Python tensorflow.python.pywrap_tensorflow.TF_PRun() Examples

The following are 4 code examples of tensorflow.python.pywrap_tensorflow.TF_PRun(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.pywrap_tensorflow , or try the search function .
Example #1
Source File: session.py    From lambda-packs with MIT License 4 votes vote down vote up 
def _do_run(self, handle, target_list, fetch_list, feed_dict,
              options, run_metadata):
    """Runs a step based on the given fetches and feeds.

    Args:
      handle: a handle for partial_run. None if this is just a call to run().
      target_list: A list of byte arrays corresponding to names of tensors
        or operations to be run to, but not fetched.
      fetch_list: A list of byte arrays corresponding to names of tensors to
        be fetched and operations to be run.
      feed_dict: A dictionary that maps tensor names (as byte arrays) to
        numpy ndarrays.
      options: A (pointer to a) [`RunOptions`] protocol buffer, or None
      run_metadata: A (pointer to a) [`RunMetadata`] protocol buffer, or None

    Returns:
      A list of numpy ndarrays, corresponding to the elements of
      `fetch_list`.  If the ith element of `fetch_list` contains the
      name of an operation, the first Tensor output of that operation
      will be returned for that element.

    Raises:
      tf.errors.OpError: Or one of its subclasses on error.
    """
    def _run_fn(session, feed_dict, fetch_list, target_list, options,
                run_metadata):
      # Ensure any changes to the graph are reflected in the runtime.
      self._extend_graph()
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_Run(session, options,
                                 feed_dict, fetch_list, target_list,
                                 status, run_metadata)

    def _prun_fn(session, handle, feed_dict, fetch_list):
      if target_list:
        raise RuntimeError('partial_run() requires empty target_list.')
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_PRun(session, handle, feed_dict, fetch_list,
                                  status)

    if handle is None:
      return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
                           target_list, options, run_metadata)
    else:
      return self._do_call(_prun_fn, self._session, handle, feed_dict,
                           fetch_list)
Example #2
Source File: session.py    From auto-alt-text-lambda-api with MIT License 4 votes vote down vote up 
def _do_run(self, handle, target_list, fetch_list, feed_dict,
              options, run_metadata):
    """Runs a step based on the given fetches and feeds.

    Args:
      handle: a handle for partial_run. None if this is just a call to run().
      target_list: A list of byte arrays corresponding to names of tensors
        or operations to be run to, but not fetched.
      fetch_list: A list of byte arrays corresponding to names of tensors to
        be fetched and operations to be run.
      feed_dict: A dictionary that maps tensor names (as byte arrays) to
        numpy ndarrays.
      options: A (pointer to a) [`RunOptions`] protocol buffer, or None
      run_metadata: A (pointer to a) [`RunMetadata`] protocol buffer, or None

    Returns:
      A list of numpy ndarrays, corresponding to the elements of
      `fetch_list`.  If the ith element of `fetch_list` contains the
      name of an operation, the first Tensor output of that operation
      will be returned for that element.

    Raises:
      tf.errors.OpError: Or one of its subclasses on error.
    """
    def _run_fn(session, feed_dict, fetch_list, target_list, options,
                run_metadata):
      # Ensure any changes to the graph are reflected in the runtime.
      self._extend_graph()
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_Run(session, options,
                                 feed_dict, fetch_list, target_list,
                                 status, run_metadata)

    def _prun_fn(session, handle, feed_dict, fetch_list):
      if target_list:
        raise RuntimeError('partial_run() requires empty target_list.')
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_PRun(session, handle, feed_dict, fetch_list,
                                  status)

    if handle is None:
      return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
                           target_list, options, run_metadata)
    else:
      return self._do_call(_prun_fn, self._session, handle, feed_dict,
                           fetch_list)
Example #3
Source File: session.py    From deep_image_model with Apache License 2.0 4 votes vote down vote up 
def _do_run(self, handle, target_list, fetch_list, feed_dict,
              options, run_metadata):
    """Runs a step based on the given fetches and feeds.

    Args:
      handle: a handle for partial_run. None if this is just a call to run().
      target_list: A list of byte arrays corresponding to names of tensors
        or operations to be run to, but not fetched.
      fetch_list: A list of byte arrays corresponding to names of tensors to
        be fetched and operations to be run.
      feed_dict: A dictionary that maps tensor names (as byte arrays) to
        numpy ndarrays.
      options: A (pointer to a) [`RunOptions`] protocol buffer, or None
      run_metadata: A (pointer to a) [`RunMetadata`] protocol buffer, or None

    Returns:
      A list of numpy ndarrays, corresponding to the elements of
      `fetch_list`.  If the ith element of `fetch_list` contains the
      name of an operation, the first Tensor output of that operation
      will be returned for that element.

    Raises:
      tf.errors.OpError: Or one of its subclasses on error.
    """
    def _run_fn(session, feed_dict, fetch_list, target_list, options,
                run_metadata):
      # Ensure any changes to the graph are reflected in the runtime.
      self._extend_graph()
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_Run(session, options,
                                 feed_dict, fetch_list, target_list,
                                 status, run_metadata)

    def _prun_fn(session, handle, feed_dict, fetch_list):
      if target_list:
        raise RuntimeError('partial_run() requires empty target_list.')
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_PRun(session, handle, feed_dict, fetch_list,
                                  status)

    if handle is None:
      return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
                           target_list, options, run_metadata)
    else:
      return self._do_call(_prun_fn, self._session, handle, feed_dict,
                           fetch_list)
Example #4
Source File: session.py    From keras-lambda with MIT License 4 votes vote down vote up 
def _do_run(self, handle, target_list, fetch_list, feed_dict,
              options, run_metadata):
    """Runs a step based on the given fetches and feeds.

    Args:
      handle: a handle for partial_run. None if this is just a call to run().
      target_list: A list of byte arrays corresponding to names of tensors
        or operations to be run to, but not fetched.
      fetch_list: A list of byte arrays corresponding to names of tensors to
        be fetched and operations to be run.
      feed_dict: A dictionary that maps tensor names (as byte arrays) to
        numpy ndarrays.
      options: A (pointer to a) [`RunOptions`] protocol buffer, or None
      run_metadata: A (pointer to a) [`RunMetadata`] protocol buffer, or None

    Returns:
      A list of numpy ndarrays, corresponding to the elements of
      `fetch_list`.  If the ith element of `fetch_list` contains the
      name of an operation, the first Tensor output of that operation
      will be returned for that element.

    Raises:
      tf.errors.OpError: Or one of its subclasses on error.
    """
    def _run_fn(session, feed_dict, fetch_list, target_list, options,
                run_metadata):
      # Ensure any changes to the graph are reflected in the runtime.
      self._extend_graph()
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_Run(session, options,
                                 feed_dict, fetch_list, target_list,
                                 status, run_metadata)

    def _prun_fn(session, handle, feed_dict, fetch_list):
      if target_list:
        raise RuntimeError('partial_run() requires empty target_list.')
      with errors.raise_exception_on_not_ok_status() as status:
        return tf_session.TF_PRun(session, handle, feed_dict, fetch_list,
                                  status)

    if handle is None:
      return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
                           target_list, options, run_metadata)
    else:
      return self._do_call(_prun_fn, self._session, handle, feed_dict,
                           fetch_list)