03a7eb89e27b70f2ca0ac932ef4bace7569d6fab,keras/layers/normalization.py,BatchNormalization,build,#BatchNormalization#Any#,85

Before Change

                                           name="{}_running_std".format(self.name),
                                           trainable=False)

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights
        self.built = True

    def call(self, x, mask=None):
        if self.mode == 0 or self.mode == 2:

After Change

    def build(self, input_shape):
        dim = input_shape[self.axis]
        if dim is None:
            raise ValueError("Axis " + str(self.axis) + " of "
                             "input tensor should have a defined dimension "
                             "but the layer received an input with shape " +
                             str(input_shape) + ".")
        self.input_spec = InputSpec(ndim=len(input_shape),
                                    axes={self.axis: dim})
        shape = (dim,)

        if self.center:
            self.beta = self.add_weight(shape,
                                        name="beta",
                                        initializer=self.beta_initializer,
                                        regularizer=self.beta_regularizer,
                                        constraint=self.beta_constraint)
        else:
            self.beta = None
        if self.scale:
            self.gamma = self.add_weight(shape,
                                         name="gamma",
                                         initializer=self.gamma_initializer,
                                         regularizer=self.gamma_regularizer,
                                         constraint=self.gamma_constraint)
        else:
            self.gamma = None
        self.moving_mean = self.add_weight(
            shape,
            name="moving_mean",
            initializer=self.moving_mean_initializer,

Instances