In both cases, you can use teacher forcing to better train the model. By doing so, the attention mechanism improves the translation of longer sentences. Instead of discarding all of the hidden states computed in the source RNN, the attention mechanism provides an approach that allows the decoder to peek at them (treating them as a dynamic memory of the source information). This works well for short and medium-length sentences however, for long sentences, the single fixed-size hidden state becomes an information bottleneck. Vanilla seq2seq: The decoder also needs to have access to the source information, and one simple way to achieve that is to initialize it with the last hidden state of the encoder, encoder_state.Īttention-based encoder-decoder: Remember that in the vanilla seq2seq model, we pass the last source state from the encoder to the decoder when starting the decoding process. This is very well summarized by this detailed NMT guide, which compares the classic seq2seq NMT against the encoder-decoder attention-based NMT architectures. Why is that missing in Tensorflow's official tutorial ? Is it a bug ? Or am I missing something here ? # Why is it not initialized with the hidden state of the encoder ?Īs per my understanding, there is a connection between the encoder and decoder, only when the decoder is initialized with the "Thought vector" or the last hidden state of the encoder. What I don't understand here is that, the GRU cell of the decoder is not connected to the encoder by initializing it with the last hidden state of the encoder. Output = tf.reshape(output, (-1, output.shape)) # output shape = (batch_size * 1, hidden_size) # passing the concatenated vector to the GRU # x shape after concatenation = (batch_size, 1, embedding_dim + hidden_size) # x shape after passing through embedding = (batch_size, 1, embedding_dim) # enc_output shape = (batch_size, max_length, hidden_size)Ĭontext_vector, attention_weights = self.attention(hidden, enc_output) Self.attention = BahdanauAttention(c_units) Self.embedding = tf.(vocab_size, embedding_dim)
#SUPER DECODER 3 CODE#
It has the following code for the Decoder : class Decoder(tf.keras.Model):ĭef _init_(self, vocab_size, embedding_dim, dec_units, batch_sz): When you get “Super”, you win everything.I am going through Tensorflow's tutorial on Neural Machine Translation using Attention mechanism. “SUPER”- the exact package make it easy taking. “SUPER”- more voltage, more non-bending, more breaking –proof, more wearing-proof. “SUPER”- it’s made by high-quality stainless steel, more acid and more alkali, all parts were soldered together firmly. What amazing is it could not broken the lock. It will never Keep work 200 times without deformation. “SUPER”- more than 10 years working-time. The new user also will open a lock within 3 mins “SUPER”- easier handle, quickly opening, read coder more efficient and accurate. “SUPER”- with polish fruity、smooth feeling、beautiful. “SUPER”- Designed by Russian –Peter, a famous Locksmith, it’s made in Taiwan with super quality This new tool have a good reputation among the locksmiths. a new tool is here now and get the industry patent. It needs him to try and practice very hard. If he can solve these problems and invent a new tool, he will make a great contribution to the world of locksmiths. with his experience and skill, he is sure he can make a super tool which can solve the problems:1,for a new who cannot find out the scales.2, for locksmith can see the scale clearly even as no light.3,for locksmith can read the scales even with trembling hands. But must be more easy handle,he thought in his mind. The biting wind bleared the vision and the cold weather trembled the hands, Mr Peter cannot see the size which show in the tool clearly, that made his work being so hard and took his more time to do so easy thing.Īfter back of his office, Mr Peter decide to make a new tool which as similar to the old lockpick.
Street lamps reflected irregular beams of light on the cobblestones. Beacause the customer locked himself out of his car.
#SUPER DECODER 3 PROFESSIONAL#
Snowing and winding in the crossroad of Russia, Mr Peter, a Professional Locksmith took one Auto 2 in 1 decoder and pick tool to help someone to open a car. The sun had gone down and it was late, but is not a common day.
0 kommentar(er)
