seq2seq

GitHub describes it as Sequence to Sequence Learning with Keras. The repository metadata lists Python as its primary language. The metadata lists the GPL-2.0 license. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as Sequence to Sequence Learning with Keras. The repository metadata lists Python as its primary language. The metadata lists the GPL-2.0 license. The README describes the project this way: Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python deep learning library Keras. Using Seq2Seq, you can build and train sequence-to-sequence neural network models in Keras. Such models are useful for machine translation, chatbots (see [[4]](http://arxiv.org/pdf/1506.05869v1.pdf)), parsers, or whatever that comes to your mind.

The README section "Getting started" states: Seq2Seq contains modular and reusable layers that you can use to build your own seq2seq models as well as built-in models that work out of the box. Seq2Seq models can be compiled as they are or added as layers to a bigger model. Every Seq2Seq model has 2 primary layers : the encoder and the decoder. Generally, the encoder encodes the input sequence to an internal representation called 'context vector' which is used by the decoder to generate the output sequence. The lengths of input and output sequences can be different, as there is no explicit one on one relation between the input and output sequences. In addition to the encoder and decoder layers, a Seq2Seq model may also contain layers such as the left-stack (Stacked LSTMs on the encoder side), the right-stack (Stacked LSTMs on the decoder side), resizers (for shape compatibility between the encoder and the decoder) and dropout layers to avoid overfitting. The source code is heavily documented, so lets go straight to the examples:

The README section "Getting started" states: That's it! You have successfully compiled a minimal Seq2Seq model! Next, let's build a 6 layer deep Seq2Seq model (3 layers for encoding, 3 layers for decoding).

The README section "Getting started" states: Notice that we have specified the depth for both encoder and decoder as 3, and your model has a total depth of 3 + 3 = 6. You can also specify different depths for the encoder and the decoder. Example:

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README