Python tensorflow.python.ops.array_ops 模块，slice() 实例源码

def _gini(self, class_counts):
    """Calculate the Gini impurity.

    If c(i) denotes the i-th class count and c = sum_i c(i) then
      score = 1 - sum_i ( c(i) / c )^2

    Args:
      class_counts: A 2-D tensor of per-class counts, usually a slice or
        gather from variables.node_sums.

    Returns:
      A 1-D tensor of the Gini impurities for each row in the input.
    """
    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
    sums = math_ops.reduce_sum(smoothed, 1)
    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)

    return 1.0 - sum_squares / (sums * sums)

def _weighted_gini(self, class_counts):
    """Our split score is the Gini impurity times the number of examples.

    If c(i) denotes the i-th class count and c = sum_i c(i) then
      score = c * (1 - sum_i ( c(i) / c )^2 )
            = c - sum_i c(i)^2 / c
    Args:
      class_counts: A 2-D tensor of per-class counts, usually a slice or
        gather from variables.node_sums.

    Returns:
      A 1-D tensor of the Gini impurities for each row in the input.
    """
    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
    sums = math_ops.reduce_sum(smoothed, 1)
    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)

    return sums - sum_squares / sums

def _variance(self, sums, squares):
    """Calculate the variance for each row of the input tensors.

    Variance is V = E[x^2] - (E[x])^2.

    Args:
      sums: A tensor containing output sums, usually a slice from
        variables.node_sums.  Should contain the number of examples seen
        in index 0 so we can calculate expected value.
      squares: Same as sums, but sums of squares.

    Returns:
      A 1-D tensor of the variances for each row in the input.
    """
    total_count = array_ops.slice(sums, [0, 0], [-1, 1])
    e_x = sums / total_count
    e_x2 = squares / total_count

    return math_ops.reduce_sum(e_x2 - math_ops.square(e_x), 1)

def _gini(self, class_counts):
    """Calculate the Gini impurity.

    If c(i) denotes the i-th class count and c = sum_i c(i) then
      score = 1 - sum_i ( c(i) / c )^2

    Args:
      class_counts: A 2-D tensor of per-class counts, usually a slice or
        gather from variables.node_sums.

    Returns:
      A 1-D tensor of the Gini impurities for each row in the input.
    """
    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
    sums = math_ops.reduce_sum(smoothed, 1)
    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)

    return 1.0 - sum_squares / (sums * sums)

def _weighted_gini(self, class_counts):
    """Our split score is the Gini impurity times the number of examples.

    If c(i) denotes the i-th class count and c = sum_i c(i) then
      score = c * (1 - sum_i ( c(i) / c )^2 )
            = c - sum_i c(i)^2 / c
    Args:
      class_counts: A 2-D tensor of per-class counts, usually a slice or
        gather from variables.node_sums.

    Returns:
      A 1-D tensor of the Gini impurities for each row in the input.
    """
    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
    sums = math_ops.reduce_sum(smoothed, 1)
    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)

    return sums - sum_squares / sums

def _variance(self, sums, squares):
    """Calculate the variance for each row of the input tensors.

    Variance is V = E[x^2] - (E[x])^2.

    Args:
      sums: A tensor containing output sums, usually a slice from
        variables.node_sums.  Should contain the number of examples seen
        in index 0 so we can calculate expected value.
      squares: Same as sums, but sums of squares.

    Returns:
      A 1-D tensor of the variances for each row in the input.
    """
    total_count = array_ops.slice(sums, [0, 0], [-1, 1])
    e_x = sums / total_count
    e_x2 = squares / total_count

    return math_ops.reduce_sum(e_x2 - math_ops.square(e_x), 1)

def average_impurity(self):
    """Constructs a TF graph for evaluating the average leaf impurity of a tree.

    If in regression mode, this is the leaf variance. If in classification mode,
    this is the gini impurity.

