{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from utils 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 we learnt some important practical techniques for training models in practice. Issues 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 other types of computer vision problems, multi-label classification and regression. 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 an image, where you may not have exactly one type of object in the image. 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 before is 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. And then, tested by passing in an image which is not of any of your recognised 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."
]
},
{
"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 fastai2.vision.all import *\n",
"path = untar_data(URLs.PASCAL_2007)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset is different to the ones we have seen before, and that it is not structured by file name or folder, but instead comes with a CSV (comma separated values) file telling us what labels to use for each image. We can have a look at the CSV file by reading it into a Pandas DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
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" \n",
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is_valid
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000005.jpg
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horse person
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000012.jpg
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000016.jpg
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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": [
"We can see that 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, it’s not actually a panda! *Pandas* is a Python library that is used to manipulate and analysis tabular and timeseries 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 you see above.\n",
"\n",
"You can access rows and columns of a DataFrame with the `iloc` property, which lets you access rows and columns as if it is a matrix:"
]
},
{
"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",
"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": "markdown",
"metadata": {},
"source": [
"TK"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas is a fast and flexible library, and is an important part of every data scientist’s Python toolbox. Unfortunately, its API can be rather confusing and surprising, so it takes a while to get familiar with it. If you haven’t used Pandas before, we’d suggest going through a tutorial; we are particularly fond of the book “*Python for Data Analysis*” by Wes McKinney, the creator of Pandas. 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 McKinney’s book."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### End sidebar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Constructing a data block"
]
},
{
"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 which returns a tuple of your independent and dependent variable for a single item\n",
"- `DataLoader`: an iterator which 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\n",
"\n",
"On top of these, fastai provides two classes for bringing your training and validation sets together:\n",
"\n",
"- `Datasets`: an object which contains a training `Dataset` and a validation `Dataset`\n",
"- `DataLoaders`: an object which 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), it’s often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after that’s working.\n",
"\n",
"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. It’s easy to debug, because you know that if there are any problems, it is in the line of code you just typed!\n",
"\n",
"Let’s 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": [
"(fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object, fname 008663.jpg\n",
" labels car person\n",
" is_valid False\n",
" Name: 4346, dtype: object)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dsets.train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, this simply returns a row of the dataframe, twice. This is because by default, the datablock 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": [
"('005620.jpg', 'aeroplane')"
]
},
"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 Python’s *lambda* keyword. This is just a shortcut for defining and then referring to a function. The above is identical to the following more verbose approach:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('002549.jpg', 'tvmonitor')"
]
},
"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, however 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 (they 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 second will need to be split on the space character (which is the default for Python’s split function) so that it becomes a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"Path.BASE_PATH = path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Path('train/002844.jpg'), ['train'])"
]
},
"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 which 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 let’s test it out:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(PILImage mode=RGB size=500x375,\n",
" TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 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 can’t 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": [
"Let’s 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) ['dog']"
]
},
"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 fastai’s L class, you 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) list 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. 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": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dls.show_batch(rows=1, cols=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And 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": [
"### Binary cross entropy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll create our `Learner`. We saw in <> that a `Learner` object contains four main things: the model, a `DataLoaders` object, an `Optimizer`, and the loss function to use. We already how our `DataLoaders`, and 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 you 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": [
"Have a 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. Here’s what one of those activations looks like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-1.0028, 0.3400, -0.5906, 0.7806, 3.1160, -0.1994, 1.3180, 1.6361, -1.7553, 0.2217, 2.8052, 1.3229, 0.9369, -1.4760, -0.3204, -2.3116, -3.8615, -1.5931, 0.0745, -3.6006],\n",
" device='cuda:5', grad_fn=)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"activs[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> note: 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 aren’t yet scaled between zero and one. We learned in <> how to scale activations to be between zero and one: the `sigmoid` function. We also saw how to calculate a loss based on this--this is our loss function from <>, 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, 1-inputs, 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 one, 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 one is not a good idea. By the same reasoning, we may want the sum to be *less* than one, 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 colums."
]
},
{
"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 it's 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 our example above.\n",
"\n",
"The equivalent for single-label datasets (like MNIST or Pets), 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=)"
]
},
"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` have 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: since we are in 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": [
"