layers.py 文件源码

def _build(self, X):
        """Build the graph of this layer."""
        n_samples, input_dim = self._get_X_dims(X)
        Wdim = (self.n_categories, self.output_dim)
        n_batch = tf.shape(X)[1]

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

        # Index into the relevant weights rather than using sparse matmul
        features = tf.gather(W, X, axis=0)
        f_dims = int(np.prod(features.shape[2:]))  # need this for placeholders
        Net = tf.reshape(features, [n_samples, n_batch, f_dims])

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

        return Net, penalty


#
# Private module stuff
#