Python ops.control_dependencies函数代码示例

    def _Update_global_variables():
      global_norm = []
      # a = a / t
      for g in grad_vars:
        global_norm.append(state_ops.assign(g, g / self._period))
      # apply
      with ops.control_dependencies(global_norm):
        apply_global_op = self._opt.apply_gradients(
            zip(grad_vars, global_center_vars))

      # pull
      with ops.control_dependencies([apply_global_op]):
        update_ops = []
        if global_step:
          with ops.colocate_with(global_step):
            update_ops.append(state_ops.assign_add(global_step, 1))

        for lvar in local_vars:
          g_val = self._global_map[lvar].read_value()
          update_ops.append(state_ops.assign(lvar, g_val))
        for grad_var in grad_vars:
          update_ops.append(
              state_ops.assign(grad_var, array_ops.zeros_like(grad_var)))
        variable_update = control_flow_ops.group(*(update_ops))
      return variable_update

  def testCaching(self):
    """Confirm caching of control output is recalculated between calls."""
    a = constant_op.constant(1)
    b = constant_op.constant(2)
    with ops.control_dependencies([a]):
      c = constant_op.constant(42)

    shared = {}

    def sub(t):
      shared[t] = shared.get(t, 0) + 1
      return t

    a = subscribe.subscribe(a,
                            lambda t: script_ops.py_func(sub, [t], [t.dtype]))

    with ops.control_dependencies([b]):
      d = constant_op.constant(11)

    # If it was using outdated cached control_outputs then
    # evaling would not trigger the new subscription.
    b = subscribe.subscribe(b,
                            lambda t: script_ops.py_func(sub, [t], [t.dtype]))

    with self.cached_session() as sess:
      c_out = self.evaluate([c])
      d_out = self.evaluate([d])

    self.assertEqual(c_out, [42])
    self.assertEqual(d_out, [11])
    self.assertEqual(shared, {2: 1, 1: 1})

  def testIgnoredArguments(self):
    """Tests that JIT computations can ignore formal parameters."""

    with self.session(config=NoRewriteSessionConfig()) as sess:
      x = array_ops.placeholder(dtypes.int32)
      y = array_ops.placeholder(dtypes.int32)
      with jit_scope():
        z = math_ops.add(x, x)
        w = math_ops.add(y, y)
        # Pulls 'w' into the same compilation via control dependencies.
        with ops.control_dependencies([w]):
          n = control_flow_ops.no_op()
        with ops.control_dependencies([n]):
          t = math_ops.add(z, z)

      run_metadata = config_pb2.RunMetadata()
      out = test_utils.RunWithWarmup(
          sess,
          t, {
              x: np.int32(7),
              y: np.int32(404)
          },
          run_metadata=run_metadata,
          options=config_pb2.RunOptions(
              trace_level=config_pb2.RunOptions.FULL_TRACE))
      self.assert_(MetadataHasXlaRunOp(run_metadata))
      self.assertAllClose(28, out)

    def _get_train_ops(self, features, targets):
        """See base class."""
        global_step = contrib_variables.get_global_step()
        assert global_step
        logits = self._logits(features, is_training=True)
        if self._enable_centered_bias:
            centered_bias_step = [self._centered_bias_step(targets, features)]
        else:
            centered_bias_step = []
        with ops.control_dependencies(centered_bias_step):
            loss = self._loss(logits, targets, features)
        logging_ops.scalar_summary("loss", loss)

        linear_vars = self._get_linear_vars()
        dnn_vars = self._get_dnn_vars()
        grads = gradients.gradients(loss, dnn_vars + linear_vars)
        if self._gradient_clip_norm:
            grads, _ = clip_ops.clip_by_global_norm(grads, self._gradient_clip_norm)

        dnn_grads = grads[0 : len(dnn_vars)]
        linear_grads = grads[len(dnn_vars) :]

        train_ops = self._get_linear_training_ops(linear_grads, linear_vars) + self._get_dnn_training_ops(
            dnn_grads, dnn_vars
        )

        train_step = control_flow_ops.group(*train_ops, name="combined_training_op")
        with ops.control_dependencies([train_step]):
            with ops.get_default_graph().colocate_with(global_step):
                return state_ops.assign_add(global_step, 1).op, loss

    def _fn():
      x = constant_op.constant(points)
      if batch_size == num_points:
        return input_lib.limit_epochs(x, num_epochs=num_epochs), None
      if randomize:
        indices = random_ops.random_uniform(
            constant_op.constant([batch_size]),
            minval=0,
            maxval=num_points - 1,
            dtype=dtypes.int32,
            seed=10)
      else:
        # We need to cycle through the indices sequentially. We create a queue
        # to maintain the list of indices.
        q = data_flow_ops.FIFOQueue(num_points, dtypes.int32, ())

