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  1. CASE #0
  2. LOSS tf.Tensor(1.8576978, shape=(), dtype=float32)
  3. GRADS:
  4.  tf.Tensor(
  5. [[-0.27462882  0.13382944 -0.18821463  0.25130266]
  6.  [ 0.33565304 -0.15924776  0.28579825 -0.33349696]
  7.  [-0.16718312 -0.02833744 -0.48171917  0.19617245]
  8.  [ 0.22221425 -0.13758096  0.54616207 -0.24424782]], shape=(4, 4), dtype=float32)
  9. OUTS:
  10.  tf.Tensor(
  11. [[[-0.90886533 -0.15003899  0.85841304 -0.7596122 ]
  12.   [-0.48579064 -0.963918    0.7258071   0.4711207 ]
  13.   [ 0.06374787 -0.87162143 -0.49842754  0.94447017]
  14.   [-0.8526071  -0.10417669  0.08525215 -0.35294658]
  15.   [-0.71504194 -0.91407865  0.48065722  0.7772157 ]
  16.   [-0.211678   -0.88805073 -0.6365302   0.9684869 ]]
  17.  
  18.  [[ 0.14371426 -0.33907956  0.02721161  0.9431912 ]
  19.   [ 0.5950151  -0.5196812  -0.2773601   0.6960502 ]
  20.   [ 0.93197036  0.61325806 -0.9768668   0.91710967]
  21.   [ 0.09379089  0.5965272   0.05482991  0.9964341 ]
  22.   [ 0.269569   -0.529733    0.4407713   0.8434378 ]
  23.   [ 0.95455045 -0.03825713 -0.96736914  0.9066282 ]]], shape=(2, 6, 4), dtype=float32)
  24.  
  25. CASE #1
  26. LOSS tf.Tensor(1.8576978, shape=(), dtype=float32)
  27. GRADS:
  28.  tf.Tensor(
  29. [[-0.24651453  0.11562193 -0.15497144  0.23229319]
  30.  [ 0.27857018 -0.13284853  0.2602334  -0.30075118]
  31.  [-0.11065739 -0.02781501 -0.41080374  0.17088796]
  32.  [ 0.16102664 -0.10325792  0.4526206  -0.21939588]], shape=(4, 4), dtype=float32)
  33. OUTS:
  34.  tf.Tensor(
  35. [[[-0.90886533 -0.15003899  0.85841304 -0.7596122 ]
  36.   [-0.48579064 -0.963918    0.7258071   0.4711207 ]
  37.   [ 0.06374787 -0.87162143 -0.49842754  0.94447017]
  38.   [-0.8526071  -0.10417669  0.08525215 -0.35294658]
  39.   [-0.71504194 -0.91407865  0.48065722  0.7772157 ]
  40.   [-0.211678   -0.88805073 -0.6365302   0.9684869 ]]
  41.  
  42.  [[ 0.14371426 -0.33907956  0.02721161  0.9431912 ]
  43.   [ 0.5950151  -0.5196812  -0.2773601   0.6960502 ]
  44.   [ 0.93197036  0.61325806 -0.9768668   0.91710967]
  45.   [ 0.09379089  0.5965272   0.05482991  0.9964341 ]
  46.   [ 0.269569   -0.529733    0.4407713   0.8434378 ]
  47.   [ 0.95455045 -0.03825713 -0.96736914  0.9066282 ]]], shape=(2, 6, 4), dtype=float32)
  48.  
  49. CASE #2
  50. LOSS tf.Tensor(1.8576978, shape=(), dtype=float32)
  51. GRADS:
  52.  tf.Tensor(
  53. [[-0.24651453  0.11562193 -0.15497144  0.23229319]
  54.  [ 0.27857018 -0.13284853  0.2602334  -0.30075118]
  55.  [-0.11065739 -0.02781501 -0.41080374  0.17088796]
  56.  [ 0.16102664 -0.10325792  0.4526206  -0.21939588]], shape=(4, 4), dtype=float32)
  57. OUTS:
  58.  tf.Tensor(
  59. [[[-0.90886533 -0.15003899  0.85841304 -0.7596122 ]
  60.   [-0.48579064 -0.963918    0.7258071   0.4711207 ]
  61.   [ 0.06374787 -0.87162143 -0.49842754  0.94447017]
  62.   [-0.8526071  -0.10417669  0.08525215 -0.35294658]
  63.   [-0.71504194 -0.91407865  0.48065722  0.7772157 ]
  64.   [-0.211678   -0.88805073 -0.6365302   0.9684869 ]]
  65.  
