Python variable_scope.get_variable函数代码示例

  def register_option2quants(self, damping):

    self.register_cov_dt1()

    if damping not in self._option2quants_by_damping:
      # It's questionable as to whether we should initialize with stuff like
      # this at all.  Ideally these values should never be used until they are
      # updated at least once.
      damping_string = scalar_or_tensor_to_string(damping)
      with variable_scope.variable_scope(self._var_scope):
        Pmat = variable_scope.get_variable(  # pylint: disable=invalid-name
            "Lmat_damp{}".format(damping_string),
            initializer=inverse_initializer,
            shape=self._cov_shape,
            trainable=False,
            dtype=self._dtype)
        Kmat = variable_scope.get_variable(  # pylint: disable=invalid-name
            "Kmat_damp{}".format(damping_string),
            initializer=inverse_initializer,
            shape=self._cov_shape,
            trainable=False,
            dtype=self._dtype)
        mu = variable_scope.get_variable(
            "mu_damp{}".format(damping_string),
            initializer=init_ops.ones_initializer,
            shape=self._vec_shape,
            trainable=False,
            dtype=self._dtype)

      self._option2quants_by_damping[damping] = (Pmat, Kmat, mu)

  def __call__(self, x, states_prev, scope=None):
    """Long short-term memory cell (LSTM)."""
    with vs.variable_scope(scope or self._names["scope"]):
      x_shape = x.get_shape().with_rank(2)
      if not x_shape[1]:
        raise ValueError("Expecting x_shape[1] to be sets: %s" % str(x_shape))
      if len(states_prev) != 2:
        raise ValueError("Expecting states_prev to be a tuple with length 2.")
      input_size = x_shape[1]
      w = vs.get_variable(self._names["W"], [input_size + self._num_units,
                                             self._num_units * 4])
      b = vs.get_variable(
          self._names["b"], [w.get_shape().with_rank(2)[1]],
          initializer=init_ops.constant_initializer(0.0))
      if self._use_peephole:
        wci = vs.get_variable(self._names["wci"], [self._num_units])
        wco = vs.get_variable(self._names["wco"], [self._num_units])
        wcf = vs.get_variable(self._names["wcf"], [self._num_units])
      else:
        wci = wco = wcf = array_ops.zeros([self._num_units])
      (cs_prev, h_prev) = states_prev
      (_, cs, _, _, _, _, h) = _lstm_block_cell(
          x,
          cs_prev,
          h_prev,
          w,
          b,
          wci=wci,
          wco=wco,
          wcf=wcf,
          forget_bias=self._forget_bias,
          use_peephole=self._use_peephole)

      return (h, (cs, h))

def _auc_hist_accumulate(hist_true, hist_false, nbins, collections):
  """Accumulate histograms in new variables."""
  with variable_scope.variable_op_scope(
      [hist_true, hist_false], None, 'hist_accumulate'):
    # Holds running total histogram of scores for records labeled True.
    hist_true_acc = variable_scope.get_variable(
        'hist_true_acc',
        initializer=array_ops.zeros_initializer(
            [nbins],
            dtype=hist_true.dtype),
        collections=collections,
        trainable=False)
    # Holds running total histogram of scores for records labeled False.
    hist_false_acc = variable_scope.get_variable(
        'hist_false_acc',
        initializer=array_ops.zeros_initializer(
            [nbins],
            dtype=hist_false.dtype),
        collections=collections,
        trainable=False)

    update_op = control_flow_ops.group(
        hist_true_acc.assign_add(hist_true),
        hist_false_acc.assign_add(hist_false),
        name='update_op')

    return hist_true_acc, hist_false_acc, update_op

 def build(self, input_shape):
   input_shape = tensor_shape.TensorShape(input_shape)
   if input_shape.ndims is None:
     raise ValueError('Inputs to `Dense` should have known rank.')
   if len(input_shape) < 2:
     raise ValueError('Inputs to `Dense` should have rank >= 2.')
   if input_shape[-1].value is None:
     raise ValueError('The last dimension of the inputs to `Dense` '
                      'should be defined. Found `None`.')
   # Note that we set `trainable=True` because this is a trainable
   # weight of the layer. If the layer is not trainable
   # (self.trainable = False), the variable will not be added to
   # tf.trainable_variables(), and self.trainable_weights will be empty.
   self.kernel = vs.get_variable('kernel',
                                 shape=[input_shape[-1].value, self.units],
                                 initializer=self.kernel_initializer,
                                 regularizer=self.kernel_regularizer,
                                 dtype=self.dtype,
                                 trainable=True)
   if self.use_bias:
     self.bias = vs.get_variable('bias',
                                 shape=[self.units,],
                                 initializer=self.bias_initializer,
                                 regularizer=self.bias_regularizer,
                                 dtype=self.dtype,
                                 trainable=True)
   else:
     self.bias = None

