Python training_ops.sparse_apply_ftrl方法代碼示例

本文整理匯總了Python中tensorflow.python.training.training_ops.sparse_apply_ftrl方法的典型用法代碼示例。如果您正苦於以下問題：Python training_ops.sparse_apply_ftrl方法的具體用法？Python training_ops.sparse_apply_ftrl怎麽用？Python training_ops.sparse_apply_ftrl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tensorflow.python.training.training_ops的用法示例。

在下文中一共展示了training_ops.sparse_apply_ftrl方法的4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: _apply_sparse

# 需要導入模塊: from tensorflow.python.training import training_ops [as 別名]
# 或者: from tensorflow.python.training.training_ops import sparse_apply_ftrl [as 別名]
def _apply_sparse(self, grad, var):
    accum = self.get_slot(var, "accum")
    linear = self.get_slot(var, "linear")
    return training_ops.sparse_apply_ftrl(
        var,
        accum,
        linear,
        grad.values,
        grad.indices,
        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
        math_ops.cast(self._l1_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._l2_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
        use_locking=self._use_locking)

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:18，代碼來源:ftrl.py

示例2: _testTypesForSparseFtrl

# 需要導入模塊: from tensorflow.python.training import training_ops [as 別名]
# 或者: from tensorflow.python.training.training_ops import sparse_apply_ftrl [as 別名]
def _testTypesForSparseFtrl(self, x, y, z, lr, grad, indices, l1=0.0, l2=0.0,
                              lr_power=-0.5):
    self.setUp()
    with self.test_session(use_gpu=False):
      var = variables.Variable(x)
      accum = variables.Variable(y)
      linear = variables.Variable(z)
      variables.global_variables_initializer().run()

      self.assertAllCloseAccordingToType(x, var.eval())
      sparse_apply_ftrl = training_ops.sparse_apply_ftrl(
          var, accum, linear, grad,
          constant_op.constant(indices, self._toType(indices.dtype)),
          lr, l1, l2, lr_power=lr_power)
      out = sparse_apply_ftrl.eval()
      self.assertShapeEqual(out, sparse_apply_ftrl)

      for (i, index) in enumerate(indices):
        self.assertAllCloseAccordingToType(
            x[index] - lr * grad[i] * (y[index] + grad[i] * grad[i]) ** (
                lr_power),
            var.eval()[index])
        self.assertAllCloseAccordingToType(y[index] + grad[i] * grad[i],
                                           accum.eval()[index])

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:26，代碼來源:training_ops_test.py

示例3: _apply_sparse

# 需要導入模塊: from tensorflow.python.training import training_ops [as 別名]
# 或者: from tensorflow.python.training.training_ops import sparse_apply_ftrl [as 別名]
def _apply_sparse(self, grad, var):
    accum = self.get_slot(var, "accum")
    linear = self.get_slot(var, "linear")
    return training_ops.sparse_apply_ftrl(
        var, accum, linear, grad.values, grad.indices,
        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
        math_ops.cast(self._l1_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._l2_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
        use_locking=self._use_locking)

開發者ID:abhisuri97，項目名稱:auto-alt-text-lambda-api，代碼行數:14，代碼來源:ftrl.py

示例4: _apply_sparse

# 需要導入模塊: from tensorflow.python.training import training_ops [as 別名]
# 或者: from tensorflow.python.training.training_ops import sparse_apply_ftrl [as 別名]
def _apply_sparse(self, grad, var):
    accum = self.get_slot(var, "accum")
    linear = self.get_slot(var, "linear")
    if self._l2_shrinkage_regularization_strength <= 0.0:
      return training_ops.sparse_apply_ftrl(
          var,
          accum,
          linear,
          grad.values,
          grad.indices,
          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
          math_ops.cast(self._l1_regularization_strength_tensor,
                        var.dtype.base_dtype),
          math_ops.cast(self._l2_regularization_strength_tensor,
                        var.dtype.base_dtype),
          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
          use_locking=self._use_locking)
    else:
      return training_ops.sparse_apply_ftrl_v2(
          var,
          accum,
          linear,
          grad.values,
          grad.indices,
          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
          math_ops.cast(self._l1_regularization_strength_tensor,
                        var.dtype.base_dtype),
          math_ops.cast(self._l2_regularization_strength_tensor,
                        var.dtype.base_dtype),
          math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
                        grad.dtype.base_dtype),
          math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
          use_locking=self._use_locking)

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:35，代碼來源:ftrl.py

注：本文中的tensorflow.python.training.training_ops.sparse_apply_ftrl方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。