Use the network to predict the test data generator

model_predict.py

STEP_SIZE_TEST = testing_generator.n // testing_generator.batch_size
testing_generator.reset()
predictions = malaria_model.predict_generator(testing_generator,
                                              steps=STEP_SIZE_TEST,
                                              verbose=1)
img_name = testing_generator.filenames
results = pd.DataFrame({'img_name': img_name,
                        'prediction': predicted_class})
_this = results.img_name.str.split('\\', n=1, expand=True)
results['reference'] = _this[0]

# Add true label
lookup_class = dict((k, v) for k, v in (training_generator.class_indices).items())
results['true_label'] = results.reference.map(lookup_class)
display(results.head(5))

'''
	img_name	prediction	reference	true_label
0	Parasitized\Parasitized_C100P61ThinF_IMG_20150918_144348_cell_143.png	0	Parasitized	0
1	Parasitized\Parasitized_C100P61ThinF_IMG_20150918_144348_cell_144.png	0	Parasitized	0
2	Parasitized\Parasitized_C100P61ThinF_IMG_20150918_145422_cell_166.png	0	Parasitized	0
3	Parasitized\Parasitized_C100P61ThinF_IMG_20150918_145422_cell_169.png	0	Parasitized	0
4	Parasitized\Parasitized_C100P61ThinF_IMG_20150918_145609_cell_145.png	0	Parasitized	0
'''