    Returns:
      The last op in the graph.
    """
    children = array_ops.squeeze(array_ops.slice(
        self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
    is_leaf = math_ops.equal(constants.LEAF_NODE, children)
    leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
                                                 squeeze_dims=[1]))
    counts = array_ops.gather(self.variables.node_sums, leaves)
    gini = self._weighted_gini(counts)
    # Guard against step 1, when there often are no leaves yet.
    def impurity():
      return gini
    # Since average impurity can be used for loss, when there's no data just
    # return a big number so that loss always decreases.
    def big():
      return array_ops.ones_like(gini, dtype=dtypes.float32) * 10000000.
    return control_flow_ops.cond(math_ops.greater(
        array_ops.shape(leaves)[0], 0), impurity, big)

def inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      routing_probabilities = self.training_ops.k_feature_routing_function(
          data,
          self.tree_parameters,
          self.tree_thresholds,
          max_nodes=self.params.num_nodes,
          num_features_per_node=self.params.num_features_per_node,
          layer_num=0,
          random_seed=self.params.base_random_seed)

      output = array_ops.slice(
          routing_probabilities,
          [0, self.params.num_nodes - self.params.num_leaves - 1],
          [-1, self.params.num_leaves])

      return output

def soft_inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      path_probability, path = (
          self.training_ops.stochastic_hard_routing_function(
              data,
              self.tree_parameters,
              self.tree_thresholds,
              tree_depth=self.params.hybrid_tree_depth,
              random_seed=self.params.base_random_seed))

      output = array_ops.slice(
          self.training_ops.unpack_path(path, path_probability),
          [0, self.params.num_nodes - self.params.num_leaves - 1],
          [-1, self.params.num_leaves])

      return output

def batch_shape(self, name="batch_shape"):
    """Shape of batches associated with this operator.

    If this operator represents the batch matrix `A` with
    `A.shape = [N1,...,Nn, k, k]`, the `batch_shape` is `[N1,...,Nn]`.

    Args:
      name:  A name scope to use for ops added by this method.

    Returns:
      `int32` `Tensor`
    """
    # Derived classes get this "for free" once .shape() is implemented.
    with ops.name_scope(self.name):
      with ops.name_scope(name, values=self.inputs):
        return array_ops.slice(self.shape(), [0], [self.rank() - 2])

def _get_identity_operator(self, v):
    """Get an `OperatorPDIdentity` to play the role of `D` in `VDV^T`."""
    with ops.name_scope("get_identity_operator", values=[v]):
      if v.get_shape().is_fully_defined():
        v_shape = v.get_shape().as_list()
        v_batch_shape = v_shape[:-2]
        r = v_shape[-1]
        id_shape = v_batch_shape + [r, r]
      else:
        v_shape = array_ops.shape(v)
        v_rank = array_ops.rank(v)
        v_batch_shape = array_ops.slice(v_shape, [0], [v_rank - 2])
        r = array_ops.gather(v_shape, v_rank - 1)  # Last dim of v
        id_shape = array_ops.concat(0, (v_batch_shape, [r, r]))
      return operator_pd_identity.OperatorPDIdentity(
          id_shape, v.dtype, verify_pd=self._verify_pd)

def _attention(self, query, attn_states):
    conv2d = nn_ops.conv2d
    reduce_sum = math_ops.reduce_sum
    softmax = nn_ops.softmax
    tanh = math_ops.tanh

    with vs.variable_scope("Attention"):
      k = vs.get_variable("AttnW", [1, 1, self._attn_size, self._attn_vec_size])
      v = vs.get_variable("AttnV", [self._attn_vec_size])
      hidden = array_ops.reshape(attn_states,
                                 [-1, self._attn_length, 1, self._attn_size])
      hidden_features = conv2d(hidden, k, [1, 1, 1, 1], "SAME")
      y = _linear(query, self._attn_vec_size, True)
      y = array_ops.reshape(y, [-1, 1, 1, self._attn_vec_size])
      s = reduce_sum(v * tanh(hidden_features + y), [2, 3])
      a = softmax(s)
      d = reduce_sum(
          array_ops.reshape(a, [-1, self._attn_length, 1, 1]) * hidden, [1, 2])
      new_attns = array_ops.reshape(d, [-1, self._attn_size])
      new_attn_states = array_ops.slice(attn_states, [0, 1, 0], [-1, -1, -1])
      return new_attns, new_attn_states

def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat(0, (outer_dimensions, [-1]))
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped

def _gini(self, class_counts):
    """Calculate the Gini impurity.

