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OperationsTest.cs
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using Microsoft.VisualStudio.TestTools.UnitTesting;
using Tensorflow.NumPy;
using System;
using System.Collections.Generic;
using System.Linq;
using Tensorflow;
using static Tensorflow.Binding;
using Buffer = Tensorflow.Buffer;
namespace TensorFlowNET.UnitTest.Basics
{
[TestClass]
public class OperationsTest : GraphModeTestBase
{
/// <summary>
/// Port from tensorflow\c\c_api_test.cc
/// `TEST(CAPI, GetAllOpList)`
/// </summary>
[TestMethod]
public void GetAllOpList()
{
var handle = c_api.TF_GetAllOpList();
var buffer = new Buffer(handle);
var op_list = OpList.Parser.ParseFrom(buffer.ToArray());
var _registered_ops = new Dictionary<string, OpDef>();
foreach (var op_def in op_list.Op)
_registered_ops[op_def.Name] = op_def;
// r1.14 added NN op
var op = _registered_ops.FirstOrDefault(x => x.Key == "NearestNeighbors");
Assert.IsTrue(op_list.Op.Count > 1000);
}
[TestMethod]
public void addInPlaceholder()
{
var a = tf.placeholder(tf.float32);
var b = tf.placeholder(tf.float32);
var c = tf.add(a, b);
var sess = tf.Session();
var o = sess.run(c,
new FeedItem(a, 3.0f),
new FeedItem(b, 2.0f));
Assert.AreEqual(o, 5.0f);
}
[TestMethod]
public void addInConstant()
{
var a = tf.constant(4.0f);
var b = tf.constant(5.0f);
var c = tf.add(a, b);
var sess = tf.Session();
var o = sess.run(c);
Assert.AreEqual(o, 9.0f);
}
[TestMethod]
public void isFinite()
{
var a = tf.constant(new[] { 1, np.nan, 2, np.nan, 3, np.nan, 4, np.nan });
var b = tf.cast(tf.is_finite(a), tf.float32);
var check = np.array(1.0f, 0.0f, 1.0f, 0.0f, 1.0f, 0.0f, 1.0f, 0.0f);
var sess = tf.Session();
var o = sess.run(b);
Assert.IsTrue(np.array_equal(o, check));
}
[TestMethod]
public void isNan()
{
var a = tf.constant(new[] { 1, np.nan, 2, np.nan, 3, np.nan, 4, np.nan });
var b = tf.cast(tf.is_nan(a), tf.float32);
var check = np.array(0.0f, 1.0f, 0.0f, 1.0f, 0.0f, 1.0f, 0.0f, 1.0f);
var sess = tf.Session();
var o = sess.run(b);
Assert.IsTrue(np.array_equal(o, check));
}
[TestMethod]
public void cumSumTest()
{
var a = tf.constant(new[] { 1, 1, 2, 3, 4, 5 });
var b = tf.cumsum(a);
var check = np.array(1, 2, 4, 7, 11, 16);
var sess = tf.Session();
var o = sess.run(b);
Assert.IsTrue(np.array_equal(o, check));
b = tf.cumsum(a, exclusive: true);
check = np.array(0, 1, 2, 4, 7, 11);
sess = tf.Session();
o = sess.run(b);
Assert.IsTrue(np.array_equal(o, check));
b = tf.cumsum(a, reverse: true);
check = np.array(16, 15, 14, 12, 9, 5);
sess = tf.Session();
o = sess.run(b);
Assert.IsTrue(np.array_equal(o, check));
b = tf.cumsum(a, exclusive: true, reverse: true);
check = np.array(15, 14, 12, 9, 5, 0);
sess = tf.Session();
o = sess.run(b);
Assert.IsTrue(np.array_equal(o, check));
}
[TestMethod]
public void logicalOpsTest()
{
var a = tf.constant(new[] { 1f, 2f, 3f, 4f, -4f, -3f, -2f, -1f });
var b = tf.less(a, 0f);
var c = tf.greater(a, 0f);
var d = tf.cast(tf.logical_and(b, c), tf.int32);
var check = np.array(new[] { 0, 0, 0, 0, 0, 0, 0, 0 });
var sess = tf.Session();
var o = sess.run(d);
Assert.IsTrue(np.array_equal(o, check));
d = tf.cast(tf.logical_not(b), tf.int32);
check = np.array(new[] { 1, 1, 1, 1, 0, 0, 0, 0 });
sess = tf.Session();
o = sess.run(d);
Assert.IsTrue(np.array_equal(o, check));
d = tf.cast(tf.logical_or(b, c), tf.int32);
check = np.array(new[] { 1, 1, 1, 1, 1, 1, 1, 1 });
sess = tf.Session();
o = sess.run(d);
Assert.IsTrue(np.array_equal(o, check));
d = tf.cast(tf.logical_xor(b, c), tf.int32);
check = np.array(new[] { 1, 1, 1, 1, 1, 1, 1, 1 });
sess = tf.Session();
o = sess.run(d);
Assert.IsTrue(np.array_equal(o, check));
}
[TestMethod]
public void addOpTests()
{
const int rows = 2; // to avoid broadcasting effect
const int cols = 10;
#region intTest
const int firstIntVal = 2;
const int secondIntVal = 3;
var firstIntFeed = Enumerable.Repeat(firstIntVal, rows * cols).ToArray();
var secondIntFeed = Enumerable.Repeat(secondIntVal, rows * cols).ToArray();
var intResult = firstIntFeed.Sum() + secondIntFeed.Sum();
var a = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var c = tf.reduce_sum(tf.reduce_sum(tf.add(a, b), 1));
var sess = tf.Session();
var o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual(o, intResult);
// Testing `operator +(Tensor x, Tensor y)`
c = tf.reduce_sum(tf.reduce_sum(a + b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual(o, intResult);
// Testing `operator +(Tensor x, int y)`
