This is the screen of the original paper: the screen of the paper. I understand the meaning of the paper is that when the value of dot-product is large, the gradient of softmax will get very small....

I am learning to apply Transform model proposed by Attention Is All You Need from tensorflow official document Transformer model for language understanding. As section Positional encoding says: ...

I'm currently studying code of transformer, but I can not understand the masked multi-head of decoder. The paper said that it is to prevent you from seeing the generating word, but I can not unsers...

I already did the implementation of the CNN part and everything seems to be working just fine. Afterwards started to implement the LSTM part and, If I understood it right, the output shape should b...

I created a Seq2Seq model for text summarization. I have two models, one with attention and one without. The one without attention was able to generate predictions but I can't do it for the one wit...

https://github.com/taoshen58/BiBloSA/blob/ec67cbdc411278dd29e8888e9fd6451695efc26c/context_fusion/self_attn.py#L29 I need to use mulit_dimensional_attention from the above link which is implemente...

I am learning the Transformer. Here is the pytorch document for MultiheadAttention. In their implementation, I saw there is a constraint: assert self.head_dim * num_heads == self.embed_dim, "...

I have been following this post in order to implement attention layer over my LSTM model. Code for the attention layer: INPUT_DIM = 2 TIME_STEPS = 20 SINGLE_ATTENTION_VECTOR = False APPLY_ATTENTION...

I am working on a machine language translation problem. The Model I am using is: Model = Sequential([ Embedding(english_vocab_size, 256, input_length=english_max_len, mask_zero=True), LSTM(256, ...

I am struggling to mask my input for the MultiHeadAttention Layer. I am using the Transformer Block from Keras documentation with self-attention. I could not find any example code online so far and...

What the difference between att_mask and key_padding_mask in MultiHeadAttnetion of pytorch: key_padding_mask – if provided, specified padding elements in the key will be ignored by the attention. ...

Using this implementation I have included attention to my RNN (which classify the input sequences into two classes) as follows. visible = Input(shape=(250,)) embed=Embedding(vocab_size,100)(visib...

Where the values are rnn_size: 512 batch_size: 128 rnn_inputs: Tensor("embedding_lookup/Identity_1:0", shape=(?, ?, 128), dtype=float32) sequence_length: Tensor("inputs_length:0&qu...

I am working on an attention model, and before running the final model, I was going through the tensor shapes which flow through the code. I have an operation where I need to reshape the tensor. Th...

Trying to use the AdditiveAttention layer in Keras. On manual implementation of the layer from tensorflow tutorial https://www.tensorflow.org/tutorials/text/nmt_with_attention import tensorflow as ...

I am trying to understand why transformers use multiple attention heads. I found the following quote: Instead of using a single attention function where the attention can be dominated by the actua...

I am following this tutorial: http://nlp.seas.harvard.edu/2018/04/03/attention.html to implement the Transformer model from the "Attention Is All You Need" paper. However I am getting the followi...

I am struggling with the concept of attention in the the context of autoencoders. I believe I understand the usage of attention with regards to seq2seq translation - after training the combined enc...

I have n-vectors which need to be influenced by each other and output n vectors with same dimensionality d. I believe this is what torch.nn.MultiheadAttention does. But the forward function expects...

I was following a paper on BERT-based lexical substitution (specifically trying to implement equation (2) - if someone has already implemented the whole paper that would also be great). Thus, I wan...

In this tutorial in tensorflow site we can see a code for the implementation of an autoencoder which it's Decoder is as follows: class Decoder(tf.keras.Model): def __init__(self, vocab_size, embed...

I have a simple network of one LSTM and two Dense layers as such: model = tf.keras.Sequential() model.add(layers.LSTM(20, input_shape=(train_X.shape[1], train_X.shape[2]))) model.add(layers.Dense(...

I am trying to understand attention model and also build one myself. After many searches I came across this website which had an atteniton model coded in keras and also looks simple. But when I tri...

With the following code: model = Sequential() num_features = data.shape[2] num_samples = data.shape[1] model.add( LSTM(16, batch_input_shape=(None, num_samples, num_features), return_sequ...

These two attentions are used in seq2seq modules. The two different attentions are introduced as multiplicative and additive attentions in this TensorFlow documentation. What is the difference?