Python session.Session方法代码示例

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def replace_variable_values_with_moving_averages(graph,
                                                 current_checkpoint_file,
                                                 new_checkpoint_file):
  """Replaces variable values in the checkpoint with their moving averages.

  If the current checkpoint has shadow variables maintaining moving averages of
  the variables defined in the graph, this function generates a new checkpoint
  where the variables contain the values of their moving averages.

  Args:
    graph: a tf.Graph object.
    current_checkpoint_file: a checkpoint containing both original variables and
      their moving averages.
    new_checkpoint_file: file path to write a new checkpoint.
  """
  with graph.as_default():
    variable_averages = tf.train.ExponentialMovingAverage(0.0)
    ema_variables_to_restore = variable_averages.variables_to_restore()
    with tf.Session() as sess:
      read_saver = tf.train.Saver(ema_variables_to_restore)
      read_saver.restore(sess, current_checkpoint_file)
      write_saver = tf.train.Saver()
      write_saver.save(sess, new_checkpoint_file) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def testDebugWhileLoopWatchingWholeGraphWorks(self):
    with session.Session() as sess:
      loop_body = lambda i: math_ops.add(i, 2)
      loop_cond = lambda i: math_ops.less(i, 16)

      i = constant_op.constant(10, name="i")
      loop = control_flow_ops.while_loop(loop_cond, loop_body, [i])

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(run_options,
                              sess.graph,
                              debug_urls=self._debug_urls())
      run_metadata = config_pb2.RunMetadata()
      self.assertEqual(
          16, sess.run(loop, options=run_options, run_metadata=run_metadata))

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)

      self.assertEqual(
          [[10]], dump.get_tensors("while/Enter", 0, "DebugIdentity"))
      self.assertEqual(
          [[12], [14], [16]],
          dump.get_tensors("while/NextIteration", 0, "DebugIdentity")) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def testDebugQueueOpsDoesNotoErrorOut(self):
    with session.Session() as sess:
      q = data_flow_ops.FIFOQueue(3, "float", name="fifo_queue")
      q_init = q.enqueue_many(([101.0, 202.0, 303.0],), name="enqueue_many")

      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_urls=self._debug_urls())

      sess.run(q_init, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)
      self.assertTrue(dump.loaded_partition_graphs())

      fifo_queue_tensor = dump.get_tensors("fifo_queue", 0, "DebugIdentity")[0]
      self.assertIsInstance(fifo_queue_tensor,
                            debug_data.InconvertibleTensorProto)
      self.assertTrue(fifo_queue_tensor.initialized)
      self.assertAllClose(
          [101.0, 202.0, 303.0],
          dump.get_tensors("enqueue_many/component_0", 0, "DebugIdentity")[0]) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def _safe_close(self, sess):
    """Closes a session without raising an exception.

    Just like sess.close() but ignores exceptions.

    Args:
      sess: A `Session`.
    """
    # pylint: disable=broad-except
    try:
      sess.close()
    except Exception:
      # Intentionally not logging to avoid user complaints that
      # they get cryptic errors.  We really do not care that Close
      # fails.
      pass
    # pylint: enable=broad-except 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def _try_run_local_init_op(self, sess):
    """Tries to run _local_init_op, if not None, and is ready for local init.

    Args:
      sess: A `Session`.

    Returns:
      A tuple (is_successful, msg), where is_successful is True if
      _local_init_op is None, or we ran _local_init_op, and False otherwise;
      and msg is a `String` with the reason why the model was not ready to run
      local init.
    """
    if self._local_init_op is not None:
      is_ready_for_local_init, msg = self._model_ready_for_local_init(sess)
      if is_ready_for_local_init:
        sess.run(self._local_init_op)
        return True, None
      else:
        return False, msg
    return True, None 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def run_one_epoch(self):
    """Creates a new 'Graph` and `Session` and runs a single epoch.

    Naturally this makes sense only for DataFrames that fit in memory.

