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Merge pull request #1202 from Wanglongzhi2001/master · MSavameri/TensorFlow.NET@079b9a3 · GitHub

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forked from SciSharp/TensorFlow.NET

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Oceania2018
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Merge pull request SciSharp#1202 from Wanglongzhi2001/master
fix: add the implementation of the tile's and GatherND's grad and add OptionalArgs
2 parents e79ecb7 + d0ec659 commit 079b9a3

7 files changed

Lines changed: 108 additions & 6 deletions

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

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@@ -140,6 +140,16 @@ public Tensor identity(Tensor input, string name = null)
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public Tensor gather(Tensor @params, Tensor indices, string name = null, int axis = 0)
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=> array_ops.gather(@params, indices, name: name, axis: ops.convert_to_tensor(axis));
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/// <summary>
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/// Gather slices from `params` into a Tensor with shape specified by `indices`.
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/// </summary>
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/// <param name="params"></param>
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/// <param name="indices"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public Tensor gather_nd(Tensor @params, Tensor indices, string name = null)
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=> gen_array_ops.gather_nd(@params, indices, 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/Gradients/array_grad.cs‎

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@@ -381,5 +381,48 @@ public static Tensor[] _ReverseV2Grad(Operation op, Tensor[] grads)
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var axis = op.inputs[1];
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return new Tensor[] { array_ops.reverse(grad, axis), null };
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}
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[RegisterGradient("Tile")]
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public static Tensor[] _TileGrad(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], out_type: op.inputs[1].dtype);
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var split_shape = array_ops.reshape(array_ops.transpose(array_ops.stack(new Tensor[] { op.inputs[1], input_shape })), new Shape(-1));
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var axes = math_ops.range(0, array_ops.size(split_shape), 2);
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//# Sum reduces grad along the first dimension for IndexedSlices
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//if isinstance(grad, indexed_slices_lib.IndexedSlices):
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//input_shape_0 = math_ops.cast(input_shape[0], grad.indices.dtype)
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//grad = math_ops.unsorted_segment_sum(
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// grad.values, math_ops.mod(grad.indices, input_shape_0), input_shape_0)
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//split_shape = array_ops.concat([[1], split_shape[1:]], axis = 0)
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var input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes);
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if (!tf.Context.executing_eagerly())
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{
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input_grad.set_shape(op.inputs[0].GetShape());
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}
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return new Tensor[] { input_grad, null };
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}
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[RegisterGradient("GatherNd")]
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public static Tensor[] _GatherNdGrad(Operation op, Tensor[] grads)
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{
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var @ref = op.inputs[0];
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var indices = op.inputs[1];
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var grad = grads[0];
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var ref_shape = array_ops.shape(@ref, out_type: indices.dtype);
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Tensor ref_grad = null;
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if (indices.shape.ndim == 2 && indices.shape.dims[indices.shape.Length - 1] == 1)
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{
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ref_grad = (Tensor)new IndexedSlices(grad, array_ops.squeeze(indices, axis: -1), ref_shape);
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}
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else
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{
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ref_grad = gen_array_ops.scatter_nd(indices, grad, ref_shape);
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}
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return new Tensor[] { ref_grad, null };
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}
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/GRUOptionalArgs.cs‎

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@@ -4,10 +4,8 @@
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class GRUOptionalArgs
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public class GRUOptionalArgs : RnnOptionalArgs
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{
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public string Identifier => "GRU";
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public Tensor Mask { get; set; } = null;
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMOptionalArgs.cs‎

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@@ -0,0 +1,11 @@
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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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{
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public class LSTMOptionalArgs : RnnOptionalArgs
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{
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public string Identifier => "LSTM";
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNOptionalArgs.cs‎

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@@ -0,0 +1,11 @@
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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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{
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public class SimpleRNNOptionalArgs : RnnOptionalArgs
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{
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public string Identifier => "SimpleRNN";
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}
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}

‎src/TensorFlowNET.Core/Operations/array_ops.cs‎

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@@ -829,7 +829,7 @@ public static Tensor strided_slice_grad(Tensor shape, Tensor begin, Tensor end,
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/// <returns>A `Tensor`. Has the same type as `input`.
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/// Contains the same data as `input`, but has one or more dimensions of
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/// size 1 removed.</returns>
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public static Tensor squeeze(Tensor input, int[] axis = null, string name = null)
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public static Tensor squeeze(Tensor input, Axis axis = null, string name = null)
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=> gen_array_ops.squeeze(input, axis, name);
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public static Tensor identity(Tensor input, string name = null)
@@ -990,7 +990,7 @@ public static Tensor gather(ResourceVariable @params, Tensor indices, string nam
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return @params.sparse_read(indices, name);
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}
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public static Tensor transpose<T1>(T1 a, Axis perm, string name = "transpose", bool conjugate = false)
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public static Tensor transpose<T1>(T1 a, Axis perm = null, string name = "transpose", bool conjugate = false)
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{
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return tf_with(ops.name_scope(name, "transpose", new { a }), scope =>
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{

‎test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs‎

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@@ -62,7 +62,7 @@ public void SquaredDifference_1D()
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// Calcute the gradient of (x1-x2)^2
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// by Automatic Differentiation in Eager mode
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// Expected is 2*(abs(x1-x2))
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Tensor x1 = new NDArray( new float[] { 1, 3, 5, 21, 19, 17 });
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Tensor x1 = new NDArray(new float[] { 1, 3, 5, 21, 19, 17 });
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Tensor x2 = new NDArray(new float[] { 29, 27, 23, 7, 11, 13 });
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float[] expected = new float[]
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{
@@ -173,5 +173,34 @@ public void ConditionalMultiply()
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var result = grad(x, 4);
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Assert.AreEqual((float)result, 4.0f);
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}
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[TestMethod]
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public void Tile()
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{
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var a = tf.constant(new int[] { 1 }, TF_DataType.TF_FLOAT);
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var b = tf.constant(new int[] { 2 });
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using (var tape = tf.GradientTape())
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{
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tape.watch(a);
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var y = tf.tile(a, b);
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var grad = tape.gradient(y, a);
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Assert.AreEqual((float)grad.numpy(), 2.0f);
188+
}
189+
}
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[TestMethod]
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public void GatherNdTest()
193+
{
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var x = tf.constant(new float[,] { { 1.0f, 2.0f, 3.0f }, { 1.0f, 2.0f, 3.0f }, { 1.0f, 2.0f, 3.0f } }, dtype: TF_DataType.TF_FLOAT);
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var indices = tf.constant(new int[,] { { 0, 1 }, { 1, 1 }, { 2, 1 } }, dtype: TF_DataType.TF_INT32);
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using (var tape = tf.GradientTape())
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{
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tape.watch(x);
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var res = tf.gather_nd(x, indices);
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var grad = tape.gradient(res, x);
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var expected = np.array(new float[,] { { 0f, 1f, 0f }, { 0f, 1f, 0f }, { 0f, 1f, 0f } });
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Assert.IsTrue(Enumerable.SequenceEqual(grad.ToArray<float>(), expected.ToArray<float>()));
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}
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}
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}
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}

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