def get_theano_variables(self, inputs=None, outputs=None):
"""
Returns a dict containing inputs, outputs and graph corresponding to
the Theano version of the pyfn.
"""
inputs = utils.as_seq(inputs, tuple)
sym_inputs = [self.get_symbolic(x) for x in inputs]
outputs = utils.as_seq(outputs, tuple)
sym_outputs = [self.get_symbolic(x) for x in outputs]
# get symbolic inputs corresponding to shared inputs in s_inputs
s_memo = OrderedDict((arg, arg.type())
for arg in utils.flat_from_doc(sym_inputs))
theano_inputs = tuple(s_memo.values())
# get new graph, replacing shared inputs with symbolic ones
graph = theano.gof.graph.clone_get_equiv(
theano.gof.graph.inputs(sym_outputs),
sym_outputs,
memo=s_memo.copy())
# get symbolic outputs
theano_outputs = tuple([graph[o] for o in sym_outputs])
return theano_inputs, theano_outputs, graph
def get_theano_variables(self, inputs=None, outputs=None):
"""
Returns a dict containing inputs, outputs and graph corresponding to
the Theano version of the pyfn.
"""
inputs = utils.as_seq(inputs, tuple)
sym_inputs = [self.get_symbolic(x) for x in inputs]
outputs = utils.as_seq(outputs, tuple)
sym_outputs = [self.get_symbolic(x) for x in outputs]
# get symbolic inputs corresponding to shared inputs in s_inputs
s_memo = OrderedDict(
(arg, arg.type()) for arg in utils.flat_from_doc(sym_inputs))
theano_inputs = tuple(s_memo.values())
# get new graph, replacing shared inputs with symbolic ones
graph = theano.gof.graph.clone_get_equiv(
theano.gof.graph.inputs(sym_outputs),
sym_outputs,
memo=s_memo.copy())
# get symbolic outputs
theano_outputs = tuple([graph[o] for o in sym_outputs])
return theano_inputs, theano_outputs, graph
def get_theano_vars(self, inputs=None, outputs=None):
"""
Returns a dict containing inputs, outputs and graph corresponding to
the Theano version of the pyfn.
"""
sym_inputs = tuple(self.get_symbolic(i)
for i in utils.as_seq(inputs))
sym_outputs = tuple(self.get_symbolic(o)
for o in utils.as_seq(outputs))
# get symbolic inputs corresponding to shared inputs in s_inputs
# this dict maps each shared variable to its (non-shared) type.
s_memo = OrderedDict((var, var.type())
for var in utils.flatten(sym_inputs))
theano_inputs = tuple(s_memo.values())
# get new graph, replacing shared inputs with symbolic ones
# graph is a dict mapping "old" variables to "new" ones, where "old"
# is the chain including shared variables, and "new" is the chain
# with the non-shared replacements.
graph = theano.gof.graph.clone_get_equiv(
inputs=theano.gof.graph.inputs(sym_outputs),
outputs=sym_outputs,
memo=s_memo.copy())
# get symbolic outputs
theano_outputs = tuple([graph[o] for o in sym_outputs])
return theano_inputs, theano_outputs, graph
def get_theano_variables(self, inputs=None, outputs=None):
"""
Returns a dict containing inputs, outputs and graph corresponding to
the Theano version of the pyfn.
This version of the function returns a single vector input.
"""
inputs = utils.as_seq(inputs, tuple)
outputs = utils.as_seq(outputs, tuple)
if inputs:
sym_inputs = [self.get_symbolic(x) for x in inputs]
else:
sym_inputs = self.s_inputs.values()
if outputs:
sym_outputs = [self.get_symbolic(x) for x in outputs]
else:
sym_outputs = self.s_outputs.values()
if len(sym_outputs) > 1:
raise ValueError(
'VectorArg functions should return a single output.')
# get symbolic inputs corresponding to shared inputs in s_inputs
s_memo = OrderedDict()
sym_args = utils.flat_from_doc(sym_inputs)
real_args = utils.flat_from_doc(self.all_init_args)
# create a symbolic vector, then split it up into symbolic input
# args
inputs_dtype = self.vector_from_args(self.all_init_args).dtype
theano_input = tt.vector(name='theta', dtype=inputs_dtype)
i = 0
for sa, ra in zip(sym_args, real_args):
if sa.ndim > 0:
vector_arg = theano_input[i: i + ra.size].reshape(ra.shape)
else:
vector_arg = theano_input[i]
s_memo[sa] = tt.patternbroadcast(
vector_arg.astype(str(sa.dtype)),
broadcastable=sa.broadcastable)
i += ra.size
# get new graph, replacing shared inputs with symbolic ones
graph = theano.gof.graph.clone_get_equiv(
theano.gof.graph.inputs(sym_outputs),
sym_outputs,
memo=s_memo.copy())
# get symbolic outputs
theano_outputs = graph[sym_outputs[0]]
f_in, f_out = self.finalize(theano_input, theano_outputs, graph)
return f_in, f_out, graph
def get_theano_variables(self, inputs=None, outputs=None):
"""
Returns a dict containing inputs, outputs and graph corresponding to
the Theano version of the pyfn.
This version of the function returns a single vector input.
"""
inputs = utils.as_seq(inputs, tuple)
outputs = utils.as_seq(outputs, tuple)
if inputs:
sym_inputs = [self.get_symbolic(x) for x in inputs]
else:
sym_inputs = self.s_inputs.values()
if outputs:
sym_outputs = [self.get_symbolic(x) for x in outputs]
else:
sym_outputs = self.s_outputs.values()
if len(sym_outputs) > 1:
raise ValueError(
'VectorArg functions should return a single output.')
# get symbolic inputs corresponding to shared inputs in s_inputs
s_memo = OrderedDict()
sym_args = utils.flat_from_doc(sym_inputs)
real_args = utils.flat_from_doc(self.all_init_args)
# create a symbolic vector, then split it up into symbolic input
# args
inputs_dtype = self.vector_from_args(self.all_init_args).dtype
theano_input = tt.vector(name='theta', dtype=inputs_dtype)
i = 0
for sa, ra in zip(sym_args, real_args):
if sa.ndim > 0:
vector_arg = theano_input[i:i + ra.size].reshape(ra.shape)
else:
vector_arg = theano_input[i]
s_memo[sa] = tt.patternbroadcast(vector_arg.astype(str(sa.dtype)),
broadcastable=sa.broadcastable)
i += ra.size
# get new graph, replacing shared inputs with symbolic ones
graph = theano.gof.graph.clone_get_equiv(
theano.gof.graph.inputs(sym_outputs),
sym_outputs,
memo=s_memo.copy())
# get symbolic outputs
theano_outputs = graph[sym_outputs[0]]
f_in, f_out = self.finalize(theano_input, theano_outputs, graph)
return f_in, f_out, graph
def orderedcallargs(fn, *args, **kwargs):
"""
Returns an OrderedDictionary containing the names and values of a
function's arguments. The arguments are ordered according to the function's
argspec:
1. named arguments
2. variable positional argument
3. variable keyword argument
"""
callargs = getcallargs(fn, *args, **kwargs)
argspec = inspect.getargspec(fn)
o_callargs = OrderedDict()
for argname in argspec.args:
o_callargs[argname] = callargs[argname]
if argspec.varargs:
o_callargs[argspec.varargs] = callargs[argspec.varargs]
if argspec.keywords:
o_callargs[argspec.keywords] = callargs[argspec.keywords]
return o_callargs
