สร้างโมเดลข้อความ RNN

Time to wire up a real model. Both PyTorch and Keras give you ready RNN layers, so you focus on shape, not raw math.

Your first layer is usually an embedding layer. It maps each integer word id to a learnable dense vector.

Next comes the RNN layer itself. It loops over the embedded sequence and outputs a hidden state for the input.

In Keras a basic text RNN is just a few stacked layers, easy to read top to bottom.

In PyTorch you define an nn.RNN and pass batches through it, reading the last hidden state for a prediction.

Real sentences vary in length, so you pad them to a common size and let the model ignore the filler positions.

On top of the RNN you add a dense layer that turns the summary state into your class scores or a probability.

For two-class problems, pair a sigmoid output with binary cross-entropy; for many classes, use categorical cross-entropy.

You then call fit or a training loop, feeding batches so the network adjusts its weights to reduce the loss.

RNNs can memorize small datasets. Add dropout or early stopping to keep your model honest on new text.

Embedding, RNN, then dense: that simple stack already reads text in order and learns to classify it end to end. 🛠️

Which layer usually comes first in an RNN text model?

A working RNN stacks an embedding, a recurrent layer, and a dense head, then trains on padded sequences to classify text. 🎯