AI News, Understanding Natural Language with Deep Neural Networks Using Torch

Understanding Natural Language with Deep Neural Networks Using Torch

When you learn a new language, you start with words: understanding their meaning, identifying similar and dissimilar words, and developing a sense of contextual appropriateness of a word.

You start with a small dictionary of words, building up your dictionary over time, mentally mapping each newly learned word close to similar words in your dictionary.

Word embeddings can either be learned in a general-purpose fashion before-hand by reading large amounts of text (like Wikipedia), or specially learned for a particular task (like sentiment analysis).

After the machine has learned word embeddings, the next problem to tackle is the ability to string words together appropriately in small, grammatically correct sentences which make sense.

For example, given a sentence (“I am eating pasta for lunch.”), and a word (“cars”), if the machine can tell you with high confidence whether or not the word is relevant to the sentence (“cars”

To fill in the blank, a good language model would likely give higher probabilities to all edibles like “pasta”, “apple”, or “chocolate”, and it would give lower probability to other words in the dictionary which are contextually irrelevant like “taxi”, “building”, or “music”.

Traditionally language modeling has been done by computing n-grams—groups of words—and processing the n-grams further with heuristics, before feeding them into machine learning models.

When you read a large body of text, like wikipedia, you could generate a new sentence by pairing together 2-grams and 3-grams and matching them with other pairs that were seen before.

Over the last few years, deep neural networks have beaten n-gram based models comfortably on a wide variety of natural language tasks.

Deep learning—neural networks that have several stacked layers of neurons, usually accelerated in computation using GPUs—has seen huge success recently in many fields such as computer vision, speech recognition, and natural language processing, beating the previous state-of-the-art results on a variety of tasks and domains such as language modeling, translation, speech recognition, and object recognition in images.

Continuing on the topic of word embeddings, let’s discuss word-level networks, where each word in the sentence is translated into a set of numbers before being fed into the neural network.

These numbers change over time while the neural net trains itself, encoding unique properties such as the semantics and contextual information for each word.

Embeddings are stored in a simple lookup table (or hash table), that given a word, returns the embedding (which is an array of numbers).

Word embeddings are usually initialized to random numbers (and learned during the training phase of the neural network), or initialized from previously trained models over large texts like Wikipedia.

Convolutional Neural Networks (ConvNets), which were covered in a previous Parallel Forall post by Evan Shelhamer, have enjoyed wide success in the last few years in several domains including images, video, audio and natural language processing.

When applied to images, ConvNets usually take raw image pixels as input, interleaving convolution layers along with pooling layers with non-linear functions in between, followed by fully connected layers.

Similarly, for language processing, ConvNets take the outputs of word embeddings as input, and then apply interleaved convolution and pooling operations, followed by fully connected layers.

Convolutional Neural Networks—and more generally, feed-forward neural networks—do not traditionally have a notion of time or experience unless you explicitly pass samples from the past as input.

The neural networks package in torch implements modules, which are different kinds of neuron layers, and containers, which can have several modules within them.

This makes it easy to calculate the derivative of any neuron in the network with respect to the objective function of the network (via the chain rule).

The following small example of modules shows how to calculate the element-wise Tanh of an input matrix, by creating an nn.Tanh module and passing the input through it.

cuBLAS, and more recently cuDNN, have accelerated deep learning research quite significantly, and the recent success of deep learning can be partly attributed to these awesome libraries from NVIDIA.

cuDNN accelerates the training of neural networks compared to Torch’s default CUDA backend (sometimes up to 30%) and is often several orders of magnitude faster than using CPUs.

This time the input consists of a character-level representation of a program in a restricted subset of python, and the target output is the result of program execution.

How to Develop a Word-Level Neural Language Model and Use it to Generate Text

A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence.

Neural network models are a preferred method for developing statistical language models because they can use a distributed representation where different words with similar meanings have similar representation and because they can use a large context of recently observed words when making predictions.

conversation) on the topic of order and justice within a city state The entire text is available for free in the public domain.

You can download the ASCII text version of the entire book (or books) here: Download the book text and place it in your current working directly with the filename ‘republic.txt‘

The specific way we prepare the data really depends on how we intend to model it, which in turn depends on how we intend to use it.

The language model will be statistical and will predict the probability of each word given an input sequence of text.

This input length will also define the length of seed text used to generate new sequences when we use the model.

We could process the data so that the model only ever deals with self-contained sentences and pad or truncate the text to meet this requirement for each input sequence.

Instead, to keep the example brief, we will let all of the text flow together and train the model to predict the next word across sentences, paragraphs, and even books or chapters in the text.

Now that we have a model design, we can look at transforming the raw text into sequences of 50 input words to 1 output word, ready to fit a model.

Based on reviewing the raw text (above), below are some specific operations we will perform to clean the text.

Below is the function clean_doc() that takes a loaded document as an argument and returns an array of clean tokens.

