7b6fd231d17cd1fd88c1fc163a33f1049d63e7b0,examples/timedelay_conv_model.py,,,#,2

Before Change

data_loader = DataLoader(from_csv=False, datapath=directory_path, time_delay=3)
dataset = data_loader.get_data()
labels = dataset.get_labels()
images = dataset.get_images()

if verbose:
    print("raw image data shape: " + str(images.shape))
label_count = len(labels[0])

print("Creating training/testing data...")
validation_split = 0.25
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(time_delay=dataset.get_time_delay()).fit(X_train, y_train)
test_gen = DataGenerator(time_delay=dataset.get_time_delay()).fit(X_test, y_test)

print("Training net...")

After Change

from src.dataloader import DataLoader
from src.neuralnets import TimeDelayConvNN

validation_split = 0.25

target_dimensions = (64, 64)
channels = 1
verbose = True

print("--------------- Time-Delay Convolutional Model -------------------")
print("Loading data...")
directory_path = "image_data/sample_image_series_directory"
data_loader = DataLoader(from_csv=False, datapath=directory_path, validation_split=validation_split, time_delay=3)
dataset = data_loader.load_data()

if verbose:
    dataset.print_data_details()

print("Preparing training/testing data...")
train_images, train_labels = dataset.get_training_data()
train_gen = DataGenerator(time_delay=dataset.get_time_delay()).fit(train_images, train_labels)
test_images, test_labels = dataset.get_test_data()
test_gen = DataGenerator(time_delay=dataset.get_time_delay()).fit(test_images, test_labels)

print("Training net...")
model = TimeDelayConvNN(target_dimensions, channels, emotion_map=dataset.get_emotion_index_map(), time_delay=dataset.get_time_delay())

Instances