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

Before Change

        self.input_spec = [InputSpec(shape=input_shape)]
        shape = (input_shape[self.axis],)

        self.gamma = self.add_weight(shape,
                                     initializer=self.gamma_init,
                                     regularizer=self.gamma_regularizer,
                                     name="{}_gamma".format(self.name))
        self.beta = self.add_weight(shape,
                                    initializer=self.beta_init,
                                    regularizer=self.beta_regularizer,
                                    name="{}_beta".format(self.name))
        self.running_mean = self.add_weight(shape, initializer="zero",
                                            name="{}_running_mean".format(self.name),
                                            trainable=False)
        self.running_std = self.add_weight(shape, initializer="one",
                                           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:
            assert self.built, "Layer must be built before being called"

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,
            trainable=False)
        self.moving_variance = self.add_weight(
            shape,
            name="moving_variance",
            initializer=self.moving_variance_initializer,
            trainable=False)

    def call(self, inputs, training=None):

Instances