This the second part of the Recurrent Neural Network Tutorial. The first part is here.
Code to follow along is on Github.
In this part we will implement a full Recurrent Neural Network from scratch using Python and optimize our implementation using Theano, a library to perform operations on a GPU. The full code is available on Github. I will skip over some boilerplate code that is not essential to understanding Recurrent Neural Networks, but all of that is also on Github.
Language Modeling
Our goal is to build a Language Model using a Recurrent Neural Network. Here’s what that means. Let’s say we have sentence of
words. A language model allows us to predict the probability of observing the sentence (in a given dataset) as:
In words, the probability of a sentence is the product of probabilities of each word given the words that came before it. So, the probability of the sentence “He went to buy some chocolate” would be the probability of “chocolate” given “He went to buy some”, multiplied by the probability of “some” given “He went to buy”, and so on.
Why is that useful? Why would we want to assign a probability to observing a sentence?
First, such a model can be used as a scoring mechanism. For example, a Machine Translation system typically generates multiple candidates for an input sentence. You could use a language model to pick the most probable sentence. Intuitively, the most probable sentence is likely to be grammatically correct. Similar scoring happens in speech recognition systems.
But solving the Language Modeling problem also has a cool side effect. Because we can predict the probability of a word given the preceding words, we are able to generate new text. It’s a generative model. Given an existing sequence of words we sample a next word from the predicted probabilities, and repeat the process until we have a full sentence. Andrej Karparthy has a great post that demonstrates what language models are capable of. His models are trained on single characters as opposed to full words, and can generate anything from Shakespeare to Linux Code.
Note that in the above equation the probability of each word is conditioned on all previous words. In practice, many models have a hard time representing such long-term dependencies due to computational or memory constraints. They are typically limited to looking at only a few of the previous words. RNNs can, in theory, capture such long-term dependencies, but in practice it’s a bit more complex. We’ll explore that in a later post.
Training Data and Preprocessing
To train our language model we need text to learn from. Fortunately we don’t need any labels to train a language model, just raw text. I downloaded 15,000 longish reddit comments from a dataset available on Google’s BigQuery. Text generated by our model will sound like reddit commenters (hopefully)! But as with most Machine Learning projects we first need to do some pre-processing to get our data into the right format.
1. Tokenize Text
We have raw text, but we want to make predictions on a per-word basis. This means we must tokenize our comments into sentences, and sentences into words. We could just split each of the comments by spaces, but that wouldn’t handle punctuation properly. The sentence “He left!” should be 3 tokens: “He”, “left”, “!”. We’ll use NLTK’s
word_tokenize
and
sent_tokenize
methods, which do most of the hard work for us.
2. Remove infrequent words
Most words in our text will only appear one or two times. It’s a good idea to remove these infrequent words. Having a huge vocabulary will make our model slow to train (we’ll talk about why that is later), and because we don’t have a lot of contextual examples for such words we wouldn’t be able to learn how to use them correctly anyway. That’s quite similar to how humans learn. To really understand how to appropriately use a word you need to have seen it in different contexts.
In our code we limit our vocabulary to the
vocabulary_size
most common words (which I set to 8000, but feel free to change it). We replace all words not included in our vocabulary by
UNKNOWN_TOKEN
. For example, if we don’t include the word “nonlinearities” in our vocabulary, the sentence “nonlineraties are important in neural networks” becomes “UNKNOWN_TOKEN are important in Neural Networks”. The word
UNKNOWN_TOKEN
will become part of our vocabulary and we will predict it just like any other word. When we generate new text we can replace
UNKNOWN_TOKEN
again, for example by taking a randomly sampled word not in our vocabulary, or we could just generate sentences until we get one that doesn’t contain an unknown token.
3. Prepend special start and end tokens
We also want to learn which words tend start and end a sentence. To do this we prepend a special
SENTENCE_START
token, and append a special
SENTENCE_END
token to each sentence. This allows us to ask: Given that the first token is
SENTENCE_START
, what is the likely next word (the actual first word of the sentence)?
4. Build training data matrices
The input to our Recurrent Neural Networks are vectors, not strings. So we create a mapping between words and indices,
index_to_word
, and
word_to_index
. For example, the word “friendly” may be at index 2001. A training example
may look like
[0, 179, 341, 416]
, where 0 corresponds to
SENTENCE_START
. The corresponding label
would be
[179, 341, 416, 1]
. Remember that our goal is to predict the next word, so y is just the x vector shifted by one position with the last element being the
SENTENCE_END
token. In other words, the correct prediction for word
179
above would be
341
, the actual next word.
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Here’s an actual training example from our text:
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Building the RNN
For a general overview of RNNs take a look at first part of the tutorial.
A recurrent neural network and the unfolding in time of the computation involved in its forward computation.
