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Oceania2018Oceania2018
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add _MinOrMaxGrad and _MaximumMinimumGrad
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6 files changed

+177
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lines changed

6 files changed

+177
-1
lines changed

‎src/TensorFlowNET.Core/APIs/tf.array.cs‎

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Original file line numberDiff line numberDiff line change
@@ -36,6 +36,16 @@ public static Tensor concat(IList<Tensor> values, int axis, string name = "conca
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public static Tensor expand_dims(Tensor input, int axis = -1, string name = null, int dim = -1)
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=> array_ops.expand_dims(input, axis, name, dim);
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/// <summary>
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/// Creates a tensor filled with a scalar value.
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/// </summary>
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/// <param name="dims"></param>
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/// <param name="value"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public static Tensor fill<T>(Tensor dims, T value, string name = null)
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=> gen_array_ops.fill(dims, value, name: name);
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/// <summary>
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/// Return the elements, either from `x` or `y`, depending on the `condition`.
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/// </summary>

‎src/TensorFlowNET.Core/APIs/tf.gradients.cs‎

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@@ -6,7 +6,7 @@ namespace Tensorflow
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{
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public static partial class tf
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{
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public static object gradients(Tensor[] ys,
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public static Tensor[] gradients(Tensor[] ys,
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Tensor[] xs,
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Tensor[] grad_ys = null,
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string name = "gradients",
@@ -41,5 +41,23 @@ public static Tensor[] gradients(Tensor ys,
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gate_gradients,
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stop_gradients: stop_gradients);
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}
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public static Tensor[] gradients(Tensor ys,
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Tensor xs,
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Tensor[] grad_ys = null,
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string name = "gradients",
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bool colocate_gradients_with_ops = false,
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bool gate_gradients = false,
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int? aggregation_method = null,
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Tensor[] stop_gradients = null)
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{
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return gradients_util._GradientsHelper(new Tensor[] { ys },
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new Tensor[] { xs },
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grad_ys,
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name,
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colocate_gradients_with_ops,
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gate_gradients,
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stop_gradients: stop_gradients);
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}
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}
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}

‎src/TensorFlowNET.Core/APIs/tf.math.cs‎

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@@ -257,6 +257,16 @@ public static Tensor multiply<Tx, Ty>(Tx x, Ty y)
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public static Tensor negative(Tensor x, string name = null)
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=> gen_math_ops.neg(x, name);
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/// <summary>
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/// Divides x / y elementwise (using Python 2 division operator semantics).
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/// </summary>
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/// <param name="x"></param>
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/// <param name="y"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public static Tensor div(Tensor x, Tensor y, string name = null)
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=> math_ops.div(x, y, name: name);
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public static Tensor divide<T>(Tensor x, T[] y, string name = null) where T : struct
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=> x / ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y");
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‎src/TensorFlowNET.Core/Gradients/math_grad.cs‎

