def training_tf_loop(model,
training_loss,
epochs: int = 1,
num_batches_per_epoch: int = 1,
logging_epoch_freq: int = 100,
manager: tf.train.CheckpointManager = None):
"""Runs Adam optimizer on model with training_loss (no monitoring).
:param model: The model to be trained.
:param training_loss: A function that returns the training objective.
:param epochs: The number of full data passes (epochs).
:param num_batches_per_epoch: The number of batches per epoch
:param logging_epoch_freq: The epoch frequency that the training loss is printed.
"""
optimizer = tf.optimizers.Adam()
@tf.function
def tf_optimization_step():
optimizer.minimize(training_loss, model.trainable_variables)
# t = time.time()
for epoch in range(epochs):
for _ in range(num_batches_per_epoch):
tf_optimization_step()
# tf_optimization_step(model, training_loss, optimizer)
epoch_id = epoch + 1
if epoch_id % logging_epoch_freq == 0:
tf.print(f"Epoch {epoch_id}: ELBO (train) {training_loss()}")
if manager is not None:
manager.save()
def train(data, class_weights, flags, net: Net, framework: Framework,
manager: tf.train.CheckpointManager):
log = get_logger()
io = SharedFlagIO(flags, subprogram=True)
flags = io.read_flags() if io.read_flags() is not None else flags
log.info('Building {} train op'.format(flags.model))
goal = len(data) * flags.epoch
first = True
for i, (x_batch,
loss_feed) in enumerate(framework.shuffle(data, class_weights)):
loss = net(x_batch, training=True, **loss_feed)
step = net.step.numpy()
lr = net.optimizer.learning_rate.numpy()
line = 'step: {} loss: {:f} lr: {:.2e} progress: {:.2f}%'
if not first:
flags.progress = i * flags.batch / goal * 100
log.info(line.format(step, loss, lr, flags.progress))
else:
log.info(f"Following gradient from step {step}...")
io.send_flags()
flags = io.read_flags()
ckpt = bool(not step % flags.save)
if ckpt and not first:
save = manager.save()
log.info(f"Saved checkpoint: {save}")
first = False
if not ckpt:
save = manager.save()
log.info(f"Finished training at checkpoint: {save}")
def save_checkpoint(strategy, step, manager: tf.train.CheckpointManager):
"""Saves model to with provided checkpoint prefix."""
if should_export_checkpoint(strategy):
saved_path = manager.save(step)
logging.info('Saving model as TF checkpoint: %s', saved_path)
else:
# In multi worker training we need every worker to save checkpoint, because variables can trigger synchronization on read and synchronization needs
# all workers to participate. To avoid workers overriding each other we save to a temporary directory on non-chief workers.
tmp_dir = tempfile.mkdtemp()
manager.save(step)
tf.io.gfile.rmtree(tmp_dir)
def checkpointing_train_SGPR(
model: gpflow.models.SGPR,
X: tf.Tensor,
Y: tf.Tensor,
epochs: int,
manager: tf.train.CheckpointManager,
optimizer: tf.optimizers = tf.optimizers.Adam(learning_rate=0.1),
logging_epoch_freq: int = 10,
epoch_var: Optional[tf.Variable] = None,
exp_tag: str = 'test',
):
"""
Training loop for Sparse GP with checkpointing
"""
set_trainable(model.mean_function, False)
tf_optimization_step = tf.function(optimization_exact)
loss = list()
for epoch in range(epochs):
tf_optimization_step(model)
if epoch_var is not None:
epoch_var.assign(epoch + 1)
epoch_id = epoch + 1
loss.append(model.training_loss())
if epoch_id % logging_epoch_freq == 0:
ckpt_path = manager.save()
tf.print(
f"Epoch {epoch_id}: LOSS (train) {model.training_loss()}, saved at {ckpt_path}"
)
tf.print(f"MSE: {mean_squared_error(Y, model.predict_y(X)[0])}")
plt.plot(range(epochs), loss)
plt.xlabel('Epoch', fontsize=25)
plt.ylabel('Loss', fontsize=25)
plt.tight_layout()
def checkpointing_training_loop(
model: gpflow.models.SVGP,
batch_size: int,
epochs: int,
manager: tf.train.CheckpointManager,
logging_epoch_freq: int = 100,
epoch_var: Optional[tf.Variable] = None,
step_var: Optional[tf.Variable] = None,
):
tf_optimization_step = tf.function(optimization_step)
batches = iter(train_dataset)
for epoch in range(epochs):
for step in range(ci_niter(num_batches_per_epoch)):
tf_optimization_step(model, next(batches))
if step_var is not None:
step_var.assign(epoch * num_batches_per_epoch + step + 1)
if epoch_var is not None:
epoch_var.assign(epoch + 1)
epoch_id = epoch + 1
if epoch_id % logging_epoch_freq == 0:
ckpt_path = manager.save()
tf.print(
f"Epoch {epoch_id}: ELBO (train) {model.elbo(data)}, saved at {ckpt_path}"
)
def monitored_training_loop(model,
training_loss,
epochs: int = 1,
num_batches_per_epoch: int = 1,
fast_tasks: gpf.monitor.MonitorTaskGroup = None,
slow_tasks: gpf.monitor.MonitorTaskGroup = None,
logging_epoch_freq: int = 100,
manager: tf.train.CheckpointManager = None):
"""Monitors (with images) Adam optimizer on model with training_loss.
Monitoring is not inside tf.function so this method will be slower than
monitored_training_tf_loop.
:param model: The model to be trained.
:param training_loss: A function that returns the training objective.
:param epochs: The number of full data passes (epochs).
:param num_batches_per_epoch: The number of batches per epoch
:param fast_tasks: gpflow monitor fast tasks e.g.
MonitorTaskGroup([ScalarToTensorBoard(log_dir, training_loss, "elbo")])
:param slow_tasks: gpflow monitor slow tasks e.g. plotting images
:param logging_epoch_freq: The epoch frequency that the training loss is printed.
"""
optimizer = tf.optimizers.Adam()
# checkpoint_path = "training_2/cp-{epoch:04d}.ckpt"
# checkpoint_dir = os.path.dirname(checkpoint_path)
@tf.function
def tf_optimization_step():
optimizer.minimize(training_loss, model.trainable_variables)
monitor = Monitor(fast_tasks, slow_tasks)
t = time.time()
for epoch in range(epochs):
for _ in range(num_batches_per_epoch):
tf_optimization_step()
# duration = t - time.time()
# print("Iteration duration: ", duration)
# t = time.time()
monitor(epoch)
epoch_id = epoch + 1
if epoch_id % logging_epoch_freq == 0:
tf.print(f"Epoch {epoch_id}: ELBO (train) {training_loss()}")
if manager is not None:
manager.save()
def monitored_training_tf_loop(model,
training_loss,
epochs: int = 1,
num_batches_per_epoch: int = 1,
fast_tasks: gpf.monitor.MonitorTaskGroup = None,
logging_epoch_freq: int = 100,
manager: tf.train.CheckpointManager = None):
"""Monitors Adam optimizer on model with training_loss.
