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提交 3ce065e3 authored 作者: Ian Goodfellow's avatar Ian Goodfellow
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got rid of generics in gradient tests, only types with a zeros_like are

supported now
上级 c811c88f
显示空白字符变更
内嵌 并排
正在显示 包含 35 行增加 和 35 行删除
theano/tests/test_gradient.py
浏览文件 @ 3ce065e3
...@@ -21,8 +21,8 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -21,8 +21,8 @@ class test_grad_sources_inputs(unittest.TestCase):
"""Test that it is not ok to return None from op.grad()""" """Test that it is not ok to return None from op.grad()"""
class retNone(gof.op.Op): class retNone(gof.op.Op):
def make_node(self): def make_node(self):
inputs = [gof.generic()] inputs = [theano.tensor.vector()]
outputs = [gof.generic()] outputs = [theano.tensor.vector()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inp, grads): def grad(self, inp, grads):
x, = inp x, = inp
...@@ -39,11 +39,11 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -39,11 +39,11 @@ class test_grad_sources_inputs(unittest.TestCase):
"""Test that it is ok to return [None] from op.grad()""" """Test that it is ok to return [None] from op.grad()"""
class retNone(gof.op.Op): class retNone(gof.op.Op):
def make_node(self, *inputs): def make_node(self, *inputs):
outputs = [gof.generic()] outputs = [theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inp, grads): def grad(self, inp, grads):
return [None] return [None]
i = gof.generic() i = theano.tensor.matrix()
a = retNone().make_node(i) a = retNone().make_node(i)
g = _grad_sources_inputs([(a.out, 1)], None) g = _grad_sources_inputs([(a.out, 1)], None)
self.assertTrue(not i in g) self.assertTrue(not i in g)
...@@ -52,13 +52,13 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -52,13 +52,13 @@ class test_grad_sources_inputs(unittest.TestCase):
"""Test that it is not ok to return the wrong number of gradients""" """Test that it is not ok to return the wrong number of gradients"""
class retNone(gof.op.Op): class retNone(gof.op.Op):
def make_node(self, *inputs): def make_node(self, *inputs):
outputs = [gof.generic()] outputs = [theano.tensor.vector()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inputs, grads): def grad(self, inputs, grads):
return [None] return [None]
i = gof.generic() i = theano.tensor.vector()
j = gof.generic() j = theano.tensor.vector()
a1 = retNone().make_node(i) a1 = retNone().make_node(i)
g = _grad_sources_inputs([(a1.out, 1)], None) g = _grad_sources_inputs([(a1.out, 1)], None)
a2 = retNone().make_node(i,j) a2 = retNone().make_node(i,j)
...@@ -71,11 +71,11 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -71,11 +71,11 @@ class test_grad_sources_inputs(unittest.TestCase):
def test_1in_1out(self): def test_1in_1out(self):
"""Test grad is called correctly for a 1-to-1 op""" """Test grad is called correctly for a 1-to-1 op"""
gval = gof.generic() gval = theano.tensor.matrix()
class O(gof.op.Op): class O(gof.op.Op):
def make_node(self): def make_node(self):
inputs = [gof.generic()] inputs = [theano.tensor.matrix()]
outputs = [gof.generic()] outputs = [theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inp, grads): def grad(self, inp, grads):
return gval, return gval,
...@@ -85,11 +85,11 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -85,11 +85,11 @@ class test_grad_sources_inputs(unittest.TestCase):
def test_1in_Nout(self): def test_1in_Nout(self):
"""Test grad is called correctly for a 1-to-many op""" """Test grad is called correctly for a 1-to-many op"""
gval = gof.generic() gval = theano.tensor.matrix()
class O(gof.op.Op): class O(gof.op.Op):
def make_node(self): def make_node(self):
inputs = [gof.generic()] inputs = [theano.tensor.matrix()]
outputs = [gof.generic(),gof.generic()] outputs = [theano.tensor.scalar(),theano.tensor.scalar()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inp, grads): def grad(self, inp, grads):
x, = inp x, = inp
...@@ -101,12 +101,12 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -101,12 +101,12 @@ class test_grad_sources_inputs(unittest.TestCase):
def test_Nin_1out(self): def test_Nin_1out(self):
"""Test grad is called correctly for a many-to-1 op""" """Test grad is called correctly for a many-to-1 op"""
gval0 = gof.generic() gval0 = theano.tensor.scalar()
gval1 = gof.generic() gval1 = theano.tensor.scalar()
class O(gof.op.Op): class O(gof.op.Op):
def make_node(self): def make_node(self):
inputs = [gof.generic(),gof.generic()] inputs = [theano.tensor.scalar(), theano.tensor.scalar()]
outputs = [gof.generic()] outputs = [theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inp, grads): def grad(self, inp, grads):
x0, x1 = inp x0, x1 = inp
...@@ -119,12 +119,12 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -119,12 +119,12 @@ class test_grad_sources_inputs(unittest.TestCase):
