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