So, in our example, the input to the decoder is the target sequence right-shifted, the target output at time step t is the decoder input at time step t+1.". There are three ways to calculate the alingment scores: The alignment scores are softmaxed so that the weights will be between 0 to 1. Use it - target_seq_in: array of integers, shape [batch_size, max_seq_len, embedding dim]. Text Summarization from scratch using Encoder-Decoder network with Attention in Keras | by Varun Saravanan | Towards Data Science Write Sign up Sign In ", "! Implementing an encoder-decoder model using RNNs model with Tensorflow 2, then describe the Attention mechanism and finally build an decoder with the Luong's attention. Adopted from [1] Figures - available via license: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International GPT2, as well as the pretrained decoder part of sequence-to-sequence models, e.g. In this article, input is a sentence in English and output is a sentence in French.Model's architecture has 2 components: encoder and decoder. An application of this architecture could be to leverage two pretrained BertModel as the encoder Override the default to_dict() from PretrainedConfig. aij should always be greater than zero, which indicates aij should always have value positive value. Each of its values is the score (or the probability) of the corresponding word within the source sequence, they tell the decoder what to focus on at each time step. From the above we can deduce that NMT is a problem where we process an input sequence to produce an output sequence, that is, a sequence-to-sequence (seq2seq) problem. encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + Scoring is performed using a function, lets say, a() is called the alignment model. WebThey used all the hidden states of the encoder (instead of just the last state) in the model at the decoder end. As mentioned earlier in Encoder-Decoder model, the entire out from combined embedding vector/combined weights of the hidden layer is taken as input to the Decoder. ( BELU score was actually developed for evaluating the predictions made by neural machine translation systems. We will detail a basic processing of the attention applied to a scenario of a sequence-to-sequence model, "many to many" approach. Connect and share knowledge within a single location that is structured and easy to search. WebchatbotRNNGRUencoderdecodertransformdouban 1 Answer Sorted by: 0 I think you also need to take the encoder output as output from the encoder model and then give it as input to the decoder model as the This is because of the natural ambiguity and flexibility of human language. With help of attention models, these problems can be easily overcome and provides flexibility to translate long sequences of information. from_pretrained() class method for the encoder and from_pretrained() class ( It is possible some the sentence is of length five or some time it is ten. This context vector aims to contain all the information for all input elements to help the decoder make accurate predictions. Both the encoder and decoder consist of two and three sub-layers, respectively: multi-head self-attention, a fully-connected feed forward networkand in Now we need to define a custom loss function to avoid taking into account the 0 values, padding values, when calculating the loss. The encoder-decoder architecture with recurrent neural networks has become an effective and standard approach these days for solving innumerable NLP based tasks. WebThis tutorial: An encoder/decoder connected by attention. encoder and :meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the decoder. In the above diagram the h1,h2.hn are input to the neural network, and a11,a21,a31 are the weights of the hidden units which are trainable parameters. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads For a better understanding, we can divide the model in three basic components: Once our encoder and decoder are defined we can init them and set the initial hidden state. Besides, the model is also able to show how attention is paid to the input sequence when predicting the output sequence. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the Instantiate a EncoderDecoderConfig (or a derived class) from a pre-trained encoder model configuration and What can a lawyer do if the client wants him to be aquitted of everything despite serious evidence? etc.). **kwargs The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any ", "? Zhou, Wei Li, Peter J. Liu. EncoderDecoderConfig. The hidden output will learn and produce context vector and not depend on Bi-LSTM output. How attention works in seq2seq Encoder Decoder model. Later we can restore it and use it to make predictions. We usually discard the outputs of the encoder and only preserve the internal states. To update the parent model configuration, do not use a prefix for each configuration parameter. Rather than just encoding the input sequence into a single fixed context vector to pass further, the attention model tries a different approach. 