    If c(i) denotes the i-th class count and c = sum_i c(i) then
      score = 1 - sum_i ( c(i) / c )^2

    Args:
      class_counts: A 2-D tensor of per-class counts, usually a slice or
        gather from variables.node_sums.

    Returns:
      A 1-D tensor of the Gini impurities for each row in the input.
    """
    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
    sums = math_ops.reduce_sum(smoothed, 1)
    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)

    return 1.0 - sum_squares / (sums * sums)

def _weighted_gini(self, class_counts):
    """Our split score is the Gini impurity times the number of examples.

    If c(i) denotes the i-th class count and c = sum_i c(i) then
      score = c * (1 - sum_i ( c(i) / c )^2 )
            = c - sum_i c(i)^2 / c
    Args:
      class_counts: A 2-D tensor of per-class counts, usually a slice or
        gather from variables.node_sums.

    Returns:
      A 1-D tensor of the Gini impurities for each row in the input.
    """
    smoothed = 1.0 + array_ops.slice(class_counts, [0, 1], [-1, -1])
    sums = math_ops.reduce_sum(smoothed, 1)
    sum_squares = math_ops.reduce_sum(math_ops.square(smoothed), 1)

    return sums - sum_squares / sums

def _variance(self, sums, squares):
    """Calculate the variance for each row of the input tensors.

    Variance is V = E[x^2] - (E[x])^2.

    Args:
      sums: A tensor containing output sums, usually a slice from
        variables.node_sums.  Should contain the number of examples seen
        in index 0 so we can calculate expected value.
      squares: Same as sums, but sums of squares.

    Returns:
      A 1-D tensor of the variances for each row in the input.
    """
    total_count = array_ops.slice(sums, [0, 0], [-1, 1])
    e_x = sums / total_count
    e_x2 = squares / total_count

    return math_ops.reduce_sum(e_x2 - math_ops.square(e_x), 1)

def average_impurity(self):
    """Constructs a TF graph for evaluating the average leaf impurity of a tree.

    If in regression mode, this is the leaf variance. If in classification mode,
    this is the gini impurity.

    Returns:
      The last op in the graph.
    """
    children = array_ops.squeeze(array_ops.slice(
        self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
    is_leaf = math_ops.equal(constants.LEAF_NODE, children)
    leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
                                                 squeeze_dims=[1]))
    counts = array_ops.gather(self.variables.node_sums, leaves)
    gini = self._weighted_gini(counts)
    # Guard against step 1, when there often are no leaves yet.
    def impurity():
      return gini
    # Since average impurity can be used for loss, when there's no data just
    # return a big number so that loss always decreases.
    def big():
      return array_ops.ones_like(gini, dtype=dtypes.float32) * 10000000.
    return control_flow_ops.cond(math_ops.greater(
        array_ops.shape(leaves)[0], 0), impurity, big)

def inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      routing_probabilities = self.training_ops.k_feature_routing_function(
          data,
          self.tree_parameters,
          self.tree_thresholds,
          max_nodes=self.params.num_nodes,
          num_features_per_node=self.params.num_features_per_node,
          layer_num=0,
          random_seed=self.params.base_random_seed)

      output = array_ops.slice(
          routing_probabilities,
          [0, self.params.num_nodes - self.params.num_leaves - 1],
          [-1, self.params.num_leaves])

      return output

def soft_inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      path_probability, path = (
          self.training_ops.stochastic_hard_routing_function(
              data,
              self.tree_parameters,
              self.tree_thresholds,
              tree_depth=self.params.hybrid_tree_depth,
              random_seed=self.params.base_random_seed))

      output = array_ops.slice(
          self.training_ops.unpack_path(path, path_probability),
          [0, self.params.num_nodes - self.params.num_leaves - 1],
          [-1, self.params.num_leaves])

      return output

def batch_shape(self, name="batch_shape"):
    """Shape of batches associated with this operator.

