Forked from harpone/Tensorflow_py_func_with_grad.py
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December 9, 2016 06:02
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import tensorflow as tf | |
from tensorflow.python.framework import ops | |
import numpy as np | |
# Define custom py_func which takes also a grad op as argument: | |
def py_func(func, inp, Tout, stateful=True, name=None, grad=None): | |
# Need to generate a unique name to avoid duplicates: | |
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8)) | |
tf.RegisterGradient(rnd_name)(grad) # see _MySquareGrad for grad example | |
g = tf.get_default_graph() | |
with g.gradient_override_map({"PyFunc": rnd_name}): | |
return tf.py_func(func, inp, Tout, stateful=stateful, name=name) | |
# Def custom square function using np.square instead of tf.square: | |
def mysquare(x, name=None): | |
with ops.op_scope([x], name, "Mysquare") as name: | |
sqr_x = py_func(np.square, | |
[x], | |
[tf.float32], | |
name=name, | |
grad=_MySquareGrad) # <-- here's the call to the gradient | |
return sqr_x[0] | |
# Actual gradient: | |
def _MySquareGrad(op, grad): | |
x = op.inputs[0] | |
return grad * 20 * x # add a "small" error just to see the difference: | |
with tf.Session() as sess: | |
x = tf.constant([1., 2.]) | |
y = mysquare(x) | |
tf.initialize_all_variables().run() | |
print(x.eval(), y.eval(), tf.gradients(y, x)[0].eval()) |
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