// to (1,?).
flattened_input = np.reshape(next_input[0], newshape=(1, -1))
with self._session.graph.as_default():
update = self._qaccumulator.add_summary(
stamp_token=self._stamp_token,
column=flattened_input,
// All weights are equal, and the weight vector is the
// same length as the input.
example_weights=np.ones_like(flattened_input))
if summary is not self._empty_summary:
self._session.run(
self._qaccumulator.add_prebuilt_summary(
stamp_token=self._stamp_token,
summary=tf.constant(summary)))
self._session.run(update)
After Change
self._session.run(
self._add_summary_op,
feed_dict={self._add_summary_input: flattened_input})
// After the flush_summary, qaccumulator will not contain any
// uncommitted information that represents the input. Instead all the