cat_to_name.json` 中找到此映射。它是一个 JSON 对象，可以使用 [`json` 模块](https://docs.python.org/2/library/json.html)读取它。这样可以获得一个从整数编码的类别到实际花卉名称的映射字典。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"with open('cat_to_name.json', 'r') as f:\n",
" cat_to_name = json.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 构建和训练分类器\n",
"\n",
"数据准备好后，就开始构建和训练分类器了。和往常一样，你应该使用 `torchvision.models` 中的某个预训练模型获取图像特征。使用这些特征构建和训练新的前馈分类器。\n",
"\n",
"这部分将由你来完成。如果你想与他人讨论这部分，欢迎与你的同学讨论！你还可以在论坛上提问或在工作时间内咨询我们的课程经理和助教导师。\n",
"\n",
"请参阅[审阅标准](https://review.udacity.com/#!/rubrics/1663/view)，了解如何成功地完成此部分。你需要执行以下操作：\n",
"\n",
"* 加载[预训练的网络](http://pytorch.org/docs/master/torchvision/models.html)（如果你需要一个起点，推荐使用 VGG 网络，它简单易用）\n",
"* 使用 ReLU 激活函数和丢弃定义新的未训练前馈网络作为分类器\n",
"* 使用反向传播训练分类器层，并使用预训练的网络获取特征\n",
"* 跟踪验证集的损失和准确率，以确定最佳超参数\n",
"\n",
"我们在下面为你留了一个空的单元格，但是你可以使用多个单元格。建议将问题拆分为更小的部分，并单独运行。检查确保每部分都达到预期效果，然后再完成下个部分。你可能会发现，当你实现每部分时，可能需要回去修改之前的代码，这很正常！\n",
"\n",
"训练时，确保仅更新前馈网络的权重。如果一切构建正确的话，验证准确率应该能够超过 70%。确保尝试不同的超参数（学习速率、分类器中的单元、周期等），寻找最佳模型。保存这些超参数并用作项目下个部分的默认值。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Build and train your network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 测试网络\n",
"\n",
"建议使用网络在训练或验证过程中从未见过的测试数据测试训练的网络。这样，可以很好地判断模型预测全新图像的效果。用网络预测测试图像，并测量准确率，就像验证过程一样。如果模型训练良好的话，你应该能够达到大约 70% 的准确率。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Do validation on the test set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 保存检查点\n",
"\n",
"训练好网络后，保存模型，以便稍后加载它并进行预测。你可能还需要保存其他内容，例如从类别到索引的映射，索引是从某个图像数据集中获取的：`image_datasets['train'].class_to_idx`。你可以将其作为属性附加到模型上，这样稍后推理会更轻松。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"attributes": {
"": "",
"classes": [],
"id": ""
}
},
"outputs": [],
"source": [
"\n",
"注意，稍后你需要完全重新构建模型，以便用模型进行推理。确保在检查点中包含你所需的任何信息。如果你想加载模型并继续训练，则需要保存周期数量和优化器状态 `optimizer.state_dict`。你可能需要在下面的下个部分使用训练的模型，因此建议立即保存它。\n",
"\n",
"\n",
"```python\n",
"# TODO: Save the checkpoint "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 加载检查点\n",
"\n",
"此刻，建议写一个可以加载检查点并重新构建模型的函数。这样的话，你可以回到此项目并继续完善它，而不用重新训练网络。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Write a function that loads a checkpoint and rebuilds the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 类别推理\n",
"\n",
"现在，你需要写一个使用训练的网络进行推理的函数。即你将向网络中传入一个图像，并预测图像中的花卉类别。写一个叫做 `predict` 的函数，该函数会接受图像和模型，然后返回概率在前 $K$ 的类别及其概率。应该如下所示："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"probs, classes = predict(image_path, model)\n",
"print(probs)\n",
"print(classes)\n",
"> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]\n",
"> ['70', '3', '45', '62', '55']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"首先，你需要处理输入图像，使其可以用于你的网络。\n",
"\n",
"## 图像处理\n",
"\n",
"你需要使用 `PIL` 加载图像（[文档](https://pillow.readthedocs.io/en/latest/reference/Image.html)）。建议写一个函数来处理图像，使图像可以作为模型的输入。该函数应该按照训练的相同方式处理图像。\n",
"\n",
"首先，调整图像大小，使最小的边为 256 像素，并保持宽高比。为此，可以使用 [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) 或 [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) 方法。然后，你需要从图像的中心裁剪出 224x224 的部分。\n",
"\n",
"图像的颜色通道通常编码为整数 0-255，但是该模型要求值为浮点数 0-1。你需要变换值。使用 Numpy 数组最简单，你可以从 PIL 图像中获取，例如 `np_image = np.array(pil_image)`。\n",
"\n",
"和之前一样，网络要求图像按照特定的方式标准化。均值应标准化为 `[0.485, 0.456, 0.406]`，标准差应标准化为 `[0.229, 0.224, 0.225]`。你需要用每个颜色通道减去均值，然后除以标准差。\n",
"\n",
"最后，PyTorch 要求颜色通道为第一个维度，但是在 PIL 图像和 Numpy 数组中是第三个维度。你可以使用 [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html)对维度重新排序。颜色通道必须是第一个维度，并保持另外两个维度的顺序。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def process_image(image):\n",
" ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
" returns an Numpy array\n",
" '''\n",
" \n",
" # TODO: Process a PIL image for use in a PyTorch model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"要检查你的项目，可以使用以下函数来转换 PyTorch 张量并将其显示在 notebook 中。如果 `process_image` 函数可行，用该函数运行输出应该会返回原始图像（但是剪裁掉的部分除外）。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def imshow(image, ax=None, title=None):\n",
" \"\"\"Imshow for Tensor.\"\"\"\n",
" if ax is None:\n",
" fig, ax = plt.subplots()\n",
" \n",
" # PyTorch tensors assume the color channel is the first dimension\n",
" # but matplotlib assumes is the third dimension\n",
" image = image.numpy().transpose((1, 2, 0))\n",
" \n",
" # Undo preprocessing\n",
" mean = np.array([0.485, 0.456, 0.406])\n",
" std = np.array([0.229, 0.224, 0.225])\n",
" image = std * image + mean\n",
" \n",
" # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
" image = np.clip(image, 0, 1)\n",
