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提交 fd628c5a authored 作者: affanv14's avatar affanv14
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Added L_op to scalar/basic.py

上级 f3844589
隐藏空白字符变更
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正在显示 包含 75 行增加 和 78 行删除
theano/scalar/basic.py
浏览文件 @ fd628c5a
......@@ -1076,6 +1076,9 @@ class ScalarOp(Op):
raise utils.MethodNotDefined("grad", type(self),
self.__class__.__name__)
def L_op(self, inputs, outputs, output_gradients):
return self.grad(inputs, output_gradients)
def __eq__(self, other):
test = (type(self) == type(other) and
getattr(self, 'output_types_preference', None) ==
......@@ -1224,10 +1227,9 @@ class FixedLogicalComparison(UnaryScalarOp):
def output_types(self, *input_dtypes):
return [bool] if getattr(self, 'bool', False) else [int8]
def grad(self, inputs, output_gradients):
def L_op(self, inputs, outputs, output_gradients):
x, = inputs
out = self(x)
assert out.type == bool
assert outputs[0].type == bool
return [x.zeros_like().astype(theano.config.floatX)]
def c_code_cache_version(self):
......@@ -1458,14 +1460,13 @@ class Switch(ScalarOp):
(z,) = outputs
return "%(z)s = %(cond)s ? %(ift)s : %(iff)s;" % locals()
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(cond, ift, iff) = inputs
(gz,) = gout
first_part = switch(cond, gz, 0.)
second_part = switch(cond, 0., gz)
out = self(cond, ift, iff)
if out.type.dtype in discrete_types:
if (outputs[0].type.dtype in discrete_types):
first_part = 0.
second_part = 0.
......@@ -1679,11 +1680,11 @@ class Add(ScalarOp):
else:
return z + " = " + op.join(inputs) + ";"
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(*inputs).type in discrete_types:
if (outputs[0].type in discrete_types):
assert gz is not None
retval = []
for ii, inp in enumerate(inputs):
......@@ -1771,13 +1772,12 @@ class Sub(BinaryScalarOp):
(z,) = outputs
return "%(z)s = %(x)s - %(y)s;" % locals()
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x, y) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if (x - y).type in discrete_types:
if outputs[0].type in discrete_types:
return [x.zeros_like().astype(theano.config.floatX),
y.zeros_like().astype(theano.config.floatX)]
......@@ -2064,11 +2064,10 @@ class Mod(BinaryScalarOp):
}
""") % locals()
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x, y) = inputs
(gz,) = gout
z = self(x, y)
if z.type.dtype in discrete_types:
if outputs[0].type.dtype in discrete_types:
# The gradient does not flow in if the output is discrete
return [x.zeros_like(dtype=theano.config.floatX),
y.zeros_like(dtype=theano.config.floatX)]
......@@ -2092,13 +2091,13 @@ class Pow(BinaryScalarOp):
raise NotImplementedError('type not supported', type)
return "%(z)s = pow(%(x)s, %(y)s);" % locals()
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x, y) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x, y).type in discrete_types:
if outputs[0].type in discrete_types:
return [x.zeros_like().astype(theano.config.floatX),
y.zeros_like().astype(theano.config.floatX)]
......@@ -2172,7 +2171,7 @@ class Clip(ScalarOp):
(z,) = outputs
return "%(z)s = %(x)s < %(min)s ? %(min)s : %(x)s > %(max)s ? %(max)s : %(x)s;" % locals()
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x, mn, mx) = inputs
(gz,) = gout
assert gz.type not in complex_types
......@@ -2180,10 +2179,8 @@ class Clip(ScalarOp):
gmn = (x < mn) * gz
gmx = (x > mx) * gz
out = self(x, mn, mx)
def handle_int(v):
if out.type in int_types:
if outputs[0].type in int_types:
return v.zeros_like().astype(config.floatX)
return v
......@@ -2366,10 +2363,10 @@ class Abs(UnaryScalarOp):
def impl(self, x):
return numpy.abs(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if self(x).type in discrete_types:
if (outputs[0].type in discrete_types):
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2624,10 +2621,10 @@ class Neg(UnaryScalarOp):
def impl(self, x):
return -x
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2659,12 +2656,12 @@ class Inv(UnaryScalarOp):
def impl(self, x):
return numpy.float32(1.0) / x
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2698,12 +2695,12 @@ class Log(UnaryScalarOp):
return numpy.log(x, sig='f')
return numpy.log(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2740,12 +2737,12 @@ class Log2(UnaryScalarOp):
return numpy.log2(x, sig='f')
return numpy.log2(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2779,12 +2776,12 @@ class Log10(UnaryScalarOp):
return numpy.log10(x, sig='f')
return numpy.log10(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2816,12 +2813,12 @@ class Log1p(UnaryScalarOp):
return numpy.log1p(x, sig='f')
