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Python layers.accuracy方法代码示例

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 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:36,代码来源:graphs.py

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 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:31,代码来源:graphs.py

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 
开发者ID:takerum,项目名称:vat_tf,代码行数:17,代码来源:train_semisup.py

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 
开发者ID:itsamitgoel,项目名称:Gun-Detector,代码行数:42,代码来源:graphs.py

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 
开发者ID:itsamitgoel,项目名称:Gun-Detector,代码行数:38,代码来源:graphs.py


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