本文整理汇总了Python中layers.classification_loss方法的典型用法代码示例。如果您正苦于以下问题：Python layers.classification_loss方法的具体用法？Python layers.classification_loss怎么用？Python layers.classification_loss使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类layers的用法示例。
在下文中一共展示了layers.classification_loss方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: classifier_graph
# 需要导入模块: import layers [as 别名]
# 或者: from layers import classification_loss [as 别名]
def classifier_graph(self):
"""Constructs classifier graph from inputs to classifier loss.
* Caches the VatxtInput object in `self.cl_inputs`
* Caches tensors: `cl_embedded`, `cl_logits`, `cl_loss`
Returns:
loss: scalar float.
"""
inputs = _inputs('train', pretrain=False)
self.cl_inputs = inputs
embedded = self.layers['embedding'](inputs.tokens)
self.tensors['cl_embedded'] = embedded
_, next_state, logits, loss = self.cl_loss_from_embedding(
embedded, return_intermediates=True)
tf.summary.scalar('classification_loss', loss)
self.tensors['cl_logits'] = logits
self.tensors['cl_loss'] = loss
acc = layers_lib.accuracy(logits, inputs.labels, inputs.weights)
tf.summary.scalar('accuracy', acc)
adv_loss = (self.adversarial_loss() * tf.constant(
FLAGS.adv_reg_coeff, name='adv_reg_coeff'))
tf.summary.scalar('adversarial_loss', adv_loss)
total_loss = loss + adv_loss
tf.summary.scalar('total_classification_loss', total_loss)
with tf.control_dependencies([inputs.save_state(next_state)]):
total_loss = tf.identity(total_loss)
return total_loss 示例2: cl_loss_from_embedding
# 需要导入模块: import layers [as 别名]
# 或者: from layers import classification_loss [as 别名]
def cl_loss_from_embedding(self,
embedded,
inputs=None,
return_intermediates=False):
"""Compute classification loss from embedding.
Args:
embedded: 3-D float Tensor [batch_size, num_timesteps, embedding_dim]
inputs: VatxtInput, defaults to self.cl_inputs.
return_intermediates: bool, whether to return intermediate tensors or only
the final loss.
Returns:
If return_intermediates is True:
lstm_out, next_state, logits, loss
Else:
loss
"""
if inputs is None:
inputs = self.cl_inputs
lstm_out, next_state = self.layers['lstm'](embedded, inputs.state,
inputs.length)
logits = self.layers['cl_logits'](lstm_out)
loss = layers_lib.classification_loss(logits, inputs.labels, inputs.weights)
if return_intermediates:
return lstm_out, next_state, logits, loss
else:
return loss 示例3: classifier_graph
# 需要导入模块: import layers [as 别名]
# 或者: from layers import classification_loss [as 别名]
def classifier_graph(self):
"""Constructs classifier graph from inputs to classifier loss.
* Caches the VatxtInput object in `self.cl_inputs`
* Caches tensors: `cl_embedded`, `cl_logits`, `cl_loss`
Returns:
loss: scalar float.
"""
inputs = _inputs('train', pretrain=False)
self.cl_inputs = inputs
embedded = self.layers['embedding'](inputs.tokens)
self.tensors['cl_embedded'] = embedded
_, next_state, logits, loss = self.cl_loss_from_embedding(
embedded, return_intermediates=True)
tf.summary.scalar('classification_loss', loss)
self.tensors['cl_logits'] = logits
self.tensors['cl_loss'] = loss
if FLAGS.single_label:
indices = tf.stack([tf.range(FLAGS.batch_size), inputs.length - 1], 1)
labels = tf.expand_dims(tf.gather_nd(inputs.labels, indices), 1)
weights = tf.expand_dims(tf.gather_nd(inputs.weights, indices), 1)
else:
labels = inputs.labels
weights = inputs.weights
acc = layers_lib.accuracy(logits, labels, weights)
tf.summary.scalar('accuracy', acc)
adv_loss = (self.adversarial_loss() * tf.constant(
FLAGS.adv_reg_coeff, name='adv_reg_coeff'))
tf.summary.scalar('adversarial_loss', adv_loss)
total_loss = loss + adv_loss
with tf.control_dependencies([inputs.save_state(next_state)]):
total_loss = tf.identity(total_loss)
tf.summary.scalar('total_classification_loss', total_loss)
return total_loss 示例4: cl_loss_from_embedding
# 需要导入模块: import layers [as 别名]
# 或者: from layers import classification_loss [as 别名]
def cl_loss_from_embedding(self,
embedded,
inputs=None,
return_intermediates=False):
"""Compute classification loss from embedding.
Args:
embedded: 3-D float Tensor [batch_size, num_timesteps, embedding_dim]
inputs: VatxtInput, defaults to self.cl_inputs.
return_intermediates: bool, whether to return intermediate tensors or only
the final loss.
Returns:
If return_intermediates is True:
lstm_out, next_state, logits, loss
Else:
loss
"""
if inputs is None:
inputs = self.cl_inputs
lstm_out, next_state = self.layers['lstm'](embedded, inputs.state,
inputs.length)
if FLAGS.single_label:
indices = tf.stack([tf.range(FLAGS.batch_size), inputs.length - 1], 1)
lstm_out = tf.expand_dims(tf.gather_nd(lstm_out, indices), 1)
labels = tf.expand_dims(tf.gather_nd(inputs.labels, indices), 1)
weights = tf.expand_dims(tf.gather_nd(inputs.weights, indices), 1)
else:
labels = inputs.labels
weights = inputs.weights
logits = self.layers['cl_logits'](lstm_out)
loss = layers_lib.classification_loss(logits, labels, weights)
if return_intermediates:
return lstm_out, next_state, logits, loss
else:
return loss