fastbook/06_multicat.ipynb
Jeremy Howard efff9626b0 fastai
2020-08-21 12:36:27 -07:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"!pip install -Uqq fastbook\n",
"import fastbook\n",
"fastbook.setup_book()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from fastbook import *"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"[[chapter_multicat]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Other Computer Vision Problems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous chapter you learned some important practical techniques for training models in practice. COnsiderations like selecting learning rates and the number of epochs are very important to getting good results.\n",
"\n",
"In this chapter we are going to look at two other types of computer vision problems: multi-label classification and regression. The first one is when you want to predict more than one label per image (or sometimes none at all), and the second is when your labels are one or several numbers—a quantity instead of a category.\n",
"\n",
"In the process will study more deeply the output activations, targets, and loss functions in deep learning models."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-Label Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Multi-label classification refers to the problem of identifying the categories of objects in images that may not contain exactly one type of object. There may be more than one kind of object, or there may be no objects at all in the classes that you are looking for.\n",
"\n",
"For instance, this would have been a great approach for our bear classifier. One problem with the bear classifier that we rolled out in <<chapter_production>> was that if a user uploaded something that wasn't any kind of bear, the model would still say it was either a grizzly, black, or teddy bear—it had no ability to predict \"not a bear at all.\" In fact, after we have completed this chapter, it would be a great exercise for you to go back to your image classifier application, and try to retrain it using the multi-label technique, then test it by passing in an image that is not of any of your recognized classes.\n",
"\n",
"In practice, we have not seen many examples of people training multi-label classifiers for this purpose—but we very often see both users and developers complaining about this problem. It appears that this simple solution is not at all widely understood or appreciated! Because in practice it is probably more common to have some images with zero matches or more than one match, we should probably expect in practice that multi-label classifiers are more widely applicable than single-label classifiers.\n",
"\n",
"First, let's see what a multi-label dataset looks like, then we'll explain how to get it ready for our model. You'll see that the architecture of the model does not change from the last chapter; only the loss function does. Let's start with the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For our example we are going to use the PASCAL dataset, which can have more than one kind of classified object per image.\n",
"\n",
"We begin by downloading and extracting the dataset as per usual:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from fastai.vision.all import *\n",
"path = untar_data(URLs.PASCAL_2007)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset is different from the ones we have seen before, in that it is not structured by filename or folder but instead comes with a CSV (comma-separated values) file telling us what labels to use for each image. We can inspect the CSV file by reading it into a Pandas DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fname</th>\n",
" <th>labels</th>\n",
" <th>is_valid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000005.jpg</td>\n",
" <td>chair</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000007.jpg</td>\n",
" <td>car</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000009.jpg</td>\n",
" <td>horse person</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000012.jpg</td>\n",
" <td>car</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000016.jpg</td>\n",
" <td>bicycle</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" fname labels is_valid\n",
"0 000005.jpg chair True\n",
"1 000007.jpg car True\n",
"2 000009.jpg horse person True\n",
"3 000012.jpg car False\n",
"4 000016.jpg bicycle True"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(path/'train.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, the list of categories in each image is shown as a space-delimited string."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sidebar: Pandas and DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No, its not actually a panda! *Pandas* is a Python library that is used to manipulate and analyze tabular and time series data. The main class is `DataFrame`, which represents a table of rows and columns. You can get a DataFrame from a CSV file, a database table, Python dictionaries, and many other sources. In Jupyter, a DataFrame is output as a formatted table, as shown here.\n",
"\n",
"You can access rows and columns of a DataFrame with the `iloc` property, as if it were a matrix:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 000005.jpg\n",
"1 000007.jpg\n",
"2 000009.jpg\n",
"3 000012.jpg\n",
"4 000016.jpg\n",
" ... \n",
"5006 009954.jpg\n",
"5007 009955.jpg\n",
"5008 009958.jpg\n",
"5009 009959.jpg\n",
"5010 009961.jpg\n",
"Name: fname, Length: 5011, dtype: object"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[:,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"fname 000005.jpg\n",
"labels chair\n",
"is_valid True\n",
"Name: 0, dtype: object"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[0,:]\n",
"# Trailing :s are always optional (in numpy, pytorch, pandas, etc.),\n",
"# so this is equivalent:\n",
"df.iloc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also grab a column by name by indexing into a DataFrame directly:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 000005.jpg\n",
"1 000007.jpg\n",
"2 000009.jpg\n",
"3 000012.jpg\n",
"4 000016.jpg\n",
" ... \n",
"5006 009954.jpg\n",
"5007 009955.jpg\n",
"5008 009958.jpg\n",
"5009 009959.jpg\n",
"5010 009961.jpg\n",
"Name: fname, Length: 5011, dtype: object"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['fname']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can create new columns and do calculations using columns:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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"text/plain": [
" a b\n",
"0 1 3\n",
"1 2 4"
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},
"execution_count": null,
"metadata": {},
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"source": [
"tmp_df = pd.DataFrame({'a':[1,2], 'b':[3,4]})\n",
"tmp_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
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" <td>4</td>\n",
