Rewrite in academic way: The model consists of an input layer with a size of (n, 33, 1) followed by a convolutional neural network (CNN) layer that has been optimally tuned through hyperparameter tuning. This CNN includes the number of filters, kernel size and activation function (ReLU), padding type (valid), strides as well as dropout rate which have all been adjusted for optimum performance. Additionally, two long short-term memory layers are present in this setup; their respective numbers of units and activations types - tanh or sigmoid - were also adjusted using hyperparameters. An attention mechanism is then used to aggregate outputs from these LSTM layers before being passed on to another dense layer featuring one unit along with its own sigmoid activation function providing the final output label upon completion. The model was compiled utilizing binary cross entropy loss alongside an optimized optimizer configured via TPE for best accuracy results during training over the training data set whilst early stopping monitored validation losses accordingly throughout optimization processes involving extensive parameter adjustments done through TPE methodologies respectively . Avoid using the following keywords: unit, stride, dropout, layer
user