def _scal_inplace(symbol):
"""Replace a symbol definition with an elementwise version of the corresponding scalar Op"""
symbolname = symbol.__name__
inplace = symbolname.endswith('_inplace')
if inplace:
scalar_op = getattr(scal, symbolname[:-len('_inplace')])
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=symbolname)
else:
scalar_op = getattr(scal, symbolname)
rval = elemwise.Elemwise(scalar_op, name=symbolname)
if getattr(symbol, '__doc__', False):
rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = 'theano.tensor.inplace'
def chk(pstate, r):
if not r.owner:
return False
return r.owner.op == rval
pprint.assign(chk, printing.FunctionPrinter(symbolname.replace('_inplace', '=')))
return rval
def construct(symbol):
symbolname = symbol.__name__
inplace = symbolname.endswith('_inplace')
if inplace:
msg = "inplace"
else:
msg = "no_inplace"
n = "Elemwise{%s,%s}" % (symbolname, msg)
if inplace:
scalar_op = getattr(scal, symbolname[:-len('_inplace')])
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=n,
nfunc_spec=(nfunc and (nfunc, nin, nout)))
else:
scalar_op = getattr(scal, symbolname)
rval = elemwise.Elemwise(scalar_op, name=n,
nfunc_spec=(nfunc and (nfunc, nin, nout)))
if getattr(symbol, '__doc__', False):
rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = 'tensor'
pprint.assign(rval, printing.FunctionPrinter(symbolname))
return rval
def _scal_inplace(symbol):
"""Replace a symbol definition with an elementwise version of the corresponding scalar Op"""
symbolname = symbol.__name__
inplace = symbolname.endswith('_inplace')
if inplace:
scalar_op = getattr(scal, symbolname[:-len('_inplace')])
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=symbolname)
else:
scalar_op = getattr(scal, symbolname)
rval = elemwise.Elemwise(scalar_op, name=symbolname)
if getattr(symbol, '__doc__', False):
rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = 'theano.tensor.inplace'
def chk(pstate, r):
if not r.owner:
return False
return r.owner.op == rval
pprint.assign(
chk, printing.FunctionPrinter(symbolname.replace('_inplace', '=')))
return rval
def construct(symbol):
symbolname = symbol.__name__
inplace = symbolname.endswith('_inplace')
if inplace:
msg = "inplace"
else:
msg = "no_inplace"
n = "Elemwise{%s,%s}" % (symbolname, msg)
if inplace:
scalar_op = getattr(scal, symbolname[:-len('_inplace')])
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
rval = elemwise.Elemwise(inplace_scalar_op, {0: 0},
name=n,
nfunc_spec=(nfunc and (nfunc, nin, nout)))
else:
scalar_op = getattr(scal, symbolname)
rval = elemwise.Elemwise(scalar_op,
name=n,
nfunc_spec=(nfunc and (nfunc, nin, nout)))
if getattr(symbol, '__doc__', False):
rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = 'tensor'
pprint.assign(rval, printing.FunctionPrinter(symbolname))
return rval
def construct(symbol):
symbolname = symbol.__name__
msg = "no_inplace"
n = "Elemwise{%s,%s}" % (symbolname, msg)
rval = Elemwise(scalar_op, name=n,
nfunc_spec=(nfunc and (nfunc, nin, nout)))
if getattr(symbol, '__doc__', False):
rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = 'tensor'
pprint.assign(rval, printing.FunctionPrinter(symbolname))
return rval
def _scal_inplace(symbol):
"""Replace a symbol definition with an elementwise version of the corresponding scalar Op"""
symbolname = symbol.__name__
inplace = symbolname.endswith("_inplace")
if inplace:
scalar_op = getattr(scal, symbolname[: -len("_inplace")])
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=symbolname)
else:
scalar_op = getattr(scal, symbolname)
rval = elemwise.Elemwise(scalar_op, name=symbolname)
if getattr(symbol, "__doc__", False):
rval.__doc__ = symbol.__doc__ + "\n" + rval.__doc__
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = "theano.tensor.inplace"
pprint.assign(rval, printing.FunctionPrinter(symbolname.replace("_inplace", "=")))
return rval
@_scal_inplace
def j0_inplace(a):
"""Bessel function of the 0'th kind"""
@_scal_inplace
def j1_inplace(a):
"""Bessel function of the 0'th kind"""
@_scal_inplace
def second_inplace(a):
"""Fill `a` with `b`"""
fill_inplace = second_inplace
pprint.assign(fill_inplace, printing.FunctionPrinter('fill='))
@_scal_inplace
def maximum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@_scal_inplace
def minimum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@_scal_inplace
def add_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
plt.savefig(fname)
print("New picture saved at", fname)
print(val_ultra.max())
print(val_ultra.min())
scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name="scalar_sigmoid")
sigmoid = elemwise.Elemwise(scalar_sigmoid, name="sigmoid")
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0: 0},
name="sigmoid_inplace",
)
pprint.assign(sigmoid, printing.FunctionPrinter("sigmoid"))
class UltraFastScalarSigmoid(scalar.UnaryScalarOp):
"""
This is just speed opt. Not for stability.
