c1b3482f575bf75557c8072b3f857672bddd3588,examples/transferlearning_model.py,,,#,4

Before Change

data_loader = DataLoader(from_csv=True, emotion_map=fer_dataset_label_map, datapath=csv_file_path,
                         image_dimensions=raw_dimensions, csv_label_col=0, csv_image_col=1, out_channels=3)
images, labels, emotion_map = data_loader.get_data()
if verbose:
    print("raw image shape: " + str(images.shape))

After Change

data_loader = DataLoader(from_csv=True, emotion_map=fer_dataset_label_map, datapath=csv_file_path,
                         image_dimensions=raw_dimensions, csv_label_col=0, csv_image_col=1, out_channels=3)
dataset = data_loader.get_data()
labels = dataset.get_labels()
images = dataset.get_images()

if verbose:
    print("raw image shape: " + str(images.shape))

print("Creating training/testing data...")
validation_split = 0.15
X_train, X_test, y_train, y_test = train_test_split(images, labels,
                                                    test_size=validation_split, random_state=42, stratify=labels)
train_gen = DataGenerator().fit(X_train, y_train)
test_gen = DataGenerator().fit(X_test, y_test)

print("--------------- Inception-V3 Model -------------------")
print("Initializing neural network with InceptionV3 base model...")
model = TransferLearningNN(model_name=model_name, emotion_map=dataset.get_emotion_index_map())

print("Training model...")
print("numLayers: " + str(len(model.model.layers)))

Instances