segmenter.model,
save_best_only=True,
save_weights_only=True,
monitor='val_loss',
verbose=0)
log = TensorBoard(log_dir='logs',
histogram_freq=0,
batch_size=data_loader.batch_size,
write_graph=True,
write_grads=False)
# Use LRFinder to find effective learning rate
lr_finder = LRFinder(1e-6, 1e-2, steps_per_epoch, epochs=1) # => (1e-4, 1e-3)
lr_scheduler = SGDRScheduler(min_lr=1e-4, max_lr=1e-3,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
cycle_length=10,
lr_decay=0.9,
mult_factor=1.2)
X_train, Y_train, X_valid, Y_valid = DataLoader.load_data(h5_dataset_path, frac=0.9)
segmenter.parallel_model.fit_generator(data_loader.generator_from_data(X_train, Y_train),
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=data_loader.generator_from_data(X_valid, Y_valid),
validation_steps=validation_steps,
callbacks=[ck, log, lr_scheduler],
initial_epoch=initial_epoch)
# lr_finder.plot_loss()
# plt.savefig("loss.png")
histogram_freq=0,
batch_size=data_loader.batch_size,
write_graph=True,
write_grads=False)
# Use LRFinder to find effective learning rate
lr_finder = LRFinder(1e-6, 1e-2, steps_per_epoch,
epochs=1) # => (2e-4, 3e-4)
lr_scheduler = WatchScheduler(lambda _, lr: lr / 2,
min_lr=2e-4,
max_lr=4e-4,
watch="val_loss",
watch_his_len=2)
lr_scheduler = SGDRScheduler(min_lr=4e-5,
max_lr=1e-3,
steps_per_epoch=steps_per_epoch,
cycle_length=15,
lr_decay=0.9,
mult_factor=1.2)
X_train, Y_train, X_valid, Y_valid = DataLoader.load_data(h5_dataset_path,
frac=0.8)
print(str(X_train))
print(str(Y_train))
file_path = "D:\\copus\\test_icwb2\\loglog"
loglog = open(file_path, 'w') # 打开文件
tokenizer.model.fit_generator(
data_loader.generator_from_data(X_train, Y_train, loglog),
epochs=1,