    If this operator represents the batch matrix `A` with
    `A.shape = [N1,...,Nn, k, k]`, the `batch_shape` is `[N1,...,Nn]`.

    Args:
      name:  A name scope to use for ops added by this method.

    Returns:
      `int32` `Tensor`
    """
    # Derived classes get this "for free" once .shape() is implemented.
    with ops.name_scope(self.name):
      with ops.name_scope(name, values=self.inputs):
        return array_ops.slice(self.shape(), [0], [self.rank() - 2])

def _get_identity_operator(self, v):
    """Get an `OperatorPDIdentity` to play the role of `D` in `VDV^T`."""
    with ops.name_scope("get_identity_operator", values=[v]):
      if v.get_shape().is_fully_defined():
        v_shape = v.get_shape().as_list()
        v_batch_shape = v_shape[:-2]
        r = v_shape[-1]
        id_shape = v_batch_shape + [r, r]
      else:
        v_shape = array_ops.shape(v)
        v_rank = array_ops.rank(v)
        v_batch_shape = array_ops.slice(v_shape, [0], [v_rank - 2])
        r = array_ops.gather(v_shape, v_rank - 1)  # Last dim of v
        id_shape = array_ops.concat(0, (v_batch_shape, [r, r]))
      return operator_pd_identity.OperatorPDIdentity(
          id_shape, v.dtype, verify_pd=self._verify_pd)

def _attention(self, query, attn_states):
    conv2d = nn_ops.conv2d
    reduce_sum = math_ops.reduce_sum
    softmax = nn_ops.softmax
    tanh = math_ops.tanh

    with vs.variable_scope("Attention"):
      k = vs.get_variable("AttnW", [1, 1, self._attn_size, self._attn_vec_size])
      v = vs.get_variable("AttnV", [self._attn_vec_size])
      hidden = array_ops.reshape(attn_states,
                                 [-1, self._attn_length, 1, self._attn_size])
      hidden_features = conv2d(hidden, k, [1, 1, 1, 1], "SAME")
      y = _linear(query, self._attn_vec_size, True)
      y = array_ops.reshape(y, [-1, 1, 1, self._attn_vec_size])
      s = reduce_sum(v * tanh(hidden_features + y), [2, 3])
      a = softmax(s)
      d = reduce_sum(
          array_ops.reshape(a, [-1, self._attn_length, 1, 1]) * hidden, [1, 2])
      new_attns = array_ops.reshape(d, [-1, self._attn_size])
      new_attn_states = array_ops.slice(attn_states, [0, 1, 0], [-1, -1, -1])
      return new_attns, new_attn_states

def _attention(self, query, attn_states):
    conv2d = nn_ops.conv2d
    reduce_sum = math_ops.reduce_sum
    softmax = nn_ops.softmax
    tanh = math_ops.tanh

    with tf.variable_scope("attention"):
      k = tf.get_variable(
          "attn_w", [1, 1, self._attn_size, self._attn_vec_size])
      v = tf.get_variable("attn_v", [self._attn_vec_size])
      hidden = array_ops.reshape(attn_states,
                                 [-1, self._attn_length, 1, self._attn_size])
      hidden_features = conv2d(hidden, k, [1, 1, 1, 1], "SAME")
      y = _linear(query, self._attn_vec_size, True)
      y = array_ops.reshape(y, [-1, 1, 1, self._attn_vec_size])
      s = reduce_sum(v * tanh(hidden_features + y), [2, 3])
      a = softmax(s)
      d = reduce_sum(
          array_ops.reshape(a, [-1, self._attn_length, 1, 1]) * hidden, [1, 2])
      new_attns = array_ops.reshape(d, [-1, self._attn_size])
      new_attn_states = array_ops.slice(attn_states, [0, 1, 0], [-1, -1, -1])
      return new_attns, new_attn_states