" <td>6</td>\n",
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"text/plain": [
" a b c\n",
"0 1 3 4\n",
"1 2 4 6"
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"output_type": "execute_result"
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"source": [
"tmp_df['c'] = tmp_df['a']+tmp_df['b']\n",
"tmp_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas is a fast and flexible library, and an important part of every data scientists Python toolbox. Unfortunately, its API can be rather confusing and surprising, so it takes a while to get familiar with it. If you havent used Pandas before, wed suggest going through a tutorial; we are particularly fond of the book [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney, the creator of Pandas (O'Reilly). It also covers other important libraries like `matplotlib` and `numpy`. We will try to briefly describe Pandas functionality we use as we come across it, but will not go into the level of detail of McKinneys book."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### End sidebar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have seen what the data looks like, let's make it ready for model training."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Constructing a DataBlock"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How do we convert from a `DataFrame` object to a `DataLoaders` object? We generally suggest using the data block API for creating a `DataLoaders` object, where possible, since it provides a good mix of flexibility and simplicity. Here we will show you the steps that we take to use the data blocks API to construct a `DataLoaders` object in practice, using this dataset as an example.\n",
"\n",
"As we have seen, PyTorch and fastai have two main classes for representing and accessing a training set or validation set:\n",
"\n",
"- `Dataset`:: A collection that returns a tuple of your independent and dependent variable for a single item\n",
"- `DataLoader`:: An iterator that provides a stream of mini-batches, where each mini-batch is a couple of a batch of independent variables and a batch of dependent variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On top of these, fastai provides two classes for bringing your training and validation sets together:\n",
"\n",
"- `Datasets`:: An object that contains a training `Dataset` and a validation `Dataset`\n",
"- `DataLoaders`:: An object that contains a training `DataLoader` and a validation `DataLoader`\n",
"\n",
"Since a `DataLoader` builds on top of a `Dataset` and adds additional functionality to it (collating multiple items into a mini-batch), its often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after thats working."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we create a `DataBlock`, we build up gradually, step by step, and use the notebook to check our data along the way. This is a great way to make sure that you maintain momentum as you are coding, and that you keep an eye out for any problems. Its easy to debug, because you know that if a problem arises, it is in the line of code you just typed!\n",
"\n",
"Lets start with the simplest case, which is a data block created with no parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dblock = DataBlock()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create a `Datasets` object from this. The only thing needed is a source—in this case, our DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dsets = dblock.datasets(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This contains a `train` and a `valid` dataset, which we can index into:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4009, 1002)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dsets.train),len(dsets.valid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(fname 000224.jpg\n",
" labels tvmonitor bottle\n",
" is_valid True\n",
" Name: 113, dtype: object, fname 000224.jpg\n",
" labels tvmonitor bottle\n",
" is_valid True\n",
" Name: 113, dtype: object)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x,y = dsets.train[0]\n",
"x,y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, this simply returns a row of the DataFrame, twice. This is because by default, the data block assumes we have two things: input and target. We are going to need to grab the appropriate fields from the DataFrame, which we can do by passing `get_x` and `get_y` functions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'000224.jpg'"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x['fname']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('009879.jpg', 'car person')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dblock = DataBlock(get_x = lambda r: r['fname'], get_y = lambda r: r['labels'])\n",
"dsets = dblock.datasets(df)\n",
"dsets.train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, rather than defining a function in the usual way, we are using Pythons `lambda` keyword. This is just a shortcut for defining and then referring to a function. The following more verbose approach is identical:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('006350.jpg', 'aeroplane')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_x(r): return r['fname']\n",
"def get_y(r): return r['labels']\n",
"dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
"dsets = dblock.datasets(df)\n",
"dsets.train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lambda functions are great for quickly iterating, but they are not compatible with serialization, so we advise you to use the more verbose approach if you want to export your `Learner` after training (lambdas are fine if you are just experimenting)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the independent variable will need to be converted into a complete path, so that we can open it as an image, and the dependent variable will need to be split on the space character (which is the default for Pythons `split` function) so that it becomes a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Path('/home/sgugger/.fastai/data/pascal_2007/train/008663.jpg'),\n",
" ['car', 'person'])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_x(r): return path/'train'/r['fname']\n",
"def get_y(r): return r['labels'].split(' ')\n",
"dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
"dsets = dblock.datasets(df)\n",
"dsets.train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To actually open the image and do the conversion to tensors, we will need to use a set of transforms; block types will provide us with those. We can use the same block types that we have used previously, with one exception: the `ImageBlock` will work fine again, because we have a path that points to a valid image, but the `CategoryBlock` is not going to work. The problem is that block returns a single integer, but we need to be able to have multiple labels for each item. To solve this, we use a `MultiCategoryBlock`. This type of block expects to receive a list of strings, as we have in this case, so lets test it out:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(PILImage mode=RGB size=500x374,\n",
" TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
" get_x = get_x, get_y = get_y)\n",
"dsets = dblock.datasets(df)\n",
"dsets.train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, our list of categories is not encoded in the same way that it was for the regular `CategoryBlock`. In that case, we had a single integer representing which category was present, based on its location in our vocab. In this case, however, we instead have a list of zeros, with a one in any position where that category is present. For example, if there is a one in the second and fourth positions, then that means that vocab items two and four are present in this image. This is known as *one-hot encoding*. The reason we cant easily just use a list of category indices is that each list would be a different length, and PyTorch requires tensors, where everything has to be the same length."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> jargon: One-hot encoding: Using a vector of zeros, with a one in each location that is represented in the data, to encode a list of integers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets check what the categories represent for this example (we are using the convenient `torch.where` function, which tells us all of the indices where our condition is true or false):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#1) ['chair']"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idxs = torch.where(dsets.train[0][1]==1.)[0]\n",
"dsets.train.vocab[idxs]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With NumPy arrays, PyTorch tensors, and fastais `L` class, we can index directly using a list or vector, which makes a lot of code (such as this example) much clearer and more concise.\n",
"\n",
"We have ignored the column `is_valid` up until now, which means that `DataBlock` has been using a random split by default. To explicitly choose the elements of our validation set, we need to write a function and pass it to `splitter` (or use one of fastai's predefined functions or classes). It will take the items (here our whole DataFrame) and must return two (or more) lists of integers:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(PILImage mode=RGB size=500x333,\n",
" TensorMultiCategory([0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def splitter(df):\n",
" train = df.index[~df['is_valid']].tolist()\n",
" valid = df.index[df['is_valid']].tolist()\n",
" return train,valid\n",
"\n",
"dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
" splitter=splitter,\n",
" get_x=get_x, \n",
" get_y=get_y)\n",
"\n",
"dsets = dblock.datasets(df)\n",
"dsets.train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we have discussed, a `DataLoader` collates the items from a `Dataset` into a mini-batch. This is a tuple of tensors, where each tensor simply stacks the items from that location in the `Dataset` item. \n",
"\n",
"Now that we have confirmed that the individual items look okay, there's one more step we need to ensure we can create our `DataLoaders`, which is to ensure that every item is of the same size. To do this, we can use `RandomResizedCrop`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
" splitter=splitter,\n",
" get_x=get_x, \n",
" get_y=get_y,\n",
" item_tfms = RandomResizedCrop(128, min_scale=0.35))\n",
"dls = dblock.dataloaders(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we can display a sample of our data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dls.show_batch(nrows=1, ncols=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember that if anything goes wrong when you create your `DataLoaders` from your `DataBlock`, or if you want to view exactly what happens with your `DataBlock`, you can use the `summary` method we presented in the last chapter."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our data is now ready for training a model. As we will see, nothing is going to change when we create our `Learner`, but behind the scenes, the fastai library will pick a new loss function for us: binary cross-entropy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Binary Cross-Entropy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll create our `Learner`. We saw in <<chapter_mnist_basics>> that a `Learner` object contains four main things: the model, a `DataLoaders` object, an `Optimizer`, and the loss function to use. We already have our `DataLoaders`, we can leverage fastai's `resnet` models (which we'll learn how to create from scratch later), and we know how to create an `SGD` optimizer. So let's focus on ensuring we have a suitable loss function. To do this, let's use `cnn_learner` to create a `Learner`, so we can look at its activations:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet18)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also saw that the model in a `Learner` is generally an object of a class inheriting from `nn.Module`, and that we can call it using parentheses and it will return the activations of a model. You should pass it your independent variable, as a mini-batch. We can try it out by grabbing a mini batch from our `DataLoader` and then passing it to the model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([64, 20])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x,y = dls.train.one_batch()\n",
"activs = learn.model(x)\n",
"activs.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Think about why `activs` has this shape—we have a batch size of 64, and we need to calculate the probability of each of 20 categories. Heres what one of those activations looks like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 2.0258, -1.3543, 1.4640, 1.7754, -1.2820, -5.8053, 3.6130, 0.7193, -4.3683, -2.5001, -2.8373, -1.8037, 2.0122, 0.6189, 1.9729, 0.8999, -2.6769, -0.3829, 1.2212, 1.6073],\n",
" device='cuda:0', grad_fn=<SelectBackward>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"activs[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> note: Getting Model Activations: Knowing how to manually get a mini-batch and pass it into a model, and look at the activations and loss, is really important for debugging your model. It is also very helpful for learning, so that you can see exactly what is going on."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"They arent yet scaled to between 0 and 1, but we learned how to do that in <<chapter_mnist_basics>>, using the `sigmoid` function. We also saw how to calculate a loss based on this—this is our loss function from <<chapter_mnist_basics>>, with the addition of `log` as discussed in the last chapter:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def binary_cross_entropy(inputs, targets):\n",
" inputs = inputs.sigmoid()\n",
