public Tensor gradient(Tensor target, ResourceVariable source)
{
var results = gradient(target, new List <IVariableV1> {
source
});
return(results[0]);
}
protected override Operation _resource_apply_dense(ResourceVariable var, EagerTensor grad, Dictionary <DeviceDType, Dictionary <string, Tensor> > _apply_state)
{
if (_momentum)
{
throw new NotImplementedException("_resource_apply_dense");
}
var device_dtype = _apply_state.Keys.FirstOrDefault(x => x.Device == var.Device && x.DType == var.dtype.as_base_dtype());
return(gen_training_ops.resource_apply_gradient_descent(var.Handle as EagerTensor,
_apply_state[device_dtype]["lr_t"] as EagerTensor,
grad,
use_locking: _use_locking));
}
public unsafe ResourceVariable[] watched_variables()
{
BindingArray result = c_api.TFE_TapeWatchedVariables(_handle);
var variables = new ResourceVariable[result.length];
for (int i = 0; i < result.length; i++)
{
var handle = *((IntPtr *)result.array + i);
var tensor = c_api.ResourceVariable_Handle(handle);
variables[i] = new ResourceVariable(handle, tensor);
}
return(variables);
}
public override Operation _apply_dense(Tensor grad, ResourceVariable var)
{
var m = get_slot(var, "m");
var v = get_slot(var, "v");
var(beta1_power, beta2_power) = _get_beta_accumulators();
return(gen_training_ops.apply_adam(
var.Handle,
m.Handle,
v.Handle,
math_ops.cast(beta1_power.Handle, var.dtype.as_base_dtype()),
math_ops.cast(beta2_power.Handle, var.dtype.as_base_dtype()),
math_ops.cast(_lr_t, var.dtype.as_base_dtype()),
math_ops.cast(_beta1_t, var.dtype.as_base_dtype()),
math_ops.cast(_beta2_t, var.dtype.as_base_dtype()),
math_ops.cast(_epsilon_t, var.dtype.as_base_dtype()),
grad,
use_locking: _use_locking).op);
}
public void VariableAccessed(ResourceVariable variable)
{
Watch(variable.Handle.Id);
}
public void VariableAccessed(ResourceVariable variable)
{
throw new NotImplementedException();
}
public static void variable_accessed(ResourceVariable variable)
{
c_api.TFE_TapeVariableAccessed(variable);
}
public Tensor gradient(Tensor target, ResourceVariable source)
{
var results = gradient(target as EagerTensor, new[] { source });
return(results[0]);
}