def multiPLSTM(cells, inputs, lens, n_input, initial_states):
    """
    Function to build multilayer PLSTM
    :param cells:
    :param inputs:
    :param lens: 2D tensor, length of the sequences in the batch (for synamic rnn use)
    :param n_input: integer, number of features in the input (without time feature)
    :param initial_states: list of tuples of initial states
    :return: 3D tensor, output of the multilayer PLSTM
    """

    assert (len(initial_states) == len(cells))
    times = tf.slice(inputs, [0, 0, n_input], [-1, -1, 1])
    newX = tf.slice(inputs, [0, 0, 0], [-1, -1, n_input])

    for k, cell, initial_state in zip(range(len(cells)), cells, initial_states):
        newX = tf.concat(2, [newX, times])
        with tf.variable_scope("{}".format(k)):
            outputs, initial_state = tf.nn.dynamic_rnn(cell, newX, dtype=tf.float32,
                                                       sequence_length=lens,
                                                       initial_state=initial_state)
            newX = outputs
    return newX

def multiPLSTM(cells, inputs, lens, n_input, initial_states):
    """
    Function to build multilayer PLSTM
    :param cells:
    :param inputs:
    :param lens: 2D tensor, length of the sequences in the batch (for synamic rnn use)
    :param n_input: integer, number of features in the input (without time feature)
    :param initial_states: list of tuples of initial states
    :return: 3D tensor, output of the multilayer PLSTM
    """

    assert (len(initial_states) == len(cells))
    times = tf.slice(inputs, [0, 0, n_input], [-1, -1, 1])
    new_x = tf.slice(inputs, [0, 0, 0], [-1, -1, n_input])

    for k, cell, initial_state in zip(range(len(cells)), cells, initial_states):
        new_x = tf.concat(axis=2, values=[new_x, times])
        with tf.variable_scope("{}".format(k)):
            outputs, initial_states[k] = tf.nn.dynamic_rnn(cell, new_x, dtype=tf.float32,
                                                       sequence_length=lens,
                                                       initial_state=initial_state)
            new_x = outputs
    return new_x

def __call__(self, inputs, state, scope=None):
    """Run this multi-layer cell on inputs, starting from state."""
    with vs.variable_scope(scope or type(self).__name__):  # "MultiRNNCell"
      cur_state_pos = 0
      cur_inp = inputs
      new_states = []
      for i, cell in enumerate(self._cells):
        with vs.variable_scope("Cell%d" % i):
          if self._state_is_tuple:
            if not nest.is_sequence(state):
              raise ValueError(
                  "Expected state to be a tuple of length %d, but received: %s"
                  % (len(self.state_size), state))
            cur_state = state[i]
          else:
            cur_state = array_ops.slice(
                state, [0, cur_state_pos], [-1, cell.state_size])
            cur_state_pos += cell.state_size
          cur_inp, new_state = cell(cur_inp, cur_state)
          new_states.append(new_state)
    new_states = (tuple(new_states) if self._state_is_tuple
                  else array_ops.concat(1, new_states))
    return cur_inp, new_states

def __call__(self, inputs, state, scope=None):
    """Run this multi-layer cell on inputs, starting from state."""
    with vs.variable_scope(scope or type(self).__name__):  # "MultiRNNCell"
      cur_state_pos = 0
      cur_inp = inputs
      new_states = []
      for i, cell in enumerate(self._cells):
        with vs.variable_scope("Cell%d" % i):
          if self._state_is_tuple:
            if not nest.is_sequence(state):
              raise ValueError(
                  "Expected state to be a tuple of length %d, but received: %s"
                  % (len(self.state_size), state))
            cur_state = state[i]
          else:
            cur_state = array_ops.slice(
                state, [0, cur_state_pos], [-1, cell.state_size])
            cur_state_pos += cell.state_size
          cur_inp, new_state = cell(cur_inp, cur_state)
          new_states.append(new_state)
    new_states = (tuple(new_states) if self._state_is_tuple
                  else array_ops.concat(1, new_states))
    return cur_inp, new_states

def _split_logits(self, logits):
    """Splits logits for heads.

