subtitles/en/28_tensorflow-predictions-and-metrics.srt

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(air whooshing)

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- In our other videos,

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and as always, there'll be links below

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if you want to check those out,

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we showed you how to
initialize and fine-tune

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a transformer model in TensorFlow.

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So the question now is,
what can we do with a model

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after we train it?

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The obvious thing to try

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is to use it to get
predictions for new data,

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so let's see how to do that.

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Again, if you're familiar
with Keras, the good news

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is that because there are
just standard Keras models,

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we can use the standard
Keras predict method,

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as shown here.

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You simply pass in tokenized
text to this method,

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like you'd get from a tokenizer,

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and you get your results.

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Our models can output
several different things,

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depending on the options you set,

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but most of the time the thing you want

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is the output logits.

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If you haven't come
across them before logits,

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sometimes pronounced to
logits because no one's sure,

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are the outputs of the
last layer of the network

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because before a softmax has been applied.

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So if you want to turn the logits

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into the model's probability outputs,

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you just apply a softmax, like so.

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What if we want to turn
those probabilities

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into class predictions?

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Again, it's very straightforward.

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We just pick the biggest
probability for each output

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and you can get that immediately
with the argmax function.

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argmax will return the index

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of the largest probability in each row

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which means that we'll
get a vector of integers.

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So zero if the largest probability
was in the zero position,

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one in the first position, and so on.

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So these are our class
predictions indicating class zero,

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class one, and so on.

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In fact, if class
predictions are all you want,

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you can skip the softmax step entirely

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because the largest logit
will always be the largest

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probability as well.

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So if probabilities and class
predictions are all you want,

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then you've seen everything
you need at this point.

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But if you're interested
in benchmarking your model

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or using it for research,

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you might want to delve deeper
into the results you get.

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And one way to do that is
to compute some metrics

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for the model's predictions.

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If you're following
along with our datasets

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and fine tuning videos,

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we got our data from the MRPC dataset,

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which is part of the GLUE benchmark.

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Each of the GLUE datasets

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as well as many other datasets
in our dataset, Light Hub

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has some predefined metrics,

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and we can load them easily

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with the datasets load metric function.

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For the MRPC dataset, the
built-in metrics are accuracy

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which just measures the
percentage of the time

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the model's prediction was correct,

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and the F1 score,

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which is a slightly more complex measure

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that measures how well
the model trades off

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precision and recall.

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To compute those metrics
to benchmark our model,

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we just pass them the model's predictions,

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and to the ground truth labels,

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and we get our results in a
straightforward Python dict.

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If you're familiar with Keras though,

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you might notice that this
is a slightly weird way

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to compute metrics,

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because we're only computing metrics

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at the very end of training.

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But in Keras, you have
this built-in ability

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to compute a wide range of metrics

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on the fly while you're training,

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which gives you a very useful insight

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into how training is going.

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So if you want to use built-in metrics,

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it's very straightforward

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and you use the standard
Keras approach again.

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You just pass a metric
argument to the compile method.

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As with things like loss and optimizer,

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you can specify the
metrics you want by string

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or you can import the
actual metric objects

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and pass specific arguments to them.

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But note that unlike loss and accuracy,

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you have to supply metrics as a list

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even if there's only one metric you want.

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Once a model has been
compiled with a metric,

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it will report that metric for training,

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validation, and predictions.

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Assuming there are labels
passed to the predictions.

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You can even write your
own metric classes.

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Although this is a bit beyond
the scope of this course,

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I'll link to the relevant TF docs below

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because it can be very handy

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if you want a metric that isn't supported

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by default in Keras,

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such as the F1 score.

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(air whooshing)