Let’s get concrete and see what the RNN for our language model looks like. The input
will be a sequence of words (just like the example printed above) and each
is a single word. But there’s one more thing: Because of how matrix multiplication works we can’t simply use a word index (like 36) as an input. Instead, we represent each word as a one-hot vector of size
vocabulary_size
. For example, the word with index 36 would be the vector of all 0’s and a 1 at position 36. So, each
will become a vector, and
will be a matrix, with each row representing a word. We’ll perform this transformation in our Neural Network code instead of doing it in the pre-processing. The output of our network
has a similar format. Each
is a vector of
vocabulary_size
elements, and each element represents the probability of that word being the next word in the sentence.
Let’s recap the equations for the RNN from the first part of the tutorial:
I always find it useful to write down the dimensions of the matrices and vectors. Let’s assume we pick a vocabulary size
and a hidden layer size
. You can think of the hidden layer size as the “memory” of our network. Making it bigger allows us to learn more complex patterns, but also results in additional computation. Then we have:
This is valuable information. Remember that
and
are the parameters of our network we want to learn from data. Thus, we need to learn a total of
parameters. In the case of
and
that’s 1,610,000. The dimensions also tell us the bottleneck of our model. Note that because
is a one-hot vector, multiplying it with
is essentially the same as selecting a column of U, so we don’t need to perform the full multiplication. Then, the biggest matrix multiplication in our network is
. That’s why we want to keep our vocabulary size small if possible.
Armed with this, it’s time to start our implementation.
Initialization
We start by declaring a RNN class an initializing our parameters. I’m calling this class
RNNNumpy
because we will implement a Theano version later. Initializing the parameters
and
is a bit tricky. We can’t just initialize them to 0’s because that would result in symmetric calculations in all our layers. We must initialize them randomly. Because proper initialization seems to have an impact on training results there has been lot of research in this area. It turns out that the best initialization depends on the activation function (
in our case) and one recommended approach is to initialize the weights randomly in the interval from
where
is the number of incoming connections from the previous layer. This may sound overly complicated, but don’t worry too much it. As long as you initialize your parameters to small random values it typically works out fine.
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Above,
word_dim
is the size of our vocabulary, and
hidden_dim
is the size of our hidden layer (we can pick it). Don’t worry about the
bptt_truncate
parameter for now, we’ll explain what that is later.
Forward Propagation
Next, let’s implement the forward propagation (predicting word probabilities) defined by our equations above:
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We not only return the calculated outputs, but also the hidden states. We will use them later to calculate the gradients, and by returning them here we avoid duplicate computation. Each
is a vector of probabilities representing the words in our vocabulary, but sometimes, for example when evaluating our model, all we want is the next word with the highest probability. We call this function
predict
:
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Let’s try our newly implemented methods and see an example output:
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For each word in the sentence (45 above), our model made 8000 predictions representing probabilities of the next word. Note that because we initialized
to random values these predictions are completely random right now. The following gives the indices of the highest probability predictions for each word:
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Calculating the Loss
To train our network we need a way to measure the errors it makes. We call this the loss function
, and our goal is find the parameters
and
that minimize the loss function for our training data. A common choice for the loss function is the cross-entropy loss. If we have
training examples (words in our text) and
classes (the size of our vocabulary) then the loss with respect to our predictions
and the true labels
is given by:
The formula looks a bit complicated, but all it really does is sum over our training examples and add to the loss based on how off our prediction are. The further away
(the correct words) and
(our predictions), the greater the loss will be. We implement the function
calculate_loss
:
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Let’s take a step back and think about what the loss should be for random predictions. That will give us a baseline and make sure our implementation is correct. We have
words in our vocabulary, so each word should be (on average) predicted with probability
, which would yield a loss of
:
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Pretty close! Keep in mind that evaluating the loss on the full dataset is an expensive operation and can take hours if you have a lot of data!
Training the RNN with SGD and Backpropagation Through Time (BPTT)
Remember that we want to find the parameters
and
that minimize the total loss on the training data. The most common way to do this is SGD, Stochastic Gradient Descent. The idea behind SGD is pretty simple. We iterate over all our training examples and during each iteration we nudge the parameters into a direction that reduces the error. These directions are given by the gradients on the loss:
. SGD also needs a learning rate, which defines how big of a step we want to make in each iteration. SGD is the most popular optimization method not only for Neural Networks, but also for many other Machine Learning algorithms. As such there has been a lot of research on how to optimize SGD using batching, parallelism and adaptive learning rates. Even though the basic idea is simple, implementing SGD in a really efficient way can become very complex. If you want to learn more about SGD this is a good place to start. Due to its popularity there are a wealth of tutorials floating around the web, and I don’t want to duplicate them here. I’ll implement a simple version of SGD that should be understandable even without a background in optimization.