c = tf.reduce_sum(tf.reduce_sum(a + secondIntVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual(o, intResult);
// Testing `operator +(int x, Tensor y)`
c = tf.reduce_sum(tf.reduce_sum(secondIntVal + a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual(o, intResult);
#endregion
#region floatTest
const float firstFloatVal = 2.0f;
const float secondFloatVal = 3.0f;
var firstFloatFeed = Enumerable.Repeat(firstFloatVal, rows * cols).ToArray();
var secondFloatFeed = Enumerable.Repeat(secondFloatVal, rows * cols).ToArray();
var floatResult = firstFloatFeed.Sum() + secondFloatFeed.Sum();
a = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.add(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual(o, floatResult);
// Testing `operator +(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a + b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual(o, floatResult);
// Testing `operator +(Tensor x, float y)
c = tf.reduce_sum(tf.reduce_sum(a + secondFloatVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual(o, floatResult);
// Testing `operator +(float x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(secondFloatVal + a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual(o, floatResult);
#endregion
#region doubleTest
const double firstDoubleVal = 2.0;
const double secondDoubleVal = 3.0;
var firstDoubleFeed = Enumerable.Repeat(firstDoubleVal, rows * cols).ToArray();
var secondDoubleFeed = Enumerable.Repeat(secondDoubleVal, rows * cols).ToArray();
var doubleResult = firstDoubleFeed.Sum() + secondDoubleFeed.Sum();
a = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.add(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator +(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a + b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual(o, doubleResult);
// Testing `operator +(Tensor x, double y)
c = tf.reduce_sum(tf.reduce_sum(a + secondDoubleVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual(o, doubleResult);
// Testing `operator +(double x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(secondDoubleVal + a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual(o, doubleResult);
#endregion
}
[TestMethod]
public void subOpTests()
{
const int rows = 2; // to avoid broadcasting effect
const int cols = 10;
#region intTest
const int firstIntVal = -2;
const int secondIntVal = 3;
var firstIntFeed = Enumerable.Repeat(firstIntVal, rows * cols).ToArray();
var secondIntFeed = Enumerable.Repeat(secondIntVal, rows * cols).ToArray();
var intResult = firstIntFeed.Sum() - secondIntFeed.Sum();
var intResultTwo = -firstIntFeed.Sum();
var a = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var c = tf.reduce_sum(tf.reduce_sum(tf.sub(a, b), 1));
var sess = tf.Session();
var o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator -(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a - b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator -(Tensor x, int y)
c = tf.reduce_sum(tf.reduce_sum(a - secondIntVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator -(int x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(secondIntVal - a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, Math.Abs(intResult));
// Testing `operator -(Tensor x)
c = tf.reduce_sum(tf.reduce_sum(-a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResultTwo);
#endregion
#region floatTest
const float firstFloatVal = -2.0f;
const float secondFloatVal = 3.0f;
var firstFloatFeed = Enumerable.Repeat(firstFloatVal, rows * cols).ToArray();
var secondFloatFeed = Enumerable.Repeat(secondFloatVal, rows * cols).ToArray();
var floatResult = firstFloatFeed.Sum() - secondFloatFeed.Sum();
var floatResultTwo = -firstFloatFeed.Sum();
a = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.sub(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator -(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a - b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator -(Tensor x, float y)
c = tf.reduce_sum(tf.reduce_sum(a - secondFloatVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator -(float x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(secondFloatVal - a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, Math.Abs(floatResult));
// Testing `operator -(Tensor x)
c = tf.reduce_sum(tf.reduce_sum(-a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResultTwo);
#endregion
#region doubleTest
const double firstDoubleVal = -2.0;
const double secondDoubleVal = 3.0;
var firstDoubleFeed = Enumerable.Repeat(firstDoubleVal, rows * cols).ToArray();
