import keras from keras.layers import GlobalMaxPooling1D, Dense, Embedding from keras.models import Sequential vocab_size = 10000 embedding_dim = 32 max_length = 100 model = Sequential() model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length)) model.add(GlobalMaxPooling1D()) model.add(Dense(1, activation='sigmoid'))
import keras from keras.layers import GlobalMaxPooling1D, Dense, Embedding, Dropout from keras.models import Sequential vocab_size = 5000 embedding_dim = 64 max_length = 250 model = Sequential() model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length)) model.add(Dropout(0.5)) model.add(GlobalMaxPooling1D()) model.add(Dense(1, activation='sigmoid'))In this example, we use Embedding, Dropout, and GlobalMaxPooling1D layers in a sequential Keras model for spam detection. The Embedding layer is used to convert input messages into a dense vector representation, the Dropout layer helps to prevent overfitting during training, and the GlobalMaxPooling1D layer extracts the most important features. The output layer has a sigmoid activation function to predict if a message is spam or not. The Keras package is the library used in the examples above.