  def testErrorConditions(self):
    self.assertRaises(ValueError, ws_util._WarmStartSettings, None)
    x = variable_scope.get_variable(
        "x",
        shape=[4, 1],
        initializer=ones(),
        partitioner=lambda shape, dtype: [2, 1])

    # List of PartitionedVariable is invalid type when warmstarting with vocab.
    self.assertRaises(TypeError, ws_util._warmstart_var_with_vocab, [x], "/tmp",
                      5, "/tmp", "/tmp")
    # Keys of type other than FeatureColumn.
    self.assertRaises(TypeError, ws_util._warmstart,
                      {"StringType": x}, ws_util._WarmStartSettings("/tmp"))

    # Unused variable names raises ValueError.
    with ops.Graph().as_default():
      with self.test_session() as sess:
        x = variable_scope.get_variable(
            "x",
            shape=[4, 1],
            initializer=ones(),
            partitioner=lambda shape, dtype: [2, 1])
        self._write_checkpoint(sess)

    self.assertRaises(ValueError, ws_util._warmstart,
                      ws_util._WarmStartSettings(
                          self.get_temp_dir(),
                          var_name_to_vocab_info={
                              "y": ws_util._VocabInfo("", 1, 0, "")
                          }))
    self.assertRaises(ValueError, ws_util._warmstart,
                      ws_util._WarmStartSettings(
                          self.get_temp_dir(),
                          var_name_to_prev_var_name={"y": "y2"}))

  def testOptimizerInit(self):
    with ops.Graph().as_default():
      layer_collection = lc.LayerCollection()

      inputs = array_ops.ones((2, 1)) * 2
      weights_val = np.ones((1, 1), dtype=np.float32) * 3.
      weights = variable_scope.get_variable(
          'w', initializer=array_ops.constant(weights_val))
      bias = variable_scope.get_variable(
          'b', initializer=init_ops.zeros_initializer(), shape=(1, 1))
      output = math_ops.matmul(inputs, weights) + bias

      layer_collection.register_fully_connected((weights, bias), inputs, output)

      logits = math_ops.tanh(output)
      targets = array_ops.constant([[0.], [1.]])
      output = math_ops.reduce_mean(
          nn.softmax_cross_entropy_with_logits(logits=logits, labels=targets))

      layer_collection.register_categorical_predictive_distribution(logits)

      optimizer.KfacOptimizer(
          0.1,
          0.2,
          0.3,
          layer_collection,
          momentum=0.5,
          momentum_type='regular')

  def build(self, input_shape):
    if len(input_shape) != self.rank + 2:
      raise ValueError('Inputs should have rank ' +
                       str(self.rank + 2) +
                       'Received input shape:', str(input_shape))
    if self.data_format == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = -1
    if input_shape[channel_axis] is None:
      raise ValueError('The channel dimension of the inputs '
                       'should be defined. Found `None`.')
    input_dim = input_shape[channel_axis]
    kernel_shape = self.kernel_size + (input_dim, self.filters)

    self.kernel = vs.get_variable('kernel',
                                  shape=kernel_shape,
                                  initializer=self.kernel_initializer,
                                  regularizer=self.kernel_regularizer,
                                  trainable=True,
                                  dtype=self.dtype)
    if self.use_bias:
      self.bias = vs.get_variable('bias',
                                  shape=(self.filters,),
                                  initializer=self.bias_initializer,
                                  regularizer=self.bias_regularizer,
                                  trainable=True,
                                  dtype=self.dtype)
    else:
      self.bias = None

  def testWarmStartMoreSettingsNoPartitioning(self):
    # Create old and new vocabs for sparse column "sc_vocab".
    prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"],
                                        "old_vocab")
    new_vocab_path = self._write_vocab(
        ["orange", "guava", "banana", "apple", "raspberry",
         "blueberry"], "new_vocab")
    # Create feature columns.
    sc_hash = fc.categorical_column_with_hash_bucket(
        "sc_hash", hash_bucket_size=15)
    sc_keys = fc.categorical_column_with_vocabulary_list(
        "sc_keys", vocabulary_list=["a", "b", "c", "e"])
    sc_vocab = fc.categorical_column_with_vocabulary_file(
        "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6)
    all_linear_cols = [sc_hash, sc_keys, sc_vocab]