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Original file line numberDiff line numberDiff line change
@@ -168,6 +168,96 @@ public static Tensor[] _MeanGrad(Operation op, Tensor[] grads)
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return new Tensor[] { math_ops.truediv(sum_grad, math_ops.cast(factor, sum_grad.dtype)), null };
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}
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/// <summary>
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/// Gradient for Max.
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/// </summary>
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/// <param name="op"></param>
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/// <param name="grads"></param>
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/// <returns></returns>
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[RegisterGradient("Max")]
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public static Tensor[] _MaxGrad(Operation op, Tensor[] grads)
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{
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return _MinOrMaxGrad(op, grads);
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}
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/// <summary>
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/// Gradient for Min.
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/// </summary>
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/// <param name="op"></param>
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/// <param name="grads"></param>
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/// <returns></returns>
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[RegisterGradient("Min")]
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public static Tensor[] _MinGrad(Operation op, Tensor[] grads)
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{
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return _MinOrMaxGrad(op, grads);
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}
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private static Tensor[] _MinOrMaxGrad(Operation op, Tensor[] grads)
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{
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var grad = grads[0];
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var input_shape = array_ops.shape(op.inputs[0]);
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var output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1]);
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var y = op.outputs[0];
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y = array_ops.reshape(y, output_shape_kept_dims);
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grad = array_ops.reshape(grad, output_shape_kept_dims);
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// Compute the number of selected (maximum or minimum) elements in each
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// reduction dimension. If there are multiple minimum or maximum elements
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// then the gradient will be divided between them.
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var indicators = math_ops.cast(math_ops.equal(y, op.inputs[0]), grad.dtype);
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var num_selected = array_ops.reshape(math_ops.reduce_sum(indicators, op.inputs[1]), output_shape_kept_dims);
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210+
return new Tensor[] { math_ops.div(indicators, num_selected) * grad, null };
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}
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/// <summary>
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/// Returns grad*(x > y, x <= y) with type of grad.
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/// </summary>
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/// <param name="op"></param>
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/// <param name="grads"></param>
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/// <returns></returns>
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[RegisterGradient("Maximum")]
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public static Tensor[] _MaximumGrad(Operation op, Tensor[] grads)
221+
{
222+
return _MaximumMinimumGrad(op, grads[0]);
223+
}
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/// <summary>
226+
/// Returns grad*(x < y, x >= y) with type of grad.
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/// </summary>
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/// <param name="op"></param>
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/// <param name="grads"></param>
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/// <returns></returns>
231+
[RegisterGradient("Minimum")]
232+
public static Tensor[] _MinimumGrad(Operation op, Tensor[] grads)
233+
{
234+
return _MaximumMinimumGrad(op, grads[0]);
235+
}
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/// <summary>
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/// Factor out the code for the gradient of Maximum or Minimum.
239+
/// </summary>
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/// <param name="op"></param>
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/// <param name="grad"></param>
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/// <returns></returns>
243+
private static Tensor[] _MaximumMinimumGrad(Operation op, Tensor grad)
244+
{
245+
var x = op.inputs[0];
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var y = op.inputs[1];
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var gdtype = grad.dtype;
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var sx = array_ops.shape(x);
249+
var sy = array_ops.shape(y);
250+
var gradshape = array_ops.shape(grad);
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var zeros = array_ops.zeros(gradshape, gdtype);
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var xmask = gen_math_ops.greater_equal(x, y);
253+
var (rx, ry) = gen_array_ops.broadcast_gradient_args(sx, sy);
254+
var xgrad = array_ops.where(xmask, grad, zeros);
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var ygrad = array_ops.where(xmask, zeros, grad);
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var gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx);
257+
var gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy);
258+
return new Tensor[] { gx, gy };
259+
}
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171261
[RegisterGradient("Neg")]
172262
public static Tensor[] _NegGrad(Operation op, Tensor[] grads)
173263
{

‎src/TensorFlowNET.Core/Operations/array_ops.py.cs‎

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@@ -36,6 +36,29 @@ public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT
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});
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}
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public static Tensor zeros(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
40+
{
41+
dtype = dtype.as_base_dtype();
42+
return with(ops.name_scope(name, "zeros", shape), scope =>
43+
{
44+
name = scope;
45+
switch (dtype)
46+
{
47+
case TF_DataType.TF_BOOL:
48+
return gen_array_ops.fill(shape, tf.constant(false, dtype: dtype), name: name);
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case TF_DataType.TF_DOUBLE:
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return gen_array_ops.fill(shape, tf.constant(0.0D, dtype: dtype), name: name);
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case TF_DataType.TF_FLOAT:
52+
return gen_array_ops.fill(shape, tf.constant(0.0F, dtype: dtype), name: name);
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case TF_DataType.TF_INT32:
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return gen_array_ops.fill(shape, tf.constant(0, dtype: dtype), name: name);
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default:
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throw new TypeError("can't find type for zeros");
57+
}
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});
60+
}
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private static Tensor _constant_if_small(int value, Tensor shape)
4063
{
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return shape < 1000;

‎src/TensorFlowNET.Core/Operations/math_ops.cs‎

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Original file line numberDiff line numberDiff line change
@@ -65,6 +65,31 @@ public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, s
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});
6666
}
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68+
/// <summary>
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/// Divide two values using Python 2 semantics. Used for Tensor.__div__.
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/// </summary>
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/// <param name="x">`Tensor` numerator of real numeric type.</param>
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/// <param name="y">`Tensor` denominator of real numeric type.</param>
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/// <param name="name">A name for the operation</param>
74+
/// <returns>`x / y` returns the quotient of x and y.</returns>
75+
public static Tensor div(Tensor x, Tensor y, string name = null)
76+
{
77+
return with(ops.name_scope(name, "div", (x, y)), name_scope =>
78+
{
79+
name = name_scope;
80+
x = ops.convert_to_tensor(x, name: "x");
81+
y = ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name = "y");
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var x_dtype = x.dtype.as_base_dtype();
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var y_dtype = y.dtype.as_base_dtype();
84+
if (x_dtype != y_dtype)
85+
throw new TypeError($"x and y must have the same dtype, got {x_dtype} != {y_dtype}");
86+
if (x_dtype.is_floating() || x_dtype.is_complex())
87+
return gen_math_ops.real_div(x, y, name: name);
88+
else
89+
return gen_math_ops.floor_div(x, y, name: name);
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});
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}
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/// <summary>
6994
/// Returns 0 if the denominator is zero.
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/// </summary>

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