var secondDoubleFeed = Enumerable.Repeat(secondDoubleVal, rows * cols).ToArray();
var doubleResult = firstDoubleFeed.Sum() - secondDoubleFeed.Sum();
var doubleResultTwo = -firstDoubleFeed.Sum();
a = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.sub(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator -(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a - b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator -(Tensor x, double y)
c = tf.reduce_sum(tf.reduce_sum(a - secondDoubleVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator -(double x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(secondDoubleVal - a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, Math.Abs(doubleResult));
// Testing `operator -(Tensor x)
c = tf.reduce_sum(tf.reduce_sum(-a, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResultTwo);
#endregion
}
private IEnumerable<int> MultiplyArray(IReadOnlyCollection<int> first, IReadOnlyCollection<int> second)
{
if (first.Count != second.Count)
throw new ArgumentException("Arrays should be of equal size!");
var firstEnumerator = first.GetEnumerator();
var secondEnumerator = second.GetEnumerator();
var result = new List<int>();
while (firstEnumerator.MoveNext())
{
secondEnumerator.MoveNext();
result.Add(firstEnumerator.Current * secondEnumerator.Current);
}
firstEnumerator.Dispose();
secondEnumerator.Dispose();
return result;
}
private IEnumerable<float> MultiplyArray(IReadOnlyCollection<float> first, IReadOnlyCollection<float> second)
{
if (first.Count != second.Count)
throw new ArgumentException("Arrays should be of equal size!");
var firstEnumerator = first.GetEnumerator();
var secondEnumerator = second.GetEnumerator();
var result = new List<float>();
while (firstEnumerator.MoveNext())
{
secondEnumerator.MoveNext();
result.Add(firstEnumerator.Current * secondEnumerator.Current);
}
firstEnumerator.Dispose();
secondEnumerator.Dispose();
return result;
}
private IEnumerable<double> MultiplyArray(IReadOnlyCollection<double> first, IReadOnlyCollection<double> second)
{
if (first.Count != second.Count)
throw new ArgumentException("Arrays should be of equal size!");
var firstEnumerator = first.GetEnumerator();
var secondEnumerator = second.GetEnumerator();
var result = new List<double>();
while (firstEnumerator.MoveNext())
{
secondEnumerator.MoveNext();
result.Add(firstEnumerator.Current * secondEnumerator.Current);
}
firstEnumerator.Dispose();
secondEnumerator.Dispose();
return result;
}
[TestMethod]
public void mulOpTests()
{
const int rows = 2; // to avoid broadcasting effect
const int cols = 10;
#region intTest
const int firstIntVal = 2;
const int secondIntVal = 3;
var firstIntFeed = Enumerable.Repeat(firstIntVal, rows * cols).ToArray();
var secondIntFeed = Enumerable.Repeat(secondIntVal, rows * cols).ToArray();
var intResult = MultiplyArray(firstIntFeed, secondIntFeed).Sum();
var a = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var c = tf.reduce_sum(tf.reduce_sum(tf.multiply(a, b), 1));
var sess = tf.Session();
var o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator *(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a * b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator *(Tensor x, int y)
c = tf.reduce_sum(tf.reduce_sum(a * secondIntVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator *(int x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(firstIntVal * b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
#endregion
#region floatTest
const float firstFloatVal = 2.0f;
const float secondFloatVal = 3.0f;
var firstFloatFeed = Enumerable.Repeat(firstFloatVal, rows * cols).ToArray();
var secondFloatFeed = Enumerable.Repeat(secondFloatVal, rows * cols).ToArray();
var floatResult = MultiplyArray(firstFloatFeed, secondFloatFeed).Sum();
a = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.multiply(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator *(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a * b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator *(Tensor x, float y)
c = tf.reduce_sum(tf.reduce_sum(a * secondFloatVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator *(float x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(firstFloatVal * b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
#endregion
#region doubleTest
const double firstDoubleVal = 2.0;
const double secondDoubleVal = 3.0;
var firstDoubleFeed = Enumerable.Repeat(firstDoubleVal, rows * cols).ToArray();