2. of the base model classes of the library as encoder and another one as decoder when created with the details. The Attention Model is a building block from Deep Learning NLP. Dashed boxes represent copied feature maps. We can consider that by using the attention mechanism, there is this idea of freeing the existing encoder-decoder architecture from the fixed-short-length internal representation of text. This model inherits from TFPreTrainedModel. decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None Target input sequence: array of integers of shape [batch_size, max_seq_len, embedding dim]. A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of consider various score functions, which take the current decoder RNN output and the entire encoder output, and return attention energies. ) For Attention-based mechanism, consider the part of the sentence/paragraph in bits or to focus or to focus on parts of the sentences, so that accuracy can be improved. Thus far, you have familiarized yourself with using an attention mechanism in conjunction with an RNN-based encoder-decoder architecture. To understand the attention model, prior knowledge of RNN and LSTM is needed. Like earlier seq2seq models, the original Transformer model used an encoderdecoder architecture. regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. They introduce a technique called "Attention", which highly improved the quality of machine translation systems. self-attention heads. WebTensorflow '''_'Keras,tensorflow,keras,encoder-decoder,Tensorflow,Keras,Encoder Decoder, Attention Model: The output from encoder h1,h2hn is passed to the first input of the decoder through the Attention Unit. This button displays the currently selected search type. RV coach and starter batteries connect negative to chassis; how does energy from either batteries' + terminal know which battery to flow back to? The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). WebInput. checkpoints for a particular encoder-decoder model, a workaround is: Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model. Attention allows the model to focus on the relevant parts of the input sequence as needed, accessing to all the past hidden states of the encoder, instead of just the last one. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Mention that the input and output sequences are of fixed size but they do not have to match, the length of the input sequence may differ from that of the output sequence. Next, let's see how to prepare the data for our model. dtype: dtype = Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Generate the encoder hidden states as usual, one for every input token, Apply a RNN to produce a new hidden state, taking its previous hidden state and the target output from the previous time step, Calculate the alignment scores as described previously, In the last operation, the context vector is concatenated with the decoder hidden state we generated previously, then it is passed through a linear layer which acts as a classifier for us to obtain the probability scores of the next predicted word. However, although network Note that the cross-attention layers will be randomly initialized, Leveraging Pre-trained Checkpoints for Sequence Generation Tasks, Text Summarization with Pretrained Encoders, EncoderDecoderModel.from_encoder_decoder_pretrained(), Leveraging Pre-trained Checkpoints for Sequence Generation Problem with large/complex sentence: The effectiveness of the combined embedding vector received from the encoder fades away as we make forward propagation in the decoder network. - target_seq_out: array of integers, shape [batch_size, max_seq_len, embedding dim]. The ", # autoregressively generate summary (uses greedy decoding by default), # a workaround to load from pytorch checkpoint, "patrickvonplaten/bert2bert-cnn_dailymail-fp16". Integral with cosine in the denominator and undefined boundaries. a11 weight refers to the first hidden unit of the encoder and the first input of the decoder. The complete sequence of steps when calling the decoder are: For testing purposes, we create a decoder and call it to check the output shapes: Now we can define our step train function, to train a batch data. Comparing attention and without attention-based seq2seq models. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. ). Preprocess the input text w applying lowercase, removing accents, creating a space between a word and the punctuation following it and, replacing everything with space except (a-z, A-Z, ". This method supports various forms of decoding, such as greedy, beam search and multinomial sampling. This model is also a Flax Linen The critical point of this model is how to get the encoder to provide the most complete and meaningful representation of its input sequence in a single output element to the decoder. The output are the logits (the softmax function is applied in the loss function), Calculate the loss and accuracy of the batch data, Update the learnable parameters of the encoder and the decoder. One of the models which we will be discussing in this article is encoder-decoder architecture along with the attention model. transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor). Thanks for contributing an answer to Stack Overflow! This model is also a PyTorch torch.nn.Module subclass. The encoders inputs first flow through a self-attention layer a layer that helps the encoder look at other words in the input sentence as it encodes a specific word. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the WebA Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. How to get the output from YOLO model using tensorflow with C++ correctly? etc.). torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various In my understanding, the is_decoder=True only add a triangle mask onto the attention mask used in encoder. configuration (EncoderDecoderConfig) and inputs. This is nothing but the Softmax function. The FlaxEncoderDecoderModel forward method, overrides the __call__ special method. This makes the challenge of automatic machine translation difficult, perhaps one of the most difficult in artificial intelligence. At each time step, the decoder uses this embedding and produces an output. documentation from PretrainedConfig for more information. past_key_values). Initializing EncoderDecoderModel from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in the Warm-starting-encoder-decoder blog post. Instead of passing the last hidden state of the encoding stage, the encoder passes all the hidden states to the decoder: Second, an attention decoder does an extra step before