test.py

import chainer
import chainer.links as L
import chainer.functions as F
import numpy as np


class CNN(chainer.Chain):
    def __init__(self):
        super(CNN, self).__init__()
        with self.init_scope():
            self.cnv1 = L.Convolution2D(None, 32, 3, 1, 1)
            self.cnv2 = L.Convolution2D(None, 64, 3, 1, 1)
            self.cnv3 = L.Convolution2D(None, 64, 3, 1, 1)

    def __call__(self, x):
        y = F.relu(self.cnv1(x))
        y.name = 'cnv1'
        y = F.relu(self.cnv2(y))
        y.name = 'cnv2'
        y = F.relu(self.cnv3(y))
        y.name = 'cnv3'
        return y


def make_grad(shape):
    return np.random.uniform(-1, 1, shape).astype(np.float32)


def get_target(y, target):
    while y is not None:
        if y.name == target:
            return y
        else:
            if y.creator is None:
                return None
            else:
                y = y.creator.inputs[0]
    return None


print(chainer.__version__)
input = np.random.uniform(-1, 1, (10, 10, 10, 10)).astype(np.float32)
net = CNN()
y = net(input)
y.grad = make_grad(y.shape)
y.backward(retain_grad=True)
target = get_target(y, 'cnv3')
print(target.grad.shape)


"""
v3
%python test.py
3.2.0
(10, 64, 10, 10)
"""

"""
v2
%python test.py
2.1.0
(10, 64, 10, 10)
"""