1374e2e6adf4d2bc3fc9983efee06c9ef3f6a08f,tensorlayer/layers/dense/dropconnect.py,DropconnectDense,build,#DropconnectDense#Any#,87

Before Change

        self.n_in = inputs_shape[-1]

        self._add_weight(scope_name=self.name, var_name="weights", shape=(n_in, self.n_units), init=self.W_init, init_args=self.W_init_args)
        // self.W = tf.compat.v1.get_variable(
        //     name=self.name + "\W", shape=(self.n_in, self.n_units), initializer=self.W_init,
        //     dtype=LayersConfig.tf_dtype, **self.W_init_args
        // )
        if self.b_init:
            self._add_weight(scope_name=self.name, var_name="biases", shape=(self.n_units), init=self.b_init, init_args=self.b_init_args)
        //     self.b = tf.compat.v1.get_variable(
        //         name=self.name + "\b", shape=(self.n_units), initializer=self.b_init, dtype=LayersConfig.tf_dtype,
        //         **self.b_init_args
        //     )

After Change

            (self.name, n_units, self.act.__name__ if self.act is not None else "No Activation")
        )

    def build(self, inputs_shape):

        if len(inputs_shape) != 2:
            raise Exception("The input dimension must be rank 2")

        self.n_in = inputs_shape[-1]

        self.W = self._get_weight("weights", shape=(n_in, self.n_units), init=self.W_init, init_args=self.W_init_args)
        // self.W = tf.compat.v1.get_variable(
        //     name=self.name + "\W", shape=(self.n_in, self.n_units), initializer=self.W_init,
        //     dtype=LayersConfig.tf_dtype, **self.W_init_args
        // )
        if self.b_init:
            self.b = self._get_weight("biases", shape=(self.n_units), init=self.b_init, init_args=self.b_init_args)
        //     self.b = tf.compat.v1.get_variable(
        //         name=self.name + "\b", shape=(self.n_units), initializer=self.b_init, dtype=LayersConfig.tf_dtype,
        //         **self.b_init_args
        //     )

Instances