ab0283366b30e1697850a1c006764d466b97afc0,theano/tensor/nnet/bn.py,AbstractBatchNormInference,make_node,#AbstractBatchNormInference#Any#Any#Any#Any#Any#Any#,515

Before Change

    def make_node(self, x, scale, bias, estimated_mean, estimated_variance, epsilon=1e-4):
        assert x.ndim == scale.ndim == bias.ndim == estimated_mean.ndim == estimated_variance.ndim
        if not isinstance(epsilon, theano.Variable):
            epsilon = as_tensor_variable(epsilon)
        return Apply(self, [x, scale, bias, estimated_mean, estimated_variance, epsilon], [x.type()])

    def grad(self, inputs, grads):
        x, scale, bias, est_mean, est_var, epsilon = inputs

After Change

    def make_node(self, x, scale, bias, estimated_mean, estimated_variance, epsilon=1e-4):
        x = as_tensor_variable(x)
        scale = as_tensor_variable(scale)
        bias = as_tensor_variable(bias)
        estimated_mean = as_tensor_variable(estimated_mean)
        estimated_variance = as_tensor_variable(estimated_variance)
        epsilon = as_tensor_variable(epsilon)

Instances