    # Save checkpoint from which to warm-start.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        variable_scope.get_variable(
            "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms())
        sc_keys_weights = variable_scope.get_variable(
            "some_other_name", shape=[4, 1], initializer=rand())
        variable_scope.get_variable(
            "linear_model/sc_vocab/weights",
            initializer=[[0.5], [1.], [2.], [3.]])
        self._write_checkpoint(sess)
        prev_keys_val = sess.run(sc_keys_weights)

    # New graph, new session with warmstarting.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as sess:
        cols_to_vars = self._create_linear_model(all_linear_cols,
                                                 partitioner=None)
        vocab_info = ws_util._VocabInfo(
            new_vocab=sc_vocab.vocabulary_file,
            new_vocab_size=sc_vocab.vocabulary_size,
            num_oov_buckets=sc_vocab.num_oov_buckets,
            old_vocab=prev_vocab_path
        )
        ws_settings = ws_util._WarmStartSettings(
            self.get_temp_dir(),
            vars_to_warmstart=".*(sc_keys|sc_vocab).*",
            var_name_to_vocab_info={
                ws_util._infer_var_name(cols_to_vars[sc_vocab]): vocab_info
            },
            var_name_to_prev_var_name={
                ws_util._infer_var_name(cols_to_vars[sc_keys]):
                    "some_other_name"
            })
        ws_util._warmstart(ws_settings)
        sess.run(variables.global_variables_initializer())
        # Verify weights were correctly warmstarted.  Var corresponding to
        # sc_hash should not be warm-started.  Var corresponding to sc_vocab
        # should be correctly warmstarted after vocab remapping.
        self._assert_cols_to_vars(cols_to_vars, {
            sc_keys: [prev_keys_val],
            sc_hash: [np.zeros([15, 1])],
            sc_vocab: [np.array([[3.], [2.], [1.], [0.5], [0.], [0.]])]
        }, sess)

def batch_normalize(tensor_in, epsilon=1e-5, convnet=False, decay=0.9, scale_after_normalization=True):
    """Batch Normalization

  Args:
    tensor_in: input Tensor, 4D shape: [batch, in_height, in_width, in_depth].
    epsilon : A float number to avoid being divided by 0.
    decay: decay rate for exponential moving average.
    convnet: Whether this is for convolutional net use. If this is True,
      moments will sum across axis [0, 1, 2]. Otherwise, only [0].
    scale_after_normalization: Whether to scale after normalization.
  """
    shape = tensor_in.get_shape().as_list()

    with vs.variable_scope("batch_norm"):
        gamma = vs.get_variable("gamma", [shape[-1]], initializer=init_ops.random_normal_initializer(1.0, 0.02))
        beta = vs.get_variable("beta", [shape[-1]], initializer=init_ops.constant_initializer(0.0))
        ema = moving_averages.ExponentialMovingAverage(decay=decay)
        if convnet:
            assign_mean, assign_var = nn.moments(tensor_in, [0, 1, 2])
        else:
            assign_mean, assign_var = nn.moments(tensor_in, [0])
        ema_assign_op = ema.apply([assign_mean, assign_var])
        ema_mean, ema_var = ema.average(assign_mean), ema.average(assign_var)

        def update_mean_var():
            """Internal function that updates mean and variance during training"""
            with ops.control_dependencies([ema_assign_op]):
                return array_ops_.identity(assign_mean), array_ops_.identity(assign_var)

        is_training = array_ops_.squeeze(ops.get_collection("IS_TRAINING"))
        mean, variance = control_flow_ops.cond(is_training, update_mean_var, lambda: (ema_mean, ema_var))
        return nn.batch_norm_with_global_normalization(
            tensor_in, mean, variance, beta, gamma, epsilon, scale_after_normalization=scale_after_normalization
        )

