What neural network to choose?
- Multilayer Perceptron (MLP): ReLU activation function.
- Convolutional Neural Network (CNN): ReLU activation function.
- Recurrent Neural Network: Tanh and/or Sigmoid activation function.
How to visualize a neural network?
- Load the image.
- Fetch the pretrained neural network.
- Run the neural network on the image.
- Find the highest probability with torch.max. pred is now a number with the index of the most likely class.
- Compute the CAM using compute_cam.
- Finally, save the CAM using save_cam.
What do you mean by a neural network?
- Neural nets are composed of layers.
- The input layer takes the data in. It’s not a computational layer.
- The computational layer is the hidden
How to understand neural networks?
- Take a fixed batch of training samples x and corresponding targets y.
- Run the network on x (a step called forward pass) to obtain predictions y_pred.
- Calculate the loss of the network on the batch, a measure of the distance between y_pred and y ( loss function also called objective function = y_pred - y ).
How does the attention mechanism work?
In essence, when the generalized attention mechanism is presented with a sequence of words, it takes the query vector attributed to some specific word in the sequence and scores it against each key in the database. In doing so, it captures how the word under consideration relates to the others in the sequence.
How does attention work NLP?
The attention mechanism is a part of a neural architecture that enables to dynamically highlight relevant features of the input data, which, in NLP, is typically a sequence of textual elements. It can be applied directly to the raw input or to its higher level representation.
How does attention work in RNN?
Attention is a mechanism combined in the RNN allowing it to focus on certain parts of the input sequence when predicting a certain part of the output sequence, enabling easier learning and of higher quality.
How does attention work in encoder decoder recurrent neural networks?
First, it works by providing a more weighted or more signified context from the encoder to the decoder and a learning mechanism where the decoder can interpret were to actually give more 'attention' to the subsequent encoding network when predicting outputs at each time step in the output sequence.
What is the process of attention?
Broadly, the attention process can be described as selective concentration on salient environmental features while ignoring other aspects.
What are the 4 components of attention?
Four processes are fundamental to attention: working memory, top-down sensitivity control, competitive selection, and automatic bottom-up filtering for salient stimuli.
What are the 3 attention networks?
According to Posner and Petersen's neurocognitive model [1,2], attention involves three neural networks: alerting, orienting, and executive control.
Why do we need attention in deep learning?
Attention is an interface connecting the encoder and decoder that provides the decoder with information from every encoder hidden state. With this framework, the model is able to selectively focus on valuable parts of the input sequence and hence, learn the association between them.
What is the use of attention layer?
attention layer can help a neural network in memorizing the large sequences of data. If we are providing a huge dataset to the model to learn, it is possible that a few important parts of the data might be ignored by the models.
Is attention necessary for encoding?
A defining feature of explicit memory, such as the hippocampal-dependent memory for place, is that it requires attention. The recruitment of attention is important not only for optimal encoding but also for subsequent retrieval (Schacter, 1996; Fernandes et al. 2005).
What is the role of attention in memory encoding?
First, memory has a limited capacity, and thus attention determines what will be encoded. Division of attention during encoding prevents the formation of conscious memories, although the role of attention in formation of unconscious memories is more complex.
What is attention in neural machine translation?
Attention in neural machine translation provides the possibility to encode relevant parts of the source sentence at each translation step. As a result, attention is considered to be an alignment model as well.
What are the 5 levels of attention?
According to Sohlberg and Mateer model (1987, 1989) there are several types: arousal, focused, sustained, selective, alternating and divided.
What is attention in memory process?
What working memory is. After gathering information, the attention funnel feeds it into the brain's short-term storage bucket. This is where new information is first held. Experts call this process “encoding.” This is also where the brain manipulates new information so it's useful.
What is the role of attention in encoding?
Attention promotes episodic encoding by stabilizing hippocampal representations | PNAS.
How does attention work in Bert?
The attention mechanism of BERT works as Query (Q), Key (K), and Value (V) that start a linear transformation to “dynamically” generate weights for different connections, and then feed them into the scaling dot product. In the definition of self-attention, Q is K itself. dk is the dimension of Q and K.
What is global attention?
Global attention is the same as what was explored in the “ Introduction to Attention ” post. It is when we use ALL encoder hidden states to define the attention based context vector for each decoder step. But as you might have guessed, this could become expensive.
What is the attention matrix in ABCNN-1?
In ABCNN-1, attention is introduced before the convolution operation. The input representation feature map (described in #2 in based model description, shown as red matrix in Fig 6) for both sentences s0 (8 x 5) and s1 (8 x 7), are “matched” to arrive at the Attention Matrix “A” (5 x 7).
