Python tensorflow.python.training.training_ops.sparse_apply_ftrl() Examples

The following are 6 code examples of tensorflow.python.training.training_ops.sparse_apply_ftrl(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.training.training_ops , or try the search function .
Example #1
Source File: ftrl.py    From lambda-packs with MIT License 6 votes vote down vote up 
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)
Example #2
Source File: training_ops_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
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])
Example #3
Source File: ftrl.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
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)
Example #4
Source File: ftrl.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
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)
Example #5
Source File: ftrl.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
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)
Example #6
Source File: ftrl.py    From keras-lambda with MIT License 5 votes vote down vote up 
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)