target_dimensions = (128, 128)
target_dimensions2 = (490, 640)
#directory_path = r'C:\Users\localadmin\Desktop\final_proj\cohn-kanade-images2'
directory_path = r'D:\Degree\Final project\cohn-kanade-formated2'
data_loader = DirectoryDataLoader(datapath=directory_path,
validation_split=validation_split,
target_dimension=target_dimensions2,
out_channels=3)
dataset = data_loader.load_data()
if verbose:
dataset.print_data_details()
print('Creating training/testing data...')
train_images, train_labels = dataset.get_training_data()
train_gen = DataGenerator().fit(train_images, train_labels)
test_images, test_labels = dataset.get_test_data()
test_gen = DataGenerator().fit(test_images, test_labels)
print('Initializing neural network with InceptionV3 base model...')
model = TransferLearningNN(model_name=model_name,
emotion_map=dataset.get_emotion_index_map())
print('Training model...')
model.fit_generator(train_gen.generate(target_dimensions, 10),
test_gen.generate(target_dimensions, 10),
epochs=10)
model = ConvolutionalNNDropout(target_dimensions,
channels,
emotion_map,
verbose=True)
pretrained_model = load_model('./output/ferplus_subset_conv_dropout_30_64.h5')
# neuralnets.fit_generator not keras.models.fit_generator
# history = model.fit_generator(train_gen.generate(target_dimensions, batch_size=batch_size),
# test_gen.generate(
# target_dimensions, batch_size=batch_size),
# epochs=epochs)
history = model.continue_training_model(
pretrained_model,
train_gen.generate(target_dimensions, batch_size=batch_size),
test_gen.generate(target_dimensions, batch_size=batch_size),
epochs=epochs)
hist_df = pd.DataFrame(history.history)
# save to json file
hist_json_file = "output/" + model_file_name + ".json"
with open(hist_json_file, mode='w') as f:
hist_df.to_json(f)
# Save model configuration (src.nueralnets.save_model)
model.save_model("output/" + model_file_name + ".h5",
"output/" + model_file_name + "_emotion_map.json",
emotion_map)
print('--------------- Convolutional Model -------------------')
print('Loading data...')
# directory_path = resource_filename('EmoPy.examples','image_data/sample_image_directory')
directory_path = r'D:\Degree\Final project\cohn-kanade-formated2'
data_loader = DirectoryDataLoader(datapath=directory_path,
validation_split=validation_split,
target_dimension=target_dimensions2)
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().fit(train_images, train_labels)
test_images, test_labels = dataset.get_test_data()
test_gen = DataGenerator().fit(test_images, test_labels)
print('Training net...')
model = ConvolutionalNN(target_dimensions,
channels,
dataset.get_emotion_index_map(),
verbose=True)
model.fit_generator(train_gen.generate(target_dimensions, batch_size=5),
test_gen.generate(target_dimensions, batch_size=5),
epochs=5)
# Save model configuration
# model.export_model('output/conv2d_model.json','output/conv2d_weights.h5',"output/conv2d_emotion_map.json", emotion_map)
dataset.print_data_details()
print('Preparing training/testing data...')
train_images, train_labels = dataset.get_training_data()
train_gen = DataGenerator().fit(train_images, train_labels)
test_images, test_labels = dataset.get_test_data()
test_gen = DataGenerator().fit(test_images, test_labels)
emotion_map = dataset.get_emotion_index_map()
print('Training net...')
model = DeepConvolutionalNNDropout(
target_dimensions, channels, emotion_map, verbose=True)
# neuralnets.fit_generator not keras.models.fit_generator
history = model.fit_generator(train_gen.generate(target_dimensions, batch_size=batch_size),
test_gen.generate(target_dimensions,
batch_size=batch_size),
epochs=epochs)
hist_df = pd.DataFrame(history.history)
# save to json file
hist_json_file = "output/" + model_file_name + ".json"
with open(hist_json_file, mode='w') as f:
hist_df.to_json(f)
# Save model configuration (src.nueralnets.save_model)
model.save_model("output/" + model_file_name + ".h5", "output/" +
model_file_name + "_emotion_map.json", emotion_map)