        # Conditionally initialize the Queue.
        def _init_q():
          with ops.control_dependencies(
              [q.enqueue_many(math_ops.range(num_points))]):
            return control_flow_ops.no_op()

        init_q = control_flow_ops.cond(q.size() <= 0, _init_q,
                                       control_flow_ops.no_op)
        with ops.control_dependencies([init_q]):
          offsets = q.dequeue_many(batch_size)
          with ops.control_dependencies([q.enqueue_many(offsets)]):
            indices = array_ops.identity(offsets)
      batch = array_ops.gather(x, indices)
      return (input_lib.limit_epochs(batch, num_epochs=num_epochs), None)

  def testTensorArrayReadTwice(self):
    with self.test_session(use_gpu=True):
      value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]])

      ta_readonce = tensor_array_ops.TensorArray(
          dtype=dtypes.float32, tensor_array_name="foo", size=2)

      w_readonce = ta_readonce.unstack(value)
      r0_readonce = w_readonce.read(0)
      with ops.control_dependencies([r0_readonce]):
        r1_readonce = w_readonce.read(0)

      with self.assertRaisesOpError(
          r"Could not read index 0 twice because it was cleared after a "
          r"previous read \(perhaps try setting clear_after_read = false\?\)"):
        r1_readonce.eval()

      ta_readtwice = tensor_array_ops.TensorArray(
          dtype=dtypes.float32,
          tensor_array_name="foo",
          size=2,
          clear_after_read=False)
      w_readtwice = ta_readtwice.unstack(value)
      r0_readtwice = w_readtwice.read(0)
      with ops.control_dependencies([r0_readtwice]):
        r1_readtwice = w_readtwice.read(0)

      self.assertAllEqual([1.0, -1.0], r1_readtwice.eval())

  def testMultidimensionalAcculumator(self):
    with self.test_session() as sess:
      accumulator = stats_accumulator_ops.StatsAccumulator(
          stamp_token=0,
          gradient_shape=tensor_shape.scalar(),
          hessian_shape=tensor_shape.scalar())
      with ops.control_dependencies([accumulator._create_op]):
        op1 = accumulator.add(
            stamp_token=0,
            partition_ids=[1, 2, 1],
            feature_ids=[[2, 2], [3, 0], [2, 2]],
            gradients=[0.1, 0.3, 0.8],
            hessians=[0.2, 0.4, -9])
        op2 = accumulator.add(0, [2, 1], [[3, 1], [2, 2]], [0.1, 1], [0.2, -1])

      with ops.control_dependencies([op1, op2]):
        num_updates, partition, bucket_ids, grads, hessians = accumulator.flush(
            stamp_token=0, next_stamp_token=1)
        num_updates, partition, bucket_ids, grads, hessians = sess.run(
            [num_updates, partition, bucket_ids, grads, hessians])

      result = _AccumulatorResultToDict(partition, bucket_ids, grads, hessians)
      self.assertEqual(num_updates, 2)
      self.assertEqual(len(result), 3)
      # Key is partion, bucket, dimension.
      self.assertAllClose(result[(1, 2, 2)], [1.9, -9.8])
      self.assertAllClose(result[(2, 3, 0)], [0.3, 0.4])
      self.assertAllClose(result[(2, 3, 1)], [0.1, 0.2])

  def test_train_max_steps_is_not_incremental(self):
    with ops.Graph().as_default() as g, self.test_session(g):
      with ops.control_dependencies(self._build_inference_graph()):
        train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
      learn.graph_actions.train(
          g,
          output_dir=self._output_dir,
          train_op=train_op,
          loss_op=constant_op.constant(2.0),
          max_steps=10)
      step = checkpoint_utils.load_variable(
          self._output_dir, variables_lib.get_global_step().name)
      self.assertEqual(10, step)

    with ops.Graph().as_default() as g, self.test_session(g):
      with ops.control_dependencies(self._build_inference_graph()):
        train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
      learn.graph_actions.train(
          g,
          output_dir=self._output_dir,
          train_op=train_op,
          loss_op=constant_op.constant(2.0),
          max_steps=15)
      step = checkpoint_utils.load_variable(
          self._output_dir, variables_lib.get_global_step().name)
      self.assertEqual(15, step)

  def _resource_apply_sparse(self, grad, var, indices):
    var_dtype = var.dtype.base_dtype
    lr_t = self._decayed_lr(var_dtype)

    beta_1_t = self._get_hyper('beta_1', var_dtype)
    beta_2_t = self._get_hyper('beta_2', var_dtype)
    local_step = math_ops.cast(self.iterations + 1, var_dtype)
    beta_1_power = math_ops.pow(beta_1_t, local_step)
    epsilon_t = self._get_hyper('epsilon', var_dtype)