var secondDoubleFeed = Enumerable.Repeat(secondDoubleVal, rows * cols).ToArray();
var doubleResult = MultiplyArray(firstDoubleFeed, secondDoubleFeed).Sum();
a = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.multiply(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator *(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a * b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator *(Tensor x, double y)
c = tf.reduce_sum(tf.reduce_sum(a * secondDoubleVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator *(double x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(firstDoubleVal * b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
#endregion
}
[Ignore]
[TestMethod]
public void divOpTests()
{
const int rows = 2; // to avoid broadcasting effect
const int cols = 10;
#region intTest
const int firstIntVal = 6;
const int secondIntVal = 3;
var firstIntFeed = Enumerable.Repeat(firstIntVal, rows * cols).ToArray();
var secondIntFeed = Enumerable.Repeat(secondIntVal, rows * cols).ToArray();
var intResult = (int)(firstIntFeed.Sum() / (float)secondIntVal);
var a = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var c = tf.reduce_sum(tf.reduce_sum(gen_math_ops.floor_div(a, b), 1));
var sess = tf.Session();
var o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator /(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a / b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator /(Tensor x, int y)
c = tf.reduce_sum(tf.reduce_sum(a / secondIntVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator /(int x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(firstIntVal / b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
#endregion
#region floatTest
const float firstFloatVal = 6.0f;
const float secondFloatVal = 3.0f;
var firstFloatFeed = Enumerable.Repeat(firstFloatVal, rows * cols).ToArray();
var secondFloatFeed = Enumerable.Repeat(secondFloatVal, rows * cols).ToArray();
var floatResult = MultiplyArray(firstFloatFeed, secondFloatFeed.Select(x => 1 / x).ToArray()).Sum();
a = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.divide(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator /(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a / b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator /(Tensor x, float y)
c = tf.reduce_sum(tf.reduce_sum(a / secondFloatVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
// Testing `operator /(float x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(firstFloatVal / b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((float)o, floatResult);
#endregion
#region doubleTest
const double firstDoubleVal = 6.0;
const double secondDoubleVal = 3.0;
var firstDoubleFeed = Enumerable.Repeat(firstDoubleVal, rows * cols).ToArray();
var secondDoubleFeed = Enumerable.Repeat(secondDoubleVal, rows * cols).ToArray();
var doubleResult = MultiplyArray(firstDoubleFeed, secondDoubleFeed.Select(x => 1 / x).ToArray()).Sum();
a = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.divide(a, b), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator /(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(a / b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator /(Tensor x, double y)
c = tf.reduce_sum(tf.reduce_sum(a / secondFloatVal, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
// Testing `operator /(double x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(firstFloatVal / b, 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((double)o, doubleResult);
#endregion
}
[TestMethod]
public void greaterThanOpTests()
{
const int rows = 2; // to avoid broadcasting effect
const int cols = 10;
#region intTest
const int intThreshold = 10;
var firstIntFeed = Enumerable.Range(0, rows * cols).ToArray();
var secondIntFeed = Enumerable.Repeat(intThreshold, rows * cols).ToArray();
var intResult = firstIntFeed.Count(elem => elem > intThreshold);
var intResultTwo = firstIntFeed.Count(elem => elem < intThreshold);
var a = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1));
var sess = tf.Session();
var o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator >(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator >(Tensor x, int y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > intThreshold, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator >(int x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold > a, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResultTwo);
#endregion
#region floatTest
const float floatThreshold = 10.0f;