Both training and monitoring are inside tf.function (no image monitoring).
This method only monitors the fast tasks as matplotlib code cannot be built
in a TF graph.
:param model: The model to be trained.
:param training_loss: A function that returns the training objective.
:param epochs: The number of full data passes (epochs).
:param num_batches_per_epoch: The number of batches per epoch
:param fast_tasks: gpflow monitor fast tasks e.g.
MonitorTaskGroup([ScalarToTensorBoard(log_dir, training_loss, "elbo")])
:param logging_epoch_freq: The epoch frequency that the training loss is printed.
"""
optimizer = tf.optimizers.Adam()
monitor = Monitor(fast_tasks)
@tf.function
def monitored_tf_opt_step(epoch):
optimizer.minimize(training_loss, model.trainable_variables)
monitor(epoch)
# t = time.time()
epochs = tf.constant(epochs) # needs to be tf.const
for epoch in tf.range(epochs):
for _ in range(num_batches_per_epoch):
monitored_tf_opt_step(epoch)
epoch_id = epoch + 1
if epoch_id % logging_epoch_freq == 0:
tf.print(f"Epoch {epoch_id}: ELBO (train) {training_loss()}")
if manager is not None:
manager.save()
def train(model: tf.keras.Model, checkpoint: tf.train.CheckpointManager, batch_size: Any, buffer_size: Any,
epochs: Any, train_data_path: AnyStr, test_data_path: AnyStr, dict_path: AnyStr, max_sentence_length: Any,
max_train_steps: Any = -1, checkpoint_save_freq: Any = 2, *args, **kwargs) -> Dict:
""" 训练器
:param model: 训练模型
:param checkpoint: 检查点管理器
:param batch_size: batch 大小
:param buffer_size: 缓冲大小
:param epochs: 训练周期
:param train_data_path: 训练数据保存路径
:param test_data_path: 测试数据保存路径
:param dict_path: 词表文件
:param max_sentence_length: 最大句子对长度
:param max_train_steps: 最大训练数据量，-1为全部
:param checkpoint_save_freq: 检查点保存频率
:return:
"""
print("训练开始，正在准备数据中")
loss_metric = tf.keras.metrics.Mean(name="train_loss_metric")
optimizer = tf.optimizers.Adam(learning_rate=1e-5)
train_steps_per_epoch = 125000 // batch_size
valid_steps_per_epoch = 10000 // batch_size
warmup_steps = train_steps_per_epoch // 3
total_steps = train_steps_per_epoch * epochs - warmup_steps
# learning_rate = CustomSchedule(d_model=768, warmup_steps=warmup_steps)
# optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999, name="optimizer")
# optimizer, _ = create_optimizer(init_lr=2e-5, num_train_steps=total_steps, num_warmup_steps=warmup_steps)
train_dataset, valid_dataset = load_raw_dataset(
train_data_path=train_data_path, max_sentence_length=max_sentence_length, batch_size=batch_size,
buffer_size=buffer_size, dict_path=dict_path, test_data_path=test_data_path)
print("训练开始")
progress_bar = ProgressBar()
for epoch in range(epochs):
print("Epoch {}/{}".format(epoch + 1, epochs))
start_time = time.time()
loss_metric.reset_states()
progress_bar.reset(total=train_steps_per_epoch, num=batch_size)
train_metric = None
for (batch, (queries, segments, labels)) in enumerate(train_dataset.take(max_train_steps)):
train_metric = _train_step(
model=model, optimizer=optimizer, loss_metric=loss_metric,
queries=queries, segments=segments, targets=labels
)
progress_bar(current=batch + 1, metrics=get_dict_string(data=train_metric))
progress_bar(current=progress_bar.total, metrics=get_dict_string(data=train_metric))
progress_bar.done(step_time=time.time() - start_time)
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0 or valid_dataset is None:
print("验证数据量过小，小于batch_size，已跳过验证轮次")
else:
progress_bar.reset(total=valid_steps_per_epoch, num=batch_size)
valid_metrics = _valid_step(model=model, dataset=valid_dataset,
progress_bar=progress_bar, loss_metric=loss_metric, **kwargs)
print("训练结束")
return {}
def train(
model: onmt.models.Model,
optimizer: tf.keras.optimizers.Optimizer,
learning_rate: tf.keras.optimizers.schedules.LearningRateSchedule,
source_file: str,
target_file: str,
checkpoint_manager: tf.train.CheckpointManager,
maximum_length=100,
shuffle_buffer_size=-1, # Uniform shuffle.
train_steps=100000,
save_every=1000,
report_every=100,
validation_source_file=None,
validation_target_file=None,
validate_every=2000,
validate_now=False,
bpe=False,
bpe_combined=False,
):
"""Train a OpenNMT model.
Arguments:
model {onmt.models.Model} -- Model to train.
optimizer {tf.keras.optimizers.Optimizer} -- Optimizer to use.
learning_rate {tf.keras.optimizers.schedules.LearningRateSchedule} --
Learning rate schedule to use.
source_file {str} -- Aligned source language file.
target_file {str} -- Aligned target language file.
checkpoint_manager {tf.train.CheckpointManager} -- Checkpoint manager.
Keyword Arguments:
maximum_length {int} -- [description] (default: {100})
shuffle_buffer_size {int} -- [description] (default: {-1})
save_every {int} -- [description] (default: {1000})
report_every {int} -- [description] (default: {100})
validation_source_file {[type]} -- [description] (default: {None})
validation_target_file {[type]} -- [description] (default: {None})
validate_every {int} -- [description] (default: {2000})
validate_now {bool} -- [description] (default: {False})
bpe {bool} -- [description] (default: {False})
bpe_combined {bool} -- [description] (default: {False})
Returns:
[type] -- [description]
"""
# Create the training dataset.
dataset = model.examples_inputter.make_training_dataset(
source_file,
target_file,
batch_size=3072,
batch_type="tokens",
shuffle_buffer_size=shuffle_buffer_size,
length_bucket_width=
1, # Bucketize sequences by the same length for efficiency.
