larroy commented on a change in pull request #14779: [WIP] Fully connected,
higher order grad
URL: https://github.com/apache/incubator-mxnet/pull/14779#discussion_r305553693
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File path: tests/python/unittest/test_higher_order_grad.py
##########
@@ -173,6 +182,87 @@ def check_second_order_unary(x, op, grad_grad_op):
# Validate the gradients.
assert_almost_equal(expected_grad_grad, x.grad.asnumpy())
+def arange_shape_like(y):
+ shape = y.shape
+ nelems = reduce(mul, shape)
+ x = nd.arange(nelems).reshape(shape)
+ return x
+
+class RandomShapes(object):
+ def __init__(self, dim, startdim=1):
+ self.dim = dim
+ self.curdim = startdim
+
+ def __iter__(self):
+ return self
+
+ @staticmethod
+ def random_shape(dimensions):
+ shape = rand_shape_nd(dimensions)
+ # x = nd.random.normal(shape=shape)
+ nelems = reduce(mul, shape)
+ x = nd.arange(nelems).reshape(shape)
+ return x
+
+ def next(self):
+ return self.__next__()
+
+ def __next__(self):
+ if self.curdim > self.dim:
+ raise StopIteration
+ x = RandomShapes.random_shape(self.curdim)
+ self.curdim += 1
+ return x
+
+
+def flatten2d_right(x):
+ s_0 = x.shape[0]
+ s_1 = reduce(mul, x.shape[1:])
+ return x.reshape((s_0, s_1))
+
+
+def flatten2d_left(x):
+ s_0 = reduce(mul, x.shape[:-1])
+ s_1 = x.shape[-1]
+ return x.reshape((s_0, s_1))
+
+
+@with_seed()
+def test_dense_backward_flatten():
+ for x in RandomShapes(4,2):
+ hidden = random.randrange(1, 4)
+ net = gluon.nn.Sequential()
+ with net.name_scope():
+ net.add(gluon.nn.Dense(hidden, flatten=True))
+ net.initialize(mxnet.initializer.Constant(.5))
+ x.attach_grad()
+ with ag.record():
Review comment:
How? Is not unary... could you propose an example?
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