specify validation_split of to extract of the training data and use it as validation dataset for evaluating the performance at each epoch the shuffle parameter helps shuffle the samples in each epoch when training the model in [ ]batch_size v_dnn fit(avg_wv_train_featuresy_trainepochs= batch_size=batch_sizeshuffle=truevalidation_split= verbose= train on samplesvalidate on samples epoch / / loss acc val_loss val_acc epoch / / loss acc val_loss val_acc epoch / / loss acc val_loss val_acc epoch / / loss acc val_loss val_acc epoch / / loss acc val_loss val_acc the preceding snippet tells us that we have trained our dnn model on the training data for five epochs with as the batch size we get validation accuracy of close to %which is quite good time now to put our model to the real testlet' evaluate our model performance on the test review word vec features (step in [ ]y_pred v_dnn predict_classes(avg_wv_test_featurespredictions le inverse_transform(y_predmeu display_model_performance_metrics(true_labels=test_sentimentspredicted_labels=predictionsclasses=['positive''negative']figure - model performance metrics for deep neural networks on word vec features the results depicted in figure - show us that we have obtained model accuracy and -score of %which is greatyou can use similar workflow to build and train dnn model for our glove based features and evaluate the model performance the following snippet depicts the workflow for steps and of our text classification system blueprint build dnn model glove_dnn construct_deepnn_architecture(num_input_features= train dnn model on glove training features batch_size glove_dnn fit(train_glove_featuresy_trainepochs= batch_size=batch_sizeshuffle=truevalidation_split= verbose= get predictions on test reviews y_pred glove_dnn predict_classes(test_glove_featurespredictions le inverse_transform(y_predevaluate model performance meu display_model_performance_metrics(true_labels=test_sentimentspredicted_ labels=predictionsclasses=['positive''negative']