813e576d5c878e33ed75c594f54d057312e66daf,tensorlayer/layers/convolution/expert_conv.py,Conv2dLayer,build,#Conv2dLayer#Any#,219

Before Change

        )
        if self.b_init:
            self.b = tf.compat.v1.get_variable(
                name=self.name + "\b_conv2d", shape=(self.shape[-1]), initializer=self.b_init,
                dtype=LayersConfig.tf_dtype, **self.b_init_args
            )
            self.add_weights([self.W, self.b])

After Change

    def build(self, inputs):
        self.W = self._get_weights("filters", shape=self.shape, init=self.W_init, init_args=self.W_init_args)
        if self.b_init:
            self.b = self._get_weights("biases", shape=(self.n_filter), init=self.b_init, init_args=self.b_init_args)

        // self.W = tf.compat.v1.get_variable(
        //     name=self.name + "\W_conv2d", shape=self.shape, initializer=self.W_init, dtype=LayersConfig.tf_dtype,
        //     **self.W_init_args
        // )
        // if self.b_init:
        //     self.b = tf.compat.v1.get_variable(
        //         name=self.name + "\b_conv2d", shape=(self.shape[-1]), initializer=self.b_init,
        //         dtype=LayersConfig.tf_dtype, **self.b_init_args
        //     )
        //     self.add_weights([self.W, self.b])
        // else:
        //     self.add_weights(self.W)

    def forward(self, inputs):
        outputs = tf.nn.conv2d(
            inputs,
            self.W,

Instances