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Oceania2018Oceania2018
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fix keras pad_sequences.
1 parent 734430d commit 16ff7a3

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10 files changed

+40
-44
lines changed

10 files changed

+40
-44
lines changed

‎src/TensorFlowNET.Core/Binding.Util.cs‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -276,8 +276,8 @@ public static float time()
276276
{
277277
if (axis == null)
278278
{
279-
var a = t1.Data<T>();
280-
var b = t2.Data<T>();
279+
var a = t1.ToArray<T>();
280+
var b = t2.ToArray<T>();
281281
for (int i = 0; i < a.Length; i++)
282282
yield return (a[i], b[i]);
283283
}

‎src/TensorFlowNET.Core/Data/MnistModelLoader.cs‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -162,7 +162,7 @@ private NDArray DenseToOneHot(NDArray labels_dense, int num_classes)
162162
var num_labels = labels_dense.dims[0];
163163
var index_offset = np.arange(num_labels) * num_classes;
164164
var labels_one_hot = np.zeros((num_labels, num_classes));
165-
var labels = labels_dense.Data<byte>();
165+
var labels = labels_dense.ToArray<byte>();
166166
for (int row = 0; row < num_labels; row++)
167167
{
168168
var col = labels[row];
@@ -176,7 +176,7 @@ private int Read32(FileStream bytestream)
176176
{
177177
var buffer = new byte[sizeof(uint)];
178178
var count = bytestream.Read(buffer, 0, 4);
179-
return np.frombuffer(buffer, ">u4").Data<int>()[0];
179+
return np.frombuffer(buffer, ">u4").ToArray<int>()[0];
180180
}
181181
}
182182
}

‎src/TensorFlowNET.Core/Gradients/math_grad.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -506,7 +506,7 @@ public static Tensor[] _SumGrad(Operation op, Tensor[] grads)
506506
if (!(axes is null))
507507
{
508508
var rank = input_0_shape.Length;
509-
if (Enumerable.SequenceEqual(Enumerable.Range(0, rank), axes.Data<int>()))
509+
if (Enumerable.SequenceEqual(Enumerable.Range(0, rank), axes.ToArray<int>()))
510510
{
511511
if (tf.Context.executing_eagerly())
512512
{

‎src/TensorFlowNET.Core/Numpy/NDArray.cs‎

Lines changed: 0 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -62,13 +62,9 @@ public NDIterator<T> AsIterator<T>(bool autoreset = false) where T : unmanaged
6262
public byte[] ToByteArray() => _tensor.BufferToArray();
6363
public static string[] AsStringArray(NDArray arr) => throw new NotImplementedException("");
6464

65-
public T[] Data<T>() where T : unmanaged
66-
=> _tensor.ToArray<T>();
6765
public T[] ToArray<T>() where T : unmanaged
6866
=> _tensor.ToArray<T>();
6967

70-
public static NDArray operator /(NDArray x, NDArray y) => throw new NotImplementedException("");
71-
7268
public override string ToString()
7369
{
7470
return tensor_util.to_numpy_string(_tensor);

‎src/TensorFlowNET.Core/Tensors/tensor_util.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -257,7 +257,7 @@ bool hasattr(Graph property, string attr)
257257
var dest_dtype_shape_array = np.array(x_).astype(cast_dtype.as_system_dtype());
258258

259259
long[] y_ = { };
260-
foreach (int y in dest_dtype_shape_array.Data<int>())
260+
foreach (int y in dest_dtype_shape_array.ToArray<int>())
261261
if (y >= 0)
262262
y_[y_.Length] = y;
263263
else

‎src/TensorFlowNET.Keras/Sequence.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -55,7 +55,7 @@ public NDArray pad_sequences(IEnumerable<int[]> sequences,
5555
value = 0f;
5656

5757
var type = dtypes.tf_dtype_from_name(dtype);
58-
var nd = new NDArray((length.Count(), maxlen.Value), dtype: type);
58+
var nd = np.zeros((length.Count(), maxlen.Value), dtype: type);
5959

6060
for (int i = 0; i < nd.dims[0]; i++)
6161
{

‎test/TensorFlowNET.Keras.UnitTest/PreprocessingTests.cs‎

Lines changed: 18 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -249,7 +249,7 @@ public void PadSequencesWithDefaults()
249249
Assert.AreNotEqual(padded[1, i], 0);
250250
}
251251

252-
[TestMethod, Ignore("slice assign doesn't work")]
252+
[TestMethod]
253253
public void PadSequencesPrePaddingTrunc()
254254
{
255255
var tokenizer = keras.preprocessing.text.Tokenizer(oov_token: OOV);
@@ -261,15 +261,15 @@ public void PadSequencesPrePaddingTrunc()
261261
Assert.AreEqual(4, padded.dims[0]);
262262
Assert.AreEqual(15, padded.dims[1]);
263263