    # m_t = beta1 * m + (1 - beta1) * g_t
    m = self.get_slot(var, 'm')
    m_slice = array_ops.gather(m, indices)
    m_t_slice = m_slice * beta_1_t + grad * (1 - beta_1_t)
    with ops.control_dependencies([m_t_slice]):
      m_t = self._resource_scatter_update(m, indices, m_t_slice)

    # u_t = max(beta2 * u, abs(g_t))
    v = self.get_slot(var, 'v')
    v_slice = array_ops.gather(v, indices)
    v_t_slice = math_ops.maximum(v_slice * beta_2_t, math_ops.abs(grad))
    with ops.control_dependencies([v_t_slice]):
      v_t = self._resource_scatter_update(v, indices, v_t_slice)
    # theta_t = theta - lr / (1 - beta1^t) * m_t / u_t
    var_slice = -lr_t / (1 - beta_1_power) * (
        m_t_slice / (v_t_slice + epsilon_t))
    with ops.control_dependencies([var_slice]):
      var_update = self._resource_scatter_add(var, indices, var_slice)
    return control_flow_ops.group(*[var_update, m_t, v_t])

  def _resource_apply_sparse(self, grad, var, indices):
    var_dtype = var.dtype.base_dtype
    lr_t = self._decayed_lr(var_dtype)
    beta_1_t = self._get_hyper('beta_1', var_dtype)
    beta_2_t = self._get_hyper('beta_2', var_dtype)
    local_step = math_ops.cast(self.iterations + 1, var_dtype)
    beta_1_power = math_ops.pow(beta_1_t, local_step)
    beta_2_power = math_ops.pow(beta_2_t, local_step)
    epsilon_t = self._get_hyper('epsilon', var_dtype)
    lr = (lr_t * math_ops.sqrt(1 - beta_2_power) / (1 - beta_1_power))

    # m_t = beta1 * m + (1 - beta1) * g_t
    m = self.get_slot(var, 'm')
    m_scaled_g_values = grad * (1 - beta_1_t)
    m_t = state_ops.assign(m, m * beta_1_t, use_locking=self._use_locking)
    with ops.control_dependencies([m_t]):
      m_t = self._resource_scatter_add(m, indices, m_scaled_g_values)
      # m_bar = (1 - beta1) * g_t + beta1 * m_t
      m_bar = m_scaled_g_values + beta_1_t * array_ops.gather(m_t, indices)

    # v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
    v = self.get_slot(var, 'v')
    v_scaled_g_values = (grad * grad) * (1 - beta_2_t)
    v_t = state_ops.assign(v, v * beta_2_t, use_locking=self._use_locking)
    with ops.control_dependencies([v_t]):
      v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)

    v_t_slice = array_ops.gather(v_t, indices)
    v_sqrt = math_ops.sqrt(v_t_slice)
    var_update = self._resource_scatter_add(var, indices,
                                            -lr * m_bar / (v_sqrt + epsilon_t))
    return control_flow_ops.group(*[var_update, m_bar, v_t])

  def test_train_skip_train_if_max_step_already_saved(self):
    with ops.Graph().as_default() as g, self.test_session(g):
      with ops.control_dependencies(self._build_inference_graph()):
        train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
      learn.graph_actions._monitored_train(  # pylint: disable=protected-access
          g,
          output_dir=self._output_dir,
          train_op=train_op,
          loss_op=constant_op.constant(2.0),
          max_steps=10)
      step = checkpoint_utils.load_variable(
          self._output_dir, variables_lib.get_global_step().name)
      self.assertEqual(10, step)

    with ops.Graph().as_default() as g, self.test_session(g):
      with ops.control_dependencies(self._build_inference_graph()):
        train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
      learn.graph_actions._monitored_train(  # pylint: disable=protected-access
          g,
          output_dir=self._output_dir,
          train_op=train_op,
          loss_op=constant_op.constant(2.0),
          max_steps=10)
      step = checkpoint_utils.load_variable(
          self._output_dir, variables_lib.get_global_step().name)
      self.assertEqual(10, step)

  def get_best(self, n):
    """Return the indices and values of the n highest scores in the TopN."""

    def refresh_shortlist():
      """Update the shortlist with the highest scores in id_to_score."""
      new_scores, new_ids = nn_ops.top_k(self.id_to_score, self.shortlist_size)
      smallest_new_score = math_ops.reduce_min(new_scores)
      new_length = math_ops.reduce_sum(
          math_ops.to_int32(math_ops.greater(new_scores, dtypes.float32.min)))
      u1 = self.sl_ids.assign(
          math_ops.to_int64(array_ops.concat([[new_length], new_ids], 0)))
      u2 = self.sl_scores.assign(
          array_ops.concat([[smallest_new_score], new_scores], 0))
      self.last_ops = [u1, u2]
      return control_flow_ops.group(u1, u2)

