0dec477ddd9d7151035315d2280b8e2fa0a391f9,examples/transferlearning_model.py,,,#,2

Before Change

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

print("Processing data...")
imageProcessor = ImageProcessor(images, target_dimensions=target_dimensions, rgb=True)
images = imageProcessor.process_training_data()
if verbose:
    print ("processed image shape: " + str(images.shape))

print("--------------- Inception-V3 Model -------------------")

After Change

K.set_image_data_format("channels_last")

verbose = True
target_dimensions = (128, 128)
raw_dimensions = (48, 48)
target_labels = [0, 1, 2, 3, 4, 5, 6]
model_name = "inception_v3"

print("Loading data...")
csv_file_path = "image_data/sample.csv"

dataLoader = DataLoader(from_csv=True, target_labels=target_labels, datapath=csv_file_path,
                        image_dimensions=raw_dimensions, csv_label_col=0, csv_image_col=1, out_channels=3)
images, labels = dataLoader.get_data()
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, target_labels=target_labels)

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

model.fit_generator(train_gen.generate(target_dimensions, 10),
                    test_gen.generate(target_dimensions, 10),
                    epochs=10)

Instances