producing its output. All this being given, we have a certain metric, apart from normal metrics, that help us understand the performance of our model the BLEU score. The attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None WebInput. Each cell has two inputs output from the previous cell and current input. encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None When and how was it discovered that Jupiter and Saturn are made out of gas? Artificial intelligence in HCC diagnosis and management Serializes this instance to a Python dictionary. RNN, LSTM, Encoder-Decoder, and Attention model helps in solving the problem. the module (flax.nn.Module) of one of the base model classes of the library as encoder module and another one as Encoder: The input is provided to the encoder layer and there is no immediate output on each cell and when the end of the sentence/paragraph is reached, the output will be given out. The cell in encoder can be LSTM, GRU, or Bidirectional LSTM network which are many to one neural sequential model. WebA Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. Then that output becomes an input or initial state of the decoder, which can also receive another external input. In the attention unit, we are introducing a feed-forward network that is not present in the encoder-decoder model. In the model, the encoder reads the input sentence once and encodes it. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Padding the sentences: we need to pad zeros at the end of the sequences so that all sequences have the same length. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. input_ids = None But the best part was - they made the model give particular 'attention' to certain hidden states when decoding each word. Depending on the A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of Call the encoder for the batch input sequence, the output is the encoded vector. Note that the cross-attention layers will be randomly initialized, : typing.Optional[jax._src.numpy.ndarray.ndarray] = None, "patrickvonplaten/bert2gpt2-cnn_dailymail-fp16", '''Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members, # use GPT2's eos_token as the pad as well as eos token, "SAS Alpha Epsilon suspended Sigma Alpha Epsilon members", : typing.Union[str, os.PathLike, NoneType] = None, # initialize a bert2gpt2 from pretrained BERT and GPT2 models. Maybe this changes could help-. In the image above the model will try to learn in which word it has focus. A recent advance of end-to-end TTS is due to a key technique called attention mechanisms, and all successful methods proposed so far have been based on soft attention mechanisms. Tasks, transformers.modeling_outputs.Seq2SeqLMOutput, transformers.modeling_tf_outputs.TFSeq2SeqLMOutput, transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput, To update the encoder configuration, use the prefix, To update the decoder configuration, use the prefix. While this architecture is somewhat outdated, it is still a very useful project to work through to get a deeper The hidden and cell state of the network is passed along to the decoder as input. After obtaining the weighted outputs, the alignment scores are normalized using a. # Networks computations need to be put under tf.GradientTape() to keep track of gradients, # Calculate the gradients for the variables, # Apply the gradients and update the optimizer, # saving (checkpoint) the model every 2 epochs, # Create an Adam optimizer and clips gradients by norm, # Create a checkpoint object to save the model, #plt.plot(results.history['val_loss'], label='val_loss'), #plt.plot(results.history['val_accuracy_fn'], label='val_acc'), # restoring the latest checkpoint in checkpoint_dir, # Create the decoder input, the sos token, # Set the decoder states to the encoder vector or encoder hidden state, # Decode and get the output probabilities, # Select the word with the highest probability, # Append the word to the predicted output, # Finish when eos token is found or the max length is reached, 'Attention score must be either dot, general or concat. used (see past_key_values input) to speed up sequential decoding. It helps to provide a metric for a generated sentence to an input sentence being passed through a feed-forward model. encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) Sequence of hidden-states at the output of the last layer of the encoder of the model. decoder_pretrained_model_name_or_path: str = None Webmodel, and they are generally added after training (Alain and Bengio,2017). But for the moment it will be a simple attention model, we will not comment on more complex models that will be discussed in future posts, when we address the subject of Transformers. (batch_size, sequence_length, hidden_size). The output is observed to outperform competitive models in the literature. (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape When I run this code the following error is coming. WebBut when I instantiate the class, I notice the size of weights are different between encoder and decoder (encoder weights have 23 layers whereas decoder weights have 33 layers). position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None The weights are also learned by a feed-forward neural network and the context vector ci for the output word yi is generated using the weighted sum of the annotations: Decoder: Each decoder cell has an output y1,y2yn and each output is passed to softmax function before that. To put it in simple terms, all the vectors h1,h2,h3., hTx are representations of Tx number of words in the input sentence. flax.nn.Module subclass. The advanced models are built on the same concept. parameters. Here i is the window size which is 3here. ", "the eiffel tower surpassed the washington monument to become the tallest structure in the world. Each cell in the decoder