def __init__(self, input_dim, target_tensor=2, clip_value=None, input_tensor=None): self.target_tensor = dim_to_var( target_tensor, "k") if type(target_tensor) == int else target_tensor self.clip_value = clip_value super(NeuralRegressor, self).__init__(input_dim, input_tensor=input_tensor)
def optimize_function(params, config=None): """ Create a optimizing function receives gradients. Parameters: params - parameters config - training configuration Returns: updating function receives gradients """ gs = [dim_to_var(p.ndim) for p in params] updates, _ = optimize_updates(params, gs, config) return theano.function(gs, [], updates=updates)
def setup_variables(self): """ Set up variables. """ if self.input_tensor: if type(self.input_tensor) == int: x = dim_to_var(self.input_tensor, name="x") else: x = self.input_tensor else: x = T.matrix('x') self.input_variables.append(x) self._output = x self._test_output = x
def __init__(self, input_dim, target_tensor=2, clip_value=None, input_tensor=None): self.target_tensor = dim_to_var(target_tensor, "k") if type(target_tensor) == int else target_tensor self.clip_value = clip_value super(NeuralRegressor, self).__init__(input_dim, input_tensor=input_tensor)