maximum_features_length=maximum_length,
maximum_labels_length=maximum_length,
)
@tf.function(input_signature=dataset.element_spec)
def training_step(source, target):
# Run the encoder.
source_inputs = model.features_inputter(source, training=True)
encoder_outputs, _, _ = model.encoder(source_inputs,
source["length"],
training=True)
# Run the decoder.
target_inputs = model.labels_inputter(target, training=True)
decoder_state = model.decoder.initial_state(
memory=encoder_outputs, memory_sequence_length=source["length"])
logits, _, _ = model.decoder(target_inputs,
target["length"],
state=decoder_state,
training=True)
# Compute the cross entropy loss.
loss_num, loss_den, _ = onmt.utils.cross_entropy_sequence_loss(
logits,
target["ids_out"],
target["length"],
label_smoothing=0.1,
average_in_time=True,
training=True,
)
loss = loss_num / loss_den
# Compute and apply the gradients.
variables = model.trainable_variables
gradients = optimizer.get_gradients(loss, variables)
optimizer.apply_gradients(list(zip(gradients, variables)))
return loss
# Runs the training loop.
for source, target in dataset:
loss = training_step(source, target)
step = optimizer.iterations.numpy()
if step % validate_every == 0 or validate_now:
output_file_name = f"predictions.{step}.txt"
if validation_source_file is not None:
tf.get_logger().info(
f"Saving validation predictions from {validation_source_file} to {output_file_name}"
)
translate(model,
validation_source_file,
output_file=output_file_name)
if bpe:
output_file_name = decode_bpe_file(output_file_name,
combined=bpe_combined)
tf.get_logger().info(
f"Computing BLEU between from {validation_target_file} to {output_file_name}"
)
per_sentence_score, mean_score = compute_bleu(
output_file_name, validation_target_file)
tf.get_logger().info(f"BLEU score {mean_score}")
if step % report_every == 0:
tf.get_logger().info(
"Step = %d ; Learning rate = %f ; Loss = %f",
step,
learning_rate(step),
loss,
)
if step % save_every == 0:
tf.get_logger().info("Saving checkpoint for step %d", step)
checkpoint_manager.save(checkpoint_number=step)
tf.get_logger().info("Checkpoint saved.")
if step == train_steps:
break
def train(encoder: tf.keras.Model,
decoder: tf.keras.Model,
optimizer: tf.keras.optimizers.Adam,
epochs: int,
checkpoint: tf.train.CheckpointManager,
train_data_path: str,
max_len: int,
vocab_size: int,
batch_size: int,
buffer_size: int,
checkpoint_save_freq: int,
num_mel: int,
tokenized_type: str = "phoneme",
dict_path: str = "",
valid_data_split: float = 0.0,
valid_data_path: str = "",
max_train_data_size: int = 0,
max_valid_data_size: int = 0):
"""
训练模块
:param encoder: 模型的encoder
:param decoder: 模型的decoder
:param optimizer: 优化器
:param checkpoint: 检查点管理器
:param epochs: 训练周期
:param train_data_path: 文本数据路径
:param max_len: 文本序列最大长度
:param vocab_size: 词汇大小
:param num_mel: 产生的梅尔带数
:param buffer_size: Dataset加载缓存大小
:param batch_size: Dataset加载批大小
:param tokenized_type: 分词类型，默认按音素分词，模式：phoneme(音素)/word(单词)/char(字符)
:param dict_path: 字典路径，若使用phoneme则不用传
:param valid_data_split: 用于从训练数据中划分验证数据
:param valid_data_path: 验证数据文本路径
:param max_train_data_size: 最大训练数据量
:param max_valid_data_size: 最大验证数据量
:param checkpoint_save_freq: 检查点保存频率
"""
train_dataset, valid_dataset, steps_per_epoch, valid_steps_per_epoch = \
_dataset.load_data(train_data_path=train_data_path, max_len=max_len, vocab_size=vocab_size,
batch_size=batch_size, buffer_size=buffer_size, tokenized_type=tokenized_type,
dict_path=dict_path, valid_data_split=valid_data_split,
valid_data_path=valid_data_path, max_train_data_size=max_train_data_size,
max_valid_data_size=max_valid_data_size)
if steps_per_epoch == 0:
print("训练数据量过小，小于batch_size，请添加数据后重试")
exit(0)
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch + 1, epochs))
start_time = time.time()
total_loss = 0
for (batch,
(mel, stop_token,
sentence)) in enumerate(train_dataset.take(steps_per_epoch)):
batch_start = time.time()
mel = tf.transpose(mel, [0, 2, 1])
mel_input = tf.concat([
tf.zeros(shape=(mel.shape[0], 1, num_mel), dtype=tf.float32),
mel[:, :-1, :]
],
axis=1)
batch_loss, mel_outputs = _train_step(encoder, decoder, optimizer,
sentence, mel, mel_input,
stop_token)
total_loss += batch_loss
print('\r{}/{} [Batch {} Loss {:.4f} {:.1f}s]'.format(
(batch + 1), steps_per_epoch, batch + 1, batch_loss.numpy(),
(time.time() - batch_start)),
end="")
print(' - {:.0f}s/step - loss: {:.4f}'.format(
(time.time() - start_time) / steps_per_epoch,
total_loss / steps_per_epoch))
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0:
print("验证数据量过小，小于batch_size，请添加数据后重试")
exit(0)
_valid_step(encoder=encoder,
decoder=decoder,
dataset=valid_dataset,
num_mel=num_mel,
steps_per_epoch=valid_steps_per_epoch)
return mel_outputs
def train(encoder: tf.keras.Model,
decoder: tf.keras.Model,
optimizer: tf.keras.optimizers.Adam,
epochs: int,
checkpoint: tf.train.CheckpointManager,
train_data_path: str,
max_len: int,
vocab_size: int,
batch_size: int,
buffer_size: int,
checkpoint_save_freq: int,
dict_path: str = "",
valid_data_split: float = 0.0,
valid_data_path: str = "",
max_train_data_size: int = 0,
max_valid_data_size: int = 0):
"""
训练模块
:param encoder: 模型的encoder
:param decoder: 模型的decoder
:param optimizer: 优化器
:param checkpoint: 检查点管理器
