/// <summary>
/// Restore-on-create for a variable be saved with this `Checkpointable`.
/// </summary>
/// <returns></returns>
protected virtual IVariableV1 _add_variable_with_custom_getter(VariableArgs args)
{
ops.init_scope();
#pragma warning disable CS0219 // Variable is assigned but its value is never used
IInitializer checkpoint_initializer = null;
#pragma warning restore CS0219 // Variable is assigned but its value is never used
if (tf.Context.executing_eagerly())
#pragma warning disable CS0642 // Possible mistaken empty statement
{
;
}
#pragma warning restore CS0642 // Possible mistaken empty statement
else
{
checkpoint_initializer = null;
}
IVariableV1 new_variable;
new_variable = args.Getter(args);
// If we set an initializer and the variable processed it, tracking will not
// assign again. It will add this variable to our dependencies, and if there
// is a non-trivial restoration queued, it will handle that. This also
// handles slot variables.
if (!args.Overwrite || new_variable is RefVariable)
{
return(_track_checkpointable(new_variable, name: args.Name,
overwrite: args.Overwrite));
}
else
{
return(new_variable);
}
}
/// <summary>
/// Adds a new variable to the layer.
/// </summary>
/// <param name="args"></param>
/// <returns></returns>
public static IVariableV1 make_variable(VariableArgs args)
{
#pragma warning disable CS0219 // Variable is assigned but its value is never used
var initializing_from_value = false;
#pragma warning restore CS0219 // Variable is assigned but its value is never used
ops.init_scope();
Func <Tensor> init_val = () => args.Initializer.Apply(new InitializerArgs
{
Shape = args.Shape,
DType = args.DType
});
var variable_dtype = args.DType.as_base_dtype();
var v = tf.Variable(init_val,
dtype: variable_dtype,
shape: args.Shape,
name: args.Name,
trainable: args.Trainable,
validate_shape: args.ValidateShape,
use_resource: args.UseResource);
return(v);
}
protected virtual IVariableV1 add_weight(string name,
TensorShape shape,
TF_DataType dtype = TF_DataType.DtInvalid,
IInitializer initializer = null,
bool?trainable = null,
Func <VariableArgs, IVariableV1> getter = null)
{
if (dtype == TF_DataType.DtInvalid)
{
dtype = TF_DataType.TF_FLOAT;
}
if (trainable == null)
{
trainable = true;
}
// Initialize variable when no initializer provided
if (initializer == null)
{
// If dtype is DT_FLOAT, provide a uniform unit scaling initializer
if (dtype.is_floating())
{
initializer = tf.glorot_uniform_initializer;
}
else if (dtype.is_integer())
{
initializer = tf.zeros_initializer;
}
else
{
throw new ValueError($"An initializer for variable {name} of type {dtype.as_base_dtype()} is required for layer {this.Name}");
}
}
var args = new VariableArgs
{
Name = name,
Shape = shape,
DType = dtype,
Getter = getter ?? base_layer_utils.make_variable,
Overwrite = true,
Initializer = initializer,
Trainable = trainable.Value
};
var variable = _add_variable_with_custom_getter(args);
//backend.track_variable(variable);
if (trainable == true)
{
trainableWeights.Add(variable);
}
else
{
nonTrainableWeights.Add(variable);
}
return(variable);
}
protected virtual IVariableV1 add_weight(string name,
Shape shape,
TF_DataType dtype = TF_DataType.TF_FLOAT,
IInitializer initializer = null,
IRegularizer regularizer = null,
VariableSynchronization synchronization = VariableSynchronization.Auto,
VariableAggregation aggregation = VariableAggregation.None,
bool trainable = true,
Func <VariableArgs, IVariableV1> getter = null)
{
// Initialize variable when no initializer provided
if (initializer == null)
{
// If dtype is DT_FLOAT, provide a uniform unit scaling initializer
if (dtype.is_floating())
{
initializer = tf.glorot_uniform_initializer;
}
else if (dtype.is_integer())
{
initializer = tf.zeros_initializer;
}
else
{
throw new ValueError($"An initializer for variable {name} of type {dtype.as_base_dtype()} is required for layer {name}");
}
}
if (synchronization == VariableSynchronization.OnRead)
{
trainable = false;
}
var args = new VariableArgs
{
Name = name,
Shape = shape,
DType = dtype,
Getter = getter ?? base_layer_utils.make_variable,
Overwrite = true,
Initializer = initializer,
Synchronization = synchronization,
Aggregation = aggregation,
Trainable = trainable
};
var variable = _add_variable_with_custom_getter(args);
if (regularizer != null)
{
var name_in_scope = variable.Name.Split(':')[0];
_handle_weight_regularization(name_in_scope, variable, regularizer);
}
//backend.track_variable(variable);
if (trainable == true)
{
trainable_weights.Add(variable);
}
else
{
non_trainable_weights.Add(variable);
}
return(variable);
}
