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how to use Defun to define custom gradients in Tensorflow
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| import tensorflow as tf | |
| from tensorflow.python.framework import function | |
| @function.Defun() | |
| def my_op_grad(op, grad): ### instead of my_op_grad(x) | |
| return tf.sigmoid(op) | |
| @function.Defun(grad_func=my_op_grad) | |
| def my_op(a): | |
| return tf.identity(a) | |
| def main(): | |
| a = tf.Variable(tf.constant([-5., 4., -3., 2., 1.], dtype=tf.float32)) | |
| grad = tf.gradients(my_op(a), a) | |
| sess = tf.Session() | |
| sess.run(tf.global_variables_initializer()) | |
| result = sess.run(grad) | |
| print(result) | |
| sess.close() | |
| if __name__ == '__main__': | |
| main() |
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