How does neural transducer work?
The input sequence is divided into multiple blocks of equal length ( except possibly the last block) and the Neural Transducer model computes attention only for the inputs in the current block, which is then used to generate the output corresponding to that block. The connection with prior blocks exists only via the hidden state connections that are part of the RNN on the encoder and decoder side. While this is similar to an extent to the local attention described earlier, there is no explicit “position alignment” as described there.
What is attention weight in pooling?
The attention weight is then used to “re-weight” the conv feature map columns. Every column in the pooling output feature map is computed as the attention weighted sum of the “w” conv feature map columns that are being pooled — in our examples above this was 3.
What is ABCNN in text?
In this paper Yin et al presented ABCNN — Attention Based CNN to model a pair of sentences, used in answer selection, paraphrase identification and textual entailment tasks. The key highlight of the proposed attention based model was that it considers the impact/relationship/influence that exists between the different parts or words or whole of one input sentence with the other, and provides an interdependent sentence pair representation that can be used in subsequent tasks. Let’s take a quick look at the base network first before looking at how attention was introduced into it.
Is convolution performed on input representation?
Now the convolution operation is performed on not just the input representation like the base model, but on both the input representation and the attention feature map just calculated.
How is attention calculated?
Attention is calculated from hidden states of encoder and recent hidden state of decoder. Source: Karim 2019.
How to visualize attention?
We can also visualize attention via heatmaps. In the figure, we map English words to translated French words. We note that sometimes a translated word is attended to by multiple English words. Lighter colours represent higher attention.
What is attention mechanism in machine translation?
Bahdanau et al. apply the concept of attention to the seq2seq model used in machine translation. This helps the decoder to "pay attention" to important parts of the source sentence. Encoder is a bidirectional RNN. Unlike earlier seq2seq models that use only the encoder's last hidden state, attention mechanism uses all hidden states of encoder and decoder to generate the context vector. It also aligns the input and output sequences, with alignment score parameterized by a feed-forward network.
What is attention in decoding?
Attention is about giving more contextual information to the decoder. At every decoding step, the decoder is informed how much "attention" it should give to each input word.
What is the difference between soft and hard attention?
They distinguish between soft attention and hard attention. Soft deterministic attention is smooth and differentiable, and is trained by standard back propagation. Hard stochastic attention is trained by maximizing an approximate variational lower bound. Soft attention is similar to Bahdanau et al.'s proposal.
What is the new neural network playbook?
Matthew Honnibal describes what he calls as the new neural network playbook for NLP. It's a four-step approach: embed, encode, attend, predict. This highlights the importance and usefulness of attention mechanism. Word embeddings do the embed part at word level. Bidirectional RNN s do the encode part at sequence level. Using a context vector, attend part produces a vector that's given to a feed-forward network. The predict part is done by this network.
What is the difference between inter-attention and intra-attention?
propose the use of both inter-attention (between encoder and decoder) and intra-attention (within encoder or decoder). Intra-attention (later to be called self-attention) is about attending to tokens within a sequence, thus uncovering lexical relations between tokens.
What is attention in RNN?
Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation.
Why is attention proposed?
Attention is proposed as a solution to the limitation of the Encoder-Decoder model encoding the input sequence to one fixed length vector from which to decode each output time step. This issue is believed to be more of a problem when decoding long sequences.
What is the role of an encoder in a model?
Encoder: The encoder is responsible for stepping through the input time steps and encoding the entire sequence into a fixed length vector called a context vector.
What is the key to a RNN model?
Key to the model is that the entire model , including encoder and decoder, is trained end-to-end, as opposed to training the elements separately. The model is described generically such that different specific RNN models could be used as the encoder and decoder.
What is the second mechanism of attention?
They develop two attention mechanisms, one they call “ soft attention ,” which resembles attention as described above with a weighted context vector, and the second “ hard attention ” where the crisp decisions are made about elements in the context vector for each word.
Who presented the paper "Neural Machine Translation by Jointly Learning to Align and Translate"?
Attention was presented by Dzmitry Bahdanau, et al. in their paper “ Neural Machine Translation by Jointly Learning to Align and Translate ” that reads as a natural extension of their previous work on the Encoder-Decoder model.
What is the effect of not providing the model with an idea of the previously decoded output, which is intended to?
This has the effect of not providing the model with an idea of the previously decoded output, which is intended to aid in alignment.
Why is attention important in neural networks?