    Args:
      logits: the logits tensor.

    Returns:
      A list of logits for the individual heads.
    """
    all_logits = []
    begin = 0
    for head in self._heads:
      current_logits_size = head.logits_dimension
      current_logits = array_ops.slice(logits, [0, begin],
                                       [-1, current_logits_size])
      all_logits.append(current_logits)
      begin += current_logits_size
    return all_logits

def call(self, inputs, state):
    """Run this multi-layer cell on inputs, starting from state."""
    cur_state_pos = 0
    cur_inp = inputs
    new_states = []
    for i, cell in enumerate(self._cells):
      with vs.variable_scope("cell_%d" % i):
        if self._state_is_tuple:
          if not nest.is_sequence(state):
            raise ValueError(
                "Expected state to be a tuple of length %d, but received: %s" %
                (len(self.state_size), state))
          cur_state = state[i]
        else:
          cur_state = array_ops.slice(state, [0, cur_state_pos],
                                      [-1, cell.state_size])
          cur_state_pos += cell.state_size
        cur_inp, new_state = cell(cur_inp, cur_state)
        new_states.append(new_state)

    new_states = (tuple(new_states) if self._state_is_tuple else
                  array_ops.concat(new_states, 1))

    return cur_inp, new_states

def crop_to_1d_bounding_box(image, offset_height, target_height,
                         dynamic_shape=False):
  """Crops an image to a specified bounding box.

  This op cuts a rectangular part out of `image`. The top-left corner of the
  returned image is at `offset_height, offset_width` in `image`, and its
  lower-right corner is at
  `offset_height + target_height, offset_width + target_width`.

  Args:
    image: 3-D tensor with shape `[height, width, channels]`
    offset_height: Vertical coordinate of the top-left corner of the result in
                   the input.
    target_height: Height of the result.
    dynamic_shape: Whether the input image has undertermined shape. If set to
      `True`, shape information will be retrieved at run time. Default to
      `False`.

  Returns:
    3-D tensor of image with shape `[target_height, target_width, channels]`

  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
    `target_*` arguments, and `dynamic_shape` is set to `False`.
  """
  image = tf.convert_to_tensor(image, name='image')
  height, _ = _ImageDimensions(image, dynamic_shape=dynamic_shape)

  cropped = array_ops.slice(image,
                            array_ops.pack([offset_height, 0]),
                            array_ops.pack([target_height, -1]))

  return cropped

def crop_to_bounding_box(image, offset_height, offset_width, target_height,
                         target_width, dynamic_shape=False):
  """Crops an image to a specified bounding box.

  This op cuts a rectangular part out of `image`. The top-left corner of the
  returned image is at `offset_height, offset_width` in `image`, and its
  lower-right corner is at
  `offset_height + target_height, offset_width + target_width`.

  Args:
    image: 3-D tensor with shape `[height, width, channels]`
    offset_height: Vertical coordinate of the top-left corner of the result in
                   the input.
    offset_width: Horizontal coordinate of the top-left corner of the result in
                  the input.
    target_height: Height of the result.
    target_width: Width of the result.
    dynamic_shape: Whether the input image has undertermined shape. If set to
      `True`, shape information will be retrieved at run time. Default to
      `False`.