264-
Assert.AreEqual(tokenizer.word_index["worst"], padded[0, 12]);
264+
Assert.AreEqual(padded[0, 12], tokenizer.word_index["worst"]);
265265
for (var i = 0; i < 3; i++)
266-
Assert.AreEqual(0, padded[0, i]);
267-
Assert.AreEqual(tokenizer.word_index["proud"], padded[1, 3]);
266+
Assert.AreEqual(padded[0, i], 0);
267+
Assert.AreEqual(padded[1, 3], tokenizer.word_index["proud"]);
268268
for (var i = 0; i < 15; i++)
269-
Assert.AreNotEqual(0, padded[1, i]);
269+
Assert.AreNotEqual(padded[1, i], 0);
270270
}
271271

272-
[TestMethod, Ignore("slice assign doesn't work")]
272+
[TestMethod]
273273
public void PadSequencesPrePaddingTrunc_Larger()
274274
{
275275
var tokenizer = keras.preprocessing.text.Tokenizer(oov_token: OOV);
@@ -281,13 +281,13 @@ public void PadSequencesPrePaddingTrunc_Larger()
281281
Assert.AreEqual(4, padded.dims[0]);
282282
Assert.AreEqual(45, padded.dims[1]);
283283

284-
Assert.AreEqual(tokenizer.word_index["worst"], padded[0, 42]);
284+
Assert.AreEqual(padded[0, 42], tokenizer.word_index["worst"]);
285285
for (var i = 0; i < 33; i++)
286-
Assert.AreEqual(0, padded[0, i]);
287-
Assert.AreEqual(tokenizer.word_index["proud"], padded[1, 33]);
286+
Assert.AreEqual(padded[0, i], 0);
287+
Assert.AreEqual(padded[1, 33], tokenizer.word_index["proud"]);
288288
}
289289

290-
[TestMethod, Ignore("slice assign doesn't work")]
290+
[TestMethod]
291291
public void PadSequencesPostPaddingTrunc()
292292
{
293293
var tokenizer = keras.preprocessing.text.Tokenizer(oov_token: OOV);
@@ -299,15 +299,15 @@ public void PadSequencesPostPaddingTrunc()
299299
Assert.AreEqual(4, padded.dims[0]);
300300
Assert.AreEqual(15, padded.dims[1]);
301301

302-
Assert.AreEqual(tokenizer.word_index["worst"], padded[0, 9]);
302+
Assert.AreEqual(padded[0, 9], tokenizer.word_index["worst"]);
303303
for (var i = 12; i < 15; i++)
304-
Assert.AreEqual(0, padded[0, i]);
305-
Assert.AreEqual(tokenizer.word_index["proud"], padded[1, 10]);
304+
Assert.AreEqual(padded[0, i], 0);
305+
Assert.AreEqual(padded[1, 10], tokenizer.word_index["proud"]);
306306
for (var i = 0; i < 15; i++)
307-
Assert.AreNotEqual(0, padded[1, i]);
307+
Assert.AreNotEqual(padded[1, i], 0);
308308
}
309309

310-
[TestMethod, Ignore("slice assign doesn't work")]
310+
[TestMethod]
311311
public void PadSequencesPostPaddingTrunc_Larger()
312312
{
313313
var tokenizer = keras.preprocessing.text.Tokenizer(oov_token: OOV);
@@ -319,10 +319,10 @@ public void PadSequencesPostPaddingTrunc_Larger()
319319
Assert.AreEqual(4, padded.dims[0]);
320320
Assert.AreEqual(45, padded.dims[1]);
321321

322-
Assert.AreEqual(tokenizer.word_index["worst"], padded[0, 9]);
322+
Assert.AreEqual(padded[0, 9], tokenizer.word_index["worst"]);
323323
for (var i = 32; i < 45; i++)
324-
Assert.AreEqual(0, padded[0, i]);
325-
Assert.AreEqual(tokenizer.word_index["proud"], padded[1, 10]);
324+
Assert.AreEqual(padded[0, i], 0);
325+
Assert.AreEqual(padded[1, 10], tokenizer.word_index["proud"]);
326326
}
327327

328328
[TestMethod]

‎test/TensorFlowNET.Native.UnitTest/Tensors/TensorTest.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -104,7 +104,7 @@ public void Tensor()
104104
EXPECT_EQ(tensor.shape[0], nd.dims[0]);
105105
EXPECT_EQ(tensor.shape[1], nd.dims[1]);
106106
EXPECT_EQ(tensor.bytesize, nd.size * sizeof(float));
107-
Assert.IsTrue(Enumerable.SequenceEqual(nd.Data<float>(), new float[] { 1, 2, 3, 4, 5, 6 }));
107+
Assert.IsTrue(Enumerable.SequenceEqual(nd.ToArray<float>(), new float[] { 1, 2, 3, 4, 5, 6 }));
108108
}
109109

110110
/// <summary>

‎test/TensorFlowNET.UnitTest/Numpy/Array.Creation.Test.cs‎

Lines changed: 13 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -26,43 +26,43 @@ public void empty_zeros_ones_full()
2626
public void arange()
2727
{
2828
var x = np.arange(3);
29-
AssetSequenceEqual(new[] { 0, 1, 2 }, x.Data<int>());
29+
AssetSequenceEqual(new[] { 0, 1, 2 }, x.ToArray<int>());
3030