return numpy.log1p(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2851,12 +2848,12 @@ class Exp(UnaryScalarOp):
return numpy.exp(x, sig='f')
return numpy.exp(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2884,12 +2881,12 @@ class Exp2(UnaryScalarOp):
return numpy.exp2(x, sig='f')
return numpy.exp2(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2917,12 +2914,12 @@ class Expm1(UnaryScalarOp):
return numpy.expm1(x, sig='f')
return numpy.expm1(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2948,12 +2945,12 @@ class Sqr(UnaryScalarOp):
def impl(self, x):
return x * x
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -2979,12 +2976,12 @@ class Sqrt(UnaryScalarOp):
return numpy.sqrt(x, sig='f')
return numpy.sqrt(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3012,12 +3009,12 @@ class Deg2Rad(UnaryScalarOp):
return numpy.deg2rad(x, sig='f')
return numpy.deg2rad(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3045,12 +3042,12 @@ class Rad2Deg(UnaryScalarOp):
return numpy.rad2deg(x, sig='f')
return numpy.rad2deg(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3080,12 +3077,12 @@ class Cos(UnaryScalarOp):
return numpy.cos(x, sig='f')
return numpy.cos(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3113,12 +3110,12 @@ class ArcCos(UnaryScalarOp):
return numpy.arccos(x, sig='f')
return numpy.arccos(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3148,12 +3145,12 @@ class Sin(UnaryScalarOp):
return numpy.sin(x, sig='f')
return numpy.sin(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3181,12 +3178,12 @@ class ArcSin(UnaryScalarOp):
return numpy.arcsin(x, sig='f')
return numpy.arcsin(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3214,12 +3211,12 @@ class Tan(UnaryScalarOp):
return numpy.tan(x, sig='f')
return numpy.tan(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3247,12 +3244,12 @@ class ArcTan(UnaryScalarOp):
return numpy.arctan(x, sig='f')
return numpy.arctan(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3282,13 +3279,13 @@ class ArcTan2(BinaryScalarOp):
return numpy.arctan2(y, x, sig='f')
return numpy.arctan2(y, x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(y, x) = inputs
(gz,) = gout
if gz.type in complex_types:
raise NotImplementedError()
else:
if self(x, y).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
gx = x.zeros_like(dtype=theano.config.floatX)
else:
......@@ -3329,12 +3326,12 @@ class Cosh(UnaryScalarOp):
return numpy.cosh(x, sig='f')
return numpy.cosh(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3362,12 +3359,12 @@ class ArcCosh(UnaryScalarOp):
return numpy.arccosh(x, sig='f')
return numpy.arccosh(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3399,12 +3396,12 @@ class Sinh(UnaryScalarOp):
return numpy.sinh(x, sig='f')
return numpy.sinh(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3432,12 +3429,12 @@ class ArcSinh(UnaryScalarOp):
return numpy.arcsinh(x, sig='f')
return numpy.arcsinh(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3470,12 +3467,12 @@ class Tanh(UnaryScalarOp):
return numpy.tanh(x, sig='f')
return numpy.tanh(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......@@ -3503,12 +3500,12 @@ class ArcTanh(UnaryScalarOp):
return numpy.arctanh(x, sig='f')
return numpy.arctanh(x)
def grad(self, inputs, gout):
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if self(x).type in discrete_types:
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
......
theano/tensor/elemwise.py
浏览文件 @ fd628c5a
......@@ -602,7 +602,7 @@ second dimension
ograds = [x.zeros_like() for x in outs]
ograds[idx] = theano.tensor.ones_like(out)
bgrads = self._bgrad(inputs, ograds)
bgrads = self._bgrad(inputs, outs, ograds)
rop_out = None
for jdx, (inp, eval_point) in enumerate(izip(inputs,
......@@ -636,7 +636,7 @@ second dimension
def L_op(self, inputs, outs, ograds):
# compute grad with respect to broadcasted input
rval = self._bgrad(inputs, ograds)
rval = self._bgrad(inputs, outs, ograds)
# TODO: make sure that zeros are clearly identifiable
# to the gradient.grad method when the outputs have
......@@ -684,7 +684,7 @@ second dimension
return rval
def _bgrad(self, inputs, ograds):
def _bgrad(self, inputs, outputs, ograds):
# returns grad, with respect to broadcasted versions of inputs
with change_flags(compute_test_value='off'):
......@@ -695,7 +695,7 @@ second dimension
scalar_inputs = list(map(as_scalar, inputs))
scalar_ograds = list(map(as_scalar, ograds))
scalar_igrads = self.scalar_op.grad(scalar_inputs, scalar_ograds)
scalar_igrads = self.scalar_op.L_op(scalar_inputs, outputs, scalar_ograds)
for igrad in scalar_igrads:
assert igrad is not None, self.scalar_op
......
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