" \n",
" ax.imshow(image)\n",
" \n",
" return ax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 类别预测\n",
"\n",
"可以获得格式正确的图像后 \n",
"\n",
"要获得前 $K$ 个值，在张量中使用 [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk)。该函数会返回前 `k` 个概率和对应的类别索引。你需要使用 `class_to_idx`（希望你将其添加到了模型中）将这些索引转换为实际类别标签，或者从用来加载数据的[ `ImageFolder`](https://pytorch.org/docs/master/torchvision/datasets.html?highlight=imagefolder#torchvision.datasets.ImageFolder)进行转换。确保颠倒字典\n",
"\n",
"同样，此方法应该接受图像路径和模型检查点，并返回概率和类别。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"probs, classes = predict(image_path, model)\n",
"print(probs)\n",
"print(classes)\n",
"> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]\n",
"> ['70', '3', '45', '62', '55']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def predict(image_path, model, topk=5):\n",
" ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
" '''\n",
" \n",
" # TODO: Implement the code to predict the class from an image file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 检查运行状况\n",
"\n",
"你已经可以使用训练的模型做出预测，现在检查模型的性能如何。即使测试准确率很高，始终有必要检查是否存在明显的错误。使用 `matplotlib` 将前 5 个类别的概率以及输入图像绘制为条形图，应该如下所示：\n",
"\n",
"<img src='assets/inference_example.png' width=300px>\n",
"\n",
"你可以使用 `cat_to_name.json` 文件（应该之前已经在 notebook 中加载该文件）将类别整数编码转换为实际花卉名称。要将 PyTorch 张量显示为图像，请使用定义如下的 `imshow` 函数。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Display an image along with the top 5 classes"
]
}
],
"metadata": {
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Developing an AI application\n",
"\n",
"Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
"\n",
"In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
"\n",
"<img src='assets/Flowers.png' width=500px>\n",
"\n",
"The project is broken down into multiple steps:\n",
"\n",
"* Load and preprocess the image dataset\n",
"* Train the image classifier on your dataset\n",
"* Use the trained classifier to predict image content\n",
"\n",
"We'll lead you through each part which you'll implement in Python.\n",
"\n",
"When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
"\n",
"First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Imports here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the data\n",
"\n",
"Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). The data should be included alongside this notebook, otherwise you can [download it here](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz). The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.\n",
"\n",
"The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.\n",
"\n",
"The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images. These values will shift each color channel to be centered at 0 and range from -1 to 1.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_dir = 'flowers'\n",
"train_dir = data_dir + '/train'\n",
"valid_dir = data_dir + '/valid'\n",
"test_dir = data_dir + '/test'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Define your transforms for the training, validation, and testing sets\n",
"data_transforms = \n",
"\n",
"# TODO: Load the datasets with ImageFolder\n",
"image_datasets = \n",
"\n",
"# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
"dataloaders = "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Label mapping\n",
"\n",
"You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"with open('cat_to_name.json', 'r') as f:\n",
" cat_to_name = json.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building and training the classifier\n",
"\n",
"Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.\n",
"\n",
"We're going to leave this part up to you. Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:\n",
"\n",
"* Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)\n",
"* Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n",
"* Train the classifier layers using backpropagation using the pre-trained network to get the features\n",
"* Track the loss and accuracy on the validation set to determine the best hyperparameters\n",
"\n",
"We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!\n",
"\n",
"When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.\n",
"\n",
"One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to\n",
"GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.\n",
"\n",