" return -torch.where(targets==1, inputs, 1-inputs).log().mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that because we have a one-hot-encoded dependent variable, we can't directly use `nll_loss` or `softmax` (and therefore we can't use `cross_entropy`):\n",
"\n",
"- `softmax`, as we saw, requires that all predictions sum to 1, and tends to push one activation to be much larger than the others (due to the use of `exp`); however, we may well have multiple objects that we're confident appear in an image, so restricting the maximum sum of activations to 1 is not a good idea. By the same reasoning, we may want the sum to be *less* than 1, if we don't think *any* of the categories appear in an image.\n",
"- `nll_loss`, as we saw, returns the value of just one activation: the single activation corresponding with the single label for an item. This doesn't make sense when we have multiple labels.\n",
"\n",
"On the other hand, the `binary_cross_entropy` function, which is just `mnist_loss` along with `log`, provides just what we need, thanks to the magic of PyTorch's elementwise operations. Each activation will be compared to each target for each column, so we don't have to do anything to make this function work for multiple columns."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> j: One of the things I really like about working with libraries like PyTorch, with broadcasting and elementwise operations, is that quite frequently I find I can write code that works equally well for a single item or a batch of items, without changes. `binary_cross_entropy` is a great example of this. By using these operations, we don't have to write loops ourselves, and can rely on PyTorch to do the looping we need as appropriate for the rank of the tensors we're working with."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PyTorch already provides this function for us. In fact, it provides a number of versions, with rather confusing names!\n",
"\n",
"`F.binary_cross_entropy` and its module equivalent `nn.BCELoss` calculate cross-entropy on a one-hot-encoded target, but do not include the initial `sigmoid`. Normally for one-hot-encoded targets you'll want `F.binary_cross_entropy_with_logits` (or `nn.BCEWithLogitsLoss`), which do both sigmoid and binary cross-entropy in a single function, as in the preceding example.\n",
"\n",
"The equivalent for single-label datasets (like MNIST or the Pet dataset), where the target is encoded as a single integer, is `F.nll_loss` or `nn.NLLLoss` for the version without the initial softmax, and `F.cross_entropy` or `nn.CrossEntropyLoss` for the version with the initial softmax.\n",
"\n",
"Since we have a one-hot-encoded target, we will use `BCEWithLogitsLoss`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(1.0082, device='cuda:5', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss_func = nn.BCEWithLogitsLoss()\n",
"loss = loss_func(activs, y)\n",
"loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We don't actually need to tell fastai to use this loss function (although we can if we want) since it will be automatically chosen for us. fastai knows that the `DataLoaders` has multiple category labels, so it will use `nn.BCEWithLogitsLoss` by default.\n",
"\n",
"One change compared to the last chapter is the metric we use: because this is a multilabel problem, we can't use the accuracy function. Why is that? Well, accuracy was comparing our outputs to our targets like so:\n",
"\n",
"```python\n",
"def accuracy(inp, targ, axis=-1):\n",
" \"Compute accuracy with `targ` when `pred` is bs * n_classes\"\n",
" pred = inp.argmax(dim=axis)\n",
" return (pred == targ).float().mean()\n",
"```\n",
"\n",
"The class predicted was the one with the highest activation (this is what `argmax` does). Here it doesn't work because we could have more than one prediction on a single image. After applying the sigmoid to our activations (to make them between 0 and 1), we need to decide which ones are 0s and which ones are 1s by picking a *threshold*. Each value above the threshold will be considered as a 1, and each value lower than the threshold will be considered a 0:\n",
"\n",
"```python\n",
"def accuracy_multi(inp, targ, thresh=0.5, sigmoid=True):\n",
" \"Compute accuracy when `inp` and `targ` are the same size.\"\n",
" if sigmoid: inp = inp.sigmoid()\n",
" return ((inp>thresh)==targ.bool()).float().mean()\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we pass `accuracy_multi` directly as a metric, it will use the default value for `threshold`, which is 0.5. We might want to adjust that default and create a new version of `accuracy_multi` that has a different default. To help with this, there is a function in Python called `partial`. It allows us to *bind* a function with some arguments or keyword arguments, making a new version of that function that, whenever it is called, always includes those arguments. For instance, here is a simple function taking two arguments:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('Hello Jeremy.', 'Ahoy! Jeremy.')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def say_hello(name, say_what=\"Hello\"): return f\"{say_what} {name}.\"\n",
"say_hello('Jeremy'),say_hello('Jeremy', 'Ahoy!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can switch to a French version of that function by using `partial`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('Bonjour Jeremy.', 'Bonjour Sylvain.')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = partial(say_hello, say_what=\"Bonjour\")\n",
"f(\"Jeremy\"),f(\"Sylvain\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now train our model. Let's try setting the accuracy threshold to 0.2 for our metric:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy_multi</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.903610</td>\n",
" <td>0.659728</td>\n",
" <td>0.263068</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.724266</td>\n",
" <td>0.346332</td>\n",
" <td>0.525458</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.415597</td>\n",
" <td>0.125662</td>\n",
" <td>0.937590</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.254987</td>\n",
" <td>0.116880</td>\n",
" <td>0.945418</td>\n",
" <td>00:07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy_multi</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.123872</td>\n",
" <td>0.132634</td>\n",
" <td>0.940179</td>\n",
" <td>00:08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.112387</td>\n",
" <td>0.113758</td>\n",
" <td>0.949343</td>\n",
" <td>00:08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.092151</td>\n",
" <td>0.104368</td>\n",
" <td>0.951195</td>\n",
" <td>00:08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))\n",
"learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Picking a threshold is important. If you pick a threshold that's too low, you'll often be failing to select correctly labeled objects. We can see this by changing our metric, and then calling `validate`, which returns the validation loss and metrics:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(#2) [0.10436797887086868,0.93057781457901]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.metrics = partial(accuracy_multi, thresh=0.1)\n",