Admittedly, attention has a lot of reasons to be effective apart from tackling the bottleneck problem. First, it usually eliminates the vanishing gradient problem, as they provide direct connections between the encoder states and the decoder. Conceptually, they act similarly as skip connections in convolutional neural networks.
How to visualize implicit attention?
One way to visualize implicit attention is by looking at the partial derivatives with respect to the input. In math, this is the Jacobian matrix, but it’s out of the scope of this article.
What are transformers used for in NLP?
In NLP, transformers and attention have been utilized successfully in a plethora of tasks including reading comprehension, abstractive summarization, word completion, and others.
What does soft attention mean?
Soft attention means that the function varies smoothly over its domain and, as a result, it is differentiable.
What is deep network?
Deep networks are very rich function approximators. So, without any further modification, they tend to ignore parts of the input and focus on others. For instance, when working on human pose estimation, the network will be more sensitive to the pixels of the human body.
Can we visualize the attention of a trained network using a heatmap?
In machine translation, we can visualize the attention of a trained network using a heatmap such as below. Note that scores are computed dynamically.
Is attention intuitive?
Attention is quite intuitive and interpretable to the human mind. Thus, by asking the network to ‘weigh’ its sensitivity to the input based on memory from previous inputs, we introduce explicit attention. From now on, we will refer to this as attention.
How does attention work in RNN?
Ultimately, the attention mechanism performs a softmax at each timestep of the RNN, so that the RNN can make the best decision it needs to at that moment. If you were trying to copy down a phone number from a website onto your phone, you might pay attention to the area code, then the next three numbers, and finally the last four digits. Attention in neural networks operate in a surprisingly similar way!
What would happen if RNN was not based on attention?
In a standard RNN without attention, the network would make a prediction merely based on the previous outputted words “L’ accord sur la zone économique” and the current word “Area”. But we don’t want the word “Area” since that word has already been translated. So attention says we have a higher probability of success if we look elsewhere.
What problem does Hinton have with convolution?
So what problem does Hinton have with convolution ? Actually he does not have any problem with convolution per se but with the architecture most CNNs use. To see the problem let us consider a typical CNN architecture:
What is a recurrent neural network?
A recurrent neural network or its variant (LSTM/GRU/..) are means of learning from sequence using back-propagation through time. In this case generally the sequence is fed to the network once. There are different elements of the network which control how much of what information is carried along through long sequences.
What is task 2 in a network?
Task 2: writing a caption from an image. This time, the attention mechanism tells the network roughly which pixels to pay attention to when writing the text.
Is logistic regression a neural network?
And in case you haven’t noticed, logistic regression is just a one-layer neural network. Whaaaaaa
What are the components of a basic attention model?
Thus, a basic attention model has three components: the encoder, the decoder, and the alignment model . Training involves learning the parameters of all these components. In the case of dot-product attention, the alignment model does not have any parameters to learn.
How to achieve selective behavior for each output token?
To achieve such selective behavior for each output token, the model can instead focus its attention on specific elements of the sequence of encodings. This is achieved by inducing attention weights for each step of the sequence of encodings.
Is attention limited to RNN?
Attention is not limited to RNN architectures. In fact, one of the coolest ideas of this decade in the deep learning domain is the Transformer, a neural network devoid of the RNN architecture that utilizes attention to implement sequence-based tasks.
Tutorial Overview
Encoder-Decoder Model
- The Encoder-Decoder model for recurrent neural networks was introduced in two papers. Both developed the technique to address the sequence-to-sequence nature of machine translation where input sequences differ in length from output sequences. Ilya Sutskever, et al. do so in the paper “Sequence to Sequence Learning with Neural Networks” using LSTMs. Kyunghyun Cho, et …
Attention Model
- Attention was presented by Dzmitry Bahdanau, et al. in their paper “Neural Machine Translation by Jointly Learning to Align and Translate” that reads as a natural extension of their previous work on the Encoder-Decoder model. Attention is proposed as a solution to the limitation of the Encoder-Decoder model encoding the input sequence to one fixed length vector from which to decode ea…
Worked Example of Attention
- In this section, we will make attention concrete with a small worked example. Specifically, we will step through the calculations with un-vectorized terms. This will give you a sufficiently detailed understanding that you could add attention to your own encoder-decoder implementation. This worked example is divided into the following 6 sections: 1. Problem 2. Encoding 3. Alignment 4. …
Summary
- In this tutorial, you discovered the attention mechanism for Encoder-Decoder model. Specifically, you learned: 1. About the Encoder-Decoder model and attention mechanism for machine translation. 2. How to implement the attention mechanism step-by-step. 3. Applications and extensions to the attention mechanism. Do you have any questions? Ask your questions in the c…