本文整理匯總了Python中layers.predictions方法的典型用法代碼示例。如果您正苦於以下問題：Python layers.predictions方法的具體用法？Python layers.predictions怎麽用？Python layers.predictions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layers的用法示例。
在下文中一共展示了layers.predictions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: eval_graph
# 需要導入模塊: import layers [as 別名]
# 或者: from layers import predictions [as 別名]
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2: eval_graph
# 需要導入模塊: import layers [as 別名]
# 或者: from layers import predictions [as 別名]
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