def _resource_apply_dense(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.resource_apply_ftrl(
var.handle,
accum.handle,
linear.handle,
grad,
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.resource_apply_ftrl_v2(
var.handle,
accum.handle,
linear.handle,
grad,
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,
var.dtype.base_dtype),
math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
use_locking=self._use_locking)
def _resource_apply_dense(self, grad, var):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
learning_rate_power = self._get_hyper('learning_rate_power', var_dtype)
l1_regularization_strength = self._get_hyper(
'l1_regularization_strength', var_dtype)
l2_regularization_strength = self._get_hyper(
'l2_regularization_strength', var_dtype)
accum = self.get_slot(var, 'accumulator')
linear = self.get_slot(var, 'linear')
if self._l2_shrinkage_regularization_strength <= 0.0:
return training_ops.resource_apply_ftrl(
var.handle,
accum.handle,
linear.handle,
grad,
lr_t,
l1_regularization_strength,
l2_regularization_strength,
learning_rate_power,
use_locking=self._use_locking)
else:
return training_ops.resource_apply_ftrl_v2(
var.handle,
accum.handle,
linear.handle,
grad,
lr_t,
l1_regularization_strength,
l2_regularization_strength,
math_ops.cast(self._l2_shrinkage_regularization_strength,
var_dtype),
learning_rate_power,
use_locking=self._use_locking)
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
accum = self.get_slot(var, 'accumulator')
linear = self.get_slot(var, 'linear')
if self._l2_shrinkage_regularization_strength <= 0.0:
return training_ops.resource_apply_ftrl(
var.handle,
accum.handle,
linear.handle,
grad,
coefficients['lr_t'],
coefficients['l1_regularization_strength'],
coefficients['l2_regularization_strength'],
coefficients['learning_rate_power'],
use_locking=self._use_locking)
else:
return training_ops.resource_apply_ftrl_v2(
var.handle,
accum.handle,
linear.handle,
grad,
coefficients['lr_t'],
coefficients['l1_regularization_strength'],
coefficients['l2_regularization_strength'],
coefficients['l2_shrinkage_regularization_strength'],
coefficients['learning_rate_power'],
use_locking=self._use_locking)
def _resource_apply_dense(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.resource_apply_ftrl(
var.handle,
accum.handle,
linear.handle,
grad,
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.resource_apply_ftrl_v2(
var.handle,
accum.handle,
linear.handle,
grad,
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,
var.dtype.base_dtype),
math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
use_locking=self._use_locking)
def _resource_apply_dense(self, grad, var):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
learning_rate_power = self._get_hyper('learning_rate_power', var_dtype)
l1_regularization_strength = self._get_hyper('l1_regularization_strength',
var_dtype)
l2_regularization_strength = self._get_hyper('l2_regularization_strength',
var_dtype)
accum = self.get_slot(var, 'accumulator')
linear = self.get_slot(var, 'linear')
if self._l2_shrinkage_regularization_strength <= 0.0:
return training_ops.resource_apply_ftrl(
var.handle,
accum.handle,
linear.handle,
grad,
lr_t,
l1_regularization_strength,
l2_regularization_strength,
learning_rate_power,
use_locking=self._use_locking)
else:
return training_ops.resource_apply_ftrl_v2(
var.handle,
accum.handle,
linear.handle,
grad,
lr_t,
l1_regularization_strength,
l2_regularization_strength,
math_ops.cast(self._l2_shrinkage_regularization_strength, var_dtype),
learning_rate_power,
use_locking=self._use_locking)
def _eval(self, var, accum, linear, grad, lr, l1, l2, l2_shrinkage=0,
lr_power=1, multiply_linear_by_lr=False):
dtype = np.float32
var = np.array(var, dtype=dtype)
accum = np.array(accum, dtype=dtype)
linear = np.array(linear, dtype=dtype)
grad = np.array(grad, dtype=dtype)
use_v2 = bool(l2_shrinkage)
with self.session() as session:
with self.test_scope():
lr = constant_op.constant(lr, dtype=dtype)
l1 = constant_op.constant(l1, dtype=dtype)
l2 = constant_op.constant(l2, dtype=dtype)
l2_shrinkage = constant_op.constant(l2_shrinkage, dtype=dtype)
lr_power = constant_op.constant(lr_power, dtype=dtype)
v_var = resource_variable_ops.ResourceVariable(var, dtype=dtype)
v_accum = resource_variable_ops.ResourceVariable(accum, dtype=dtype)
v_linear = resource_variable_ops.ResourceVariable(linear, dtype=dtype)
session.run(v_var.create)
session.run(v_accum.create)
session.run(v_linear.create)
assert not (use_v2 and multiply_linear_by_lr)
if use_v2:
session.run(training_ops.resource_apply_ftrl_v2(
v_var.handle, v_accum.handle, v_linear.handle,
grad, lr, l1, l2, l2_shrinkage, lr_power,
multiply_linear_by_lr=multiply_linear_by_lr))
else:
session.run(training_ops.resource_apply_ftrl(
v_var.handle, v_accum.handle, v_linear.handle,
grad, lr, l1, l2, lr_power,
multiply_linear_by_lr=multiply_linear_by_lr))
return (v_var.read_value().eval().reshape(var.shape),
v_accum.read_value().eval().reshape(accum.shape),
v_linear.read_value().eval().reshape(linear.shape))
