Understanding Seq2Seq Neural Networks – Part 7: Generating the Output with Softmax

In the previous article , we were transforming the outputs to the fully connected layer. A fully connected layer is just another name for a basic neural network . This fully connected layer has two inputs , corresponding to the two values that come from the top layer of LSTM cells . It has four outputs , one for each token in the Spanish vocabulary . In between, we have connections between each input and output , each with their own weights and biases . Then, we run the output of the fully connected layer through a Softmax function to select the output word . Now, going back to the full Encoder–Decoder model : We can see that the output from the Softmax function is “Vamos,” which is the Spanish translation for “Let’s go.” We will continue further in the next article . Looking for an easier way to install tools, libraries, or entire repositories? Try Installerpedia : a community-driven, structured installation platform that lets you install almost anything with minimal hassle and clear,