:param epochs: 训练周期
:param train_data_path: 文本数据路径
:param max_len: 文本序列最大长度
:param vocab_size: 词汇大小
:param buffer_size: Dataset加载缓存大小
:param batch_size: Dataset加载批大小
:param dict_path: 字典路径，若使用phoneme则不用传
:param valid_data_split: 用于从训练数据中划分验证数据
:param valid_data_path: 验证数据文本路径
:param max_train_data_size: 最大训练数据量
:param max_valid_data_size: 最大验证数据量
:param checkpoint_save_freq: 检查点保存频率
"""
_, train_dataset, valid_dataset, steps_per_epoch, valid_steps_per_epoch = \
load_data(train_data_path=train_data_path, max_len=max_len, vocab_size=vocab_size,
batch_size=batch_size, buffer_size=buffer_size, dict_path=dict_path,
valid_data_split=valid_data_split, valid_data_path=valid_data_path,
max_train_data_size=max_train_data_size, max_valid_data_size=max_valid_data_size)
if steps_per_epoch == 0:
print("训练数据量过小，小于batch_size，请添加数据后重试")
exit(0)
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch + 1, epochs))
start_time = time.time()
total_loss = 0
for (batch,
(audio_feature,
sentence)) in enumerate(train_dataset.take(steps_per_epoch)):
batch_start = time.time()
sentence_input = sentence[:, :-1]
sentence_real = sentence[:, 1:]
batch_loss, sentence_predictions = _train_step(
encoder, decoder, optimizer, sentence_input, sentence_real,
audio_feature)
total_loss += batch_loss
print('\r{}/{} [Batch {} Loss {:.4f} {:.1f}s]'.format(
(batch + 1), steps_per_epoch, batch + 1, batch_loss.numpy(),
(time.time() - batch_start)),
end="")
print(' - {:.0f}s/step - loss: {:.4f}'.format(
(time.time() - start_time) / steps_per_epoch,
total_loss / steps_per_epoch))
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0:
print("验证数据量过小，小于batch_size，请添加数据后重试")
exit(0)
_valid_step(encoder=encoder,
decoder=decoder,
dataset=valid_dataset,
steps_per_epoch=valid_steps_per_epoch)
def train(self,
optimizer: tf.optimizers.Adam,
checkpoint: tf.train.CheckpointManager,
train_data_path: str,
epochs: int,
checkpoint_save_freq: int,
valid_data_split: float = 0.0,
max_train_data_size: int = 0,
valid_data_path: str = "",
max_valid_data_size: int = 0,
history: dict = {},
**kwargs) -> Dict:
""" 训练模块
:param optimizer: 优化器
:param checkpoint: 检查点管理器
:param train_data_path: 文本数据路径
:param epochs: 训练周期
:param checkpoint_save_freq: 检查点保存频率
:param valid_data_split: 用于从训练数据中划分验证数据
:param max_train_data_size: 最大训练数据量
:param valid_data_path: 验证数据文本路径
:param max_valid_data_size: 最大验证数据量
:param history: 用于保存训练过程中的历史指标数据
:return: 返回历史指标数据
"""
print("训练开始，正在准备数据中")
train_dataset, valid_dataset, train_steps_per_epoch, valid_steps_per_epoch = load_data(
dict_path=self.dict_path,
train_data_path=train_data_path,
buffer_size=self.buffer_size,
batch_size=self.batch_size,
max_sentence=self.max_sentence,
valid_data_split=valid_data_split,
valid_data_path=valid_data_path,
max_train_data_size=max_train_data_size,
valid_data_type=self.valid_data_type,
max_valid_data_size=max_valid_data_size,
train_data_type=self.train_data_type,
**kwargs)
progress_bar = ProgressBar()
for epoch in range(epochs):
print("Epoch {}/{}".format(epoch + 1, epochs))
start_time = time.time()
self.loss_metric.reset_states()
self.accuracy_metric.reset_states()
progress_bar.reset(total=train_steps_per_epoch,
num=self.batch_size)
for (batch, batch_dataset) in enumerate(
train_dataset.take(train_steps_per_epoch)):
train_metrics = self._train_step(batch_dataset=batch_dataset,
optimizer=optimizer,
**kwargs)
progress_bar(current=batch + 1,
metrics=get_dict_string(data=train_metrics))
progress_bar.done(step_time=time.time() - start_time)
for key, value in train_metrics.items():
history[key].append(value)
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0 or valid_dataset is None:
print("验证数据量过小，小于batch_size，已跳过验证轮次")
else:
valid_metrics = self._valid_step(
dataset=valid_dataset,
progress_bar=progress_bar,
steps_per_epoch=valid_steps_per_epoch,
**kwargs)
for key, value in valid_metrics.items():
history[key].append(value)
print("训练结束")
self._save_model(**kwargs)
return history
def train(model: tf.keras.Model,
optimizer: tf.keras.optimizers.Adam,
epochs: int,
checkpoint: tf.train.CheckpointManager,
train_data_path: str,
batch_size: int,
buffer_size: int,
checkpoint_save_freq: int,
dict_path: str = "",
valid_data_split: float = 0.0,
valid_data_path: str = "",
train_length_path: str = "",
valid_length_path: str = "",
stop_early_limits: int = 0,
max_train_data_size: int = 0,
max_valid_data_size: int = 0,
history_img_path: str = ""):
"""
训练模块
:param model: 模型
:param optimizer: 优化器
:param checkpoint: 检查点管理器
:param epochs: 训练周期
:param train_data_path: 文本数据路径
:param buffer_size: Dataset加载缓存大小
:param batch_size: Dataset加载批大小
:param dict_path: 字典路径，若使用phoneme则不用传
:param valid_data_split: 用于从训练数据中划分验证数据
:param valid_data_path: 验证数据文本路径
:param max_train_data_size: 最大训练数据量
:param train_length_path: 训练样本长度保存路径
:param valid_length_path: 验证样本长度保存路径
:param stop_early_limits: 不增长停止个数
:param max_valid_data_size: 最大验证数据量
:param checkpoint_save_freq: 检查点保存频率
:param history_img_path: 历史指标数据图表保存路径
:return: 返回历史指标数据
"""
train_dataset, valid_dataset, steps_per_epoch, valid_steps_per_epoch = \
load_data(train_data_path=train_data_path, batch_size=batch_size, buffer_size=buffer_size,
valid_data_split=valid_data_split, valid_data_path=valid_data_path,
