b2e6cccd53bd6c076c32421b8c4d562a96437524,tensorlayer/layers/normalization.py,LayerNormLayer,__init__,#LayerNormLayer#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,228

Before Change

                 begin_params_axis=-1,
                 name="layernorm"):

        if tf.__version__ < "1.3":
            raise Exception("Please use TF 1.3+")

        Layer.__init__(self, name=name)
        self.inputs = layer.outputs
        logging.info("LayerNormLayer %s: act:%s" % (self.name, act.__name__))
        with tf.variable_scope(name) as vs:
            self.outputs = tf.contrib.layers.layer_norm(
                self.inputs,
                center=center,
                scale=scale,
                activation_fn=act,
                reuse=reuse,
                variables_collections=variables_collections,
                outputs_collections=outputs_collections,
                trainable=trainable,
                begin_norm_axis=begin_norm_axis,
                begin_params_axis=begin_params_axis,
                scope="var",
            )
            variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)

        self.all_layers = list(layer.all_layers)
        self.all_params = list(layer.all_params)
        self.all_drop = dict(layer.all_drop)
        self.all_layers.extend([self.outputs])
        self.all_params.extend(variables)

After Change

        if tf.__version__ < "1.3":
            // raise Exception("Please use TF 1.3+")
            with tf.variable_scope(name) as vs:
                self.outputs = tf.contrib.layers.layer_norm(
                    self.inputs,
                    center=center,
                    scale=scale,
                    activation_fn=act,
                    reuse=reuse,
                    variables_collections=variables_collections,
                    outputs_collections=outputs_collections,
                    trainable=trainable,
                    // begin_norm_axis=begin_norm_axis,
                    // begin_params_axis=begin_params_axis,
                    scope="var",
                )
                variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
        else:
            with tf.variable_scope(name) as vs:
                self.outputs = tf.contrib.layers.layer_norm(
                    self.inputs,

Instances