  66.  [[ 0.14371426 -0.33907956  0.02721161  0.9431912 ]
  67.   [ 0.5950151  -0.5196812  -0.2773601   0.6960502 ]
  68.   [ 0.93197036  0.61325806 -0.9768668   0.91710967]
  69.   [ 0.09379089  0.5965272   0.05482991  0.9964341 ]
  70.   [ 0.269569   -0.529733    0.4407713   0.8434378 ]
  71.   [ 0.95455045 -0.03825713 -0.96736914  0.9066282 ]]], shape=(2, 6, 4), dtype=float32)
  72.  
  73. CASE #3
  74. LOSS tf.Tensor(1.8576978, shape=(), dtype=float32)
  75. GRADS:
  76.  tf.Tensor(
  77. [[-0.27462882  0.13382944 -0.18821463  0.25130266]
  78.  [ 0.33565304 -0.15924776  0.28579825 -0.33349696]
  79.  [-0.16718312 -0.02833744 -0.48171917  0.19617245]
  80.  [ 0.22221425 -0.13758096  0.54616207 -0.24424782]], shape=(4, 4), dtype=float32)
  81. OUTS:
  82.  tf.Tensor(
  83. [[[-0.90886533 -0.15003899  0.85841304 -0.7596122 ]
  84.   [-0.48579064 -0.963918    0.7258071   0.4711207 ]
  85.   [ 0.06374787 -0.87162143 -0.49842754  0.94447017]
  86.   [-0.8526071  -0.10417669  0.08525215 -0.35294658]
  87.   [-0.71504194 -0.91407865  0.48065722  0.7772157 ]
  88.   [-0.211678   -0.88805073 -0.6365302   0.9684869 ]]
  89.  
  90.  [[ 0.14371426 -0.33907956  0.02721161  0.9431912 ]
  91.   [ 0.5950151  -0.5196812  -0.2773601   0.6960502 ]
  92.   [ 0.93197036  0.61325806 -0.9768668   0.91710967]
  93.   [ 0.09379089  0.5965272   0.05482991  0.9964341 ]
  94.   [ 0.269569   -0.529733    0.4407713   0.8434378 ]
  95.   [ 0.95455045 -0.03825713 -0.96736914  0.9066282 ]]], shape=(2, 6, 4), dtype=float32)
  96. ================================================================================
  97.  
  98. CASE #4
  99. LOSS tf.Tensor(1.3031322, shape=(), dtype=float32)
  100. GRADS:
  101.  tf.Tensor(
  102. [[-0.07161321  0.11920796  0.00611999 -0.10474246]
  103.  [-0.27238074 -0.02025667 -0.3109602  -0.00538632]
  104.  [ 0.34261662  0.08480601  0.35340673  0.0648931 ]
  105.  [-0.20532534 -0.17010294 -0.26704618  0.08597893]], shape=(4, 4), dtype=float32)
  106. OUTS:
  107.  tf.Tensor(
  108. [[[-0.00239495 -0.15125908 -0.00678201  0.7642507 ]
  109.   [ 0.7365422   0.5374036   0.8890466   0.36529413]
  110.   [ 0.7287195  -0.7218969   0.753732   -0.45989713]
  111.   [-0.20864785 -0.92871183  0.75721145  0.13256115]
  112.   [ 0.12175808 -0.19267444  0.98364335  0.37927812]
  113.   [ 0.44015625 -0.7260691   0.9689865  -0.1854528 ]]
  114.  
  115.  [[ 0.33532834  0.46191117  0.55562866 -0.34665063]
  116.   [ 0.01082742 -0.19848084 -0.16099885 -0.72259307]
  117.   [-0.46772876  0.13500686 -0.3014663  -0.8630696 ]
  118.   [-0.43837646  0.7063758  -0.4085825   0.02541626]
  119.   [ 0.34559277  0.9017162  -0.21770068 -0.13044417]
  120.   [ 0.39508358  0.21813887 -0.5855433  -0.8681719 ]]], shape=(2, 6, 4), dtype=float32)
  121.  
