Building an LSTM Neural Network for Binary Classification with TensorFlow
In this example we're using TensorFlow to build an LSTM neural network for a binary classification problem. We define the architecture of the LSTM model using the Sequential class from TensorFlow's Keras API. We add an LSTM layer with 64 units and a dense output layer with a sigmoid activation function.
Then we compile the model using the binary cross-entropy loss function, the Adam optimizer and accuracy as the evaluation metric. We then train the model on the training set using 10 epochs and a batch size of 32. Finally, we evaluate the performance of the model on the testing set and print the test loss and accuracy.