train_length_path=train_length_path, valid_length_path=valid_length_path,
max_train_data_size=max_train_data_size, max_valid_data_size=max_valid_data_size)
tokenizer = load_tokenizer(dict_path=dict_path)
history = {"loss": [], "wers": [], "norm_lers": []}
if steps_per_epoch == 0:
print("训练数据量过小，小于batch_size，请添加数据后重试")
exit(0)
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch + 1, epochs))
start_time = time.time()
total_loss = 0
for (batch,
(audio_feature, sentence,
length)) in enumerate(train_dataset.take(steps_per_epoch)):
batch_start = time.time()
batch_loss = _train_step(model, optimizer, sentence, length,
audio_feature)
total_loss += batch_loss
print('\r{}/{} [Batch {} Loss {:.4f} {:.1f}s]'.format(
(batch + 1), steps_per_epoch, batch + 1, batch_loss.numpy(),
(time.time() - batch_start)),
end="")
print(' - {:.0f}s/step - loss: {:.4f}'.format(
(time.time() - start_time) / steps_per_epoch,
total_loss / steps_per_epoch))
history["loss"].append(total_loss / steps_per_epoch)
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0:
print("验证数据量过小，小于batch_size，请添加数据后重试")
exit(0)
valid_loss, valid_wer, valid_ler = _valid_step(
model=model,
dataset=valid_dataset,
steps_per_epoch=valid_steps_per_epoch,
tokenizer=tokenizer)
history["wers"].append(valid_wer)
history["norm_lers"].append(valid_ler)
if stop_early_limits != 0 and len(
history["wers"]) >= stop_early_limits:
if can_stop(history["wers"][-stop_early_limits:]) \
or can_stop(history["norm_lers"][-stop_early_limits:]):
print("指标反弹，停止训练！")
break
plot_history(history=history,
valid_epoch_freq=checkpoint_save_freq,
history_img_path=history_img_path)
return history
def train(self,
epochs,
hpars_optimizer,
lr_scheduler,
kl_scheduler,
monitor: Monitor,
ckpt_manager: tf.train.CheckpointManager,
ckpt_period=1,
use_natural=True,
clip_natgrad_value=None,
first_epoch=1):
bar = tqdm(total=self.experiment.len_train_data)
avg_loss = tf.keras.metrics.Mean(name='loss', dtype=tf_floatx())
avg_breaked_loss = tf.keras.metrics.MeanTensor(name='breaked_loss',
dtype=tf_floatx())
optimizer = None if use_natural else self.default_optimizer()
self._optimizer_ = optimizer
# TODO: initial monitoring
if monitor:
try:
epoch = 0
kl_weight = kl_scheduler(epoch)
for y in self.experiment.train_dataset:
loss, breaked_loss = self._initial_monitor_on_batch(
y, epoch, hpars_optimizer, kl_weight, lr_scheduler)
avg_loss.update_state(loss)
avg_breaked_loss.update_state(breaked_loss)
bar.update()
monitor(epoch,
epoch=epoch,
kl_scheduler=kl_scheduler,
train_loss=avg_loss.result(),
train_bloss=avg_breaked_loss.result())
avg_loss.reset_states()
avg_breaked_loss.reset_states()
except:
pass
for epoch in tf.range(first_epoch, epochs + 1, dtype=tf_floatx()):
bar.reset()
bar.set_description(f'Epoch {epoch}')
kl_weight = kl_scheduler(epoch)
for y in self.experiment.train_dataset:
loss, breaked_loss = self._train_on_batch(
y,
epoch,
hpars_optimizer,
kl_weight,
lr_scheduler,
use_natural,
optimizer,
clip_value=clip_natgrad_value)
avg_loss.update_state(loss)
avg_breaked_loss.update_state(breaked_loss)
bar.update()
if monitor:
try:
monitor(epoch,
epoch=epoch,
kl_scheduler=kl_scheduler,
train_loss=avg_loss.result(),
train_bloss=avg_breaked_loss.result())
except:
pass
avg_loss.reset_states()
avg_breaked_loss.reset_states()
if ckpt_manager and epoch % ckpt_period == 0:
ckpt_manager.save()
def train(model: tf.keras.Model,
checkpoint: tf.train.CheckpointManager,
batch_size: Any,
buffer_size: Any,
epochs: Any,
embedding_dim: Any,
train_data_path: AnyStr,
valid_data_path: AnyStr,
max_sentence_length: Any,
max_train_steps: Any = -1,
checkpoint_save_freq: Any = 2,
*args,
**kwargs) -> Dict:
""" 训练器
:param model: 训练模型
:param checkpoint: 检查点管理器
:param batch_size: batch 大小
:param buffer_size: 缓冲大小
:param epochs: 训练周期
:param embedding_dim: 词嵌入大小
:param train_data_path: 训练数据保存路径
:param valid_data_path: 验证数据保存路径
:param max_sentence_length: 最大句子对长度
:param max_train_steps: 最大训练数据量，-1为全部
:param checkpoint_save_freq: 检查点保存频率
:return:
"""
print("训练开始，正在准备数据中")
learning_rate = CustomSchedule(d_model=embedding_dim)
loss_metric = tf.keras.metrics.Mean(name="train_loss_metric")
optimizer = tf.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.9,
beta_2=0.98,
name="optimizer")
train_dataset = load_raw_dataset(data_path=train_data_path,
max_sentence_length=max_sentence_length,
batch_size=batch_size,
buffer_size=buffer_size,
data_type="train")
valid_dataset = load_raw_dataset(data_path=valid_data_path,
max_sentence_length=max_sentence_length,
batch_size=batch_size,
buffer_size=buffer_size,
data_type="valid")
train_steps_per_epoch = max_train_steps if max_train_steps != -1 else (
90000 // batch_size)
valid_steps_per_epoch = 10000 // batch_size
progress_bar = ProgressBar()
for epoch in range(epochs):
print("Epoch {}/{}".format(epoch + 1, epochs))
start_time = time.time()
loss_metric.reset_states()
progress_bar.reset(total=train_steps_per_epoch, num=batch_size)
train_metric = None
result, targets = tf.convert_to_tensor(
[], dtype=tf.float32), tf.convert_to_tensor([], dtype=tf.int32)
for (batch,
(queries, _, outputs,
labels)) in enumerate(train_dataset.take(max_train_steps)):
train_metric, prediction = _train_step(model=model,
optimizer=optimizer,
batch_size=batch_size,