    Returns:
      A dictionary mapping column names to numpy arrays that contain a single
      epoch of the `DataFrame`.
    """
    # batches is a list of dicts of numpy arrays
    batches = [b for b in self.run(num_epochs=1)]

    # first invert that to make a dict of lists of numpy arrays
    pivoted_batches = {}
    for k in batches[0].keys():
      pivoted_batches[k] = []
    for b in batches:
      for k, v in b.items():
        pivoted_batches[k].append(v)

    # then concat the arrays in each column
    result = {k: np.concatenate(column_batches)
              for k, column_batches in pivoted_batches.items()}
    return result 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def _export_graph(graph, saver, checkpoint_path, export_dir,
                  default_graph_signature, named_graph_signatures,
                  exports_to_keep):
  """Exports graph via session_bundle, by creating a Session."""
  with graph.as_default():
    with tf_session.Session('') as session:
      variables.local_variables_initializer()
      lookup_ops.tables_initializer()
      saver.restore(session, checkpoint_path)

      export = exporter.Exporter(saver)
      export.init(
          init_op=control_flow_ops.group(
              variables.local_variables_initializer(),
              lookup_ops.tables_initializer()),
          default_graph_signature=default_graph_signature,
          named_graph_signatures=named_graph_signatures,
          assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS))
      return export.export(export_dir, contrib_variables.get_global_step(),
                           session, exports_to_keep=exports_to_keep) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def setUp(self):
    self._tmp_dir = tempfile.mktemp()

    self.v = variables.Variable(10.0, name="v")
    self.delta = constant_op.constant(1.0, name="delta")
    self.inc_v = state_ops.assign_add(self.v, self.delta, name="inc_v")

    self.ph = array_ops.placeholder(dtypes.float32, name="ph")
    self.xph = array_ops.transpose(self.ph, name="xph")
    self.m = constant_op.constant(
        [[0.0, 1.0, 2.0], [-4.0, -1.0, 0.0]], dtype=dtypes.float32, name="m")
    self.y = math_ops.matmul(self.m, self.xph, name="y")

    self.sess = session.Session()

    # Initialize variable.
    self.sess.run(self.v.initializer) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def setUp(self):
    self.a = variables.Variable(10.0, name="a")
    self.b = variables.Variable(20.0, name="b")

    self.c = math_ops.add(self.a, self.b, name="c")  # Should be 30.0.
    self.d = math_ops.subtract(self.a, self.c, name="d")  # Should be -20.0.
    self.e = math_ops.multiply(self.c, self.d, name="e")  # Should be -600.0.

    self.ph = array_ops.placeholder(dtypes.float32, shape=(2, 2), name="ph")
    self.f = math_ops.multiply(self.e, self.ph, name="f")

    self.opt = gradient_descent.GradientDescentOptimizer(0.1).minimize(
        self.e, name="opt")

    self.sess = session.Session()

    self.sess.run(self.a.initializer)
    self.sess.run(self.b.initializer) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def setUp(self):
    self.a = variables.Variable(2.0, name="a")
    self.b = variables.Variable(3.0, name="b")

    self.c = math_ops.multiply(self.a, self.b, name="c")  # Should be 6.0.
    self.d = math_ops.multiply(self.a, self.a, name="d")  # Should be 4.0.

    self.e = math_ops.multiply(self.d, self.c, name="e")  # Should be 24.0.

    self.f_y = constant_op.constant(0.30, name="f_y")
    self.f = math_ops.div(self.b, self.f_y, name="f")  # Should be 10.0.

