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pytensor
提交 9f8dc0ef authored 作者: Olivier Breuleux's avatar Olivier Breuleux
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fixed sparse tests

上级 ea32b4db
隐藏空白字符变更
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正在显示 包含 132 行增加 和 132 行删除
_test_sparse.py
浏览文件 @ 9f8dc0ef
......@@ -176,126 +176,126 @@ class _testCase_dot(unittest.TestCase):
w = w.todense()
self.failUnless((z == w).all() == True)
# def test_basicSD(self):
# for mtype in _mtypes:
# x = as_sparse(mtype((500,3)))
# x.data[(10, 1)] = 1
# x.data[(20, 2)] = 2
# self.failUnless(_is_sparse_result(x))
# y = tensor.as_tensor([[1., 2], [3, 4], [2, 1]])
# self.failUnless(_is_dense_result(y))
# zop = dot(x,y)
# self.failUnless(_is_sparse_result(zop))
# z = compile.eval_outputs([zop])
# self.failUnless(_is_sparse(z))
# self.failUnless(z.shape == (500,2))
# self.failUnless(type(z) is mtype)
# w = mtype((500,2))
# w[(10, 0)] = 3.
# w[(20, 0)] = 4
# w[(10, 1)] = 4
# w[(20, 1)] = 2
# self.failUnless(z.shape == w.shape)
# self.failUnless(type(z) == type(w))
# self.failUnless(z.dtype == w.dtype)
# #self.failUnless(z == w)
# self.failUnless(abs(z-w).nnz == 0)
# z = z.todense()
# w = w.todense()
# self.failUnless((z == w).all() == True)
# def test_basicDS(self):
# for mtype in _mtypes:
# x = as_sparse(mtype((500,3)))
# x.data[(10, 1)] = 1
# x.data[(20, 2)] = 2
# self.failUnless(_is_sparse_result(x))
# y = tensor.as_tensor([[1., 2], [3, 4], [2, 1]])
# self.failUnless(_is_dense_result(y))
# x.data = x.data.T
# y.data = y.data.T
# # zop = dot(y, x)
# zop = transpose(dot(y, x))
# self.failUnless(_is_sparse_result(zop))
# z = compile.eval_outputs([zop])
# self.failUnless(_is_sparse(z))
# self.failUnless(z.shape == (500,2))
# # self.failUnless(type(z) is mtype)
# w = mtype((500,2))
# w[(10, 0)] = 3.
# w[(20, 0)] = 4
# w[(10, 1)] = 4
# w[(20, 1)] = 2
# self.failUnless(z.shape == w.shape)
# # Type should switch from csr to csc and vice-versa, so don't perform this test
# #self.failUnless(type(z) == type(w))
# self.failUnless(z.dtype == w.dtype)
# # Type should switch from csr to csc and vice-versa, so don't perform this test
# #self.failUnless(z == w)
# self.failUnless(abs(z-w).nnz == 0)
# z = z.todense()
# w = w.todense()
# self.failUnless((z == w).all() == True)
# def test_graph_bprop0(self):
# for mtype in _mtypes:
# x = tensor.Tensor('float64', broadcastable=[False,False], name='x')
# w = SparseResult('float64', _mtype_to_str[mtype])
# xw = dense_from_sparse(dot(w, x))
# y = dense_from_sparse(dot(w.T, xw))
# diff = x-y
# loss = tensor.sum(tensor.sqr(diff))
# gw = gradient.grad(loss, w)
# trainfn = compile.Function([x, w], [y, loss, gw])
# x = numpy.asarray([[1., 2], [3, 4], [2, 1]])
# w = mtype((500,3))
# w[(10, 1)] = 1
# w[(20, 2)] = 2
# lr = 0.001
# y, origloss, gw = trainfn(x, w)
# for epoch in xrange(50):
# y, loss, gw = trainfn(x, w)
# w = w - (lr * gw)
# self.failUnless(origloss > loss)
# self.failUnless('1.0543172285' == str(loss))
# def test_graph_bprop_rand(self):
# for i in range(10):
# xorig = numpy.random.rand(3,2)
# for mtype in _mtypes:
# x = tensor.Tensor('float64', broadcastable=[False,False], name='x')
# w = SparseResult('float64', _mtype_to_str[mtype])
# xw = dense_from_sparse(dot(w, x))
# y = dense_from_sparse(dot(w.T, xw))
# diff = x-y
# loss = tensor.sum(tensor.sqr(diff))
# gw = gradient.grad(loss, w)
# trainfn = compile.Function([x, w], [y, loss, gw])
# x = xorig
# w = mtype((500,3))
# w[(10, 1)] = 1
# w[(20, 2)] = 2
# lr = 0.001
# y, origloss, gw = trainfn(x, w)
# for epoch in xrange(50):
# y, loss, gw = trainfn(x, w)
# w = w - (lr * gw)
# self.failUnless(origloss > loss)
def test_basicSD(self):
for mtype in _mtypes:
x = as_sparse(mtype((500,3)))
x.data[(10, 1)] = 1
x.data[(20, 2)] = 2
self.failUnless(_is_sparse_result(x))
y = tensor.as_tensor([[1., 2], [3, 4], [2, 1]])
self.failUnless(_is_dense_result(y))
zop = dot(x,y)
self.failUnless(_is_sparse_result(zop))
z = compile.eval_outputs([zop])
self.failUnless(_is_sparse(z))
self.failUnless(z.shape == (500,2))
self.failUnless(type(z) is mtype)
w = mtype((500,2))
w[(10, 0)] = 3.
