def summary(self, feeds=None): from ..scalar import current_step if self.as_tensor() is None: return value = get_default_session().run(self.as_tensor(), self.get_feed_dict(feeds)) self.nodes['summary_writer'].add_summary(value, current_step())
def summary(self, feeds=None): from ..scalar import current_step mrf = self._get_multiple_run_feeds(feeds) feed_dict = self.get_feed_dict(feeds) feed_dict.update(mrf) value = get_default_session().run(self.nodes[SummaryKeys.MERGED], feed_dict=feed_dict) self.nodes['summary_writer'].add_summary(value, current_step())
def save(self, feeds): from ..scalar import global_step if self._saver is None: self._saver = tf.train.Saver() from dxpy.learn.session import get_default_session sess = get_default_session() step = sess.run(global_step()) print("[SAVE] model to: {}.".format(self._model_path())) self._saver.save(sess, self._model_path(), global_step=step)
def _get_multiple_run_feeds(self, feeds=None): result = dict() for i in range(self.nb_max_runs): current_result = get_default_session().run( self.inputs, self.get_feed_dict(feeds)) for k in self.inputs: if k in self.nb_runs and self.nb_runs[k] > i: result[self.multi_runs[k][i]] = current_result[k] return result
def load(self, feeds): from ..scalar import global_global_step import sys if self._saver is None: self._saver = tf.train.Saver() from dxpy.learn.session import get_default_session sess = get_default_session() path_load, flag = self.__resolve_path_load(feeds) if flag is False: if isinstance(path_load, int): msg = "[ERROR][LOAD] Save for given step {} not found. Skip restore." print(msg.format(path_load), file=sys.stderr) return else: msg = "[ERROR][LOAD] Checkpoint file {} not found. Skip restore." print(msg.format(path_load), file=sys.stderr) return print("[LOAD] model from: {}.".format(path_load)) self._saver.restore(sess, path_load)
def __create_writer(self, feeds): self.register_node( 'summary_writer', tf.summary.FileWriter(self.param('path', feeds), get_default_session().graph))
def set_value(self, feeds): from dxpy.learn.session import get_default_session get_default_session().run(self.assign_op, feed_dict={self.nodes['new_value']: feeds})
def get_value(): from dxpy.learn.session import get_default_session return get_default_session().run(global_step())
def _train(self, feeds): sess = get_default_session() sess.run(self.as_tensor(), feed_dict=feeds)
def get_value(self): from dxpy.learn.session import get_default_session return get_default_session().run(self.as_tensor())
def session(self): from dxpy.learn.session import get_default_session return get_default_session()
def get_value(): from dxpy.learn.session import get_default_session return get_default_session().run(keep_prob())