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allow multi-input and multi-output models

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Connor Olding 2017-09-16 18:13:13 +00:00
parent 3386869b30
commit d38e2076f0
1 changed files with 40 additions and 14 deletions
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54
onn_core.py
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@ -932,23 +932,49 @@ class Model:
assert inner_offset >= node.size, "Layer {} allocated less weights than it said it would".format(node) assert inner_offset >= node.size, "Layer {} allocated less weights than it said it would".format(node)
offset += node.size offset += node.size
def evaluate(self, inputs, deterministic=True): def evaluate(self, input_, deterministic=True):
values = dict() assert len(self.nodes_in) == 1, "ambiguous input in multi-input network; use evaluate_multi() instead"
input_node = self.nodes[0] assert len(self.nodes_out) == 1, "ambiguous output in multi-output network; use evaluate_multi() instead"
output_node = self.nodes[-1] node_in = self.nodes_in[0]
values[input_node] = input_node._propagate(np.expand_dims(inputs, 0), deterministic) node_out = self.nodes_out[0]
for node in self.nodes[1:]: outputs = self.evaluate_multi({node_in: input_}, deterministic)
values[node] = node.propagate(values, deterministic) return outputs[node_out]
return values[output_node]
def apply(self, error): # TODO: better name? def apply(self, error): # TODO: better name?
assert len(self.nodes_in) == 1, "ambiguous input in multi-input network; use apply_multi() instead"
assert len(self.nodes_out) == 1, "ambiguous output in multi-output network; use apply_multi() instead"
node_in = self.nodes_in[0]
node_out = self.nodes_out[0]
inputs = self.apply_multi({node_out: error})
return inputs[node_in]
def evaluate_multi(self, inputs, deterministic=True):
values = dict() values = dict()
input_node = self.nodes[0] outputs = dict()
output_node = self.nodes[-1] for node in self.nodes:
values[output_node] = output_node._backpropagate(np.expand_dims(error, 0)) if node in self.nodes_in:
for node in reversed(self.nodes[:-1]): assert node in inputs, "missing input for node {}".format(node.name)
values[node] = node.backpropagate(values) X = inputs[node]
return values[input_node] values[node] = node._propagate(np.expand_dims(X, 0), deterministic)
else:
values[node] = node.propagate(values, deterministic)
if node in self.nodes_out:
outputs[node] = values[node]
return outputs
def apply_multi(self, outputs):
values = dict()
inputs = dict()
for node in reversed(self.nodes):
if node in self.nodes_out:
assert node in outputs, "missing output for node {}".format(node.name)
X = outputs[node]
values[node] = node._backpropagate(np.expand_dims(X, 0))
else:
values[node] = node.backpropagate(values)
if node in self.nodes_in:
inputs[node] = values[node]
return inputs
def forward(self, inputs, outputs, measure=False, deterministic=False): def forward(self, inputs, outputs, measure=False, deterministic=False):
predicted = self.evaluate(inputs, deterministic=deterministic) predicted = self.evaluate(inputs, deterministic=deterministic)