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)
        W_shape, _ = self._weight_shapes(self.n_categories)
        n_batch = tf.shape(X)[1]

        # Layer weights
        self.pW = _make_prior(self.std, self.pW, W_shape)
        self.qW = _make_posterior(self.std, self.qW, W_shape, self.full)

        # Index into the relevant weights rather than using sparse matmul
        Wsamples = _sample_W(self.qW, n_samples)
        features = tf.map_fn(lambda wx: tf.gather(*wx, axis=0), (Wsamples, X),
                             dtype=Wsamples.dtype)

        # Now concatenate the resulting features on the last axis
        f_dims = int(np.prod(features.shape[2:]))  # need this for placeholders
        Net = tf.reshape(features, [n_samples, n_batch, f_dims])

        # Regularizers
        KL = kl_sum(self.qW, self.pW)

        return Net, KL