print( classification_report(labels_test, svcPred, target_names=category_id_df.category.values)) nnmodel = KerasClassifier(build_fn=baseline_model, epochs=60, batch_size=10, verbose=0) nnModel = baseline_model() nnModel.fit(features_train2, train_labels_ff2, shuffle=True, epochs=60, batch_size=10, verbose=0) nnmodel._name = 'ffNN' ffPred = nnModel.predict_classes(features_test) print("Feedforward accuracy is ") print(accuracy_score(labels_test, ffPred)) conf_mat_ff = confusion_matrix(labels_test, ffPred) ax = sns.heatmap(conf_mat_ff, annot=True, fmt='d', xticklabels=category_id_df.category.values, yticklabels=category_id_df.category.values) plt.ylabel('Actual') plt.xlabel('Predicted') plt.show() print(