a94f7b93251013d51d9918c27ab2569eb526a494,examples/timedelay_conv_model.py,,,#,2

Before Change

label_count = len(labels[0])

print("Processing data...")
imageProcessor = ImageProcessor(image_data, target_dimensions=target_dimensions, time_series=True)
image_array = imageProcessor.process_training_data()
image_data = np.array([np.array([image]).reshape([time_delay]+list(target_dimensions)+[channels]) for image in image_array])
if verbose:
    print ("processed image data shape: " + str(image_data.shape))

After Change

validation_split = 0.25
X_train, X_test, y_train, y_test = train_test_split(image_data, labels,
                                                    test_size=validation_split, random_state=42, stratify=labels)
train_gen = DataGenerator(time_delay=time_delay).fit(X_train, y_train)
test_gen = DataGenerator(time_delay=time_delay).fit(X_test, y_test)

print("Training net...")
model = TimeDelayConvNN(target_dimensions, time_delay, channels, label_count)

// model.fit(image_data, labels, validation_split)
model.fit_generator(train_gen.generate(target_dimensions, batch_size=10),
                    test_gen.generate(target_dimensions, batch_size=10),
                    epochs=10)

Instances