  Returns:
    3-D tensor of image with shape `[target_height, target_width, channels]`

  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
    `target_*` arguments, and `dynamic_shape` is set to `False`.
  """
  image = tf.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=(not dynamic_shape))
  shapes = _ImageDimensions(image, dynamic_shape=dynamic_shape)

  cropped = array_ops.slice(image,
                            array_ops.pack([offset_height, 0]),
                            array_ops.pack([target_height, -1]))

  return cropped


# In[3]:

def _get_input_for_group(self, inpt, group_id, group_size):
        """
        Slices inputs into groups to prepare for processing by cell's groups
        :param inpt: inputs
        :param group_id: group id, for which to prepare extract input_group_id
        :param group_size: size of the group
        :return: subset of inputs, correspoinding to group group_id
        """
        return array_ops.slice(input_=inpt,
                               begin=[0, group_id * group_size],
                               size=[inpt.get_shape()[0].value, group_size],
                               name="GLSTMinputGroupCreation")

def __call__(self, inputs, question, state, m_input_size, m_size, scope=None):
        """Run this multi-layer cell on inputs, starting from state."""
        with vs.variable_scope(scope or type(self).__name__):   # "MultiRNNCell"
            cur_state_pos = 0
            cur_inp = inputs
            new_states = []
            for i, cell in enumerate(self._cells):
                with vs.variable_scope("Cell%d" % i):
                    cur_state = array_ops.slice(
                            state, [0, cur_state_pos], [-1, cell.state_size])
                    cur_state_pos += cell.state_size
                    cur_inp, new_state = cell(cur_inp, question, cur_state, m_input_size, m_size)
                    new_states.append(new_state)
        return cur_inp, array_ops.concat(1, new_states)

def __call__(self, inputs, state, episodic_gate, scope=None):
        """Run this multi-layer cell on inputs, starting from state."""
        with vs.variable_scope(scope or type(self).__name__):   # "MultiRNNCell"
            cur_state_pos = 0
            cur_inp = inputs
            new_states = []
            for i, cell in enumerate(self._cells):
                with vs.variable_scope("Cell%d" % i):
                    cur_state = array_ops.slice(
                            state, [0, cur_state_pos], [-1, cell.state_size])
                    cur_state_pos += cell.state_size
                    cur_inp, new_state = cell(cur_inp, cur_state, episodic_gate)
                    new_states.append(new_state)
        return cur_inp, array_ops.concat(1, new_states)

def tree_initialization(self):
    def _init_tree():
      return state_ops.scatter_update(self.variables.tree, [0], [[-1, -1]]).op

    def _nothing():
      return control_flow_ops.no_op()

    return control_flow_ops.cond(
        math_ops.equal(array_ops.squeeze(array_ops.slice(
            self.variables.tree, [0, 0], [1, 1])), -2),
        _init_tree, _nothing)

def get_stats(self, session):
    num_nodes = self.variables.end_of_tree.eval(session=session) - 1
    num_leaves = array_ops.where(
        math_ops.equal(array_ops.squeeze(array_ops.slice(
            self.variables.tree, [0, 0], [-1, 1])), constants.LEAF_NODE)
        ).eval(session=session).shape[0]
    return TreeStats(num_nodes, num_leaves)

def random_crop(value, size, seed=None, name=None):
  """Randomly crops a tensor to a given size.

  Slices a shape `size` portion out of `value` at a uniformly chosen offset.
  Requires `value.shape >= size`.

  If a dimension should not be cropped, pass the full size of that dimension.
  For example, RGB images can be cropped with
  `size = [crop_height, crop_width, 3]`.

  Args:
    value: Input tensor to crop.
    size: 1-D tensor with size the rank of `value`.
    seed: Python integer. Used to create a random seed. See
      @{tf.set_random_seed}
      for behavior.
    name: A name for this operation (optional).