loss_metric=loss_metric,
queries=queries,
targets=outputs)
result = tf.concat([result, prediction[:, 1]], axis=0)
targets = tf.concat([targets, labels], axis=0)
progress_bar(current=batch + 1,
metrics=get_dict_string(data=train_metric))
auc_score = roc_auc_score(y_true=targets, y_score=result)
train_metric["train_auc"] = auc_score
progress_bar(current=progress_bar.total,
metrics=get_dict_string(data=train_metric))
progress_bar.done(step_time=time.time() - start_time)
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0 or valid_dataset is None:
print("验证数据量过小，小于batch_size，已跳过验证轮次")
else:
progress_bar.reset(total=valid_steps_per_epoch, num=batch_size)
valid_metrics = _valid_step(model=model,
dataset=valid_dataset,
batch_size=batch_size,
progress_bar=progress_bar,
loss_metric=loss_metric,
**kwargs)
print("训练结束")
return {}
def train(model: tf.keras.Model, checkpoint: tf.train.CheckpointManager, batch_size: Any, buffer_size: Any,
epochs: Any, embedding_dim: Any, train_data_path: AnyStr, valid_data_path: AnyStr,
max_sentence_length=Any, max_train_steps: Any = -1, checkpoint_save_freq: Any = 2, *args, **kwargs) -> Dict:
""" 训练器
:param model: 训练模型
:param checkpoint: 检查点管理器
:param batch_size: batch 大小
:param buffer_size: 缓冲大小
:param epochs: 训练周期
:param embedding_dim: 词嵌入大小
:param train_data_path: 训练数据文件保存路径
:param valid_data_path: 验证数据文件保存路径
:param max_sentence_length: 最大句子长度
:param max_train_steps: 最大训练数据量，-1为全部
:param checkpoint_save_freq: 检查点保存频率
:return:
"""
print("训练开始，正在准备数据中")
# learning_rate = CustomSchedule(d_model=embedding_dim)
loss_metric = tf.keras.metrics.Mean(name="train_loss_metric")
optimizer = tf.optimizers.Adam(learning_rate=1e-4, beta_1=0.9, beta_2=0.98, name="optimizer")
train_dataset = load_pair_dataset(data_path=train_data_path, max_sentence_length=max_sentence_length,
batch_size=batch_size, buffer_size=buffer_size, data_type="train")
valid_dataset = load_pair_dataset(data_path=valid_data_path, max_sentence_length=max_sentence_length,
batch_size=batch_size, buffer_size=buffer_size, data_type="valid")
train_steps_per_epoch = max_train_steps if max_train_steps != -1 else (206588 // batch_size)
valid_steps_per_epoch = 10000 // batch_size
progress_bar = ProgressBar()
for epoch in range(epochs):
print("Epoch {}/{}".format(epoch + 1, epochs))
start_time = time.time()
loss_metric.reset_states()
progress_bar.reset(total=train_steps_per_epoch, num=batch_size)
train_metric = None
for (batch, (first_queries, second_queries, labels)) in enumerate(train_dataset.take(max_train_steps)):
train_metric, prediction = _train_step(
model=model, optimizer=optimizer, loss_metric=loss_metric,
first_queries=first_queries, second_queries=second_queries, labels=labels
)
progress_bar(current=batch + 1, metrics=get_dict_string(data=train_metric))
progress_bar(current=progress_bar.total, metrics=get_dict_string(data=train_metric))
progress_bar.done(step_time=time.time() - start_time)
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0 or valid_dataset is None:
print("验证数据量过小，小于batch_size，已跳过验证轮次")
else:
progress_bar.reset(total=valid_steps_per_epoch, num=batch_size)
valid_metrics = _valid_step(model=model, dataset=valid_dataset,
progress_bar=progress_bar, loss_metric=loss_metric, **kwargs)
print("训练结束")
return {}
def train_model(batcher, model: Seq2Seq,
ckpt_manager: tf.train.CheckpointManager) -> float:
# 训练参数
epochs = config.epochs
batch_size = config.batch_size
learning_rate = config.lr
max_enc_len = config.max_enc_steps
max_dec_len = config.max_dec_steps
# 优化器
optimizer = tf.keras.optimizers.Adam(name='Adam',
learning_rate=learning_rate)
# 损失函数
# loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
# 定义损失函数
# @tf.function
# def loss_func(real: tf.Tensor, pred: tf.Tensor) -> float:
# # 计算 损失
# loss = loss_object(real, pred)
# # 忽略 pad 和 unk
# pad_mask = tf.math.equal(real, pad_idx)
# # unk_mask = tf.math.equal(real, unk_idx)
# # mask = tf.math.logical_not(tf.math.logical_or(pad_mask, unk_mask))
# # unk mask会导致预测结果中缺少UNK项，故而去掉，只保留pad mask
# mask = tf.math.logical_not(pad_mask)
# mask = tf.cast(mask, dtype=loss.dtype)
# loss *= mask
# # 返回按 平均的 loss
# return tf.reduce_mean(loss)
# 训练
# @tf.function
def train_step(batcher) -> float:
with tf.GradientTape() as tape:
# 获取训练输入数据
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t_1, coverage = get_input_from_batch(
batcher)
dec_batch, dec_padding_mask, max_dec_len, dec_lens_var, target_batch = get_output_from_batch(
batcher)
# # 解码器的首次输入 = 句子开始标签
# dec_input = tf.expand_dims([bos_idx] * dec_target.shape[0], 1)
# 计算输出
step_losses, coverage = model.teacher_forcing_decode(
enc_batch, dec_batch, enc_padding_mask, dec_padding_mask,
extra_zeros, enc_batch_extend_vocab, coverage)
# 计算 batch loss (句子开始标识不计算在内)
sum_losses = tf.reduce_sum(tf.stack(step_losses, 1), 1)
batch_avg_loss = sum_losses / dec_lens_var
batch_loss = tf.reduce_mean(batch_avg_loss)
# print(batch_loss)
# 可训练参数
variables = model.trainable_variables
# 计算梯度
gradients = tape.gradient(batch_loss, variables)
# 更新梯度
optimizer.apply_gradients(zip(gradients, variables))
# 返回 batch loss
return batch_loss
# 构建训练数据集
# 进行训练
epoch_loss = -1
train_loss = []
eval_loss = []
for epoch in tqdm(range(epochs)):
start = time.time()
total_loss = 0.