"learn.validate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you pick a threshold that's too high, you'll only be selecting the objects for which your model is very confident:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(#2) [0.10436797887086868,0.9416930675506592]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.metrics = partial(accuracy_multi, thresh=0.99)\n",
"learn.validate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can find the best threshold by trying a few levels and seeing what works best. This is much faster if we just grab the predictions once:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"preds,targs = learn.get_preds()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we can call the metric directly. Note that by default `get_preds` applies the output activation function (sigmoid, in this case) for us, so we'll need to tell `accuracy_multi` to not apply it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorMultiCategory(0.9554)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_multi(preds, targs, thresh=0.9, sigmoid=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now use this approach to find the best threshold level:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"xs = torch.linspace(0.05,0.95,29)\n",
"accs = [accuracy_multi(preds, targs, thresh=i, sigmoid=False) for i in xs]\n",
"plt.plot(xs,accs);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, we're using the validation set to pick a hyperparameter (the threshold), which is the purpose of the validation set. Sometimes students have expressed their concern that we might be *overfitting* to the validation set, since we're trying lots of values to see which is the best. However, as you see in the plot, changing the threshold in this case results in a smooth curve, so we're clearly not picking some inappropriate outlier. This is a good example of where you have to be careful of the difference between theory (don't try lots of hyperparameter values or you might overfit the validation set) versus practice (if the relationship is smooth, then it's fine to do this).\n",
"\n",
"This concludes the part of this chapter dedicated to multi-label classification. Next, we'll take a look at a regression problem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's easy to think of deep learning models as being classified into domains, like *computer vision*, *NLP*, and so forth. And indeed, that's how fastai classifies its applications—largely because that's how most people are used to thinking of things.\n",
"\n",
"But really, that's hiding a more interesting and deeper perspective. A model is defined by its independent and dependent variables, along with its loss function. That means that there's really a far wider array of models than just the simple domain-based split. Perhaps we have an independent variable that's an image, and a dependent that's text (e.g., generating a caption from an image); or perhaps we have an independent variable that's text and dependent that's an image (e.g., generating an image from a caption—which is actually possible for deep learning to do!); or perhaps we've got images, texts, and tabular data as independent variables, and we're trying to predict product purchases... the possibilities really are endless.\n",
"\n",
"To be able to move beyond fixed applications, to crafting your own novel solutions to novel problems, it helps to really understand the data block API (and maybe also the mid-tier API, which we'll see later in the book). As an example, let's consider the problem of *image regression*. This refers to learning from a dataset where the independent variable is an image, and the dependent variable is one or more floats. Often we see people treat image regression as a whole separate application—but as you'll see here, we can treat it as just another CNN on top of the data block API.\n",
"\n",
"We're going to jump straight to a somewhat tricky variant of image regression, because we know you're ready for it! We're going to do a key point model. A *key point* refers to a specific location represented in an image—in this case, we'll use images of people and we'll be looking for the center of the person's face in each image. That means we'll actually be predicting *two* values for each image: the row and column of the face center. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assemble the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use the [Biwi Kinect Head Pose dataset](https://icu.ee.ethz.ch/research/datsets.html) for this section. We'll begin by downloading the dataset as usual:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path = untar_data(URLs.BIWI_HEAD_POSE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"Path.BASE_PATH = path"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what we've got!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#50) [Path('01'),Path('01.obj'),Path('02'),Path('02.obj'),Path('03'),Path('03.obj'),Path('04'),Path('04.obj'),Path('05'),Path('05.obj')...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path.ls().sorted()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 24 directories numbered from 01 to 24 (they correspond to the different people photographed), and a corresponding *.obj* file for each (we won't need them here). Let's take a look inside one of these directories:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#1000) [Path('01/depth.cal'),Path('01/frame_00003_pose.txt'),Path('01/frame_00003_rgb.jpg'),Path('01/frame_00004_pose.txt'),Path('01/frame_00004_rgb.jpg'),Path('01/frame_00005_pose.txt'),Path('01/frame_00005_rgb.jpg'),Path('01/frame_00006_pose.txt'),Path('01/frame_00006_rgb.jpg'),Path('01/frame_00007_pose.txt')...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(path/'01').ls().sorted()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inside the subdirectories, we have different frames, each of them come with an image (*\\_rgb.jpg*) and a pose file (*\\_pose.txt*). We can easily get all the image files recursively with `get_image_files`, then write a function that converts an image filename to its associated pose file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Path('13/frame_00349_pose.txt')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img_files = get_image_files(path)\n",
"def img2pose(x): return Path(f'{str(x)[:-7]}pose.txt')\n",
"img2pose(img_files[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at our first image:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(480, 640)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im = PILImage.create(img_files[0])\n",