We can run this cleaning operation on our loaded document and print out some of the tokens and statistics as a sanity check.

We can organize the long list of tokens into sequences of 50 input words and 1 output word.

We can do this by iterating over the list of tokens from token 51 onwards and taking the prior 50 tokens as a sequence, then repeating this process to the end of the list of tokens.

The code to split the list of clean tokens into sequences with a length of 51 tokens is listed below.

It has a few unique characteristics: Specifically, we will use an Embedding Layer to learn the representation of words, and a Long Short-Term Memory (LSTM) recurrent neural network to learn to predict words based on their context.

Once loaded, we can split the data into separate training sequences by splitting based on new lines.

First, the Tokenizer must be trained on the entire training dataset, which means it finds all of the unique words in the data and assigns each a unique integer.

We can then use the fit Tokenizer to encode all of the training sequences, converting each sequence from a list of words to a list of integers.

The Embedding layer needs to allocate a vector representation for each word in this vocabulary from index 1 to the largest index and because indexing of arrays is zero-offset, the index of the word at the end of the vocabulary will be 7,409;

This means converting it from an integer to a vector of 0 values, one for each word in the vocabulary, with a 1 to indicate the specific word at the index of the words integer value.

This is so that the model learns to predict the probability distribution for the next word and the ground truth from which to learn from is 0 for all words except the actual word that comes next.

We know that there are 50 words because we designed the model, but a good generic way to specify that is to use the second dimension (number of columns) of the input data’s shape.

dense fully connected layer with 100 neurons connects to the LSTM hidden layers to interpret the features extracted from the sequence.

The output layer predicts the next word as a single vector the size of the vocabulary with a probability for each word in the vocabulary.

Finally, the model is fit on the data for 100 training epochs with a modest batch size of 128 to speed things up.

During training, you will see a summary of performance, including the loss and accuracy evaluated from the training data at the end of each batch update.

We can determine this from the input sequences by calculating the length of one line of the loaded data and subtracting 1 for the expected output word that is also on the same line.

The model can predict the next word directly by calling model.predict_classes() that will return the index of the word with the highest probability.

We can wrap all of this into a function called generate_seq() that takes as input the model, the tokenizer, input sequence length, the seed text, and the number of words to generate.

when he said that a man when he grows old may learn many things for he can no more learn much than he can run much youth is the time for any extraordinary toil of course and therefore calculation and geometry and all the other elements of instruction which are a Then 50 words of generated text are printed.

preparation for dialectic should be presented to the name of idle spendthrifts of whom the other is the manifold and the unjust and is the best and the other which delighted to be the opening of the soul of the soul and the embroiderer will have to be said at You will get different results.

In this tutorial, you discovered how to develop a word-based language model using a word embedding and a recurrent neural network.

7 types of Artificial Neural Networks for Natural Language Processing

An artificial neural network (ANN) is a computational nonlinear model based on the neural structure of the brain that is able to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples.

An artificial neural network consists of artificial neurons or processing elements and is organized in three interconnected layers: input, hidden that may include more than one layer, and output.

Artificial neuron with four inputs http://en.citizendium.org/wiki/File:Artificialneuron.png The weighted sum of the inputs produces the activation signal that is passed to the activation function to obtain one output from the neuron.

Linear function f(x)=ax Step function Logistic (Sigmoid) Function Tanh Function Rectified linear unit (ReLu) function Training is the weights optimizing process in which the error of predictions is minimized and the network reaches a specified level of accuracy.

Artificial neural networks with multiple hidden layers between the input and output layers are called deep neural networks (DNNs), and they can model complex nonlinear relationships.

convolutional neural network (CNN) contains one or more convolutional layers, pooling or fully connected, and uses a variation of multilayer perceptrons discussed above.

He presents a model built on top of word2vec, conducts a series of experiments with it, and tests it against several benchmarks, demonstrating that the model performs excellent.

In Text Understanding from Scratch, Xiang Zhang and Yann LeCun, demonstrate that CNNs can achieve outstanding performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language [2].

recursive neural network (RNN) is a type of deep neural network formed by applying the same set of weights recursively over a structure to make a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order [6].

recurrent neural network (RNN), unlike a feedforward neural network, is a variant of a recursive artificial neural network in which connections between neurons make a directed cycle.

These blocks have three or four “gates” (for example, input gate, forget gate, output gate) that control information flow drawing on the logistic function.

In this paper, we described different variants of artificial neural networks, such as deep multilayer perceptron (MLP), convolutional neural network (CNN), recursive neural network (RNN), recurrent neural network (RNN), long short-term memory (LSTM), sequence-to-sequence model, and shallow neural networks including word2vec for word embeddings.

We demonstrated that convolutional neural networks are primarily utilized for text classification tasks while recurrent neural networks are commonly used for natural language generation or machine translation.

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