# 遍历所有的Batch
with tqdm(range(config.step_per_epoch), unit='batch',
desc='训练进度') as pbar:
for step in pbar:
batch = batcher.next_batch()
# batch=batcher.next_batch()
batch_loss = train_step(batch)
total_loss += batch_loss
pbar.set_postfix({
'size':
'{:d}'.format(batch_size),
'loss':
'{:.6f}'.format(batch_loss),
'average':
'{:.6f}'.format(total_loss / (step + 1))
})
pbar.update(1)
# 定期保存模型: 每个Epoch
path_ckpt_save = ckpt_manager.save()
# 计算平均loss:
epoch_loss = total_loss / config.step_per_epoch #steps_per_epoch
logger.info('Epoch {:3d}: 新存档点保存在 {}'.format(epoch + 1,
path_ckpt_save))
logger.info('Epoch {:3d}: 训练花费时间为 {:.2f} 分钟, Loss = {:.6f}'.format(
epoch + 1, (time.time() - start) / 60., epoch_loss))
print('Epoch:', epoch + 1, '\ntrain_loss:', epoch_loss.numpy())
train_loss.append(epoch_loss.numpy())
eval_epoch_loss = evaluate()
eval_loss.append(eval_epoch_loss.numpy())
print('train_loss:', train_loss)
print('eval_loss:', eval_loss)
# 返回loss
return epoch_loss
def train(epochs: int,
train_data_path: str,
batch_size: int,
buffer_size: int,
checkpoint_save_freq: int,
checkpoint: tf.train.CheckpointManager,
model: tf.keras.Model,
optimizer: tf.keras.optimizers.Adam,
dict_path: str = "",
valid_data_split: float = 0.0,
valid_data_path: str = "",
train_length_path: str = "",
valid_length_path: str = "",
max_train_data_size: int = 0,
max_valid_data_size: int = 0,
history_img_path: str = ""):
"""
训练模块
:param epochs: 训练周期
:param train_data_path: 文本数据路径
:param dict_path: 字典路径，若使用phoneme则不用传
:param buffer_size: Dataset加载缓存大小
:param batch_size: Dataset加载批大小
:param checkpoint: 检查点管理器
:param model: 模型
:param optimizer: 优化器
:param valid_data_split: 用于从训练数据中划分验证数据
:param valid_data_path: 验证数据文本路径
:param max_train_data_size: 最大训练数据量
:param train_length_path: 训练样本长度保存路径
:param valid_length_path: 验证样本长度保存路径
:param max_valid_data_size: 最大验证数据量
:param checkpoint_save_freq: 检查点保存频率
:param history_img_path: 历史指标数据图表保存路径
:return:
"""
train_dataset, valid_dataset, steps_per_epoch, valid_steps_per_epoch = \
load_data(train_data_path=train_data_path, batch_size=batch_size, buffer_size=buffer_size,
valid_data_split=valid_data_split, valid_data_path=valid_data_path,
train_length_path=train_length_path, valid_length_path=valid_length_path,
max_train_data_size=max_train_data_size, max_valid_data_size=max_valid_data_size)
tokenizer = load_tokenizer(dict_path=dict_path)
history = {"loss": [], "wers": [], "norm_lers": []}
if steps_per_epoch == 0:
print("训练数据量过小，小于batch_size，请添加数据后重试")
exit(0)
for epoch in range(epochs):
total_loss = 0
start_time = time.time()
enc_hidden = model.initialize_hidden_state()
dec_input = tf.cast(tf.expand_dims(
[tokenizer.word_index.get('<start>')] * batch_size, 1),
dtype=tf.int64)
print("Epoch {}/{}".format(epoch + 1, epochs))
for (batch,
(audio_feature, sentence,
length)) in enumerate(train_dataset.take(steps_per_epoch)):
batch_start = time.time()
batch_loss = _train_step(model, optimizer, audio_feature, sentence,
enc_hidden, dec_input)
total_loss += batch_loss
print('\r{}/{} [Batch {} Loss {:.4f} {:.1f}s]'.format(
(batch + 1), steps_per_epoch, batch + 1, batch_loss.numpy(),
(time.time() - batch_start)),
end="")
print(' - {:.0f}s/step - loss: {:.4f}'.format(
(time.time() - start_time) / steps_per_epoch,
total_loss / steps_per_epoch))
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0:
print("验证数据量过小，小于batch_size，请添加数据后重试")
exit(0)
valid_loss, valid_wer, valid_ler = _valid_step(
model=model,
dataset=valid_dataset,
enc_hidden=enc_hidden,
dec_input=dec_input,
steps_per_epoch=valid_steps_per_epoch,
tokenizer=tokenizer)
history["wers"].append(valid_wer)
history["norm_lers"].append(valid_ler)
plot_history(history=history,
valid_epoch_freq=checkpoint_save_freq,
history_img_path=history_img_path)
return history
def train(model: tf.keras.Model,
checkpoint: tf.train.CheckpointManager,
batch_size: Any,
epochs: Any,
train_dataset: Any,
valid_dataset: AnyStr = None,
max_train_steps: Any = -1,
checkpoint_save_freq: Any = 2,
*args,
**kwargs) -> Dict:
""" 训练器
:param model: 训练模型
:param checkpoint: 检查点管理器
:param batch_size: batch 大小
:param epochs: 训练周期
:param train_dataset: 训练数据集
:param valid_dataset: 验证数据集
:param max_train_steps: 最大训练数据量，-1为全部
:param checkpoint_save_freq: 检查点保存频率
:return:
"""
print("训练开始，正在准备数据中")
# learning_rate = CustomSchedule(d_model=embedding_dim)
loss_metric = tf.keras.metrics.Mean(name="train_loss_metric")
optimizer = tf.optimizers.Adam(learning_rate=2e-5,
beta_1=0.9,
beta_2=0.999,
name="optimizer")
train_steps_per_epoch = max_train_steps if max_train_steps != -1 else (
40000 // batch_size)
valid_steps_per_epoch = 3944 // batch_size
progress_bar = ProgressBar()
for epoch in range(epochs):
print("Epoch {}/{}".format(epoch + 1, epochs))
start_time = time.time()
loss_metric.reset_states()
progress_bar.reset(total=train_steps_per_epoch, num=batch_size)
train_metric = None
for (batch,
(train_enc, train_dec, month_enc, month_dec,
labels)) in enumerate(train_dataset.take(max_train_steps)):
train_metric, prediction = _train_step(model=model,
optimizer=optimizer,
loss_metric=loss_metric,
train_enc=train_enc,
train_dec=train_dec,
month_enc=month_enc,
month_dec=month_dec,
labels=labels)
progress_bar(current=batch + 1,
metrics=get_dict_string(data=train_metric))
progress_bar(current=progress_bar.total,
metrics=get_dict_string(data=train_metric))
progress_bar.done(step_time=time.time() - start_time)
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
if valid_steps_per_epoch == 0 or valid_dataset is None:
print("验证数据量过小，小于batch_size，已跳过验证轮次")
else:
progress_bar.reset(total=valid_steps_per_epoch, num=batch_size)
valid_metrics = _valid_step(model=model,
dataset=valid_dataset,
progress_bar=progress_bar,
loss_metric=loss_metric,
**kwargs)
print("训练结束")
return {}
def fitTrainData(model: tf.keras.Model, optimizer: tf.keras.optimizers,
metrics: List[tf.keras.metrics.Mean], lossFunc, PSNRFunc,
X: np.ma.array, y: np.ma.array, batchSize: int, epochs: int,
bufferSize: int, valData: List[np.ma.array], valSteps: int,
checkpoint: tf.train.Checkpoint,