 def testRestoreOnAssign(self):
   checkpoint_directory = self.get_temp_dir()
   checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
   save_graph = ops.Graph()
   with save_graph.as_default(), self.test_session(save_graph):
     first = checkpointable.Checkpointable()
     first.var1 = variable_scope.get_variable(
         name="outside_var", initializer=0.)
     first.var2 = variable_scope.get_variable(
         name="blah", initializer=0.)
     self.evaluate(first.var1.assign(4.))
     self.evaluate(first.var2.assign(8.))
     save_path = checkpointable_utils.CheckpointableSaver(first).save(
         checkpoint_prefix)
   restore_graph = ops.Graph()
   with restore_graph.as_default(), self.test_session(restore_graph):
     second = checkpointable.Checkpointable()
     second.var2 = variable_scope.get_variable(
         name="blah", initializer=0.)
     status = checkpointable_utils.CheckpointableSaver(
         second).restore(save_path)
     recreated_var1 = variable_scope.get_variable(
         name="outside_var", initializer=0.)
     status.run_restore_ops()
     self.assertEqual(8., self.evaluate(second.var2))
     self.evaluate(recreated_var1.assign(-2.))
     self.assertEqual(-2., self.evaluate(recreated_var1))
     second.var1 = recreated_var1
     status.run_restore_ops()
     self.assertEqual(4., self.evaluate(recreated_var1))

  def _project_input(self, inputs, c_prev, m_prev, with_c):
    """Fills in c_prev and m_prev with projected input, for input dimensions
    """
    conf = self._config

    if (inputs is not None and inputs.get_shape().with_rank(2)[1].value > 0
        and len(conf.inputs) > 0):
      if isinstance(inputs, tuple):
        if len(conf.inputs) != len(inputs):
          raise ValueError("Expect inputs as a tuple of {} "
                           "tensors".format(len(conf.inputs)))
        input_splits = inputs
      else:
        input_splits = array_ops.split(
          value=inputs, num_or_size_splits=len(conf.inputs), axis=1)
      input_sz = input_splits[0].get_shape().with_rank(2)[1].value

      for i, j in enumerate(conf.inputs):
        input_project_m = vs.get_variable(
          'project_m_{}'.format(j), [input_sz, conf.num_units],
          dtype=inputs.dtype)
        m_prev[j] = math_ops.matmul(input_splits[i], input_project_m)

        if with_c:
          input_project_c = vs.get_variable(
            'project_c_{}'.format(j), [input_sz, conf.num_units],
            dtype=inputs.dtype)
          c_prev[j] = math_ops.matmul(input_splits[i], input_project_c)

  def testPartitionConcatenatesAlongCorrectAxis(self):

    def _part_axis_0(**unused_kwargs):
      return (2, 1, 1)

    def _part_axis_1(**unused_kwargs):
      return (1, 2, 1)

    with variable_scope.variable_scope("root"):
      v0 = variable_scope.get_variable(
          "n0", shape=(2, 2, 2), partitioner=_part_axis_0)
      v1 = variable_scope.get_variable(
          "n1", shape=(2, 2, 2), partitioner=_part_axis_1)

    self.assertEqual(v0.get_shape(), (2, 2, 2))
    self.assertEqual(v1.get_shape(), (2, 2, 2))

    n0_0 = ops.get_default_graph().get_tensor_by_name("root/n0/part_0:0")
    n0_1 = ops.get_default_graph().get_tensor_by_name("root/n0/part_1:0")
    self.assertEqual(n0_0.get_shape(), (1, 2, 2))
    self.assertEqual(n0_1.get_shape(), (1, 2, 2))

    n1_0 = ops.get_default_graph().get_tensor_by_name("root/n1/part_0:0")
    n1_1 = ops.get_default_graph().get_tensor_by_name("root/n1/part_1:0")
    self.assertEqual(n1_0.get_shape(), (2, 1, 2))
    self.assertEqual(n1_1.get_shape(), (2, 1, 2))

def weighted_moving_average(value,
                            decay,
                            weight,
                            truediv=True,
                            collections=None,
                            name=None):
  """Compute the weighted moving average of `value`.

  Conceptually, the weighted moving average is:
    `moving_average(value * weight) / moving_average(weight)`,
  where a moving average updates by the rule
    `new_value = decay * old_value + (1 - decay) * update`
  Internally, this Op keeps moving average variables of both `value * weight`
  and `weight`.

