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denoisor_training.py
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denoisor_training.py
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import keras.losses
import utility_functions
from callbacks import *
from loss_functions import *
from pathlib import Path
import pandas as pd
def train(model_wrapper, X_train, y_train, X_test, y_test, batch_size=64, lr_factor=0.5, lr_patience=20, epochs=1000, sub_path = "", verbose=True, load_if_possible=True, add_info="", save=False, initial_learning_rate=0.001):
model = model_wrapper.denoise_model
model.summary()
cfg_str = "bs" + str(batch_size) + "lrp" + str(lr_patience) + "e" + str(epochs) + add_info
paths = utility_functions.get_paths(model, cfg_str, sub_path)
if load_if_possible and os.path.exists(paths.weight_path):
model.load_weights(paths.weight_path)
print("loaded denoisor : ", paths.weight_path)
return
print("train denoisor : ", paths.weight_path)
lr_plateau = tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss",
factor=lr_factor,
patience=lr_patience,
min_lr=1e-7, mode="min")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=paths.tensorboard_path, histogram_freq=1)
display_callback = DenoiseDisplayCallback(X_test, y_test, 5)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=5*lr_patience)
callbacks = [lr_plateau, display_callback, tensorboard_callback, early_stopping]
model.compile(
loss=keras.losses.MeanSquaredError(),
optimizer=k.optimizers.RMSprop(learning_rate=initial_learning_rate)
)
print("train denoiser")
history = model.fit(
verbose=verbose,
x=X_train,
y=y_train,
batch_size=batch_size,
epochs=epochs,
shuffle=True,
validation_data=[X_test, y_test],
callbacks=callbacks)
if save:
Path(paths.cur_folder_name).mkdir(parents=True, exist_ok=True)
model.save_weights(paths.weight_path)
model.save_weights(paths.cur_weight_path)
history_df = pd.DataFrame.from_dict(history.history)
history_df.to_csv(os.path.join(paths.cur_folder_name, "history.csv"))
os.rename(paths.cur_folder_name, (paths.folder_name + "/trained" + add_info).strip())