checkpointManager: tf.train.CheckpointManager, logDir: str,
ckptDir: str, saveBestOnly: bool):
trainSet = loadTrainDataAsTFDataSet(X, y[0], y[1], epochs, batchSize,
bufferSize)
valSet = loadValDataAsTFDataSet(valData[0], valData[1], valData[2],
valSteps, batchSize, bufferSize)
# Logger
w = tf.summary.create_file_writer(logDir)
dataSetLength = len(X)
totalSteps = tf.cast(dataSetLength / batchSize, tf.int64)
globalStep = tf.cast(checkpoint.step, tf.int64)
step = globalStep % totalSteps
epoch = 0
# Metrics
trainLoss, trainPSNR, testLoss, testPSNR = metrics
with w.as_default():
for x_batch_train, y_batch_train, y_mask_batch_train in trainSet:
if (totalSteps - step) == 0:
epoch += 1
step = globalStep % totalSteps
logger.info('Start of epoch %d' % (epoch))
# Reset metrics
trainLoss.reset_states()
trainPSNR.reset_states()
testLoss.reset_states()
testPSNR.reset_states()
step += 1
globalStep += 1
trainStep(x_batch_train, y_batch_train, y_mask_batch_train,
checkpoint, lossFunc, PSNRFunc, trainLoss, trainPSNR)
checkpoint.step.assign_add(1)
t = f"step {step}/{int(totalSteps)}, loss: {trainLoss.result():.3f}, psnr: {trainPSNR.result():.3f}"
logger.info(t)
tf.summary.scalar('Train PSNR',
trainPSNR.result(),
step=globalStep)
tf.summary.scalar('Train loss',
trainLoss.result(),
step=globalStep)
if step != 0 and (step % opt.evalTestStep) == 0:
# Reset states for test
testLoss.reset_states()
testPSNR.reset_states()
for x_batch_val, y_batch_val, y_mask_batch_val in valSet:
testStep(x_batch_val, y_batch_val, y_mask_batch_val,
checkpoint, lossFunc, PSNRFunc, testLoss,
testPSNR)
tf.summary.scalar('Test loss',
testLoss.result(),
step=globalStep)
tf.summary.scalar('Test PSNR',
testPSNR.result(),
step=globalStep)
t = f"Validation results... val_loss: {testLoss.result():.3f}, val_psnr: {testPSNR.result():.3f}"
logger.info(t)
w.flush()
if saveBestOnly and (testPSNR.result() <= checkpoint.psnr):
continue
checkpoint.psnr = testPSNR.result()
checkpointManager.save()
def train(epochs: int,
train_data_path: str,
max_len: int,
vocab_size: int,
batch_size: int,
buffer_size: int,
checkpoint_save_freq: int,
checkpoint: tf.train.CheckpointManager,
model: tf.keras.Model,
optimizer: tf.keras.optimizers.Adam,
tokenized_type: str = "phoneme",
dict_path: str = "",
valid_data_split: float = 0.0,
valid_data_path: str = "",
max_train_data_size: int = 0,
max_valid_data_size: int = 0):
"""
训练模块
:param epochs: 训练周期
:param train_data_path: 文本数据路径
:param max_len: 文本序列最大长度
:param vocab_size: 词汇大小
:param tokenized_type: 分词类型，默认按音素分词，模式：phoneme(音素)/word(单词)/char(字符)
:param dict_path: 字典路径，若使用phoneme则不用传
:param buffer_size: Dataset加载缓存大小
:param batch_size: Dataset加载批大小
:param checkpoint: 检查点管理器
:param model: 模型
:param optimizer: 优化器
:param valid_data_split: 用于从训练数据中划分验证数据
:param valid_data_path: 验证数据文本路径
:param max_train_data_size: 最大训练数据量
:param max_valid_data_size: 最大验证数据量
:param checkpoint_save_freq: 检查点保存频率
:return:
"""
train_dataset, valid_dataset, steps_per_epoch, valid_steps_per_epoch = \
_dataset.load_data(train_data_path=train_data_path, max_len=max_len, vocab_size=vocab_size,
batch_size=batch_size, buffer_size=buffer_size, tokenized_type=tokenized_type,
dict_path=dict_path, valid_data_split=valid_data_split,
valid_data_path=valid_data_path, max_train_data_size=max_train_data_size,
max_valid_data_size=max_valid_data_size)
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch + 1, epochs))
start_time = time.time()
total_loss = 0
for (batch,
(mel, stop_token,
sentence)) in enumerate(train_dataset.take(steps_per_epoch)):
batch_start = time.time()
batch_loss, mel_outputs = _train_step(model, optimizer, sentence,
mel,
stop_token) # 训练一个批次，返回批损失
total_loss += batch_loss
print('\r{}/{} [Batch {} Loss {:.4f} {:.1f}s]'.format(
(batch + 1), steps_per_epoch, batch + 1, batch_loss.numpy(),
(time.time() - batch_start)),
end='')
print(' - {:.0f}s/step - loss: {:.4f}'.format(
(time.time() - start_time) / steps_per_epoch,
total_loss / steps_per_epoch))
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
_valid_step(model=model,
dataset=valid_dataset,
steps_per_epoch=valid_steps_per_epoch)
return mel_outputs
def train(epochs: int,
train_data_path: str,
max_len: int,
vocab_size: int,
batch_size: int,
buffer_size: int,
checkpoint_save_freq: int,
checkpoint: tf.train.CheckpointManager,
model: tf.keras.Model,
optimizer: tf.keras.optimizers.Adam,
dict_path: str = "",
valid_data_split: float = 0.0,
valid_data_path: str = "",
max_train_data_size: int = 0,
max_valid_data_size: int = 0):
"""
训练模块
:param epochs: 训练周期
:param train_data_path: 文本数据路径
:param max_len: 文本序列最大长度
:param vocab_size: 词汇大小
:param dict_path: 字典路径，若使用phoneme则不用传
:param buffer_size: Dataset加载缓存大小
:param batch_size: Dataset加载批大小
:param checkpoint: 检查点管理器
:param model: 模型
:param optimizer: 优化器
:param valid_data_split: 用于从训练数据中划分验证数据
:param valid_data_path: 验证数据文本路径
:param max_train_data_size: 最大训练数据量
:param max_valid_data_size: 最大验证数据量
:param checkpoint_save_freq: 检查点保存频率
:return:
"""
tokenizer, train_dataset, valid_dataset, steps_per_epoch, valid_steps_per_epoch = \
load_data(train_data_path=train_data_path, max_len=max_len, vocab_size=vocab_size,
batch_size=batch_size, buffer_size=buffer_size, dict_path=dict_path,
valid_data_split=valid_data_split, valid_data_path=valid_data_path,
max_train_data_size=max_train_data_size, max_valid_data_size=max_valid_data_size)
for epoch in range(epochs):
start = time.time()
enc_hidden = model.initialize_hidden_state()
total_loss = 0
batch_start = time.time()
print("Epoch {}/{}".format(epoch + 1, epochs))
for (batch,
(audio_feature,
sentence)) in enumerate(train_dataset.take(steps_per_epoch)):
batch_loss = _train_step(audio_feature, sentence, enc_hidden,
tokenizer, model, optimizer, batch_size)
total_loss += batch_loss
print('Epoch {} Batch {} Loss {:.4f} - {:.4f} sec'.format(
epoch + 1, batch, batch_loss.numpy(),
time.time() - batch_start))
batch_start = time.time()
print('Epoch {} Loss {:.4f} - {:.4f} sec'.format(
epoch + 1, total_loss / steps_per_epoch,
time.time() - start))
if (epoch + 1) % checkpoint_save_freq == 0:
checkpoint.save()
