# Compile the model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
In RNNs, applying dropout across recurrent connections is tricky. A common pattern is to apply dropout only on the input or output dimensions. With a hidden state dimension of 20, you can use dropout in nn.LSTM : dropout dimension 20
# Define the model architecture model = Sequential() model.add(Dense(20, activation='relu', input_shape=(20,))) model.add(Dropout(0.2)) # Dropout dimension 20 with 20% dropout rate model.add(Dense(10, activation='softmax')) # Compile the model model