"im.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=160x120 at 0x7F4213FAD350>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im.to_thumb(160)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Biwi dataset website used to explain the format of the pose text file associated with each image, which shows the location of the center of the head. The details of this aren't important for our purposes, so we'll just show the function we use to extract the head center point:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cal = np.genfromtxt(path/'01'/'rgb.cal', skip_footer=6)\n",
"def get_ctr(f):\n",
" ctr = np.genfromtxt(img2pose(f), skip_header=3)\n",
" c1 = ctr[0] * cal[0][0]/ctr[2] + cal[0][2]\n",
" c2 = ctr[1] * cal[1][1]/ctr[2] + cal[1][2]\n",
" return tensor([c1,c2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function returns the coordinates as a tensor of two items:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([384.6370, 259.4787])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_ctr(img_files[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can pass this function to `DataBlock` as `get_y`, since it is responsible for labeling each item. We'll resize the images to half their input size, just to speed up training a bit.\n",
"\n",
"One important point to note is that we should not just use a random splitter. The reason for this is that the same people appears in multiple images in this dataset, but we want to ensure that our model can generalize to people that it hasn't seen yet. Each folder in the dataset contains the images for one person. Therefore, we can create a splitter function that returns true for just one person, resulting in a validation set containing just that person's images.\n",
"\n",
"The only other difference tfrom the previous data block examples is that the second block is a `PointBlock`. This is necessary so that fastai knows that the labels represent coordinates; that way, it knows that when doing data augmentation, it should do the same augmentation to these coordinates as it does to the images:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"biwi = DataBlock(\n",
" blocks=(ImageBlock, PointBlock),\n",
" get_items=get_image_files,\n",
" get_y=get_ctr,\n",
" splitter=FuncSplitter(lambda o: o.parent.name=='13'),\n",
" batch_tfms=[*aug_transforms(size=(240,320)), \n",
" Normalize.from_stats(*imagenet_stats)]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> important: Points and Data Augmentation: We're not aware of other libraries (except for fastai) that automatically and correctly apply data augmentation to coordinates. So, if you're working with another library, you may need to disable data augmentation for these kinds of problems."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before doing any modeling, we should look at our data to confirm it seems okay:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dls = biwi.dataloaders(path)\n",
"dls.show_batch(max_n=9, figsize=(8,6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's looking good! As well as looking at the batch visually, it's a good idea to also look at the underlying tensors (especially as a student; it will help clarify your understanding of what your model is really seeing):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([64, 3, 240, 320]), torch.Size([64, 1, 2]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xb,yb = dls.one_batch()\n",
"xb.shape,yb.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure that you understand *why* these are the shapes for our mini-batches."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's an example of one row from the dependent variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-0.0753, 0.0237]], device='cuda:5')"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yb[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, we haven't had to use a separate *image regression* application; all we've had to do is label the data, and tell fastai what kinds of data the independent and dependent variables represent."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's the same for creating our `Learner`. We will use the same function as before, with one new parameter, and we will be ready to train our model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training a Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As usual, we can use `cnn_learner` to create our `Learner`. Remember way back in <<chapter_intro>> how we used `y_range` to tell fastai the range of our targets? We'll do the same here (coordinates in fastai and PyTorch are always rescaled between -1 and +1):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet18, y_range=(-1,1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`y_range` is implemented in fastai using `sigmoid_range`, which is defined as:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def sigmoid_range(x, lo, hi): return torch.sigmoid(x) * (hi-lo) + lo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is set as the final layer of the model, if `y_range` is defined. Take a moment to think about what this function does, and why it forces the model to output activations in the range `(lo,hi)`.\n",
"\n",
"Here's what it looks like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EGRUIkXZq2Xs7eezdnXzl7BHMmTIo6DgSh6JaIMws3cyeM7NyM9thZvOb6HezmW0ws1Iz22ZmNzfYvt3MKs2sLPx4LZo5Rdq7VTsO8b3nNzBzdB++fd7YoONInIr2GMT9QA3QD5gMvGRm2e7ecD1pI7Ri3DpgJPCamX3k7k9G9LnI3V+Pcj6Rdm9/SRXXPbaKAT2SuG/eFDprxpK0kqgdQZhZMjAXuM3dy9x9OfACsLBhX3e/191Xu/thd99MaD3qGdHKIhKvag6HBqXLqg+zZNE0enZPDDqSxLFonmIaA9S5e25EWzYw/mgvstDN6WcCDY8yHjezAjN7zcwmHeX1i80sy8yyCgoKjje7SLtw559yWL2ziP++ZBJj+6cFHUfiXDQLRApQ3KCtGGjuTmF3hHP8LqJtATAcGAa8CbxqZo1O0XD3Je6e6e6ZGRkZxxFbpH14etUuHnt3J4vPHsGFEwcEHUc6gGgWiDKg4a80aUBpUy8wsxsIjUVc6O7VR9rdfYW7V7p7hbv/ECgidJQh0iFt2F3Mfz23nk+N6M23z9WSodI2olkgcoEEMxsd0TaJT546AsDMrgJuBf7V3Xc1s28nNLAt0uEUVdRw7WOrSE9O5L75U0jQqnDSRqL2TnP3cuBZ4E4zSzazGcBs4NGGfc1sAXA38Fl339pg21Azm2FmiWbWLTwFtg+wIlpZRdqL+nrnG0+tZX9JFb9aMJU+KVr4R9pOtH8VuR5IAvKBZcB17p5jZjPNrCyi311Ab+D9iGsd/je8LRV4ADgE7AbOA85398IoZxWJefe/mcebmwv43ufHMWVor6DjSAcT1esg3P0gMKeR9rcJDWIfeX7SUfaRA0yMZi6R9ujtLQX89PVc5kweyJfOGBZ0HOmAdDJTJAbtKarka0+uZXTfFO6+eAKh2eAibUsFQiTG1NbVc8MTq6mureOBL02je6JuuizB0DtPJMb86JVNrN5ZxC/nT2FkRkrzLxBpJTqCEIkhf96wlweXb+PyTw3j8xMHBh1HOjgVCJEYsaOwnJv/sI5Jg3vwnQtPCTqOiAqESCyoqq3jq0+sxgx+OX8qXRO0bKgET2MQIjHg7pc3smF3Cb9ZlMmQ9O5BxxEBdAQhErg/rdvD0nd2cM3Mk/jsuH5BxxH5JxUIkQBtP1DOrc+sZ8rQnloZTmKOCoRIQI6MO3TuZPxy/lS66CZ8EmM0BiESkLtf3kjOnhJ+uyiTQT2Tgo4j8gn6lUUkAC+v3/vPcYfPaNxBYpQKhEgb21lYwS1Pr2PyEI07SGxTgRBpQ9WH/+96h/vmTdG4g8Q0jUGItKF7XtnM+t3F/HrhNF3vIDFPv76ItJG/fLCfh1Zs44ozh3Pu+P5BxxFpVlQLhJmlm9lzZlZuZjvMbH4T/czM7jGzwvDjXou44b2ZTTazVWZWEf5zcjRzirS13UWV/McfspkwqAf/eYHGHaR9iPYRxP1ADdAPWAA8YGbjG+m3mNDKc5MIrR73eeArAGaWCDwPPAb0Ah4Bng+3i7Q7tXX13PjEaurqnfvmTdF9lqTdiFqBMLNkYC5wm7uXufty4AVgYSPdLwd+4u673H038BPgivC2WYTGRv7H3avd/ReAAZ+OVlaRtvST13JZvbOIH82dwPA+yUHHETlm0TyCGAPUuXtuRFs20NgRxPjwtsb6jQfWubtHbF/XxH4ws8VmlmVmWQUFBccdXqQ1vLU5n//924fMP32o1