def save_hard_images(
threshold_list=(0.9999, 0.999, 0.99, 0.97, 0.95, 0.9, 0.8, 0.7, 0.5),
image_number=1250):
CustomModel = import_model()
model = CustomModel()
factor = 1
for i, threshold in enumerate(threshold_list):
print("acquisition::get_hard_images : threshold :", threshold)
try:
shutil.rmtree(
os.path.join(negatives_hard_directory, str(threshold)))
except FileNotFoundError:
pass
os.makedirs(os.path.join(negatives_hard_directory, str(threshold)))
model.train(image_number, image_number)
pyramid = Pyramid(model, threshold)
pyramid.add_false_positive_to_negative_db(directory_path=added_images,
save_path=os.path.join(
negatives_hard_directory,
str(threshold)),
strides=(9, 9))
pyramid = Pyramid(model, threshold_list[max(0, i - 1)])
pyramid.test_pyramide('photo_famille.jpeg')
factor *= 0.8
model.recompile(factor)
def main(model_num=1):
preprocess = Preprocess()
texts_train, labels_train = preprocess.preprocessData(
'../projet2/train.txt', mode="train")
texts_dev, labels_dev = preprocess.preprocessData('../projet2/dev.txt',
mode="train")
MAX_SEQUENCE_LENGTH = 24
LSTM_DIM = 64
HIDDEN_LAYER_DIM = 30
NUM_CLASSES = 4
GAUSSIAN_NOISE = 0.1
DROPOUT = 0.2
DROPOUT_LSTM = 0.2
BATCH_SIZE = 200
X_train, X_val, y_train, y_val = train_test_split(texts_train,
labels_train,
test_size=0.2,
random_state=42)
labels_categorical_train = to_categorical(np.asarray(y_train))
labels_categorical_val = to_categorical(np.asarray(y_val))
labels_categorical_dev = to_categorical(np.asarray(labels_dev))
embedding = Embedding('../projet2/emosense.300d.txt')
embeddings = embedding.getMatrix()
tokenizer = embedding.getTokenizer()
message_first_message_train, message_second_message_train, message_third_message_train = get_sequences(
X_train, MAX_SEQUENCE_LENGTH, tokenizer)
message_first_message_val, message_second_message_val, message_third_message_val = get_sequences(
X_val, MAX_SEQUENCE_LENGTH, tokenizer)
message_first_message_dev, message_second_message_dev, message_third_message_dev = get_sequences(
texts_dev, MAX_SEQUENCE_LENGTH, tokenizer)
model = CustomModel(model_num)
model.build(embeddings,
MAX_SEQUENCE_LENGTH,
LSTM_DIM,
HIDDEN_LAYER_DIM,
NUM_CLASSES,
noise=GAUSSIAN_NOISE,
dropout_lstm=DROPOUT_LSTM,
dropout=DROPOUT)
model.summary()
history = model.train(message_first_message_train,
message_second_message_train,
message_third_message_train,
labels_categorical_train, message_first_message_val,
message_second_message_val,
message_third_message_val, labels_categorical_val)
y_pred = model.predict([
message_first_message_dev, message_second_message_dev,
message_third_message_dev
])