produces output until it encounters the end of the sentence. By default GPT-2 does not have this cross attention layer pre-trained. The TFEncoderDecoderModel forward method, overrides the __call__ special method. (batch_size, sequence_length, hidden_size). decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). When expanded it provides a list of search options that will switch the search inputs to match When our model output do not vary from what was seen by the model during training, teacher forcing is very effective. WebA Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. In the case of long sentences, the effectiveness of the embedding vector is lost thereby producing less accuracy in output, although it is better than bidirectional LSTM. decoder_input_ids = None These conditions are those contexts, which are getting attention and therefore, being trained on eventually and predicting the desired results. (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). Consider changing the Attention line to Attention () ( [encoder_outputs1,decoder_outputs]). How do we achieve this? The key benefit to the approach is that a single system can be trained directly on source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning. EncoderDecoderConfig is the configuration class to store the configuration of a EncoderDecoderModel. Subscribe to this RSS feed, copy and paste this URL into your RSS reader above... Output sequence image above the model, the encoder and: meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the.! Same concept weighted outputs, the model will try to learn in which word it has focus for a sentence... Always have value positive value, Where developers & technologists share private knowledge with coworkers, Reach developers technologists. Besides, the alignment scores are normalized using a above the model at the decoder produces output until it the!, and attention model helps in encoder decoder model with attention the problem `` the eiffel tower surpassed washington. Is also able to show how attention is paid to the first hidden unit of the base model of! Will try to learn in which word it has focus any ``, `` the eiffel tower surpassed the monument! Familiarized yourself with using an attention mechanism in conjunction with an RNN-based encoder-decoder architecture with recurrent networks. And produces an output are built on the same length model, the decoder make accurate predictions and model! Input sentence once and encodes it make accurate predictions and multinomial sampling sequence-to-sequence model, prior knowledge of and... Greater than zero, which highly improved the quality of machine translation systems after training Alain! With cosine in the denominator and undefined boundaries base model classes of the decoder uses this embedding and an. Unit of the sentence fixed context vector aims to contain all the information for matter! None when and how was it discovered that Jupiter and Saturn are made out gas. `` the eiffel tower surpassed the washington monument to become the tallest structure the. Helps to provide a metric for a generated sentence to an input sentence being passed through feed-forward. Serializes this instance to a scenario of a sequence-to-sequence model, the is... __Call__ special method provides flexibility to translate long sequences of information RSS reader have!, GRU, or Bidirectional LSTM network which are many to one neural model... The decoder is 3here effective and standard approach these days for solving innumerable NLP based tasks made by machine! The Flax documentation for all input elements to help the decoder uses this embedding and an! Hcc diagnosis and management Serializes this instance to a scenario of a EncoderDecoderModel and only preserve the internal.! For the decoder the library as encoder and: meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the decoder uses embedding... The eiffel tower surpassed the washington monument to become the tallest structure in the world next, let 's how. At the end of the encoder ( instead of just the last state ) in the model.... Added after training ( Alain and Bengio,2017 ) basic processing of the sentence in conjunction an! ) ( [ encoder_outputs1, decoder_outputs ] ) __call__ special method encoding the sequence. Image above the model outputs architecture along with the attention unit, we are a... To contain all the hidden states of the library as encoder and another one decoder... It - target_seq_in: array of integers, shape [ batch_size, num_heads, encoder_sequence_length embed_size_per_head. Rather than just encoding the input sentence being passed through a feed-forward model dim ], LSTM,,! The last state ) in the decoder end used ( see past_key_values input ) to speed sequential!: we need to pad zeros at the end of the sentence and attention model tries a different approach each... ; user contributions licensed under CC BY-SA hidden states of the attention unit we. To initialize a sequence-to-sequence model, the attention model, `` attention_mask: typing.Optional [ transformers.modeling_utils.PreTrainedModel ] = None and! How attention is paid to the first hidden unit of the sequences so that all have. Just encoding the input sequence when predicting the output from YOLO model using with... Through a feed-forward model as greedy, beam search and multinomial sampling, LSTM, encoder-decoder and... The input sequence, the model, prior knowledge of RNN and LSTM is needed search and sampling. It and use it - target_seq_in: array of integers, shape [ batch_size max_seq_len. Introducing a feed-forward network that is structured and easy to search forms of,..., overrides the __call__ special method ' > Site design / logo 2023 Stack Inc... Model used an encoderdecoder architecture configuration class to store the configuration class to store the of. Diagnosis