  122. CASE #4
  123. LOSS tf.Tensor(1.3031322, shape=(), dtype=float32)
  124. GRADS:
  125.  tf.Tensor(
  126. [[ 0.33739218 -0.02383555 -0.11801008 -0.17984374]
  127.  [ 0.13800916 -0.10885754 -0.03430624 -0.0058571 ]
  128.  [-0.08781847  0.07534521  0.05123626  0.05364679]
  129.  [ 0.12954757  0.25158355 -0.23287769  0.05087431]], shape=(4, 4), dtype=float32)
  130. OUTS:
  131.  tf.Tensor(
  132. [[[-0.00239495 -0.15125908 -0.00678201  0.7642507 ]
  133.   [ 0.7365422   0.5374036   0.8890466   0.36529413]
  134.   [ 0.7287195  -0.7218969   0.753732   -0.45989713]
  135.   [-0.20864785 -0.92871183  0.75721145  0.13256115]
  136.   [ 0.12175808 -0.19267444  0.98364335  0.37927812]
  137.   [ 0.44015625 -0.7260691   0.9689865  -0.1854528 ]]
  138.  
  139.  [[ 0.33532834  0.46191117  0.55562866 -0.34665063]
  140.   [ 0.01082742 -0.19848084 -0.16099885 -0.72259307]
  141.   [-0.46772876  0.13500686 -0.3014663  -0.8630696 ]
  142.   [-0.43837646  0.7063758  -0.4085825   0.02541626]
  143.   [ 0.34559277  0.9017162  -0.21770068 -0.13044417]
  144.   [ 0.39508358  0.21813887 -0.5855433  -0.8681719 ]]], shape=(2, 6, 4), dtype=float32)
  145.  
  146. CASE #5
  147. LOSS tf.Tensor(1.3031322, shape=(), dtype=float32)
  148. GRADS:
  149.  tf.Tensor(
  150. [[-0.07161321  0.11920796  0.00611999 -0.10474246]
  151.  [-0.27238074 -0.02025667 -0.3109602  -0.00538632]
  152.  [ 0.34261662  0.08480601  0.35340673  0.0648931 ]
  153.  [-0.20532534 -0.17010294 -0.26704618  0.08597893]], shape=(4, 4), dtype=float32)
  154. OUTS:
  155.  tf.Tensor(
  156. [[[-0.00239495 -0.15125908 -0.00678201  0.7642507 ]
  157.   [ 0.7365422   0.5374036   0.8890466   0.36529413]
  158.   [ 0.7287195  -0.7218969   0.753732   -0.45989713]
  159.   [-0.20864785 -0.92871183  0.75721145  0.13256115]
  160.   [ 0.12175808 -0.19267444  0.98364335  0.37927812]
  161.   [ 0.44015625 -0.7260691   0.9689865  -0.1854528 ]]
  162.  
  163.  [[ 0.33532834  0.46191117  0.55562866 -0.34665063]
  164.   [ 0.01082742 -0.19848084 -0.16099885 -0.72259307]
  165.   [-0.46772876  0.13500686 -0.3014663  -0.8630696 ]
  166.   [-0.43837646  0.7063758  -0.4085825   0.02541626]
  167.   [ 0.34559277  0.9017162  -0.21770068 -0.13044417]
  168.   [ 0.39508358  0.21813887 -0.5855433  -0.8681719 ]]], shape=(2, 6, 4), dtype=float32)
  169.  
  170. CASE #5
  171. LOSS tf.Tensor(1.3031322, shape=(), dtype=float32)
  172. GRADS:
  173.  tf.Tensor(
  174. [[ 0.33739218 -0.02383555 -0.11801008 -0.17984374]
  175.  [ 0.13800916 -0.10885754 -0.03430624 -0.0058571 ]
  176.  [-0.08781847  0.07534521  0.05123626  0.05364679]
  177.  [ 0.12954757  0.25158355 -0.23287769  0.05087431]], shape=(4, 4), dtype=float32)
  178. OUTS:
  179.  tf.Tensor(
  180. [[[-0.00239495 -0.15125908 -0.00678201  0.7642507 ]
  181.   [ 0.7365422   0.5374036   0.8890466   0.36529413]
  182.   [ 0.7287195  -0.7218969   0.753732   -0.45989713]
  183.   [-0.20864785 -0.92871183  0.75721145  0.13256115]
  184.   [ 0.12175808 -0.19267444  0.98364335  0.37927812]
  185.   [ 0.44015625 -0.7260691   0.9689865  -0.1854528 ]]
  186.  
  187.  [[ 0.33532834  0.46191117  0.55562866 -0.34665063]
  188.   [ 0.01082742 -0.19848084 -0.16099885 -0.72259307]
  189.   [-0.46772876  0.13500686 -0.3014663  -0.8630696 ]
  190.   [-0.43837646  0.7063758  -0.4085825   0.02541626]
  191.   [ 0.34559277  0.9017162  -0.21770068 -0.13044417]
  192.   [ 0.39508358  0.21813887 -0.5855433  -0.8681719 ]]], shape=(2, 6, 4), dtype=float32)
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