  Args:
    value: A numeric `Tensor`.
    decay: A float `Tensor` or float value.  The moving average decay.
    weight:  `Tensor` that keeps the current value of a weight.
      Shape should be able to multiply `value`.
    truediv:  Boolean, if `True`, dividing by `moving_average(weight)` is
      floating point division.  If `False`, use division implied by dtypes.
    collections:  List of graph collections keys to add the internal variables
      `value * weight` and `weight` to.
      Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
    name: Optional name of the returned operation.
      Defaults to "WeightedMovingAvg".

  Returns:
    An Operation that updates and returns the weighted moving average.
  """
  # Unlike assign_moving_average, the weighted moving average doesn't modify
  # user-visible variables. It is the ratio of two internal variables, which are
  # moving averages of the updates.  Thus, the signature of this function is
  # quite different than assign_moving_average.
  if collections is None:
    collections = [ops.GraphKeys.GLOBAL_VARIABLES]
  with variable_scope.variable_scope(name, "WeightedMovingAvg",
                                     [value, weight, decay]) as scope:
    value_x_weight_var = variable_scope.get_variable(
        "value_x_weight",
        initializer=init_ops.zeros_initializer(value.get_shape(),
                                               dtype=value.dtype),
        trainable=False,
        collections=collections)
    weight_var = variable_scope.get_variable(
        "weight",
        initializer=init_ops.zeros_initializer(weight.get_shape(),
                                               dtype=weight.dtype),
        trainable=False,
        collections=collections)
    numerator = assign_moving_average(
        value_x_weight_var, value * weight, decay, zero_debias=False)
    denominator = assign_moving_average(
        weight_var, weight, decay, zero_debias=False)

    if truediv:
      return math_ops.truediv(numerator, denominator, name=scope.name)
    else:
      return math_ops.div(numerator, denominator, name=scope.name)

  def __call__(self, x, h_prev, scope=None):
    """GRU cell."""
    with vs.variable_scope(scope or type(self).__name__):
      input_size = x.get_shape().with_rank(2)[1]

      # Check if the input size exist.
      if input_size is None:
        raise ValueError("Expecting input_size to be set.")

      # Check cell_size == state_size from h_prev.
      cell_size = h_prev.get_shape().with_rank(2)[1]
      if cell_size != self._cell_size:
        raise ValueError("Shape of h_prev[1] incorrect: cell_size %i vs %s" %
                         (self._cell_size, cell_size))

      if cell_size is None:
        raise ValueError("cell_size from `h_prev` should not be None.")

      w_ru = vs.get_variable("w_ru", [input_size + self._cell_size,
                                      self._cell_size * 2])
      b_ru = vs.get_variable(
          "b_ru", [self._cell_size * 2],
          initializer=init_ops.constant_initializer(1.0))
      w_c = vs.get_variable("w_c",
                            [input_size + self._cell_size, self._cell_size])
      b_c = vs.get_variable(
          "b_c", [self._cell_size],
          initializer=init_ops.constant_initializer(0.0))

      _gru_block_cell = gen_gru_ops.gru_block_cell  # pylint: disable=invalid-name
      _, _, _, new_h = _gru_block_cell(
          x=x, h_prev=h_prev, w_ru=w_ru, w_c=w_c, b_ru=b_ru, b_c=b_c)

      return new_h, new_h

 def _annotated_graph(self):
   graph = ops.Graph()
   with graph.as_default():
     random_seed.set_random_seed(2)
     current_activation = variable_scope.get_variable(
         name='start', shape=[1, 2, 2, 5])
     conv_filter = variable_scope.get_variable(
         name='filter', shape=[5, 5, 5, 5])
     for layer_number in range(3):
       with variable_scope.variable_scope('layer_{}'.format(layer_number)):
         after_conv = nn.conv2d(current_activation, conv_filter, [1, 1, 1, 1],
                                'SAME')
         current_activation = 2. * after_conv
         current_activation.op._set_attr(
             '_recompute_hint',
             # The value of the attribute does not matter; just that the key
             # exists in the op's attributes.
             attr_value_pb2.AttrValue(i=1))
         current_activation += 5.
         current_activation.op._set_attr(
             '_recompute_hint', attr_value_pb2.AttrValue(i=0))
         current_activation = nn.relu(current_activation)
         current_activation.op._set_attr(
             '_recompute_hint', attr_value_pb2.AttrValue(i=1))
     loss = math_ops.reduce_mean(current_activation)
     optimizer = train.AdamOptimizer(0.001)
     train_op = optimizer.minimize(loss)
     init_op = variables.global_variables_initializer()
   return graph, init_op, train_op