Hi Patric,
Yes, I tried to define a new op and it works. But I don't know why the
speed become much slower....
class round_2(theano.Op):
__props__ = ()
def make_node(self, x):
x = theano.tensor.as_tensor_variable(x)
# Note: using x_.type() is dangerous, as it copies x's broadcasting
# behaviour
return theano.Apply(self, [x], [x.type()])
def perform(self, node, inputs, output_storage):
x = inputs[0]
z = output_storage[0]
z[0] = np.round(x)
def infer_shape(self, node, i0_shapes):
return i0_shapes
def grad(self, inputs, output_grads):
return [output_grads[0]]
def R_op(self, inputs, eval_points):
# R_op can receive None as eval_points.
# That mean there is no diferientiable path through that input
# If this imply that you cannot compute some outputs,
# return None for those.
if eval_points[0] is None:
return eval_points
return self.grad(inputs, eval_points)
On Monday, 24 April 2017 13:48:37 UTC+9:30, Patric wrote:
>
> Does the below link help?
>
>
> http://stackoverflow.com/questions/40613225/theano-how-to-override-gradient-for-part-of-op-graph
>
>
>
> On Sunday, April 23, 2017 at 3:27:49 PM UTC+8, Bohan Zhuang wrote:
>>
>> Recently, I found a piece of code written in Tensorflow. It serves to
>> overriding tf.round()'s gradient function with tf.identity()'s using
>> gradient_override_map function:
>>
>> import tensorflow as tf
>> G = tf.get_default_graph()
>> def quantize(x, k):
>> n = float(2**k - 1)
>> with G.gradient_override_map({"Round": "Identity"}):
>> return tf.round(x * n) / n
>>
>> And I want to implement it in Theano but have no idea....So could anyone
>> provide some advice on that?
>>
>
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