def show_summary():
print("\n" + cf.skyBlue("Training set"))
print(get_summary_matrix(common.TRAINING_DIR))
print("\n" + cf.salmon("Validation set"))
print(get_summary_matrix(common.VALIDATION_DIR))
print("\n" + cf.lightGreen("Testing set"))
print(get_summary_matrix(common.TESTING_DIR))
def fit(model, training, validation, batch_size):
print("\nFitting model...")
history = model.fit_generator(
training,
epochs=MAX_EPOCHS,
validation_data=validation,
steps_per_epoch=training.n // batch_size,
validation_steps=validation.n // batch_size,
callbacks=setup_callbacks(),
workers=2,
verbose=2)
best_epoch = np.argmin(history.history['val_loss']) + 1
print("\n" + cf.lightGreen("Best epoch: {}".format(best_epoch)))
def main(src_subdir, verbose, model_name):
common.create_directories()
src_dir = common.DATA_DIR + src_subdir + "/"
model = tensorflow.keras.models.load_model(common.MODEL_DIR + model_name)
# real (index) x predicted (column)
confusion_matrix = pd.DataFrame(np.zeros((4, 4), dtype=np.int32),
index=('down', 'left', 'right', 'up'),
columns=('down', 'left', 'right', 'up'))
classification_matrix = pd.DataFrame(np.zeros((4, 3)),
index=('down', 'left', 'right', 'up'),
columns=('precision', 'recall', 'f1'))
type_matrix = pd.DataFrame(np.zeros((4, 2), dtype=np.int32),
index=('round', 'wide', 'narrow', 'total'),
columns=('correct', 'incorrect'))
images = common.get_files(src_dir)
print("Processing {} file(s) in {}/...\n".format(len(images), src_subdir))
for path, filename in images:
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
data = np.reshape(img, (1, ) + common.INPUT_SHAPE)
prediction = model.predict(data)
class_index = np.argmax(prediction)
predicted_class = common.CLASSES[class_index]
real_class, arrow_type = common.arrow_labels(filename)
if verbose and real_class != predicted_class:
print(path)
print("Expected {} but got {}: {}\n".format(
cf.lightGreen(real_class), cf.lightCoral(predicted_class),
str(prediction[0])))
confusion_matrix[predicted_class][real_class] += 1
if real_class == predicted_class:
type_matrix['correct'][arrow_type] += 1
type_matrix['correct']['total'] += 1
else:
type_matrix['incorrect'][arrow_type] += 1
type_matrix['incorrect']['total'] += 1
print("\n" + cf.sandyBrown("Confusion matrix"))
print(confusion_matrix)
classification_matrix['precision'] = confusion_matrix.apply(precision)
classification_matrix['recall'] = confusion_matrix.apply(recall, axis=1)
classification_matrix['f1'] = classification_matrix.apply(f1, axis=1)
print("\n" + cf.skyBlue("Classification summary"))
print(classification_matrix)
type_matrix['accuracy'] = type_matrix.apply(type_accuracy, axis=1)
print("\n" + cf.plum("Accuracy by type"))
print(type_matrix)
print("\nFinished!")