def __call__(self, model, X, Y = None):
if self.X_callback is not None:
orig_X = X
X = CallbackOp(self.X_callback)(X)
assert len(X.owner.inputs) == 1
assert orig_X is X.owner.inputs[0]
if self.y_callback is not None:
Y = CallbackOp(self.y_callback)(Y)
cost = X.sum()
if self.supervised and Y is not None:
cost = cost + Y.sum()
model_terms = sum([param.sum() for param in model.get_params()])
cost = cost * model_terms
return cost
def expr(self, model, data):
self.get_data_specs(model)[0].validate(data)
callbacks = self.data_callbacks
cb_tuple = self._mapping.flatten(callbacks, return_tuple=True)
data_tuple = self._mapping.flatten(data, return_tuple=True)
costs = []
for (callback, data_var) in safe_zip(cb_tuple, data_tuple):
orig_var = data_var
data_var = CallbackOp(callback)(data_var)
assert len(data_var.owner.inputs) == 1
assert orig_var is data_var.owner.inputs[0]
costs.append(data_var.sum())
# sum() will call theano.add on the symbolic variables
cost = sum(costs)
model_terms = sum([param.sum() for param in model.get_params()])
cost = cost * model_terms
return cost
def expr(self, model, data):
self.get_data_specs(model)[0].validate(data)
callbacks = self.data_callbacks
cb_tuple = self._mapping.flatten(callbacks, return_tuple=True)
data_tuple = self._mapping.flatten(data, return_tuple=True)
costs = []
for (callback, data_var) in safe_zip(cb_tuple, data_tuple):
orig_var = data_var
data_var = CallbackOp(callback)(data_var)
assert len(data_var.owner.inputs) == 1
assert orig_var is data_var.owner.inputs[0]
costs.append(data_var.sum())
# sum() will call theano.add on the symbolic variables
cost = sum(costs)
model_terms = sum([param.sum() for param in model.get_params()])
cost = cost * model_terms
return cost
def __call__(self, model, X, Y=None):
if self.X_callback is not None:
orig_X = X
X = CallbackOp(self.X_callback)(X)
assert len(X.owner.inputs) == 1
assert orig_X is X.owner.inputs[0]
if self.y_callback is not None:
Y = CallbackOp(self.y_callback)(Y)
cost = X.sum()
if self.supervised and Y is not None:
cost = cost + Y.sum()
model_terms = sum([param.sum() for param in model.get_params()])
cost = cost * model_terms
return cost