"""
@staticmethod
def st_impl(x):
x = 0.5 * x
# The if is a tanh approximate.
if x >= 0:
if x < 1.7:
z = 1.5 * x / (1 + x)
elif x < 3:
plt.savefig(fname)
print("New picture saved at", fname)
print(val_ultra.max())
print(val_ultra.min())
scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid')
sigmoid = elemwise.Elemwise(scalar_sigmoid, name='sigmoid')
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0: 0},
name='sigmoid_inplace',
)
pprint.assign(sigmoid, printing.FunctionPrinter('sigmoid'))
class UltraFastScalarSigmoid(scalar.UnaryScalarOp):
"""
This is just speed opt. Not for stability.
"""
@staticmethod
def st_impl(x):
x = 0.5 * x
# The if is a tanh approximate.
if x >= 0:
if x < 1.7:
z = (1.5 * x / (1 + x))
elif x < 3:
os.path.dirname(theano.__file__), "..", "doc", "library", "tensor", "nnet", "sigmoid_prec.png"
)
plt.savefig(fname)
print("New picture saved at", fname)
print(val_ultra.max())
print(val_ultra.min())
scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name="scalar_sigmoid")
sigmoid = elemwise.Elemwise(scalar_sigmoid, name="sigmoid")
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)), inplace_pattern={0: 0}, name="sigmoid_inplace"
)
pprint.assign(sigmoid, printing.FunctionPrinter("sigmoid"))
class UltraFastScalarSigmoid(scalar.UnaryScalarOp):
"""
This is just speed opt. Not for stability.
"""
@staticmethod
def st_impl(x):
x = 0.5 * x
# The if is a tanh approximate.
if x >= 0:
if x < 1.7:
z = 1.5 * x / (1 + x)
plt.savefig(fname)
print "New picture saved at", fname
print val_ultra.max()
print val_ultra.min()
scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid')
sigmoid = elemwise.Elemwise(scalar_sigmoid, name='sigmoid')
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0: 0},
name='sigmoid_inplace',
)
pprint.assign(sigmoid, printing.FunctionPrinter('sigmoid'))
class UltraFastScalarSigmoid(scalar.UnaryScalarOp):
"""
This is just speed opt. Not for stability.
"""
@staticmethod
def st_impl(x):
x = 0.5 * x
# The if is a tanh approximate.
if x >= 0:
if x < 1.7:
z = (1.5 * x / (1 + x))
elif x < 3:
z = (0.935409070603099 + 0.0458812946797165 * (x - 1.7))
def i1_inplace(x):
"""Modified Bessel function of the first kind of order 1."""
@_scal_inplace
def iv_inplace(v, x):
"""Modified Bessel function of the first kind of order v (real)."""
@_scal_inplace
def second_inplace(a):
"""Fill `a` with `b`"""
fill_inplace = second_inplace
pprint.assign(fill_inplace, printing.FunctionPrinter('fill='))
@_scal_inplace
def maximum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@_scal_inplace
def minimum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@_scal_inplace
def add_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
def c_code_cache_version(self):
v = super(ScalarSigmoid, self).c_code_cache_version()
if v:
return (2,) + v
else:
return v
scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid')
sigmoid = elemwise.Elemwise(scalar_sigmoid, name='sigmoid')
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0:0},
name='sigmoid_inplace',
)
pprint.assign(sigmoid, printing.FunctionPrinter('sigmoid'))
class ScalarSoftplus(scalar.UnaryScalarOp):
@staticmethod
def static_impl(x):
if x < -30.0:
return 0.0
if x > 30.0:
return x
return numpy.log1p(numpy.exp(x))
def impl(self, x):
return ScalarSoftplus.static_impl(x)
def grad(self, inp, grads):
x, = inp
gz, = grads
def erf_inplace(a):
"""error function"""
@_scal_inplace
def erfc_inplace(a):
"""complementary error function"""
@_scal_inplace
def second_inplace(a):
"""Fill `a` with `b`"""
fill_inplace = second_inplace
pprint.assign(fill_inplace, printing.FunctionPrinter("fill="))
@_scal_inplace
def maximum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@_scal_inplace
def minimum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@_scal_inplace
def add_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
if v:
return (2, ) + v
else:
return v
scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid')
sigmoid = elemwise.Elemwise(scalar_sigmoid, name='sigmoid')
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0: 0},
name='sigmoid_inplace',
)
pprint.assign(sigmoid, printing.FunctionPrinter('sigmoid'))
class ScalarSoftplus(scalar.UnaryScalarOp):
@staticmethod
def static_impl(x):
if x < -30.0:
return 0.0
if x > 30.0:
return x
return numpy.log1p(numpy.exp(x))
def impl(self, x):
return ScalarSoftplus.static_impl(x)
def grad(self, inp, grads):
def i1_inplace(x):
"""Modified Bessel function of the first kind of order 1."""
@scalar_elemwise
def iv_inplace(v, x):
"""Modified Bessel function of the first kind of order v (real)."""
@scalar_elemwise
def second_inplace(a):
"""Fill `a` with `b`"""
fill_inplace = second_inplace
pprint.assign(fill_inplace, printing.FunctionPrinter("fill="))
@scalar_elemwise(symbolname="scalar_maximum_inplace")
def maximum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@scalar_elemwise(symbolname="scalar_minimum_inplace")
def minimum_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
@scalar_elemwise
def add_inplace(a, b):
"""elementwise addition (inplace on `a`)"""
