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Oceania2018Oceania2018
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add CumsumGrad, BroadcastToGrad
1 parent d986603 commit 5a414f0

7 files changed

Lines changed: 94 additions & 2 deletions

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

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@@ -53,6 +53,16 @@ public Tensor batch_to_space_nd<T>(T input, int[] block_shape, int[,] crops, str
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public Tensor boolean_mask<T1, T2>(T1 tensor, T2 mask, string name = "boolean_mask", int axis = 0)
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=> array_ops.boolean_mask(tensor, mask, name: name, axis: axis);
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/// <summary>
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/// Broadcast an array for a compatible shape.
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/// </summary>
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/// <param name="input"></param>
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/// <param name="shape"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public Tensor broadcast_to(Tensor input, TensorShape shape, string name = null)
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=> gen_array_ops.broadcast_to(input, shape, name: name);
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public Tensor check_numerics(Tensor tensor, string message, string name = null)
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=> gen_array_ops.check_numerics(tensor, message, name: name);
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‎src/TensorFlowNET.Core/Gradients/array_grad.cs‎

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@@ -27,6 +27,27 @@ namespace Tensorflow.Gradients
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[RegisterGradient("array_grad")]
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public class array_grad
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{
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[RegisterGradient("BroadcastTo")]
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public static Tensor[] _BroadcastToGrad(Operation op, Tensor[] grads)
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{
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var grad = grads[0];
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var input_value = op.inputs[0];
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var broadcast_shape = op.inputs[1];
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var input_value_shape = array_ops.shape(input_value);
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var (_, reduction_axes) = gen_array_ops.broadcast_gradient_args(broadcast_shape,
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input_value_shape);
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var updates_grad_reshaped = math_ops.reduce_sum(grad,
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axis: reduction_axes,
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keepdims: true);
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var updates_grad = array_ops.reshape(updates_grad_reshaped, input_value_shape);
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return new Tensor[]
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{
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updates_grad,
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null
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};
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}
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[RegisterGradient("ConcatV2")]
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public static Tensor[] _ConcatGradV2(Operation op, Tensor[] grads)
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{

‎src/TensorFlowNET.Core/Gradients/math_grad.cs‎

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@@ -58,6 +58,20 @@ public static Tensor[] _AddGrad(Operation op, Tensor[] grads)
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return new Tensor[] { r1, r2 };
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}
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[RegisterGradient("Cumsum")]
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public static Tensor[] _CumsumGrad(Operation op, Tensor[] grads)
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{
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var grad = grads[0];
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var axis = op.inputs[1];
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var exclusive = op.get_attr<bool>("exclusive");
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var reverse = op.get_attr<bool>("reverse");
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return new Tensor[]
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{
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math_ops.cumsum(grad, axis, exclusive: exclusive, reverse: !reverse),
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null
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};
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}
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[RegisterGradient("DivNoNan")]
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public static Tensor[] _DivNoNanGrad(Operation op, Tensor[] grads)
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{

‎src/TensorFlowNET.Core/Operations/gen_array_ops.cs‎

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@@ -515,5 +515,19 @@ public static Tensor broadcast_args(Tensor s0, Tensor s1, string name = null)
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return _op.outputs[0];
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}
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/// <summary>
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/// Broadcast an array for a compatible shape.
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/// </summary>
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/// <param name="input"></param>
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/// <param name="shape"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public static Tensor broadcast_to(Tensor input, int[] shape, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("BroadcastTo", name, args: new { input, shape, name });
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return _op.outputs[0];
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}
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}
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}

‎src/TensorFlowNET.Core/Operations/gen_math_ops.cs‎

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@@ -238,7 +238,7 @@ public static Tensor cosh(Tensor x, string name = null)
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return _op.outputs[0];
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}
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public static Tensor cumsum(Tensor x, int axis = 0, bool exclusive = false, bool reverse = false, string name = null)
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public static Tensor cumsum<T>(Tensor x, T axis, bool exclusive = false, bool reverse = false, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Cumsum", name, args: new { x, axis, exclusive, reverse });
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‎src/TensorFlowNET.Core/Operations/math_ops.cs‎

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@@ -80,7 +80,7 @@ public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, s
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});
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}
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public static Tensor cumsum(Tensor x, int axis = 0, bool exclusive = false, bool reverse = false, string name = null)
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public static Tensor cumsum<T>(Tensor x, T axis = default, bool exclusive = false, bool reverse = false, string name = null)
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{
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return tf_with(ops.name_scope(name, "Cumsum", new {x}), scope =>
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{

‎test/TensorFlowNET.UnitTest/gradients_test/GradientsTest.cs‎

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@@ -11,6 +11,39 @@ namespace TensorFlowNET.UnitTest.gradients_test
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[TestClass]
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public class GradientsTest : PythonTest
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{
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[TestMethod]
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public void BroadcastToGrad()
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{
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var graph = tf.Graph().as_default();
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var x = tf.constant(2, dtype: dtypes.float32);
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var y = tf.broadcast_to(x, (2, 4, 3));
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var grad = tf.gradients(y, x);
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using (var sess = tf.Session(graph))
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{
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float result = sess.run(grad[0]);
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Assert.AreEqual(result, 24.0f);
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}
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}
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[TestMethod]
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public void CumsumGrad()
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{
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var graph = tf.Graph().as_default();
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var x = tf.constant(2, dtype: dtypes.float32);
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var y = tf.broadcast_to(x, (2, 4, 3));
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var z = tf.cumsum(y, axis: 1);
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var grad = tf.gradients(z, x);
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using (var sess = tf.Session(graph))
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{
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float result = sess.run(grad[0]);
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Assert.AreEqual(result, 60.0f);
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}
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}
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[Ignore("TODO")]
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[TestMethod]
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public void testGradients()

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