raw_dimensions = (48, 48)
target_dimensions = (64, 64)
channels = 1
verbose = True
print('--------------- Convolutional LSTM Model -------------------')
print('Loading data...')
directory_path = "image_data/sample_image_series_directory"
data_loader = DirectoryDataLoader(datapath=directory_path, validation_split=validation_split, time_delay=2)
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 = ConvolutionalLstmNN(target_dimensions, channels, dataset.get_emotion_index_map(), time_delay=dataset.get_time_delay())
model.fit_generator(train_gen.generate(target_dimensions, batch_size=5),
test_gen.generate(target_dimensions, batch_size=5),
epochs=5)
## if you want to save a graph of your model layers.
model.save_model_graph()
# Save model configuration
# model.export_model('output/conv_lstm_model.json','output/conv_lstm_weights.h5',"output/conv_lstm_emotion_map.json", emotion_map)
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))
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=emotion_map)
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)
# Save model configuration
# model.export_model('output/transfer_learning_model.json','output/transfer_learning_weights.h5',"output/transfer_learning_emotion_map.json", emotion_map)