def test_Nin_Nout(self): def test_Nin_Nout(self):
"""Test grad is called correctly for a many-to-many op""" """Test grad is called correctly for a many-to-many op"""
gval0 = gof.generic() gval0 = theano.tensor.matrix()
gval1 = gof.generic() gval1 = theano.tensor.matrix()
class O(gof.op.Op): class O(gof.op.Op):
def make_node(self): def make_node(self):
inputs = [gof.generic(),gof.generic()] inputs = [theano.tensor.matrix(),theano.tensor.matrix()]
outputs = [gof.generic(),gof.generic()] outputs = [theano.tensor.matrix(),theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inp, grads): def grad(self, inp, grads):
return gval0, gval1 return gval0, gval1
...@@ -139,11 +139,11 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -139,11 +139,11 @@ class test_grad_sources_inputs(unittest.TestCase):
def __init__(self, tst): def __init__(self, tst):
self.tst = tst self.tst = tst
def make_node(self, *inputs): def make_node(self, *inputs):
outputs = [gof.generic(),gof.generic()] outputs = [theano.tensor.matrix(),theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inputs, g_out): def grad(self, inputs, g_out):
return [1] return [1]
i = gof.generic() i = theano.tensor.matrix()
a1 = O(self).make_node(i) a1 = O(self).make_node(i)
g = grad_sources_inputs([(a1.outputs[0], 1)], None, warn_type=False) g = grad_sources_inputs([(a1.outputs[0], 1)], None, warn_type=False)
self.assertTrue(g[i] is 1) self.assertTrue(g[i] is 1)
...@@ -155,16 +155,16 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -155,16 +155,16 @@ class test_grad_sources_inputs(unittest.TestCase):
self.tst = tst self.tst = tst
self.grad_ok = grad_ok self.grad_ok = grad_ok
def make_node(self, *inputs): def make_node(self, *inputs):
outputs = [gof.generic(),gof.generic()] outputs = [theano.tensor.matrix(),theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inputs, g_out): def grad(self, inputs, g_out):
if not self.grad_ok: if not self.grad_ok:
self.tst.fail() self.tst.fail()
else: else:
return [1, None] return [1, None]
i = gof.generic() i = theano.tensor.matrix()
j = gof.generic() j = theano.tensor.matrix()
k = gof.generic() k = theano.tensor.matrix()
a1 = O(self, True).make_node(i,j) a1 = O(self, True).make_node(i,j)
a2 = O(self, True).make_node(a1.outputs[1], k) a2 = O(self, True).make_node(a1.outputs[1], k)
g = grad_sources_inputs([(a2.outputs[0], 1)], None, warn_type=False) g = grad_sources_inputs([(a2.outputs[0], 1)], None, warn_type=False)
...@@ -182,7 +182,7 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -182,7 +182,7 @@ class test_grad_sources_inputs(unittest.TestCase):
self.tst = tst self.tst = tst
self.grad_ok = grad_ok self.grad_ok = grad_ok
def make_node(self, *inputs): def make_node(self, *inputs):
outputs = [gof.generic(),gof.generic()] outputs = [theano.tensor.matrix(),theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inputs, grads): def grad(self, inputs, grads):
g0, g1 = grads g0, g1 = grads
...@@ -193,9 +193,9 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -193,9 +193,9 @@ class test_grad_sources_inputs(unittest.TestCase):
return [g0, g0+g1] return [g0, g0+g1]
else: else:
return [g0, g0] return [g0, g0]
i = gof.generic() i = theano.tensor.matrix()
j = gof.generic() j = theano.tensor.matrix()
k = gof.generic() k = theano.tensor.matrix()
a1 = O(self, True).make_node(i,j) a1 = O(self, True).make_node(i,j)
a2 = O(self, True).make_node(k,a1.outputs[1]) a2 = O(self, True).make_node(k,a1.outputs[1])
g = _grad_sources_inputs([(a2.outputs[0], 1), (a1.outputs[1],4), g = _grad_sources_inputs([(a2.outputs[0], 1), (a1.outputs[1],4),
...@@ -214,7 +214,7 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -214,7 +214,7 @@ class test_grad_sources_inputs(unittest.TestCase):
self.tst = tst self.tst = tst
self.grad_ok = grad_ok self.grad_ok = grad_ok
def make_node(self, *inputs): def make_node(self, *inputs):
outputs = [gof.generic(),gof.generic()] outputs = [theano.tensor.matrix(),theano.tensor.matrix()]
return gof.Apply(self, inputs, outputs) return gof.Apply(self, inputs, outputs)
def grad(self, inputs, grads): def grad(self, inputs, grads):
g0, g1 = grads g0, g1 = grads
...@@ -225,9 +225,9 @@ class test_grad_sources_inputs(unittest.TestCase): ...@@ -225,9 +225,9 @@ class test_grad_sources_inputs(unittest.TestCase):
return [g0, g0+g1] return [g0, g0+g1]
else: else:
return [g0, g0] return [g0, g0]
i = gof.generic() i = theano.tensor.matrix()
j = gof.generic() j = theano.tensor.matrix()
k = gof.generic() k = theano.tensor.matrix()
a1 = O(self,True).make_node(i,j) a1 = O(self,True).make_node(i,j)
a2 = O(self,True).make_node(k,a1.outputs[1]) a2 = O(self,True).make_node(k,a1.outputs[1])
g = _grad_sources_inputs([(a2.outputs[0], 1), (a1.outputs[1],4), g = _grad_sources_inputs([(a2.outputs[0], 1), (a1.outputs[1],4),
......
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