update TF

exps-tf/test-invH.py

@@ -0,0 +1,46 @@
+import os, sys, math, time, random, argparse
+import tensorflow as tf
+from pathlib import Path
+
+
+def test_a():
+  x = tf.Variable([[1.], [2.], [4.0]])
+  with tf.GradientTape(persistent=True) as g:
+    trn = tf.math.exp(tf.math.reduce_sum(x))
+    val = tf.math.cos(tf.math.reduce_sum(x))
+    dT_dx = g.gradient(trn, x)
+    dV_dx = g.gradient(val, x)
+    hess_vector = g.gradient(dT_dx, x, output_gradients=dV_dx)
+  print ('calculate ok : {:}'.format(hess_vector))
+
+def test_b():
+  cce = tf.keras.losses.SparseCategoricalCrossentropy()
+  L1 = tf.convert_to_tensor([0, 1, 2])
+  L2 = tf.convert_to_tensor([2, 0, 1])
+  B = tf.Variable([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]])
+  with tf.GradientTape(persistent=True) as g:
+    trn = cce(L1, B)
+    val = cce(L2, B)
+    dT_dx = g.gradient(trn, B)
+    dV_dx = g.gradient(val, B)
+    hess_vector = g.gradient(dT_dx, B, output_gradients=dV_dx)
+  print ('calculate ok : {:}'.format(hess_vector))
+
+def test_c():
+  cce = tf.keras.losses.CategoricalCrossentropy()
+  L1 = tf.convert_to_tensor([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
+  L2 = tf.convert_to_tensor([[0., 0., 1.], [0., 1., 0.], [1., 0., 0.]])
+  B = tf.Variable([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]])
+  with tf.GradientTape(persistent=True) as g:
+    trn = cce(L1, B)
+    val = cce(L2, B)
+    dT_dx = g.gradient(trn, B)
+    dV_dx = g.gradient(val, B)
+    hess_vector = g.gradient(dT_dx, B, output_gradients=dV_dx)
+  print ('calculate ok : {:}'.format(hess_vector))
+
+if __name__ == '__main__':
+  print(tf.__version__)
+  test_c()
+  #test_b()
+  #test_a()