    # The there nodes x, y and z form a graph with "cross-links" in. I.e., x
    # and y are both direct inputs to z, but x is also a direct input to y.
    self.x = variables.Variable(2.0, name="x")  # Should be 2.0
    self.y = math_ops.negative(self.x, name="y")  # Should be -2.0.

    self.z = math_ops.multiply(self.x, self.y, name="z")  # Should be -4.0.

    self.sess = session.Session()
    self.sess.run(variables.global_variables_initializer())

    self.sess = session.Session()
    self.sess.run(variables.global_variables_initializer()) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def testClearDevices(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_clear_devices")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Specify a device and save a variable.
    ops.reset_default_graph()
    with session.Session(
        target="",
        config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
      with sess.graph.device("/cpu:0"):
        self._init_and_validate_variable(sess, "v", 42)
        builder.add_meta_graph_and_variables(
            sess, [tag_constants.TRAINING], clear_devices=True)

    # Save the SavedModel to disk.
    builder.save()

    # Restore the graph with a single predefined tag whose variables were saved
    # without any device information.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, [tag_constants.TRAINING], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def testTFRecordDataset(self):
    dataset_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(),
                                                       'tfrecord_dataset'))

    height = 300
    width = 280

    with self.test_session():
      provider = dataset_data_provider.DatasetDataProvider(
          _create_tfrecord_dataset(dataset_dir))
      image, label = provider.get(['image', 'label'])
      image = _resize_image(image, height, width)

      with session.Session('') as sess:
        with queues.QueueRunners(sess):
          image, label = sess.run([image, label])
      self.assertListEqual([height, width, 3], list(image.shape))
      self.assertListEqual([1], list(label.shape)) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def testTFRecordSeparateGetDataset(self):
    dataset_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(),
                                                       'tfrecord_separate_get'))

    height = 300
    width = 280

    with self.test_session():
      provider = dataset_data_provider.DatasetDataProvider(
          _create_tfrecord_dataset(dataset_dir))
    [image] = provider.get(['image'])
    [label] = provider.get(['label'])
    image = _resize_image(image, height, width)

    with session.Session('') as sess:
      with queues.QueueRunners(sess):
        image, label = sess.run([image, label])
      self.assertListEqual([height, width, 3], list(image.shape))
      self.assertListEqual([1], list(label.shape)) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def testIndexedSlicesGradIsClippedCorrectly(self):
    sparse_grad_indices = np.array([0, 1, 4])
    sparse_grad_dense_shape = [self._grad_vec.size]

    values = constant_op.constant(self._grad_vec, dtype=dtypes.float32)
    indices = constant_op.constant(sparse_grad_indices, dtype=dtypes.int32)
    dense_shape = constant_op.constant(
        sparse_grad_dense_shape, dtype=dtypes.int32)

    gradient = ops.IndexedSlices(values, indices, dense_shape)
    variable = variables_lib.Variable(self._zero_vec, dtype=dtypes.float32)

    gradients_to_variables = (gradient, variable)
    gradients_to_variables = learning.clip_gradient_norms(
        [gradients_to_variables], self._max_norm)[0]

    # Ensure the built IndexedSlice has the right form.
    self.assertEqual(gradients_to_variables[1], variable)
    self.assertEqual(gradients_to_variables[0].indices, indices)
    self.assertEqual(gradients_to_variables[0].dense_shape, dense_shape)

    with session.Session() as sess:
      actual_gradient = sess.run(gradients_to_variables[0].values)
    np_testing.assert_almost_equal(actual_gradient, self._clipped_grad_vec) 

# 需要导入模块: from tensorflow.python.client import session [as 别名]
# 或者: from tensorflow.python.client.session import Session [as 别名]
def write_graph_and_checkpoint(inference_graph_def,
                               model_path,
                               input_saver_def,
                               trained_checkpoint_prefix):
  """Writes the graph and the checkpoint into disk."""
  for node in inference_graph_def.node:
    node.device = ''
  with tf.Graph().as_default():
    tf.import_graph_def(inference_graph_def, name='')
    with session.Session() as sess:
      saver = saver_lib.Saver(saver_def=input_saver_def,
                              save_relative_paths=True)
      saver.restore(sess, trained_checkpoint_prefix)
      saver.save(sess, model_path)