neQdieaBSIFKG7QVgykHkPfYiAlPA7Rkv3g7kvcPdPdMzMyMo4ruEhr2F9SxTd/n83Y/ql87/Pjgo4j0mLRLBBlQFqDtjSg9Bj6pgFl4aOGluxHJCbV1Ts3LVtDZU0dv5w/lW5dNO4g7U80C0QukGBmoyPaJgE5jfTNCW9rrF8OMDFyVhOhgezG9iMSk37+1y2s3HaQu+acyqi+Wlda2qeoFQh3LweeBe40s2QzmwHMBh5tpPtS4JtmNsjMBgLfAh4Ob3sLqANuMrOuZnZDuP2NaGUVaU3/yDvAfW9sYe7UwcydNjjoOCLHLdrTXK8HkoB8YBlwnbvnmNlMMyuL6Pdr4EVgPbABeCnchrvXEJoCuwgoAq4C5oTbRWJaQWk1X3tqLSP6JPODOY3OqxBpN6J6qw13P0joy71h+9uEBp+PPHfg2+FHY/tZA0yLZjaR1lZX73zjqbWUVNby6NXT6Z6oO9lI+6Z3sEiU/OrNPJbnHeCHF09gbP+G8yxE2h/di0kkCt7dWsjPXs9l9uSBXHbakKDjiESFCoTICTpQVs1Ny9YwvHcy/+8LE/j4BDyR9kunmEROQH143KGospbfXXkaKV31kZL4oSMIkRPwq7fyeHvLAW6/aBzjB/YIOo5IVKlAiByndz4s5Kd/yeXfJg1k/vShQccRiToVCJHjUFBazU1PhsYd7r5Y4w4Sn3TCVKSF6uqdrz+1hpLKWpZeNV3jDhK39M4WaaGf/3ULK/IKuXfuRE4ZoOsdJH7pFJNIC/w9t4D73tjCJdMGc6mud5A4pwIhcoz2Flfy9afWcnK/VH4w+9Sg44i0OhUIkWNQc7ierz6+muraOu5fMJWkRK3vIPFPYxAix+DulzeyemcR98+fysgMre8gHYOOIESa8UL2Hh7+x3auPuskLpw4IOg4Im1GBULkKLbsL+XWZ9aROawXt54/Nug4Im1KBUKkCaVVtXzlsVV0T+zM/Qum0qWzPi7SsUTlHW9m6Wb2nJmVm9kOM5t/lL43m9kGMys1s21mdnOD7dvNrNLMysKP16KRUaQl3J3/+EM2Owor+OX8qfRL6xZ0JJE2F61B6vuBGqAfMBl4ycyy3T2nkb5GaDnRdcBI4DUz+8jdn4zoc5G7vx6lbCIt9sDfPuTVnP1898JTOGNE76DjiATihI8gzCwZmAvc5u5l7r4ceAFY2Fh/d7/X3Ve7+2F33ww8D8w40Rwi0bJ8ywF+/OpmPj9xAFefdVLQcUQCE41TTGOAOnfPjWjLBppdsd1CdzibCTQ80njczArM7DUzm9TMPhabWZaZZRUUFLQ0u8jHfHSwghuXrWZU3xTumTtRN+GTDi0aBSIFKG7QVgykHsNr7whn+F1E2wJgODAMeBN41cx6NrUDd1/i7pnunpmRkdGC2CIfV1FzmMWPrqKu3lmyMJNk3YRPOrhmC4SZvWVm3sRjOVAGNLxjWRpQ2sx+byA0FnGhu1cfaXf3Fe5e6e4V7v5DoIjQUYZIq3F3vv30OjbtK+EX86YwvE9y0JFEAtfsr0juPuto28NjEAlmNtrdt4SbJ/HJ00aRr7kKuBU42913NReB0MC2SKv59d+38qd1e7nlvLHMOrlv0HFEYsIJn2Jy93LgWeBOM0s2sxnAbODRxvqb2QLgbuCz7r61wbahZjbDzBLNrFt4CmwfYMWJ5hRpypub8rnnz5u4cOIArj1nRNBxRGJGtK78uR5IAvKBZcB1R6a4mtlMMyuL6HsX0Bt4P+Jah/8Nb0sFHgAOAbuB84Dz3b0wSjlFPiYvv5Sblq1h3IA0fnzJJA1Ki0SIyiicux8E5jSx7W1CA9lHnjc5bzBcVCZGI5NIc4oqarj6kSy6dunEbxZl6g6tIg1omoZ0SLV19Xz1idXsLapi2eLTGdgzKehIIjFHBUI6HHfn9hdyWJFXyH9fMpFpw9KDjiQSk3T3MelwfrdiO0+s3Ml1s0byxUwtGyrSFBUI6VDe2LSfu176gHPH9+Pmz50cdByRmKYCIR1Gzp5ibnxiDeMGpvGzf59Mp06asSRyNCoQ0iHsKarkqoffp0dSFx68/DS6J2r4TaQ5+pRI3CupquWqh9+norqOp687U2s7iBwjFQiJazWH6/nq46vJyy/jkaumc3L/Y7mHpIiACoTEsfp655Zn1vH2lgP89yUTmTGqT9CRRNoVjUFI3Lrnz5t4bs1ubj73ZE1nFTkOKhASl3779lZ+/fetLPrUMK6fNTLoOCLtkgqExJ3n1uzirpc2cv6p/bn9ovG6AZ/IcVKBkLjy+gf7+Y8/rOPMkb352b9PprOudRA5bioQEjfe+bCQ659YzakD01iyKJNuXXR3VpEToQIhcSH7oyKuWZrFsPTuPHzldFK0nrTICYtagTCzdDN7zszKzWyHmc0/St87zKw2YsGgMjMbEbF9spmtMrOK8J+To5VT4k/OnmIWPriSXsldePTq0+mVnBh0JJG4EM0jiPuBGqAfsAB4wMzGH6X/U+6eEvHYCmBmicDzwGNAL+AR4Plwu8jH5O4vZeGD75HSNYEnvnwG/XvoKmmRaIlKgTCzZGAucJu7l7n7cuAFYOFx7G4WoQv4/sfdq939F4ABn45GVokfHxaUseC3K0noZDxxzRkMSe8edCSRuBKtI4gxQJ2750a0ZQNHO4K4yMwOmlmOmV0X0T4eWOfuHtG2rql9mdliM8sys6yCgoLjzS/tTF5+GfOWvIu788Q1pzO8T3LQkUTiTrQKRApQ3KCtGGjqxje/B04BMoBrgO+Z2bzj2Ze7L3H3THfPzMjIOJ7s0s7k5Zcy7zfvUu+w7JozGNVX91cSaQ3HVCDM7C0z8yYey4EyIK3By9KA0sb25+4fuPsed69z938APwcuCW9u0b6kY9myv5TLlqzEHZ5cfDqj+6k4iLSWYyoQ7j7L3a2Jx1lALpBgZqMjXjYJyDnGHE5onIHwaybaxy9/ndiCfUmc2rC7mEt//Q5m8ORiHTmItLaonGJy93LgWeBOM0s2sxnAbODRxvqb2Wwz62Uh04GbCM1cAngLqANuMrOuZnZDuP2NaGSV9mnVjoPMW/Iu3RMT+MNXPsWovilBRxKJe9Gc5no9kATkA8uA69w9B8DMZppZWUTfy4A8QqeNlgL3uPsjAO5eA8wBFgFFwFXAnHC7dEDLtxxg4YPv0Se1K3+49lMakBZpI1G73NTdDxL6Ym9s29uEBp+PPJ/XWL+I7WuAadHKJu3Xi9l7+Obv1zIyI4WlV0+nb6qucxBpK7ofgcSsR/6xnTtezOG0Yen85vJMeiR1CTqSSIeiAiExx9358Wubuf/ND/nsuH7cN2+KbrwnEgAVCIkp1Yfr+PbT63h+7R7mTR/CD2afSkJn3VNSJAgqEBIziipqWPzoKt7bdpCbzz2Z62eN1GI/IgFSgZCYkJdfxjVLs9h9qJKfXzaZ2ZMHBR1JpMNTgZDAvbU5nxuXrSGxcycev+Z0ThueHnQkEUEFQgLk7jy4fBt3v7yRk/un8ZtF0xjcS3dkFYkVKhASiPLqw9zyzDr+tG4v543vz08unUSyVoETiSn6REqb23agnK88mkVefhm3nDeWa88ZocFokRikAiFt6oXsPXzn2fV06Wwsvep0zhrdJ+hIItIEFQhpE1W1dXz/xQ9Y9t5Opg3rxS/mTWFQz6SgY4nIUahASKvbtK+Erz+5lk37Srn2nJF863Nj6KKL30RingqEtJr6euehFdu498+bSUvqwsNXnsask/sGHUtEjpEKhLSKXYcquOWZdazIK+Qzp/TjnrkT6J3SNehYItICKhASVe7OE+/t5O6XNgJw9xcmMG/6EM1SEmmHVCAkanYUlvOd59azIq+QGaN686OLJzIkXRe+ibRXURkpNLN0M3vOzMrNbIeZzT9K31fMrCziUWNm6yO2bzezyojtr0Ujo7SemsP13P9mHp/72d9Z91Exd805lceuPl3FQaSdi9YRxP1ADdAPmAy8ZGbZR5YcjeTu50c+N7O3+OR60xe5++tRyiat6J0PC7n9hQ3k7i/j/FP7c8e/jadfmlZ9E4kHJ1wgzCwZmAuc6u5lwHIzewFYCNzazGuHAzOBK080h7StvcWV3P3yJl7M3sOgnkn8ZlEmnx3XL+hYIhJF0TiCGAPUuXtuRFs2cM4xvHYR8La7b2vQ/riZdQLWADe7e3ZTOzCzxcBigKFDh7YouLRcRc1hlvx9K7/+21bq3fnav47mulkjteKbSByKRoFIAYobtBUDqcfw2kXAXQ3aFgCrAQO+BrxqZmPdvaixHbj7EmAJQGZmprcgt7RAXb3zzKpd/Pi1zeSXVnPBhP785/mnaJxBJI41WyDCYwRNHQ2sAG4E0hq0pwGlzez3LKA/8HRku7uviHj6QzO7nNBpqBeby5KciCMAAAlOSURBVCrR5+78ecM+fvKXXPLyy5gytCcPfGkq04ZpzQaReNdsgXD3WUfbHh6DSDCz0e6+Jdw8CfjEAHUDlwPPhsctjhqB0NGEtCF3568b8/n5X7ewfncxo/qm8MCCqZx3an9d0yDSQZzwKSZ3LzezZ4E7zezLhGYxzQbObOo1ZpYEfBG4uEH7UGAI8D6hKbg3An0IHalIG6ivd177YD/3vbGFnD0lDElP4sdfnMQXpgyicycVBpGOJFrTXK8HHgLygULguiNTXM1sJvCKu6dE9J9DaJzizQb7SQUeAEYCVcBa4Hx3L4xSTmlC9eE6/rhmN0v+vpUPC8oZ3rs7P/7iJGZPHqgb64l0UOYeP+O6mZmZnpWVFXSMdqWwrJpl7+1k6Ts7yC+tZtyANL5yzggunDCABBUGkbhnZqvcPbOxbbrVRge1blcRS9/ZwQvZe6g5XM/M0X34yaWTOGtUH40xiAigAtGhlFUf5sXsPTy+cgcbdpeQ1KUzl2YO5oozhzOq77HMShaRjkQFIs7V1zvvbivk6VW7eGX9Pipr6xjbP5U7Z49nzpRBpHXrEnREEYlRKhBxyN3ZuLeU57N38+LaPewpriK1awJzpgzkkmmDmTq0l04jiUizVCDixJGi8MqGvby8fi8fFpST0Mk4e0wGt5w/ls+N609Som6HISLHTgWiHTtcV8/72w/x+sb9/OWD/ew8WEEngzNG9OaKGSdx4YQBpCcnBh1TRNopFYh2Zl9xFX/LzedvuQW8veUApVWHSUzoxIyRvbn2nJGcO76flvYUkahQgYhxRRU1rNx2kHc+LGR53gHy8kN3JumX1pULTh3Av4zNYOboDJK76n+liESXvlViiLuzp7iKrO0HWbXjEO9tO8imfaF7HiZ16cz0k9K5NHMwM0dnMLZ/qgaaRaRVqUAEqKSqlpzdJWTvKmLtziLWflTEvpIqIFQQpg3rxbc+O4DTR/Rm0pAedE3QILOItB0ViDbg7uwrqWLj3hI27i3lg70l5OwuZnthxT/7DOvdneknpTNtWC+mDevF2P6putWFiARKBSKK6uudPcWVfFhQTl5+GXn5ZeTuLyV3fymlVYf/2W9wryQmDOrBFzOHMH5gGpMG96SXZhuJSIxRgWih6sN17C2q4qNDFeworOCjgxVsLyxn+4HQn9WH6//Zt2f3Lozpm8q/TRrImH6pnDIgjbEDUnX1soi0CyoQEWrr6jlQVs3+kmr2FVeyr7iKvcVV7CmuYk9RJbsPVbK/tIrIG+AmJnRiaHp3hvdO5uwxfTipTwqj+qYwMiOZ9OREDSSLSLvV4QuEu3PRL5ezr7iKwvIaGt79PDGhEwN7dGNAjyRmjOrDkPQkBvfqzuBeSQzr3Z1+qd3opIV0RCQORaVAmNkNwBXABGCZu1/RTP9vALcAScAzhBYYqg5vGw78Djgd2Anc4O6vRyNnE1kYlZHChEE96Jvajb5pXemf1o1+ad0Y0KObjgJEpMOK1hHEHuAu4FxCX/pNMrNzgVuBT4df9xzw/XAbwDLgHeCC8OPp8HrXBVHK+gn/c9mU1tq1iEi7FZV5lO7+rLv/kdByo825HHjQ3XPc/RDwA0JHH5jZGGAqcLu7V7r7M8B6YG40coqIyLELYqL9eCA74nk20M/Meoe3bXX30gbbx7dhPhERIZgCkQIURzw/8vfURrYd2d7kcmdmttjMsswsq6Cg1c5CiYh0OM0WCDN7y8y8icfy4/g3y4C0iOdH/l7ayLYj20tpgrsvcfdMd8/MyMg4jjgiItKYZguEu89yd2vicdZx/Js5wKSI55OA/e5eGN42wsxSG2zPOY5/R0RETkBUTjGZWYKZdQM6A53NrJuZNTVDailwtZmNM7NewHeBhwHcPRdYC9we3scXgImEpsKKiEgbitYYxHeBSkJTVb8U/vt3AcxsqJmVmdlQAHf/M3Av8CawI/y4PWJflwGZwCHgR8AlrTnFVUREGmfe8NLhdiwzM9OzsrKCjiEi0m6Y2Sp3z2x0WzwVCDMrIHREcjz6AAeiGCdalKtllKtllKtl4jHXMHdvdIZPXBWIE2FmWU1V0SApV8soV8soV8t0tFxakUZERBqlAiEiIo1Sgfg/S4IO0ATlahnlahnlapkOlUtjECIi0igdQYiISKNUIEREpFEqECIi0igViEaY2WgzqzKzx4LOAmBmj5nZXjMrMbNcM/tyDGTqamYPmtkOMys1szVmdn7QuSC0BG74FvDVZvZwwFnSzew5MysP/6zmB5nniFj6GR0R4++pmPsMRmqt76xoLTkab+4H3g86RIQfAle7e7WZjQXeMrM17r4qwEwJwEfAOYTWDr8A+L2ZTXD37QHmghYsgdsG7gdqgH7AZOAlM8t296DvUBxLP6MjYvk9FYufwUit8p2lI4gGzOwyoAj4a9BZjggvz1p95Gn4MTLASLh7ubvf4e7b3b3e3f8EbAOmBZkrnK0lS+C2GjNLJrRc7m3uXubuy4EXgIVB5oLY+RlFivH3VMx9Bo9oze8sFYgIZpYG3Al8K+gsDZnZr8ysAtgE7AVeDjjSx5hZP2AMWrsj0higLnwb+yO0hO4xirX3VCx+Blv7O0sF4uN+ADzo7h8FHaQhd7+e0NKrM4Fngeqjv6LtmFkX4HHgEXffFHSeGNLiJXQlJBbfUzH6GWzV76wOUyCaWzrVzCYDnwF+Fku5Ivu6e134NMVg4LpYyGVmnYBHCZ1nv6E1M7UkV4xo8RK60vbvqZZoy89gc9riO6vDDFK7+6yjbTezrwPDgZ1mBqHf/jqb2Th3nxpUriYk0MrnP48ll4V+UA8SGoC9wN1rWzPTseaKIblAgpmNdvct4TYtoXsUQbynjlOrfwaPwSxa+TurwxxBHIMlhP6HTw4//hd4idAsj8CYWV8zu8zMUsyss5mdC8wD3ggyV9gDwCnARe5eGXSYI6xlS+C2GncvJ3Qq4k4zSzazGcBsQr8dBypWfkaNiLn3VAx/Blv/O8vd9WjkAdwBPBYDOTKAvxGapVACrAeuiYFcwwjN5KgidCrlyGNBDGS7g/+baXLkcUdAWdKBPwLlhKZuzg/65xNrP6NYf0/F6mewif+nUf3O0s36RESkUTrFJCIijVKBEBGRRqlAiIhIo1QgRESkUSoQIiLSKBUIERFplAqEiIg0SgVCREQa9f8Ba2yC1VH8xekAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_function(partial(sigmoid_range,lo=-1,hi=1), min=-4, max=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We