The code that will be used for this experiment is as follows:
# Model architecturemodel <- keras_model_sequential() %>% layer_embedding(input_dim = 1500, output_dim = 32, input_length = 400) %>% layer_conv_1d(filters = 64, kernel_size = 4, padding = "valid", activation = "relu", strides = 1) %>% layer_max_pooling_1d(pool_size = 4) %>% layer_dropout(0.25) %>% layer_lstm(units = 32) %>% layer_dense(units = 50, activation = "softmax")# Compiling the model model %>% compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = c("acc")) # Fitting the model model_three <- model %>% fit(trainx, trainy, epochs = 30, batch_size = 8, validation_data = list(validx, validy)) ...
