Python session.run方法代碼示例

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def on_run_start(self, request):
    """Callback invoked on run() calls to the debug-wrapper session.

    This is a blocking callback.
    The invocation happens after the wrapper's run() call is entered,
    after an increment of run call counter.

    Args:
      request: (OnRunStartRequest) callback request object carrying information
        about the run call such as the fetches, feed dict, run options, run
        metadata, and how many run() calls to this wrapper session has occurred.

    Returns:
      An instance of OnRunStartResponse, carrying information to
        1) direct the wrapper session to perform a specified action (e.g., run
          with or without debug tensor watching, invoking the stepper.)
        2) debug URLs used to watch the tensors.
    """
    pass 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def __init__(self, fetches, feed_dict, run_options, run_metadata,
               run_call_count):
    """Constructor of `OnRunStartRequest`.

    Args:
      fetches: Fetch targets of the run() call.
      feed_dict: The feed dictionary to the run() call.
      run_options: RunOptions input to the run() call.
      run_metadata: RunMetadata input to the run() call.
        The above four arguments are identical to the input arguments to the
        run() method of a non-wrapped TensorFlow session.
      run_call_count: 1-based count of how many run calls (including this one)
        has been invoked.
    """
    self.fetches = fetches
    self.feed_dict = feed_dict
    self.run_options = run_options
    self.run_metadata = run_metadata
    self.run_call_count = run_call_count 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def on_run_start(self, request):
    """Callback invoked on run() calls to the debug-wrapper session.

    This is a blocking callback.
    The invocation happens after the wrapper's run() call is entered,
    after an increment of run call counter.

    Args:
      request: (`OnRunStartRequest`) callback request object carrying
        information about the run call such as the fetches, feed dict, run
        options, run metadata, and how many `run()` calls to this wrapper
        session have occurred.

    Returns:
      An instance of `OnRunStartResponse`, carrying information to
        1) direct the wrapper session to perform a specified action (e.g., run
          with or without debug tensor watching, invoking the stepper.)
        2) debug URLs used to watch the tensors.
    """ 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def invoke_node_stepper(self,
                          node_stepper,
                          restore_variable_values_on_exit=True):
    """Callback invoked when the client intends to step through graph nodes.

    Args:
      node_stepper: (stepper.NodeStepper) An instance of NodeStepper to be used
        in this stepping session.
      restore_variable_values_on_exit: (bool) Whether any variables whose values
        have been altered during this node-stepper invocation should be restored
        to their old values when this invocation ends.

    Returns:
      The same return values as the `Session.run()` call on the same fetches as
        the NodeStepper.
    """ 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def _prepare_run_watch_config(self, fetches, feed_dict):
    """Get the debug_urls, and node/op whitelists for the current run() call.

    Args:
      fetches: Same as the `fetches` argument to `Session.run()`.
      feed_dict: Same as the `feed_dict argument` to `Session.run()`.

    Returns:
      debug_urls: (str or list of str) Debug URLs for the current run() call.
        Currently, the list consists of only one URL that is a file:// URL.
      watch_options: (WatchOptions) The return value of a watch_fn, containing
        options including debug_ops, and whitelists.
    """

    debug_urls = self.prepare_run_debug_urls(fetches, feed_dict)
    if self._watch_fn is None:
      watch_options = WatchOptions()
    else:
      watch_options = self._watch_fn(fetches, feed_dict)
      if isinstance(watch_options, tuple):
        # For legacy return type (tuples).
        watch_options = WatchOptions(*watch_options)

    return debug_urls, watch_options 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def run(args):
  """Function triggered by run command.

  Args:
    args: A namespace parsed from command line.

  Raises:
    AttributeError: An error when neither --inputs nor --input_exprs is passed
    to run command.
  """
  if not args.inputs and not args.input_exprs:
    raise AttributeError(
        'At least one of --inputs and --input_exprs must be required')
  tensor_key_feed_dict = load_inputs_from_input_arg_string(
      args.inputs, args.input_exprs)
  run_saved_model_with_feed_dict(args.dir, args.tag_set, args.signature_def,
                                 tensor_key_feed_dict, args.outdir,
                                 args.overwrite, tf_debug=args.tf_debug) 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def __init__(self, fetches, feed_dict, run_options, run_metadata,
               run_call_count):
    """Constructor of OnRunStartRequest.