3131
x = np.arange(3f);
32-
Assert.IsTrue(Equal(new float[] { 0, 1, 2 }, x.Data<float>()));
32+
Assert.IsTrue(Equal(new float[] { 0, 1, 2 }, x.ToArray<float>()));
3333

3434
var y = np.arange(3, 7);
35-
AssetSequenceEqual(new[] { 3, 4, 5, 6 }, y.Data<int>());
35+
AssetSequenceEqual(new[] { 3, 4, 5, 6 }, y.ToArray<int>());
3636

3737
y = np.arange(3, 7, 2);
38-
AssetSequenceEqual(new[] { 3, 5 }, y.Data<int>());
38+
AssetSequenceEqual(new[] { 3, 5 }, y.ToArray<int>());
3939
}
4040

4141
[TestMethod]
4242
public void array()
4343
{
4444
var x = np.array(1, 2, 3);
45-
AssetSequenceEqual(new[] { 1, 2, 3 }, x.Data<int>());
45+
AssetSequenceEqual(new[] { 1, 2, 3 }, x.ToArray<int>());
4646

4747
x = np.array(new[,] { { 1, 2 }, { 3, 4 }, { 5, 6 } });
48-
AssetSequenceEqual(new[] { 1, 2, 3, 4, 5, 6 }, x.Data<int>());
48+
AssetSequenceEqual(new[] { 1, 2, 3, 4, 5, 6 }, x.ToArray<int>());
4949
}
5050

5151
[TestMethod]
5252
public void eye()
5353
{
5454
var x = np.eye(3, k: 1);
55-
Assert.IsTrue(Equal(new double[] { 0, 1, 0, 0, 0, 1, 0, 0, 0 }, x.Data<double>()));
55+
Assert.IsTrue(Equal(new double[] { 0, 1, 0, 0, 0, 1, 0, 0, 0 }, x.ToArray<double>()));
5656
}
5757

5858
[TestMethod]
5959
public void linspace()
6060
{
6161
var x = np.linspace(2.0, 3.0, num: 5);
62-
Assert.IsTrue(Equal(new double[] { 2, 2.25, 2.5, 2.75, 3 }, x.Data<double>()));
62+
Assert.IsTrue(Equal(new double[] { 2, 2.25, 2.5, 2.75, 3 }, x.ToArray<double>()));
6363

6464
x = np.linspace(2.0, 3.0, num: 5, endpoint: false);
65-
Assert.IsTrue(Equal(new double[] { 2, 2.2, 2.4, 2.6, 2.8 }, x.Data<double>()));
65+
Assert.IsTrue(Equal(new double[] { 2, 2.2, 2.4, 2.6, 2.8 }, x.ToArray<double>()));
6666
}
6767

6868
[TestMethod]
@@ -71,13 +71,13 @@ public void meshgrid()
7171
var x = np.linspace(0, 1, num: 3);
7272
var y = np.linspace(0, 1, num: 2);
7373
var (xv, yv) = np.meshgrid(x, y);
74-
Assert.IsTrue(Equal(new double[] { 0, 0.5, 1, 0, 0.5, 1 }, xv.Data<double>()));
75-
Assert.IsTrue(Equal(new double[] { 0, 0, 0, 1, 1, 1 }, yv.Data<double>()));
74+
Assert.IsTrue(Equal(new double[] { 0, 0.5, 1, 0, 0.5, 1 }, xv.ToArray<double>()));
75+
Assert.IsTrue(Equal(new double[] { 0, 0, 0, 1, 1, 1 }, yv.ToArray<double>()));
7676

7777
(xv, yv) = np.meshgrid(x, y, sparse: true);
78-
Assert.IsTrue(Equal(new double[] { 0, 0.5, 1 }, xv.Data<double>()));
78+
Assert.IsTrue(Equal(new double[] { 0, 0.5, 1 }, xv.ToArray<double>()));
7979
AssetSequenceEqual(new long[] { 1, 3 }, xv.shape.dims);
80-
Assert.IsTrue(Equal(new double[] { 0, 1 }, yv.Data<double>()));
80+
Assert.IsTrue(Equal(new double[] { 0, 1 }, yv.ToArray<double>()));
8181
AssetSequenceEqual(new long[] { 2, 1 }, yv.shape.dims);
8282
}
8383
}

‎test/TensorFlowNET.UnitTest/Numpy/Math.Test.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@ public void prod()
2525

2626
p = np.prod(new[,] { { 1.0, 2.0 }, { 3.0, 4.0 } }, axis: 1);
2727
Assert.AreEqual(p.shape, 2);
28-
Assert.IsTrue(Equal(p.Data<double>(), new[] { 2.0, 12.0 }));
28+
Assert.IsTrue(Equal(p.ToArray<double>(), new[] { 2.0, 12.0 }));
2929
}
3030
}
3131
}

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