Unexpected behavior from `tf.math.greaterEqual`

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
  Custom code
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  macOS BigSur 11.1
• TensorFlow installed from (source or binary):
  TensorFlow Java latest GitHub, based on karl/tensor-ttype

I have the following code snippet.

       Operand<TFloat32> r = tf.random.randomUniform(tf.constant(Shape.of(3,2)), TFloat32.class,   
                                                         RandomUniform.seed(1001L));
      Operand<TFloat32> rate1 = tf.constant(0.5f);
      Operand<TBool> mask = tf.math.greaterEqual(r, rate1);

The generated random sequence (shape 3,2) is:

0,0). 0.028465
0,1). 0.936395
1.0). 0.035310
1,1). 0.230565
2,0). 0.269415
2,1). 0.145439

Describe the current behavior
When I run this code, I get mask values of:

0,0). false
0,1). true
1,0). false
1,1). false
2,0). false
2,1). true

Describe the expected behavior
I expected mask values to be:

0,0). false
0,1). true
1,0). false
1,1). false
2,0). false
2,1). false

The last random number, 0.145439, is less than 0.5.

Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.

try (TestSession session = TestSession.createTestSession(TestSession.Mode.GRAPH)) {
      Ops tf = session.getTF();
       Operand<TFloat32> random = tf.random.randomUniform(tf.constant(Shape.of(3,2), TFloat32.class,   
                                                         RandomUniform.seed(1001L));
      Operand<TFloat32> rate = tf.constant(0.5f);
      Operand<TBool> mask = tf.math.greaterEqual(r, rate);

      System.out.println("******** MASK ***************");
      System.out.println("random:");
      session.print(random);
      System.out.println("rate1:");
      session.print(rate1);
      System.out.println("mask:");
      session.print(mask);
      System.out.println("******** MASK ***************");
}

If I do this code in Python it works as expected:

import tensorflow as tf
a = [
    [0.028465 ,  0.936395],
    [0.035310 , 0.230565],
    [0.269415 , 0.145439]
]
print(a);
rate = 0.5
b = tf.math.greater_equal(a, rate);
print(b)

Output:

[[0.028465, 0.936395], [0.03531, 0.230565], [0.269415, 0.145439]]
tf.Tensor(
[[False  True]
 [False False]
 [False False]], shape=(3, 2), dtype=bool)

Java スニペットと GRAPH モードの TestSession から始め、生成されたテンソルと tf.math.greaterEqual の結果を Python の例と比較します。報告された最後の要素の不一致を再現し、それが Java バインディングによるものかテスト設定によるものかを特定します。動作が説明され、回帰テストまたは修正変更が検証されれば完了です。