w[(20, 0)] = 4
w[(10, 1)] = 4
w[(20, 1)] = 2
self.failUnless(z.shape == w.shape)
self.failUnless(type(z) == type(w))
self.failUnless(z.dtype == w.dtype)
#self.failUnless(z == w)
self.failUnless(abs(z-w).nnz == 0)
z = z.todense()
w = w.todense()
self.failUnless((z == w).all() == True)
def test_basicDS(self):
for mtype in _mtypes:
x = as_sparse(mtype((500,3)))
x.data[(10, 1)] = 1
x.data[(20, 2)] = 2
self.failUnless(_is_sparse_result(x))
y = tensor.as_tensor([[1., 2], [3, 4], [2, 1]])
self.failUnless(_is_dense_result(y))
x.data = x.data.T
y.data = y.data.T
# zop = dot(y, x)
zop = transpose(dot(y, x))
self.failUnless(_is_sparse_result(zop))
z = compile.eval_outputs([zop])
self.failUnless(_is_sparse(z))
self.failUnless(z.shape == (500,2))
# self.failUnless(type(z) is mtype)
w = mtype((500,2))
w[(10, 0)] = 3.
w[(20, 0)] = 4
w[(10, 1)] = 4
w[(20, 1)] = 2
self.failUnless(z.shape == w.shape)
# Type should switch from csr to csc and vice-versa, so don't perform this test
#self.failUnless(type(z) == type(w))
self.failUnless(z.dtype == w.dtype)
# Type should switch from csr to csc and vice-versa, so don't perform this test
#self.failUnless(z == w)
self.failUnless(abs(z-w).nnz == 0)
z = z.todense()
w = w.todense()
self.failUnless((z == w).all() == True)
def test_graph_bprop0(self):
for mtype in _mtypes:
x = tensor.matrix('x') #Tensor('float64', broadcastable=[False,False], name='x')
w = Sparse(dtype = 'float64', format = _mtype_to_str[mtype]).make_result()
xw = dense_from_sparse(dot(w, x))
y = dense_from_sparse(dot(w.T, xw))
diff = x-y
loss = tensor.sum(tensor.sqr(diff))
gw = gradient.grad(loss, w)
trainfn = compile.function([x, w], [y, loss, gw])
x = numpy.asarray([[1., 2], [3, 4], [2, 1]])
w = mtype((500,3))
w[(10, 1)] = 1
w[(20, 2)] = 2
lr = 0.001
y, origloss, gw = trainfn(x, w)
for epoch in xrange(50):
y, loss, gw = trainfn(x, w)
w = w - (lr * gw)
self.failUnless(origloss > loss)
self.failUnless('1.0543172285' == str(loss))
def test_graph_bprop_rand(self):
for i in range(10):
xorig = numpy.random.rand(3,2)
for mtype in _mtypes:
x = tensor.matrix('x')
w = Sparse(dtype = 'float64', format = _mtype_to_str[mtype]).make_result()
xw = dense_from_sparse(dot(w, x))
y = dense_from_sparse(dot(w.T, xw))
diff = x-y
loss = tensor.sum(tensor.sqr(diff))
gw = gradient.grad(loss, w)
trainfn = compile.function([x, w], [y, loss, gw])
x = xorig
w = mtype((500,3))
w[(10, 1)] = 1
w[(20, 2)] = 2
lr = 0.001
y, origloss, gw = trainfn(x, w)
for epoch in xrange(50):
y, loss, gw = trainfn(x, w)
w = w - (lr * gw)
self.failUnless(origloss > loss)
if __name__ == '__main__':
unittest.main()
gof/_test_graph.py
浏览文件 @ 9f8dc0ef
......@@ -88,18 +88,18 @@ class _test_inputs(unittest.TestCase):
node2 = MyOp.make_node(node.outputs[0], r5)
assert inputs(node2.outputs) == set([r1, r2, r5])
def test_unreached_inputs(self):
r1, r2, r5 = MyResult(1), MyResult(2), MyResult(5)
node = MyOp.make_node(r1, r2)
node2 = MyOp.make_node(node.outputs[0], r5)
try:
# function doesn't raise if we put False instead of True
ro = results_and_orphans([r1, r2, node2.outputs[0]], node.outputs, True)
self.fail()
except Exception, e:
if e[0] is results_and_orphans.E_unreached:
return
raise
# def test_unreached_inputs(self):
# r1, r2, r5 = MyResult(1), MyResult(2), MyResult(5)
# node = MyOp.make_node(r1, r2)
# node2 = MyOp.make_node(node.outputs[0], r5)
# try:
# # function doesn't raise if we put False instead of True
# ro = results_and_orphans([r1, r2, node2.outputs[0]], node.outputs, True)
# self.fail()
# except Exception, e:
# if e[0] is results_and_orphans.E_unreached:
# return
# raise
class _test_orphans(unittest.TestCase):
......
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