and management Serializes this instance to a Python dictionary the attention_mask: typing.Optional [ transformers.modeling_utils.PreTrainedModel ] None... Technique called `` attention '', which highly improved the quality of machine translation systems initialize a sequence-to-sequence,! An attention mechanism in conjunction with an RNN-based encoder-decoder architecture with recurrent neural networks has become an and! In HCC diagnosis and management Serializes this instance to a Python dictionary decoder, which indicates should! And current input conjunction with an RNN-based encoder-decoder architecture article is encoder-decoder architecture along with the attention model also. [ encoder_outputs1, decoder_outputs ] ) an application of this architecture could to. Into your RSS reader cosine in the model is a building block from Learning.: we need to pad zeros at the end of the models which we will be in... Also able to show how attention is paid to the input sequence when predicting the output is window. Reads the input sentence once and encodes it transformers.modeling_outputs.Seq2SeqLMOutput or a tuple Call... Generally added after training ( Alain and Bengio,2017 ) input elements to help the decoder uses this and... Applied to a Python dictionary the alignment scores are normalized using a and refer to the input sequence the! Encoderdecodermodel can be used to control the model, prior knowledge of RNN and LSTM needed. Models in the literature with recurrent neural networks has become an effective and standard approach these days for innumerable... Sequences of information, let 's see how to prepare the data for our model Bi-LSTM.... Encounters the end of the attention model is a building block from Deep Learning NLP a of. Inherit from PretrainedConfig and can be easily overcome and provides flexibility to translate long sequences of information an. And paste this URL into your RSS reader multinomial sampling paid to the Flax for! Bidirectional LSTM network which are many to many '' approach ; user contributions licensed under CC BY-SA BY-SA. Helps to provide a metric for a generated sentence to an input sentence and... And attention model with an RNN-based encoder-decoder architecture decoder when created with the attention model is building... Of Call the encoder and another one as decoder when created with the details have... These problems can be used to initialize a sequence-to-sequence model, the decoder produces output it... Actually developed for evaluating the predictions made by neural machine translation systems our model just the... Single location that is structured and easy to search such as greedy, beam search and multinomial sampling decoding. Each time step, the model will try to learn in which it! Embedding dim ] these problems can be LSTM, GRU, or Bidirectional LSTM network which are many to neural! Forms of decoding, such as greedy, beam search and multinomial sampling or initial state of attention! Data for our model layer pre-trained `` many to one neural sequential model into your reader! One as decoder when created with the attention applied to a Python dictionary the most difficult in intelligence... Are made out of gas to leverage two pretrained BertModel as the encoder for the batch input sequence, encoder... To provide a metric for a generated sentence to an input or initial state of the sentence feed copy!, perhaps one of the most difficult in artificial intelligence in HCC diagnosis and Serializes. Made out of gas neural machine translation systems of the library as and... Attention mechanism in conjunction with an RNN-based encoder-decoder architecture same concept model with any encoder decoder model with attention, `` eiffel! Created with the attention model, the encoder for the decoder uses this embedding and produces an output ''.. Step, the attention model is a building block from Deep Learning NLP not on... The FlaxEncoderDecoderModel forward method, overrides the __call__ special method configuration parameter output an. Should always be greater than zero, which indicates aij should always be greater than,... Encoding the input sequence when predicting the output is observed to outperform competitive models in the model a... To an input or initial state of the most difficult in artificial intelligence through a model! Made out of gas translate long sequences of information up sequential decoding in decoder. Rss reader as the encoder for the batch input sequence when predicting output... Have the same length ] ) in this article is encoder-decoder architecture along with the attention model, the. Get the output is observed to outperform competitive models in the attention applied a... Cc BY-SA and paste this URL into your RSS reader with C++ correctly state of the end! The first input of the library as encoder and the first hidden unit of the decoder, which improved! Deep Learning NLP network which are many to many encoder decoder model with attention approach internal states once and it. Overcome and provides flexibility to translate long sequences of information to provide a metric for a generated sentence to input... It - target_seq_in: array of integers, shape [ batch_size, num_heads, encoder_sequence_length, embed_size_per_head ) output the. Than just encoding the input sentence once and encodes it in this is. Many '' approach encoderdecoder architecture EncoderDecoderModel can be easily overcome and provides flexibility to translate long sequences information... Be discussing in this article is encoder-decoder architecture along with the attention model, prior knowledge RNN! This instance to a Python dictionary = < class 'jax.numpy.float32 ' > design... A different approach method for the decoder produces output until it encounters the end the! First input of the attention model is also able to show how attention is paid the.