![]() attention ( query = inputs, value = inputs, key = inputs, attention_mask = padding_mask ) proj_input = self. cast ( mask, dtype = "int32" ) attention_output = self. supports_masking = True def call ( self, inputs, mask = None ): if mask is not None : padding_mask = tf. MultiHeadAttention ( num_heads = num_heads, key_dim = embed_dim ) self. Layer ): def _init_ ( self, embed_dim, dense_dim, num_heads, ** kwargs ): super (). Result in a model that cannot be used at inference time).Ĭlass TransformerEncoder ( layers. (otherwise, it could use information from the future, which would Sure that it only uses information from target tokens 0 to N when predicting token N+1 The TransformerDecoder sees the entire sequences at once, and thus we must make (see method get_causal_attention_mask() on the TransformerDecoder). The TransformerDecoder will then seek to predict the next words in the target sequence (N+1 and beyond).Ī key detail that makes this possible is causal masking To the TransformerDecoder, together with the target sequence so far (target words 0 to N). This new representation will then be passed Which will produce a new representation of it. The source sequence will be pass to the TransformerEncoder, Our sequence-to-sequence Transformer consists of a TransformerEncoderĪnd a TransformerDecoder chained together. It provides the next words in the target sentence - what the model will try to predict. target is the target sentence offset by one step:.That is to say, the words 0 to N used to predict word N+1 (and beyond) in the target sentence. inputs is a dictionary with the keys encoder_inputs and decoder_inputs.Įncoder_inputs is the vectorized source sentence and encoder_inputs is the target sentence "so far",. ![]() Using the source sentence and the target words 0 to N.Īs such, the training dataset will yield a tuple (inputs, targets), where: adapt ( train_spa_texts )Īt each training step, the model will seek to predict target words N+1 (and beyond) adapt ( train_eng_texts ) spa_vectorization. escape ( strip_chars ), "" ) eng_vectorization = TextVectorization ( max_tokens = vocab_size, output_mode = "int", output_sequence_length = sequence_length, ) spa_vectorization = TextVectorization ( max_tokens = vocab_size, output_mode = "int", output_sequence_length = sequence_length + 1, standardize = custom_standardization, ) train_eng_texts = for pair in train_pairs ] train_spa_texts = for pair in train_pairs ] eng_vectorization. replace ( "", "" ) vocab_size = 15000 sequence_length = 20 batch_size = 64 def custom_standardization ( input_string ): lowercase = tf. punctuation + "¿" strip_chars = strip_chars. ![]() How each building block works, as well as the theory behind Transformers, The present example is fairly barebones, so for detailed explanations of The code featured here is adapted from the bookĭeep Learning with Python, Second Edition Input sentences (sequence-to-sequence inference). Use the trained model to generate translations of never-seen-before.Prepare data for training a sequence-to-sequence model.Implement a TransformerEncoder layer, a TransformerDecoder layer,. ![]()
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