"**Note for Workspace users:** If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with `ls -lh`), you should reduce the size of your hidden layers and train again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Build and train your network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing your network\n",
"\n",
"It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Do validation on the test set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save the checkpoint\n",
"\n",
"Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: `image_datasets['train'].class_to_idx`. You can attach this to the model as an attribute which makes inference easier later on.\n",
"\n",
"```model.class_to_idx = image_datasets['train'].class_to_idx```\n",
"\n",
"Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, `optimizer.state_dict`. You'll likely want to use this trained model in the next part of the project, so best to save it now."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Save the checkpoint "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the checkpoint\n",
"\n",
"At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Write a function that loads a checkpoint and rebuilds the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inference for classification\n",
"\n",
"Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n",
"\n",
"```python\n",
"probs, classes = predict(image_path, model)\n",
"print(probs)\n",
"print(classes)\n",
"> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]\n",
"> ['70', '3', '45', '62', '55']\n",
"```\n",
"\n",
"First you'll need to handle processing the input image such that it can be used in your network. \n",
"\n",
"## Image Preprocessing\n",
"\n",
"You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
"\n",
"First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
"\n",
"Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
"\n",
"As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n",
"\n",
"And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def process_image(image):\n",
" ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
" returns an Numpy array\n",
" '''\n",
" \n",
" # TODO: Process a PIL image for use in a PyTorch model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def imshow(image, ax=None, title=None):\n",
" \"\"\"Imshow for Tensor.\"\"\"\n",
" if ax is None:\n",
" fig, ax = plt.subplots()\n",
" \n",
" # PyTorch tensors assume the color channel is the first dimension\n",
" # but matplotlib assumes is the third dimension\n",
" image = image.numpy().transpose((1, 2, 0))\n",
" \n",
" # Undo preprocessing\n",
" mean = np.array([0.485, 0.456, 0.406])\n",
" std = np.array([0.229, 0.224, 0.225])\n",
" image = std * image + mean\n",
" \n",
" # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
" image = np.clip(image, 0, 1)\n",
" \n",
" ax.imshow(image)\n",
" \n",
" return ax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class Prediction\n",
"\n",
"Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
"\n",
"To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
"\n",
"Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
"\n",
"```python\n",
"probs, classes = predict(image_path, model)\n",
"print(probs)\n",
"print(classes)\n",
"> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]\n",
"> ['70', '3', '45', '62', '55']\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def predict(image_path, model, topk=5):\n",
" ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
" '''\n",
" \n",
" # TODO: Implement the code to predict the class from an image file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sanity Checking\n",
"\n",
"Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use `matplotlib` to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:\n",
"\n",
"<img src='assets/inference_example.png' width=300px>\n",
"\n",
"You can convert from the class integer encoding to actual flower names with the `cat_to_name.json` file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the `imshow` function defined above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Display an image along with the top 5 classes"
]
}
],
"metadata": {
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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MIT License
Copyright (c) 2018 Udacity
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# AI Programming with Python Project
Project code for Udacity's AI Programming with Python Nanodegree program. In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application.

