AI News, Recurrent Neural Networks by Example in Python
Recurrent Neural Networks by Example in Python
Keras is an incredible library: it allows us to build state-of-the-art models in a few lines of understandable Python code.
The code for a simple LSTM is below with an explanation following: We are using the Keras Sequential API which means we build the network up one layer at a time.
The input to the LSTM layer is (None, 50, 100) which means that for each batch (the first dimension), each sequence has 50 timesteps (words), each of which has 100 features after embedding.
We can quickly load in the pre-trained embeddings from disk and make an embedding matrix with the following code: What this does is assign a 100-dimensional vector to each word in the vocab.
To explore the embeddings, we can use the cosine similarity to find the words closest to a given query word in the embedding space: Embeddings are learned which means the representations apply specifically to one task.
If these embeddings were trained on tweets, we might not expect them to work well, but since they were trained on Wikipedia data, they should be generally applicable to a range of language processing tasks.
How to Use Word Embedding Layers for Deep Learning with Keras
Word embeddings provide a dense representation of words and their relative meanings.
word embedding is a class of approaches for representing words and documents using a dense vector representation.
It is an improvement over more the traditional bag-of-word model encoding schemes where large sparse vectors were used to represent each word or to score each word within a vector to represent an entire vocabulary.
These representations were sparse because the vocabularies were vast and a given word or document would be represented by a large vector comprised mostly of zero values.
Instead, in an embedding, words are represented by dense vectors where a vector represents the projection of the word into a continuous vector space.
The position of a word within the vector space is learned from text and is based on the words that surround the word when it is used.
Two popular examples of methods of learning word embeddings from text include: In addition to these carefully designed methods, a word embedding can be learned as part of a deep learning model.
It must specify 3 arguments: It must specify 3 arguments: For example, below we define an Embedding layer with a vocabulary of 200 (e.g.
integer encoded words from 0 to 199, inclusive), a vector space of 32 dimensions in which words will be embedded, and input documents that have 50 words each.
If you wish to connect a Dense layer directly to an Embedding layer, you must first flatten the 2D output matrix to a 1D vector using the Flatten layer.
In this section, we will look at how we can learn a word embedding while fitting a neural network on a text classification problem.
We will define a small problem where we have 10 text documents, each with a comment about a piece of work a student submitted.
We will estimate the vocabulary size of 50, which is much larger than needed to reduce the probability of collisions from the hash function.
For example, the researchers behind GloVe method provide a suite of pre-trained word embeddings on their website released under a public domain license.
You can download this collection of embeddings and we can seed the Keras Embedding layer with weights from the pre-trained embedding for the words in your training dataset.
Keras provides a Tokenizer class that can be fit on the training data, can convert text to sequences consistently by calling the texts_to_sequences() method on the Tokenizer class, and provides access to the dictionary mapping of words to integers in a word_index attribute.
Next, we need to load the entire GloVe word embedding file into memory as a dictionary of word to embedding array.
In practice, I would encourage you to experiment with learning a word embedding using a pre-trained embedding that is fixed and trying to perform learning on top of a pre-trained embedding.
The Keras Blog
In this tutorial, we will walk you through the process of solving a text classification problem using pre-trained word embeddings and a convolutional neural network.
The task we will try to solve will be to classify posts coming from 20 different newsgroup, into their original 20 categories --the infamous '20 Newsgroup dataset'.
Here are a few sample categories: Here's how we will solve the classification problem: First, we will simply iterate over the folders in which our text samples are stored, and format them into a list of samples.
We will also prepare at the same time a list of class indices matching the samples: Then we can format our text samples and labels into tensors that can be fed into a neural network.
Next, we compute an index mapping words to known embeddings, by parsing the data dump of pre-trained embeddings: At this point we can leverage our embedding_index dictionary and our word_index to compute our embedding matrix: We load this embedding matrix into an Embedding layer.
These input sequences should be padded so that they all have the same length in a batch of input data (although an Embedding layer is capable of processing sequence of heterogenous length, if you don't pass an explicit input_length argument to the layer).
Finally we can then build a small 1D convnet to solve our classification problem: This model reaches 95% classification accuracy on the validation set after only 2 epochs.
In general, using pre-trained embeddings is relevant for natural processing tasks were little training data is available (functionally the embeddings act as an injection of outside information which might prove useful for your model).
Machine Learning — Word Embedding Sentiment Classification using Keras
In the previous post, we discussed various steps of text processing involved in Nature Language Processing (NLP) and also implemented a basic Sentiment Analyzer using some of the classical ML techniques.
The input X is a piece of text and the output Y is the sentiment which we want to predict, such as the star rating of a movie review.
If we can train a system to map from X to Y based on a labelled data set like above, then such a system can be used to predict sentiment of a reviewer after watching a movie.
In this post we will focus on below tasks: Deep learning text classification model architectures generally consist of the following components connected in sequence: The IMDB movie review set can be downloaded from here.
This dataset for binary sentiment classification contains set of 25,000 highly polar movie reviews for training, and 25,000 for testing.
First we load the IMDb dataset, the text reviews are labelled as 1 or 0 for positive and negative sentiment respectively.
The Embedding layer requires the specification of the vocabulary size (vocab_size), the size of the real-valued vector space EMBEDDING_DIM = 100, and the maximum length of input documents max_length .
The Embedding layer is initialized with random weights and will learn an embedding for all of the words in the training dataset during training of the model.
The output gives the prediction of the word either to be 1 (positive sentiment) or 0 (negative sentiment).
Value closer to 1 is strong positive sentiment and a value close to 0 is a strong negative sentiment.
The first step is to prepare the text corpus for learning the embedding by creating word tokens, removing punctuation, removing stop words etc.
workers– Number of threads used in training parallelization, to speed up training After we train the model on our IMDb dataset, it builds a vocabulary size = 134156 .
Now we will map embeddings from the loaded word2vec model for each word to the tokenizer_obj.word_index vocabulary and create a matrix with of word vectors.
In the below code, the only change from previous model is using the embedding_matrix as input to the Embedding layer and setting trainable = False, since the embedding is already learned.
Finally training the classification model on train and validation test set, we get improvement in accuracy with each epoch run.
Text Classification Using CNN, LSTM and Pre-trained Glove Word Embeddings: Part-3
This is a part of series articles on classifying Yelp review comments using deep learning techniques and word embeddings.
Followings are the list of brief contents of different part : Data processing involves the following steps: Detail code of the data processing can be found on Part-1.
It was trained on a dataset of one billion tokens (words) with a vocabulary of 400 thousand words.
The glove has embedding vector sizes, including 50, 100, 200 and 300 dimensions.
- On Sunday, January 20, 2019
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