提交 9fc1f281 authored 作者: AlexLamb's avatar AlexLamb

Added documentation on output_keys argument. Switched str to basestring

上级 d50d1e2c
...@@ -2196,6 +2196,10 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions ...@@ -2196,6 +2196,10 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
:param on_unused_input: What to do if a variable in the 'inputs' list is :param on_unused_input: What to do if a variable in the 'inputs' list is
not used in the graph. Possible values are 'raise', 'warn', and 'ignore'. not used in the graph. Possible values are 'raise', 'warn', and 'ignore'.
:param output_keys: If the outputs argument for theano.function was a
list, then output_keys is None. If the outputs argument was a dict,
then output_keys is a sorted list of the keys from that dict.
:note: this function sets TensorType.filter_checks_isfinite :note: this function sets TensorType.filter_checks_isfinite
when `mode.check_isfinite` is True when `mode.check_isfinite` is True
......
...@@ -49,7 +49,8 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None, ...@@ -49,7 +49,8 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
:param inputs: function parameters, these are not allowed to be shared :param inputs: function parameters, these are not allowed to be shared
variables variables
:type outputs: list of Variables or Out instances :type outputs: list or dict of Variables or Out instances. If it is a
dict, the keys must be strings
:param outputs: expressions to compute :param outputs: expressions to compute
:type mode: string or `Mode` instance. :type mode: string or `Mode` instance.
...@@ -193,7 +194,7 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None, ...@@ -193,7 +194,7 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
output_keys = [] output_keys = []
outputs = [] outputs = []
for pair in output_items_sorted: for pair in output_items_sorted:
assert isinstance(pair[0], str) assert isinstance(pair[0], basestring)
output_keys.append(pair[0]) output_keys.append(pair[0])
outputs.append(pair[1]) outputs.append(pair[1])
......
...@@ -1442,6 +1442,11 @@ def orig_function(inputs, outputs, mode=None, accept_inplace=False, ...@@ -1442,6 +1442,11 @@ def orig_function(inputs, outputs, mode=None, accept_inplace=False,
:param on_unused_input: What to do if a variable in the 'inputs' list is :param on_unused_input: What to do if a variable in the 'inputs' list is
not used in the graph. Possible values are 'raise', 'warn', 'ignore' not used in the graph. Possible values are 'raise', 'warn', 'ignore'
and None and None
:param output_keys: If the outputs were provided to theano.function as a
list, then output_keys is None. Otherwise, if outputs were provided
as a dict, output_keys is the sorted list of keys from the outputs
""" """
# Every element of the input list will be upgraded to an `In` instance if # Every element of the input list will be upgraded to an `In` instance if
......
...@@ -506,7 +506,7 @@ def pfunc(params, outputs=None, mode=None, updates=None, givens=None, ...@@ -506,7 +506,7 @@ def pfunc(params, outputs=None, mode=None, updates=None, givens=None,
mutable=False, borrow=True, shared=True) mutable=False, borrow=True, shared=True)
inputs.append(si) inputs.append(si)
return orig_function(inputs, cloned_outputs, mode, return orig_function(inputs, cloned_outputs, mode,
accept_inplace=accept_inplace, name=name, profile=profile, accept_inplace=accept_inplace, name=name, profile=profile,
on_unused_input=on_unused_input, output_keys=output_keys) on_unused_input=on_unused_input, output_keys=output_keys)
......
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