    # We only need to refresh the shortlist if n is greater than the
    # current shortlist size (which is stored in sl_ids[0]).
    with ops.control_dependencies(self.last_ops):
      cond_op = control_flow_ops.cond(n > self.sl_ids[0], refresh_shortlist,
                                      control_flow_ops.no_op)
      with ops.control_dependencies([cond_op]):
        topk_values, topk_indices = nn_ops.top_k(
            self.sl_scores,
            math_ops.minimum(n, math_ops.to_int32(self.sl_ids[0])))
        # topk_indices are the indices into the shortlist, we want to return
        # the indices into id_to_score
        gathered_indices = array_ops.gather(self.sl_ids, topk_indices)
        return gathered_indices, topk_values

def _check_labels(labels, expected_labels_dimension):
  """Check labels type and shape."""
  with ops.name_scope(None, 'labels', (labels,)) as scope:
    labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
    if isinstance(labels, sparse_tensor.SparseTensor):
      raise ValueError('SparseTensor labels are not supported.')
    labels_shape = array_ops.shape(labels)
    err_msg = 'labels shape must be [batch_size, {}]'.format(
        expected_labels_dimension)
    assert_rank = check_ops.assert_rank(labels, 2, message=err_msg)
    with ops.control_dependencies([assert_rank]):
      static_shape = labels.shape
      if static_shape is not None:
        dim1 = static_shape[1]
        if (dim1 is not None) and (dim1 != expected_labels_dimension):
          raise ValueError(
              'Mismatched label shape. '
              'Classifier configured with n_classes=%s.  Received %s. '
              'Suggested Fix: check your n_classes argument to the estimator '
              'and/or the shape of your label.' %
              (expected_labels_dimension, dim1))
      assert_dimension = check_ops.assert_equal(
          expected_labels_dimension, labels_shape[1], message=err_msg)
      with ops.control_dependencies([assert_dimension]):
        return array_ops.identity(labels, name=scope)

 def _get_sparse_tensors(self, inputs, weight_collections=None,
                         trainable=None):
   sparse_tensors = self.categorical_column._get_sparse_tensors(inputs)
   id_tensor = sparse_tensors.id_tensor
   weight_tensor = sparse_tensors.weight_tensor
   # Expands final dimension, so that embeddings are not combined during
   # embedding lookup.
   check_id_rank = check_ops.assert_equal(
       array_ops.rank(id_tensor), 2,
       data=[
           'Column {} expected ID tensor of rank 2. '.format(self.name),
           'id_tensor shape: ', array_ops.shape(id_tensor)])
   with ops.control_dependencies([check_id_rank]):
     id_tensor = sparse_ops.sparse_reshape(
         id_tensor,
         shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0))
   if weight_tensor is not None:
     check_weight_rank = check_ops.assert_equal(
         array_ops.rank(weight_tensor), 2,
         data=[
             'Column {} expected weight tensor of rank 2.'.format(self.name),
             'weight_tensor shape:', array_ops.shape(weight_tensor)])
     with ops.control_dependencies([check_weight_rank]):
       weight_tensor = sparse_ops.sparse_reshape(
           weight_tensor,
           shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0))
   return fc._CategoricalColumn.IdWeightPair(id_tensor, weight_tensor)

  def update_weights(self, train_op):
    """Updates the model weights.

    This function must be called on at least one worker after `minimize`.
    In distributed training this call can be omitted on non-chief workers to
    speed up training.

    Args:
      train_op: The operation returned by the `minimize` call.

    Returns:
      An Operation that updates the model weights.
    """
    with ops.control_dependencies([train_op]):
      update_ops = []
      # Copy over unshrinked weights to user provided variables.
      for name in ['sparse_features_weights', 'dense_features_weights']:
        for var, slot_var in zip(self._variables[name],
                                 self._slots['unshrinked_' + name]):
          update_ops.append(var.assign(slot_var))

    # Apply proximal step.
    with ops.control_dependencies(update_ops):
      update_ops = []
      for name in ['sparse_features_weights', 'dense_features_weights']:
        for var in self._variables[name]:
          with ops.device(var.device):
            update_ops.append(
                sdca_shrink_l1(
                    self._convert_n_to_tensor(
                        [var], as_ref=True),
                    l1=self._symmetric_l1_regularization(),
                    l2=self._symmetric_l2_regularization()))
      return control_flow_ops.group(*update_ops)