layers.py 文件源码

def _build(self, X):
        """Build the graph of this layer."""
        n_samples, input_shape = self._get_X_dims(X)
        Wdim = tuple(input_shape) + (self.output_dim,)

        W = tf.Variable(tf.random_normal(shape=Wdim, seed=next(seedgen)),
                        name="W_map")

        # We don't want to copy tf.Variable W so map over X
        Net = tf.map_fn(lambda x: tf.matmul(x, W), X)

        # Regularizers
        penalty = self.l2 * tf.nn.l2_loss(W) + self.l1 * _l1_loss(W)

        # Optional Bias
        if self.use_bias is True:
            b = tf.Variable(tf.random_normal(shape=(1, self.output_dim),
                                             seed=next(seedgen)), name="b_map")
            Net += b
            penalty += self.l2 * tf.nn.l2_loss(b) + self.l1 * _l1_loss(b)

        return Net, penalty