def _train_step(model: tf.keras.Model, optimizer: tf.keras.optimizers.Adam,
loss_metric: tf.keras.metrics.Mean, train_enc: Any,
train_dec: Any, month_enc: Any, month_dec: Any,
labels: Any) -> Tuple:
""" 单个训练步
:param model: 训练模型
:param optimizer: 优化器
:param loss_metric: 损失计算器
:param train_enc: enc输入
:param train_dec: dec输入
:param month_enc: enc月份输入
:param month_dec: dec月份输入
:param labels: 标签
:return: 训练指标
"""
with tf.GradientTape() as tape:
train_enc = tf.squeeze(train_enc, axis=0)
train_dec = tf.squeeze(train_dec, axis=0)
outputs = model(inputs=[train_enc, train_dec, month_enc, month_dec])
treat_outputs = tf.squeeze(input=outputs[:, -24:, :], axis=-1)
loss = tf.keras.losses.mean_squared_error(labels, treat_outputs)
# acikill_metrics = eval_score(preds=outputs, label=labels)
loss_metric(loss)
variables = model.trainable_variables
gradients = tape.gradient(target=loss, sources=variables)
optimizer.apply_gradients(zip(gradients, variables))
return {"train_loss": loss_metric.result()}, treat_outputs
def _train_step(model: tf.keras.Model, optimizer: tf.keras.optimizers.Adam,
audio_feature: tf.Tensor, sentence: tf.Tensor,
enc_hidden: tf.Tensor, dec_input: tf.Tensor):
"""
训练步
:param model: 模型
:param sentence: sentence序列
:param audio_feature: 音频特征序列
:param enc_hidden: encoder初始化隐藏层
:param optimizer 优化器
:param dec_input: 解码器输入
:return: batch损失和post_net输出
"""
loss = 0
with tf.GradientTape() as tape:
for t in range(1, sentence.shape[1]):
predictions, _ = model(audio_feature, enc_hidden, dec_input)
loss += loss_func_mask(sentence[:, t], predictions)
if sum(sentence[:, t]) == 0:
break
dec_input = tf.expand_dims(sentence[:, t], 1)
batch_loss = (loss / int(sentence.shape[0]))
variables = model.trainable_variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
return batch_loss
def _train_step(model: tf.keras.Model, optimizer: tf.keras.optimizers.Adam,
sentence: tf.Tensor, length: tf.Tensor,
audio_feature: tf.Tensor):
"""
训练步
:param model: 模型
:param sentence: sentence序列
:param audio_feature: 音频特征序列
:param length: 样本长度序列
:param optimizer 优化器
:return: batch损失和post_net输出
"""
with tf.GradientTape() as tape:
predictions = model(audio_feature)
input_length = compute_ctc_input_length(audio_feature.shape[1],
predictions.shape[1],
length[:, 1:])
loss = tf.keras.backend.ctc_batch_cost(y_true=sentence,
y_pred=predictions,
input_length=input_length,
label_length=length[:, 0:1])
batch_loss = tf.reduce_mean(loss)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return batch_loss
def _train_step(model: tf.keras.Model, optimizer: tf.keras.optimizers.Adam,
batch_size: Any, loss_metric: tf.keras.metrics.Mean,
queries: Any, targets: Any) -> Tuple:
""" 单个训练步
:param model: 训练模型
:param optimizer: 优化器
:param batch_size: batch大小
:param loss_metric: 损失计算器
:param queries: 第一个查询句子
:param targets: 第二个查询句子
:return: 训练指标
"""
with tf.GradientTape() as tape:
outputs = model(inputs=queries)
loss = tf.keras.losses.SparseCategoricalCrossentropy(
reduction=tf.keras.losses.Reduction.NONE)(targets, outputs)
mask = tf.cast(x=tf.math.not_equal(targets, 0), dtype=tf.float32)
batch_loss = tf.reduce_sum(mask * loss) / batch_size
loss_metric(batch_loss)
variables = model.trainable_variables
gradients = tape.gradient(target=batch_loss, sources=variables)
optimizer.apply_gradients(zip(gradients, variables))
return {
"train_loss": loss_metric.result()
}, tf.nn.softmax(logits=outputs[:, 0, 5:7], axis=-1)
def _backpropagates_gradient(
self,
tape: tf.GradientTape,
models: List[tf.keras.Model],
loss: tf.float32,
optimizer: tf.keras.optimizers.Adam,
) -> None:
""" Backpropagates the gradient of the loss into the given networks"""
trainable_variables = sum(
[model.trainable_variables for model in models], [])
gradients = tape.gradient(loss, trainable_variables)
optimizer.apply_gradients(zip(gradients, trainable_variables))
def _train_step(model: tf.keras.Model, optimizer: tf.keras.optimizers.Adam,
loss_metric: tf.keras.metrics.Mean, first_queries: Any, second_queries: Any, labels: Any) -> Tuple:
""" 单个训练步
:param model: 训练模型
:param optimizer: 优化器
:param loss_metric: 损失计算器
:param first_queries: 第一个查询句子
:param second_queries: 第二个查询句子
:param labels: 标签
:return: 训练指标
"""
with tf.GradientTape() as tape:
outputs = model(inputs=[first_queries, second_queries])
loss = tf.keras.losses.SparseCategoricalCrossentropy()(labels, outputs)
loss_metric(loss)
variables = model.trainable_variables
gradients = tape.gradient(target=loss, sources=variables)
optimizer.apply_gradients(zip(gradients, variables))
return {"train_loss": loss_metric.result()}, outputs
def _train_step(model: tf.keras.Model, optimizer: tf.keras.optimizers.Adam,
input_ids: tf.Tensor, mel_gts: tf.Tensor,
stop_token: tf.Tensor):
"""
训练步
:param input_ids: sentence序列
:param mel_gts: ground-true的mel
:param model: 模型
:param optimizer 优化器
:param stop_token: ground-true的stop_token
:return: batch损失和postnet输出
"""
loss = 0
with tf.GradientTape() as tape:
mel_outputs, mel_outputs_postnet, gate_outputs, alignments = model(
input_ids, mel_gts)
loss += _loss_function(mel_outputs, mel_outputs_postnet, mel_gts,
stop_token, gate_outputs)
batch_loss = loss
variables = model.trainable_variables
gradients = tape.gradient(loss, variables) # 计算损失对参数的梯度
optimizer.apply_gradients(zip(gradients, variables)) # 优化器反向传播更新参数
return batch_loss, mel_outputs_postnet