didn't specify a loss function, which means we're getting whatever fastai chooses as the default. Let's see what it picked for us:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FlattenedLoss of MSELoss()"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dls.loss_func"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This makes sense, since when coordinates are used as the dependent variable, most of the time we're likely to be trying to predict something as close as possible; that's basically what `MSELoss` (mean squared error loss) does. If you want to use a different loss function, you can pass it to `cnn_learner` using the `loss_func` parameter.\n",
"\n",
"Note also that we didn't specify any metrics. That's because the MSE is already a useful metric for this task (although it's probably more interpretable after we take the square root). \n",
"\n",
"We can pick a good learning rate with the learning rate finder:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.03981071710586548)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn.lr_find()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll try an LR of 2e-2:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.049488</td>\n",
" <td>0.022839</td>\n",
" <td>00:39</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.008415</td>\n",
" <td>0.005187</td>\n",
" <td>00:54</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.003400</td>\n",
" <td>0.000343</td>\n",
" <td>00:55</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.001462</td>\n",
" <td>0.000100</td>\n",
" <td>00:55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lr = 1e-2\n",
"learn.fine_tune(3, lr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generally when we run this we get a loss of around 0.0001, which corresponds to an average coordinate prediction error of:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.01"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.sqrt(0.0001)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This sounds very accurate! But it's important to take a look at our results with `Learner.show_results`. The left side are the actual (*ground truth*) coordinates and the right side are our model's predictions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x576 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn.show_results(ds_idx=1, max_n=3, figsize=(6,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's quite amazing that with just a few minutes of computation we've created such an accurate key points model, and without any special domain-specific application. This is the power of building on flexible APIs, and using transfer learning! It's particularly striking that we've been able to use transfer learning so effectively even between totally different tasks; our pretrained model was trained to do image classification, and we fine-tuned for image regression."
]
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"source": [
"## Conclusion"
]
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"In problems that are at first glance completely different (single-label classification, multi-label classification, and regression), we end up using the same model with just different numbers of outputs. The loss function is the one thing that changes, which is why it's important to double-check that you are using the right loss function for your problem.\n",
"\n",
"fastai will automatically try to pick the right one from the data you built, but if you are using pure PyTorch to build your `DataLoader`s, make sure you think hard when you have to decide on your about your choice of loss function, and remember that you most probably want:\n",
"\n",
"- `nn.CrossEntropyLoss` for single-label classification\n",
"- `nn.BCEWithLogitsLoss` for multi-label classification\n",
"- `nn.MSELoss` for regression"
]
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"source": [
"## Questionnaire"
]
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"source": [
"1. How could multi-label classification improve the usability of the bear classifier?\n",
"1. How do we encode the dependent variable in a multi-label classification problem?\n",
"1. How do you access the rows and columns of a DataFrame as if it was a matrix?\n",
"1. How do you get a column by name from a DataFrame?\n",
"1. What is the difference between a `Dataset` and `DataLoader`?\n",
"1. What does a `Datasets` object normally contain?\n",
"1. What does a `DataLoaders` object normally contain?\n",
"1. What does `lambda` do in Python?\n",
"1. What are the methods to customize how the independent and dependent variables are created with the data block API?\n",
"1. Why is softmax not an appropriate output activation function when using a one hot encoded target?\n",
"1. Why is `nll_loss` not an appropriate loss function when using a one-hot-encoded target?\n",
"1. What is the difference between `nn.BCELoss` and `nn.BCEWithLogitsLoss`?\n",
"1. Why can't we use regular accuracy in a multi-label problem?\n",
"1. When is it okay to tune a hyperparameter on the validation set?\n",
"1. How is `y_range` implemented in fastai? (See if you can implement it yourself and test it without peeking!)\n",
"1. What is a regression problem? What loss function should you use for such a problem?\n",
"1. What do you need to do to make sure the fastai library applies the same data augmentation to your inputs images and your target point coordinates?"
]
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"source": [
"### Further Research"
]
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"1. Read a tutorial about Pandas DataFrames and experiment with a few methods that look interesting to you. See the book's website for recommended tutorials.\n",
"1. Retrain the bear classifier using multi-label classification. See if you can make it work effectively with images that don't contain any bears, including showing that information in the web application. Try an image with two different kinds of bears. Check whether the accuracy on the single-label dataset is impacted using multi-label classification."
]
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