    Args:
      fetches: Fetch targets of the run() call.
      feed_dict: The feed dictionary to the run() call.
      run_options: RunOptions input to the run() call.
      run_metadata: RunMetadata input to the run() call.
        The above four arguments are identical to the input arguments to the
        run() method of a non-wrapped TensorFlow session.
      run_call_count: 1-based count of how many run calls (including this one)
        has been invoked.
    """
    self.fetches = fetches
    self.feed_dict = feed_dict
    self.run_options = run_options
    self.run_metadata = run_metadata
    self.run_call_count = run_call_count 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def __init__(self, fetches, feed_dict, run_options, run_metadata,
               run_call_count, is_callable_runner=False):
    """Constructor of `OnRunStartRequest`.

    Args:
      fetches: Fetch targets of the run() call.
      feed_dict: The feed dictionary to the run() call.
      run_options: RunOptions input to the run() call.
      run_metadata: RunMetadata input to the run() call.
        The above four arguments are identical to the input arguments to the
        run() method of a non-wrapped TensorFlow session.
      run_call_count: 1-based count of how many run calls (including this one)
        has been invoked.
      is_callable_runner: (bool) whether a runner returned by
        Session.make_callable is being run.
    """
    self.fetches = fetches
    self.feed_dict = feed_dict
    self.run_options = run_options
    self.run_metadata = run_metadata
    self.run_call_count = run_call_count
    self.is_callable_runner = is_callable_runner 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def __enter__(self):
    self.old_run = getattr(session.BaseSession, 'run', None)
    self.old_init = getattr(session.BaseSession, '__init__', None)
    if not self.old_run:
      raise errors.InternalError(None, None, 'BaseSession misses run method.')
    elif not self.old_init:
      raise errors.InternalError(None, None,
                                 'BaseSession misses __init__ method.')
    elif getattr(session.BaseSession, '_profiler_run_internal', None):
      raise errors.InternalError(None, None,
                                 'Already in context or context not cleaned.')
    elif getattr(session.BaseSession, '_profiler_init_internal', None):
      raise errors.InternalError(None, None,
                                 'Already in context or context not cleaned.')
    else:
      setattr(session.BaseSession, 'run', _profiled_run)
      setattr(session.BaseSession, '__init__', _profiled_init)
      setattr(session.BaseSession, '_profiler_run_internal', self.old_run)
      setattr(session.BaseSession, '_profiler_init_internal', self.old_init)
      setattr(session.BaseSession, 'profile_context', self)
      return self 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def prepare_run_debug_urls(self, fetches, feed_dict):
    """Abstract method to be implemented by concrete subclasses.

    This method prepares the run-specific debug URL(s).

    Args:
      fetches: Same as the `fetches` argument to `Session.run()`
      feed_dict: Same as the `feed_dict` argument to `Session.run()`

    Returns:
      debug_urls: (`str` or `list` of `str`) Debug URLs to be used in
        this `Session.run()` call.
    """ 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def _prepare_run_debug_urls(self, fetches, feed_dict):
    """Abstract method to be implemented by concrete subclasses.

    This method prepares the run-specific debug URL(s).

    Args:
      fetches: Same as the `fetches` argument to `Session.run()`
      feed_dict: Same as the `feed_dict` argument to `Session.run()`

    Returns:
      debug_urls: (`str` or `list` of `str`) Debug URLs to be used in
        this `Session.run()` call.
    """ 

# 需要導入模塊: from tensorflow.python.client import session [as 別名]
# 或者: from tensorflow.python.client.session import run [as 別名]
def _prepare_run_watch_config(self, fetches, feed_dict):
    """Get the debug_urls, and node/op whitelists for the current run() call.

    Args:
      fetches: Same as the `fetches` argument to `Session.run()`.
      feed_dict: Same as the `feed_dict argument` to `Session.run()`.

    Returns:
      debug_urls: (str or list of str) Debug URLs for the current run() call.
        Currently, the list consists of only one URL that is a file:// URL.
      debug_ops: (str or list of str) Debug op(s) to be used by the
        debugger.
      node_name_regex_whitelist: (str or regex) Regular-expression whitelist for
        node name. Same as the same-name argument to debug_utils.watch_graph.
      op_type_regex_whitelist: (str or regex) Regular-expression whitelist for
        op type. Same as the same-name argument to debug_utils.watch_graph.
    """

    debug_urls = self._prepare_run_debug_urls(fetches, feed_dict)
    debug_ops = "DebugIdentity"
    node_name_regex_whitelist = None
    op_type_regex_whitelist = None
    if self._watch_fn is not None:
      debug_ops, node_name_regex_whitelist, op_type_regex_whitelist = (
          self._watch_fn(fetches, feed_dict))

    return (debug_urls, debug_ops, node_name_regex_whitelist,
            op_type_regex_whitelist)