Add default_output to the ConnectionistTemporalClassification's properties

上级 b464d595
...@@ -28,7 +28,7 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp): ...@@ -28,7 +28,7 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp):
compute_grad compute_grad
If set to True, enables the computation of gradients of the CTC loss function. If set to True, enables the computation of gradients of the CTC loss function.
""" """
__props__ = ('compute_grad',) __props__ = ('compute_grad', 'default_output',)
_cop_num_inputs = 3 _cop_num_inputs = 3
_cop_num_outputs = 2 _cop_num_outputs = 2
...@@ -50,6 +50,8 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp): ...@@ -50,6 +50,8 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp):
gof.OpenMPOp.__init__(self) gof.OpenMPOp.__init__(self)
self.compute_grad = compute_grad self.compute_grad = compute_grad
# Return only the cost. Gradient will be returned by grad()
self.default_output = 0
def c_lib_dirs(self): def c_lib_dirs(self):
dirs = [] dirs = []
...@@ -109,9 +111,6 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp): ...@@ -109,9 +111,6 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp):
gradients = T.ftensor3(name="ctc_grad") gradients = T.ftensor3(name="ctc_grad")
outputs += [gradients] outputs += [gradients]
# Return only the cost. Gradient will be returned by grad()
self.default_output = 0
return gof.Apply(self, inputs=[t_activations, t_labels, t_input_lengths], return gof.Apply(self, inputs=[t_activations, t_labels, t_input_lengths],
outputs=outputs) outputs=outputs)
......
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