def validate_all(sizes, droput_rates):
learn_histories_dict = {}
for row_idx, size in enumerate(sizes):
for col_idx, rate in enumerate(droput_rates):
print(f'class_first_size_{size}_dr_rate{rate}')
model_creator = ModelCreator()
model_creator.load_base_pretrained_model()
model_creator.read_prepared_data()
model_creator.train_last_fully_connected_layer(
final_training_mode=True,
first_classif_layer_size=size,
dropout_rate=rate,
logs_file=f'class_first_size_{size}_dr_rate{rate}.csv')
learn_histories_dict[(size, rate)] = model_creator.learn_history
dd.draw_learning_history(
model_creator.learn_history,
title='Uczenie części klasyfikującej',
out_filename=f'class_first_size_{size}_dr_rate{rate}')
dd.draw_learning_history_top_k(
model_creator.learn_history,
title='Uczenie części klasyfikującej',
out_filename=f'class_first_size_{size}_dr_rate{rate}')
dd.draw_two_used_params_values_comparision(
histories_dict=learn_histories_dict,
out_filename='sizes_and_rates_first_classif')
def validate_loss_functions(functions):
for func in functions:
model_creator = ModelCreator()
model_creator.load_base_pretrained_model()
model_creator.read_prepared_data()
model_creator.train_last_fully_connected_layer(
final_training_mode=False, loss=func)
dd.draw_learning_history(model_creator.learn_history,
f'Funkcja kary: {func}')
def validate_dropout_rates(dropout_rates):
learn_histories = []
for dropout_rate in dropout_rates:
model_creator = ModelCreator()
model_creator.load_base_pretrained_model()
model_creator.read_prepared_data()
model_creator.train_last_fully_connected_layer(
final_training_mode=False,
dropout_rate=dropout_rate,
logs_file=f'valid_dropout_{dropout_rate}.csv')
learn_histories.append(model_creator.learn_history)
dd.draw_used_params_values_comparision(histories=learn_histories,
values=dropout_rates,
out_filename='dropout_validation')
def validate_first_classif_sizes(sizes):
learn_histories = []
for size in sizes:
model_creator = ModelCreator()
model_creator.load_base_pretrained_model()
model_creator.read_prepared_data()
model_creator.train_last_fully_connected_layer(
final_training_mode=False,
first_classif_layer_size=size,
logs_file=f'valid_size_{size}.csv')
learn_histories.append(model_creator.learn_history)
dd.draw_used_params_values_comparision(histories=learn_histories,
values=sizes,
out_filename='sizes_validation')
def validate_removal_coef(removal_coefs):
for removal_coef in removal_coefs:
model_creator = ModelCreator()
model_creator.load_base_pretrained_model()
model_creator.read_prepared_data()
model_creator.train_whole_convolutional_network(
removal_coef, final_training_mode=False)
dd.draw_learning_history(
model_creator.learn_history,
f'Wspolczynnik usunietych warstw: {removal_coef}')
model_creator.save_model(
f'models/{model_creator.base_model.name}_{AppParams.layers_trainable_mode.name}_{AppParams.epochs}_{removal_coef}.h5'
)
dd.draw_learning_history(model_creator.learn_history)
del model_creator
def valuate_first_classif_sizes_from_last_conv(sizes):
learn_histories = []
for size in sizes:
model_creator = ModelCreator()
model_creator.load_base_pretrained_model()
model_creator.read_prepared_data()
model_creator.train_from_last_convolutional_layer(
final_training_mode=True,
first_classif_layer_size=size,
logs_file=f'valid_size_{size}_last_conv.csv')
learn_histories.append(model_creator.learn_history)
dd.draw_learning_history(model_creator.learn_history,
title='Uczenie od warstwy splotowej',
out_filename=f'last_conv_size_{size}')
dd.draw_learning_history_top_k(model_creator.learn_history,
title='Uczenie części klasyfikującej',
out_filename=f'last_conv_size_{size}')
dd.draw_used_params_values_comparision(
histories=learn_histories,
values=sizes,
out_filename='sizes_validation_last_conv')
import datetime
from keras.engine.saving import load_model
import params_validator as pv
from app_params import AppParams
from draw_utils import draw_data as dd
from layers_trainable_modes import LayersTrainableMode
from model_creator import ModelCreator
from svm_model import train_svm
model_creator = ModelCreator()
model_creator.load_base_pretrained_model(
) # załadowanie wytrenowanego już modelu
model_creator.read_prepared_data(
) # odczytanie odpowiednio podzielonych danych
# ZADANIE 1
if AppParams.layers_trainable_mode is LayersTrainableMode.ONLY_CLASSIF:
model_creator.train_last_fully_connected_layer(
logs_file='only_classif.csv')
dd.draw_learning_history(model_creator.learn_history,
title='Uczenie części klasyfikującej',
out_filename='only_classif')
# ZADANIE 2
elif AppParams.layers_trainable_mode is LayersTrainableMode.FROM_LAST_CONV:
model_creator.train_from_last_convolutional_layer(
logs_file='from_last_conv.csv')
dd.draw_learning_history(model_creator.learn_history,
title='Uczenie od ostatniej warstwy splotowej',
def validate_optimizers(optimizers):
for optimizer in optimizers:
model_creator = ModelCreator()
model_creator.load_base_pretrained_model()
model_creator.read_prepared_data()
model_creator.train_last_fully_connected_layer()