But how do we calculate those gradients we mentioned above? In a traditional Neural Network we do this through the backpropagation algorithm. In RNNs we use a slightly modified version of the this algorithm called Backpropagation Through Time (BPTT). Because the parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. If you know calculus, it really is just applying the chain rule. The next part of the tutorial will be all about BPTT, so I won’t go into detailed derivation here. For a general introduction to backpropagation check out this and this post. For now you can treat BPTT as a black box. It takes as input a training example
and returns the gradients
.
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Gradient Checking
Whenever you implement backpropagation it is good idea to also implement gradient checking, which is a way of verifying that your implementation is correct. The idea behind gradient checking is that derivative of a parameter is equal to the slope at the point, which we can approximate by slightly changing the parameter and then dividing by the change:
We then compare the gradient we calculated using backpropagation to the gradient we estimated with the method above. If there’s no large difference we are good. The approximation needs to calculate the total loss for every parameter, so that gradient checking is very expensive (remember, we had more than a million parameters in the example above). So it’s a good idea to perform it on a model with a smaller vocabulary.
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SGD Implementation
Now that we are able to calculate the gradients for our parameters we can implement SGD. I like to do this in two steps: 1. A function
sdg_step
that calculates the gradients and performs the updates for one batch. 2. An outer loop that iterates through the training set and adjusts the learning rate.
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Done! Let’s try to get a sense of how long it would take to train our network:
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Uh-oh, bad news. One step of SGD takes approximately 350 milliseconds on my laptop. We have about 80,000 examples in our training data, so one epoch (iteration over the whole data set) would take several hours. Multiple epochs would take days, or even weeks! And we’re still working with a small dataset compared to what’s being used by many of the companies and researchers out there. What now?
Fortunately there are many ways to speed up our code. We could stick with the same model and make our code run faster, or we could modify our model to be less computationally expensive, or both. Researchers have identified many ways to make models less computationally expensive, for example by using a hierarchical softmax or adding projection layers to avoid the large matrix multiplications (see also here or here). But I want to keep our model simple and go the first route: Make our implementation run faster using a GPU. Before doing that though, let’s just try to run SGD with a small dataset and check if the loss actually decreases:
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Good, it seems like our implementation is at least doing something useful and decreasing the loss, just like we wanted.
Training our Network with Theano and the GPU
I have previously written a tutorial on Theano, and since all our logic will stay exactly the same I won’t go through optimized code here again. I defined a
RNNTheano
class that replaces the numpy calculations with corresponding calculations in Theano. Just like the rest of this post, the code is also available Github.
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This time, one SGD step takes 70ms on my Mac (without GPU) and 23ms on a g2.2xlarge Amazon EC2 instance with GPU. That’s a 15x improvement over our initial implementation and means we can train our model in hours/days instead of weeks. There are still a vast number of optimizations we could make, but we’re good enough for now.
To help you avoid spending days training a model I have pre-trained a Theano model with a hidden layer dimensionality of 50 and a vocabulary size of 8000. I trained it for 50 epochs in about 20 hours. The loss was was still decreasing and training longer would probably have resulted in a better model, but I was running out of time and wanted to publish this post. Feel free to try it out yourself and trian for longer. You can find the model parameters in
data/trained-model-theano.npz
in the Github repository and load them using the
load_model_parameters_theano
method:
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Generating Text
Now that we have our model we can ask it to generate new text for us! Let’s implement a helper function to generate new sentences:
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A few selected (censored) sentences. I added capitalization.
- Anyway, to the city scene you’re an idiot teenager.
- What ? ! ! ! ! ignore!
- Screw fitness, you’re saying: https
- Thanks for the advice to keep my thoughts around girls.
- Yep, please disappear with the terrible generation.
Looking at the generated sentences there are a few interesting things to note. The model successfully learn syntax. It properly places commas (usually before and’s and or’s) and ends sentence with punctuation. Sometimes it mimics internet speech such as multiple exclamation marks or smileys.
However, the vast majority of generated sentences don’t make sense or have grammatical errors (I really picked the best ones above). One reason could be that we did not train our network long enough (or didn’t use enough training data). That may be true, but it’s most likely not the main reason. Our vanilla RNN can’t generate meaningful text because it’s unable to learn dependencies between words that are several steps apart. That’s also why RNNs failed to gain popularity when they were first invented. They were beautiful in theory but didn’t work well in practice, and we didn’t immediately understand why.
Fortunately, the difficulties in training RNNs are much better understood now. In the next part of this tutorial we will explore the Backpropagation Through Time (BPTT) algorithm in more detail and demonstrate what’s called the vanishing gradient problem. This will motivate our move to more sophisticated RNN models, such as LSTMs, which are the current state of the art for many tasks in NLP (and can generate much better reddit comments!). Everything you learned in this tutorial also applies to LSTMs and other RNN models, so don’t feel discouraged if the results for a vanilla RNN are worse then you expected.
That’s it for now. Please leave questions or feedback in the comments! and don’t forget to check out the /code.
原文位址: http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/