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(
