ab50820c352bef9a551ec6349ad73397c1529cf5,tensorlayer/layers/dense/dorefa_dense.py,DorefaDense,build,#DorefaDense#Any#,83

Before Change

        )

    def build(self, inputs):
        if inputs.get_shape().ndims != 2:
            raise Exception("The input dimension must be rank 2, please reshape or flatten it")
        if self.use_gemm:
            raise Exception("TODO. The current version use tf.matmul for inferencing.")

        n_in = int(inputs.get_shape()[-1])
        self.W = tf.compat.v1.get_variable(
            name=self.name + "\W", shape=(n_in, self.n_units), initializer=self.W_init, dtype=LayersConfig.tf_dtype,
            **self.W_init_args
        )
        if self.b_init is not None:
            try:
                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
                )

            except Exception:  // If initializer is a constant, do not specify shape.
                self.b = tf.compat.v1.get_variable(
                    name=self.name + "\b", 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):
        inputs = quantize_active(cabs(inputs), self.bitA)
        W_ = quantize_weight(self.W, self.bitW)
        outputs = tf.matmul(inputs, W_)

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, please reshape or flatten it")
        if self.use_gemm:
            raise Exception("TODO. The current version use tf.matmul for inferencing.")

        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=(n_in, self.n_units), initializer=self.W_init, dtype=LayersConfig.tf_dtype,
        //     **self.W_init_args
        // )
        if self.b_init is not None:
            self._add_weight(scope_name=self.name, var_name="biases", shape=(self.n_units), init=self.b_init, init_args=self.b_init_args)
        //     try:
        //         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
        //         )
        //
        //     except Exception:  // If initializer is a constant, do not specify shape.
        //         self.b = tf.compat.v1.get_variable(
        //             name=self.name + "\b", 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):
        inputs = quantize_active(cabs(inputs), self.bitA)
        W_ = quantize_weight(self.weights, self.bitW)
        outputs = tf.matmul(inputs, W_)

Instances