var firstFloatFeed = Enumerable.Range(0, rows * cols).Select(elem => (float)elem).ToArray();
var secondFloatFeed = Enumerable.Repeat(floatThreshold, rows * cols).ToArray();
var floatResult = firstFloatFeed.Count(elem => elem > floatThreshold);
var floatResultTwo = firstFloatFeed.Count(elem => elem < floatThreshold);
a = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResult);
// Testing `operator >(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResult);
// Testing `operator >(Tensor x, float y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > floatThreshold, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResult);
// Testing `operator >(float x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold > a, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResultTwo);
#endregion
#region doubleTest
const double doubleThreshold = 10.0;
var firstDoubleFeed = Enumerable.Repeat(0, rows * cols).Select(elem => (double)elem).ToArray();
var secondDoubleFeed = Enumerable.Repeat(doubleThreshold, rows * cols).ToArray();
var doubleResult = firstDoubleFeed.Count(elem => elem > doubleThreshold);
var doubleResultTwo = firstDoubleFeed.Count(elem => elem < doubleThreshold);
a = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, doubleResult);
// Testing `operator >(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, doubleResult);
// Testing `operator >(Tensor x, double y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > doubleThreshold, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, doubleResult);
// Testing `operator >(double x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(doubleThreshold > a, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, doubleResultTwo);
#endregion
}
[TestMethod]
public void lessThanOpTests()
{
const int rows = 2; // to avoid broadcasting effect
const int cols = 10;
#region intTest
const int intThreshold = 10;
var firstIntFeed = Enumerable.Range(0, rows * cols).ToArray();
var secondIntFeed = Enumerable.Repeat(intThreshold, rows * cols).ToArray();
var intResult = firstIntFeed.Count(elem => elem < intThreshold);
var intResultTwo = firstIntFeed.Count(elem => elem > intThreshold);
var a = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols));
var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1));
var sess = tf.Session();
var o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator <(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator <(Tensor x, int y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < intThreshold, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResult);
// Testing `operator <(int x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold < a, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, intResultTwo);
#endregion
#region floatTest
const float floatThreshold = 10.0f;
var firstFloatFeed = Enumerable.Range(0, rows * cols).Select(elem => (float)elem).ToArray();
var secondFloatFeed = Enumerable.Repeat(floatThreshold, rows * cols).ToArray();
var floatResult = firstFloatFeed.Count(elem => elem < floatThreshold);
var floatResultTwo = firstFloatFeed.Count(elem => elem > floatThreshold);
a = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float32, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResult);
// Testing `operator <(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResult);
// Testing `operator <(Tensor x, float y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < floatThreshold, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResult);
// Testing `operator <(float x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold < a, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, floatResultTwo);
#endregion
#region doubleTest
const double doubleThreshold = 10.0;
var firstDoubleFeed = Enumerable.Repeat(0, rows * cols).Select(elem => (double)elem).ToArray();
var secondDoubleFeed = Enumerable.Repeat(doubleThreshold, rows * cols).ToArray();
var doubleResult = firstDoubleFeed.Count(elem => elem < doubleThreshold);
var doubleResultTwo = firstDoubleFeed.Count(elem => elem > doubleThreshold);
a = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
b = tf.placeholder(tf.float64, shape: new Shape(rows, cols));
c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, doubleResult);
// Testing `operator <(Tensor x, Tensor y)
c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1));
sess = tf.Session();
o = sess.run(c,
new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
Assert.AreEqual((int)o, doubleResult);
// Testing `operator <(Tensor x, double y)