def evaluate(model_root, model, images, image_size, seed, top_n=None):
imagegen = image.ImageDataGenerator()
test_gen = image.DirectoryIterator(os.path.join(images, 'testing'),
imagegen,
target_size=(image_size, image_size),
seed=seed,
shuffle=True)
# Get classes sorted by their value
classes = [
x[0]
for x in sorted(test_gen.class_indices.items(), key=lambda x: x[1])
]
metrics_path = model_root.with_name(model_root.name + "_metrics.csv")
cm_path = model_root.with_name(model_root.name + "_cm.png")
metrics, _, _ = score_keras.evaluate_model(model,
test_gen,
classes,
metrics_path,
cm_path,
top_n=top_n)
class_map = model_root.with_name(model_root.name + "_classes.csv")
try:
with class_map.open('w', encoding='utf-8') as cmfp:
cmfp.write('Class,ID\n')
for i, c in enumerate(classes):
cmfp.write('"{}",{}\n'.format(c, i))
except:
logger.warn('Failed to write class map file.')
return classes, cm_path, metrics
def evaluate(model_root,
model,
images,
image_size,
num_batches,
seed,
top_n=None):
imagegen = image.ImageDataGenerator()
test_gen = image.DirectoryIterator(os.path.join(images, 'testing'),
imagegen,
target_size=(image_size, image_size),
seed=seed,
shuffle=True)
# Get classes sorted by their value
classes = [
x[0]
for x in sorted(test_gen.class_indices.items(), key=lambda x: x[1])
]
metrics_path = model_root + "_metrics.csv"
cm_path = model_root + "_cm.png"
metrics, _, _ = score_keras.evaluate_model(model,
test_gen,
classes,
num_batches,
metrics_path,
cm_path,
top_n=top_n)
return classes, cm_path, metrics