本文整理汇总了Python中layers.accuracy方法的典型用法代码示例。如果您正苦于以下问题:Python layers.accuracy方法的具体用法?Python layers.accuracy怎么用?Python layers.accuracy使用的例子?那么, 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.accuracy方法的5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 accuracy [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: eval_graph
# 需要导入模块: import layers [as 别名]
# 或者: from layers import accuracy [as 别名]
def eval_graph(self, dataset='test'):
"""Constructs classifier evaluation graph.
Args:
dataset: the labeled dataset to evaluate, {'train', 'test', 'valid'}.
Returns:
eval_ops: dict<metric name, tuple(value, update_op)>
var_restore_dict: dict mapping variable restoration names to variables.
Trainable variables will be mapped to their moving average names.
"""
inputs = _inputs(dataset, pretrain=False)
embedded = self.layers['embedding'](inputs.tokens)
_, next_state, logits, _ = self.cl_loss_from_embedding(
embedded, inputs=inputs, return_intermediates=True)
eval_ops = {
'accuracy':
tf.contrib.metrics.streaming_accuracy(
layers_lib.predictions(logits), inputs.labels, inputs.weights)
}
with tf.control_dependencies([inputs.save_state(next_state)]):
acc, acc_update = eval_ops['accuracy']
acc_update = tf.identity(acc_update)
eval_ops['accuracy'] = (acc, acc_update)
var_restore_dict = make_restore_average_vars_dict()
return eval_ops, var_restore_dict
示例3: build_eval_graph
# 需要导入模块: import layers [as 别名]
# 或者: from layers import accuracy [as 别名]
def build_eval_graph(x, y, ul_x):
losses = {}
logit = vat.forward(x, is_training=False, update_batch_stats=False)
nll_loss = L.ce_loss(logit, y)
losses['NLL'] = nll_loss
acc = L.accuracy(logit, y)
losses['Acc'] = acc
scope = tf.get_variable_scope()
scope.reuse_variables()
at_loss = vat.adversarial_loss(x, y, nll_loss, is_training=False)
losses['AT_loss'] = at_loss
ul_logit = vat.forward(ul_x, is_training=False, update_batch_stats=False)
vat_loss = vat.virtual_adversarial_loss(ul_x, ul_logit, is_training=False)
losses['VAT_loss'] = vat_loss
return losses
示例4: classifier_graph
# 需要导入模块: import layers [as 别名]
# 或者: from layers import accuracy [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5: eval_graph
# 需要导入模块: import layers [as 别名]
# 或者: from layers import accuracy [as 别名]
def eval_graph(self, dataset='test'):
"""Constructs classifier evaluation graph.
Args:
dataset: the labeled dataset to evaluate, {'train', 'test', 'valid'}.
Returns:
eval_ops: dict<metric name, tuple(value, update_op)>
var_restore_dict: dict mapping variable restoration names to variables.
Trainable variables will be mapped to their moving average names.
"""
inputs = _inputs(dataset, pretrain=False)
embedded = self.layers['embedding'](inputs.tokens)
_, next_state, logits, _ = self.cl_loss_from_embedding(
embedded, inputs=inputs, return_intermediates=True)
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
eval_ops = {
'accuracy':
tf.contrib.metrics.streaming_accuracy(
layers_lib.predictions(logits), labels, weights)
}
with tf.control_dependencies([inputs.save_state(next_state)]):
acc, acc_update = eval_ops['accuracy']
acc_update = tf.identity(acc_update)
eval_ops['accuracy'] = (acc, acc_update)
var_restore_dict = make_restore_average_vars_dict()
return eval_ops, var_restore_dict