public static Tensor softmax_cross_entropy_with_logits_v2_helper(Tensor labels,
Tensor logits,
int axis = -1,
string name = null)
{
return(Python.with(ops.name_scope(name, "softmax_cross_entropy_with_logits", new { }), scope =>
{
var precise_logits = logits;
var input_rank = array_ops.rank(precise_logits);
var shape = logits.getShape();
if (axis != -1)
{
throw new NotImplementedException("softmax_cross_entropy_with_logits_v2_helper axis != -1");
}
var input_shape = array_ops.shape(precise_logits);
// Do the actual op computation.
// The second output tensor contains the gradients. We use it in
// _CrossEntropyGrad() in nn_grad but not here.
var(cost, unused_backprop) = gen_nn_ops.softmax_cross_entropy_with_logits(precise_logits, labels, name: name);
// The output cost shape should be the input minus axis.
var output_shape = array_ops.slice(input_shape,
new int[] { 0 },
new Tensor[] { math_ops.subtract(input_rank, 1) });
cost = array_ops.reshape(cost, output_shape);
return cost;
}));
}
private void _init_from_args(object initial_value,
bool trainable = true,
List <string> collections = null,
bool validate_shape = true,
string caching_device = "",
string name = null,
TF_DataType dtype = TF_DataType.DtInvalid)
{
if (initial_value is null)
{
throw new ValueError("initial_value must be specified.");
}
var init_from_fn = initial_value.GetType().Name == "Func`1";
if (collections == null)
{
collections = new List <string> {
ops.GraphKeys.GLOBAL_VARIABLES
};
}
// Store the graph key so optimizers know how to only retrieve variables from
// this graph.
_graph_key = ops.get_default_graph()._graph_key;
_trainable = trainable;
if (!collections.Contains(ops.GraphKeys.TRAINABLE_VARIABLES))
{
collections.Add(ops.GraphKeys.TRAINABLE_VARIABLES);
}
ops.init_scope();
var values = init_from_fn ? new object[0] : new object[] { initial_value };
with(new ops.name_scope(name, "Variable", values), scope =>
{
name = scope;
if (init_from_fn)
{
// Use attr_scope and device(None) to simulate the behavior of
// colocate_with when the variable we want to colocate with doesn't
// yet exist.
string true_name = ops._name_from_scope_name(name);
var attr = new AttrValue
{
List = new AttrValue.Types.ListValue()
};
attr.List.S.Add(ByteString.CopyFromUtf8($"loc:{true_name}"));
with(new ops.name_scope("Initializer"), scope2 =>
{
_initial_value = (initial_value as Func <Tensor>)();
_initial_value = ops.convert_to_tensor(_initial_value, name: "initial_value", dtype: dtype);
_variable = state_ops.variable_op_v2(_initial_value.shape, _initial_value.dtype.as_base_dtype(), name: name);
});
}
// Or get the initial value from a Tensor or Python object.
else
{
_initial_value = ops.convert_to_tensor(initial_value, name: "initial_value");
var shape = _initial_value.shape;
dtype = _initial_value.dtype;
_variable = gen_state_ops.variable_v2(shape, dtype.as_base_dtype(), scope);
}
// Manually overrides the variable's shape with the initial value's.
if (validate_shape)
{
var initial_value_shape = _initial_value.getShape();
if (!initial_value_shape.is_fully_defined())
{
throw new ValueError($"initial_value must have a shape specified: {_initial_value}");
}
}
// If 'initial_value' makes use of other variables, make sure we don't
// have an issue if these other variables aren't initialized first by
// using their initialized_value() method.
var _initial_value2 = _try_guard_against_uninitialized_dependencies(_initial_value);
_initializer_op = gen_state_ops.assign(_variable, _initial_value2, validate_shape).op;
if (!String.IsNullOrEmpty(caching_device))
{
}
else
{
ops.colocate_with(_initializer_op);
_snapshot = gen_array_ops.identity(_variable, name = "read");
}
ops.add_to_collections(collections, this);
});
}