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{"21": "fire lily", "3": "canterbury bells", "45": "bolero deep blue", "1": "pink primrose", "34": "mexican aster", "27": "prince of wales feathers", "7": "moon orchid", "16": "globe-flower", "25": "grape hyacinth", "26": "corn poppy", "79": "toad lily", "39": "siam tulip", "24": "red ginger", "67": "spring crocus", "35": "alpine sea holly", "32": "garden phlox", "10": "globe thistle", "6": "tiger lily", "93": "ball moss", "33": "love in the mist", "9": "monkshood", "102": "blackberry lily", "14": "spear thistle", "19": "balloon flower", "100": "blanket flower", "13": "king protea", "49": "oxeye daisy", "15": "yellow iris", "61": "cautleya spicata", "31": "carnation", "64": "silverbush", "68": "bearded iris", "63": "black-eyed susan", "69": "windflower", "62": "japanese anemone", "20": "giant white arum lily", "38": "great masterwort", "4": "sweet pea", "86": "tree mallow", "101": "trumpet creeper", "42": "daffodil", "22": "pincushion flower", "2": "hard-leaved pocket orchid", "54": "sunflower", "66": "osteospermum", "70": "tree poppy", "85": "desert-rose", "99": "bromelia", "87": "magnolia", "5": "english marigold", "92": "bee balm", "28": "stemless gentian", "97": "mallow", "57": "gaura", "40": "lenten rose", "47": "marigold", "59": "orange dahlia", "48": "buttercup", "55": "pelargonium", "36": "ruby-lipped cattleya", "91": "hippeastrum", "29": "artichoke", "71": "gazania", "90": "canna lily", "18": "peruvian lily", "98": "mexican petunia", "8": "bird of paradise", "30": "sweet william", "17": "purple coneflower", "52": "wild pansy", "84": "columbine", "12": "colt's foot", "11": "snapdragon", "96": "camellia", "23": "fritillary", "50": "common dandelion", "44": "poinsettia", "53": "primula", "72": "azalea", "65": "californian poppy", "80": "anthurium", "76": "morning glory", "37": "cape flower", "56": "bishop of llandaff", "60": "pink-yellow dahlia", "82": "clematis", "58": "geranium", "75": "thorn apple", "41": "barbeton daisy", "95": "bougainvillea", "43": "sword lily", "83": "hibiscus", "78": "lotus lotus", "88": "cyclamen", "94": "foxglove", "81": "frangipani", "74": "rose", "89": "watercress", "73": "water lily", "46": "wallflower", "77": "passion flower", "51": "petunia"}

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import signal
from contextlib import contextmanager
import requests
DELAY = INTERVAL = 4 * 60 # interval time in seconds
MIN_DELAY = MIN_INTERVAL = 2 * 60
KEEPALIVE_URL = "https://nebula.udacity.com/api/v1/remote/keep-alive"
TOKEN_URL = "http://metadata.google.internal/computeMetadata/v1/instance/attributes/keep_alive_token"
TOKEN_HEADERS = {"Metadata-Flavor":"Google"}
def _request_handler(headers):
def _handler(signum, frame):
requests.request("POST", KEEPALIVE_URL, headers=headers)
return _handler
@contextmanager
def active_session(delay=DELAY, interval=INTERVAL):
"""
Example:
from workspace_utils import active session
with active_session():
# do long-running work here
"""
token = requests.request("GET", TOKEN_URL, headers=TOKEN_HEADERS).text
headers = {'Authorization': "STAR " + token}
delay = max(delay, MIN_DELAY)
interval = max(interval, MIN_INTERVAL)
original_handler = signal.getsignal(signal.SIGALRM)
try:
signal.signal(signal.SIGALRM, _request_handler(headers))
signal.setitimer(signal.ITIMER_REAL, delay, interval)
yield
finally:
signal.signal(signal.SIGALRM, original_handler)
signal.setitimer(signal.ITIMER_REAL, 0)
def keep_awake(iterable, delay=DELAY, interval=INTERVAL):
"""
Example:
from workspace_utils import keep_awake
for i in keep_awake(range(5)):
# do iteration with lots of work here
"""
with active_session(delay, interval): yield from iterable