本文整理匯總了Python中tensor2tensor.layers.common_layers.sepconv_relu_sepconv方法的典型用法代碼示例。如果您正苦於以下問題：Python common_layers.sepconv_relu_sepconv方法的具體用法？Python common_layers.sepconv_relu_sepconv怎麽用？Python common_layers.sepconv_relu_sepconv使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensor2tensor.layers.common_layers的用法示例。
在下文中一共展示了common_layers.sepconv_relu_sepconv方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: transformer_decoder_ffn_unit
# 需要導入模塊: from tensor2tensor.layers import common_layers [as 別名]
# 或者: from tensor2tensor.layers.common_layers import sepconv_relu_sepconv [as 別名]
def transformer_decoder_ffn_unit(x,
hparams,
nonpadding_mask=None):
"""Applies a feed-forward function which is parametrised for decoding.
Args:
x: input
hparams: model hyper-parameters
nonpadding_mask: optional Tensor with shape [batch_size, encoder_length]
indicating what positions are not padding. This is used
to mask out padding in convoltutional layers. We generally only
need this mask for "packed" datasets, because for ordinary datasets,
no padding is ever followed by nonpadding.
Returns:
the output tensor
"""
with tf.variable_scope("ffn"):
if hparams.transformer_ffn_type == "fc":
y = transformer.transformer_ffn_layer(
common_layers.layer_preprocess(x, hparams),
hparams,
conv_padding="LEFT",
nonpadding_mask=nonpadding_mask)
if hparams.transformer_ffn_type == "sepconv":
y = common_layers.sepconv_relu_sepconv(
common_layers.layer_preprocess(x, hparams),
filter_size=hparams.filter_size,
output_size=hparams.hidden_size,
first_kernel_size=(3, 1),
second_kernel_size=(5, 1),
padding="LEFT",
nonpadding_mask=nonpadding_mask,
dropout=hparams.relu_dropout)
x = common_layers.layer_postprocess(x, y, hparams)
return x 示例2: transformer_encoder_ffn_unit
# 需要導入模塊: from tensor2tensor.layers import common_layers [as 別名]
# 或者: from tensor2tensor.layers.common_layers import sepconv_relu_sepconv [as 別名]
def transformer_encoder_ffn_unit(x,
hparams,
nonpadding_mask=None,
pad_remover=None):
"""Applies a feed-forward function which is parametrised for encoding.
Args:
x: input
hparams: model hyper-parameters
nonpadding_mask: optional Tensor with shape [batch_size, encoder_length]
indicating what positions are not padding. This is used
to mask out padding in convoltutional layers. We generally only
need this mask for "packed" datasets, because for ordinary datasets,
no padding is ever followed by nonpadding.
pad_remover: to mask out padding in convolutional layers (efficiency).
Returns:
the output tensor
"""
with tf.variable_scope("ffn"):
if hparams.transformer_ffn_type == "fc":
y = transformer.transformer_ffn_layer(
common_layers.layer_preprocess(x, hparams),
hparams,
pad_remover,
conv_padding="SAME",
nonpadding_mask=nonpadding_mask)
if hparams.transformer_ffn_type == "sepconv":
assert nonpadding_mask is not None, (
"The nonpadding_mask should be provided, otherwise the model uses "
"the leaked padding information to estimate the length!")
y = common_layers.sepconv_relu_sepconv(
common_layers.layer_preprocess(x, hparams),
filter_size=hparams.filter_size,
output_size=hparams.hidden_size,
first_kernel_size=(3, 1),
second_kernel_size=(5, 1),
padding="SAME",
nonpadding_mask=nonpadding_mask,
dropout=hparams.relu_dropout)
x = common_layers.layer_postprocess(x, y, hparams)
return x