  Returns:
    A cropped tensor of the same rank as `value` and shape `size`.
  """
  # TODO(shlens): Implement edge case to guarantee output size dimensions.
  # If size > value.shape, zero pad the result so that it always has shape
  # exactly size.
  with ops.name_scope(name, "random_crop", [value, size]) as name:
    value = ops.convert_to_tensor(value, name="value")
    size = ops.convert_to_tensor(size, dtype=dtypes.int32, name="size")
    shape = array_ops.shape(value)
    check = control_flow_ops.Assert(
        math_ops.reduce_all(shape >= size),
        ["Need value.shape >= size, got ", shape, size],
        summarize=1000)
    shape = control_flow_ops.with_dependencies([check], shape)
    limit = shape - size + 1
    offset = random_uniform(
        array_ops.shape(shape),
        dtype=size.dtype,
        maxval=size.dtype.max,
        seed=seed) % limit
    return array_ops.slice(value, offset, size, name=name)

def multinomial(logits, num_samples, seed=None, name=None):
  """Draws samples from a multinomial distribution.

  Example:

  ```python
  # samples has shape [1, 5], where each value is either 0 or 1 with equal
  # probability.
  samples = tf.multinomial(tf.log([[10., 10.]]), 5)

def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None):
  """Draws `shape` samples from each of the given Poisson distribution(s).

  `lam` is the rate parameter describing the distribution(s).

  Example:

    samples = tf.random_poisson([0.5, 1.5], [10])
    # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
    # the samples drawn from each distribution

    samples = tf.random_poisson([12.2, 3.3], [7, 5])
    # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
    # represents the 7x5 samples drawn from each of the two distributions

  Args:
    lam: A Tensor or Python value or N-D array of type `dtype`.
      `lam` provides the rate parameter(s) describing the poisson
      distribution(s) to sample.
    shape: A 1-D integer Tensor or Python array. The shape of the output samples
      to be drawn per "rate"-parameterized distribution.
    dtype: The type of `lam` and the output: `float16`, `float32`, or
      `float64`.
    seed: A Python integer. Used to create a random seed for the distributions.
      See
      @{tf.set_random_seed}
      for behavior.
    name: Optional name for the operation.

  Returns:
    samples: a `Tensor` of shape `tf.concat(shape, tf.shape(lam))` with
      values of type `dtype`.
  """
  with ops.name_scope(name, "random_poisson", [lam, shape]):
    lam = ops.convert_to_tensor(lam, name="lam", dtype=dtype)
    shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
    seed1, seed2 = random_seed.get_seed(seed)
    return gen_random_ops._random_poisson(shape, lam, seed=seed1, seed2=seed2)

def crf_log_norm(inputs, sequence_lengths, transition_params):
  """Computes the normalization for a CRF.

  Args:
    inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
        to use as input to the CRF layer.
    sequence_lengths: A [batch_size] vector of true sequence lengths.
    transition_params: A [num_tags, num_tags] transition matrix.
  Returns:
    log_norm: A [batch_size] vector of normalizers for a CRF.
  """
  # Split up the first and rest of the inputs in preparation for the forward
  # algorithm.
  first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1])
  first_input = array_ops.squeeze(first_input, [1])
  rest_of_input = array_ops.slice(inputs, [0, 1, 0], [-1, -1, -1])

  # Compute the alpha values in the forward algorithm in order to get the
  # partition function.
  forward_cell = CrfForwardRnnCell(transition_params)
  _, alphas = rnn.dynamic_rnn(
      cell=forward_cell,
      inputs=rest_of_input,
      sequence_length=sequence_lengths - 1,
      initial_state=first_input,
      dtype=dtypes.float32)
  log_norm = math_ops.reduce_logsumexp(alphas, [1])
  return log_norm

def crf_binary_score(tag_indices, sequence_lengths, transition_params):
  """Computes the binary scores of tag sequences.

  Args:
    tag_indices: A [batch_size, max_seq_len] matrix of tag indices.
    sequence_lengths: A [batch_size] vector of true sequence lengths.
    transition_params: A [num_tags, num_tags] matrix of binary potentials.
  Returns:
    binary_scores: A [batch_size] vector of binary scores.
  """
  # Get shape information.
  num_tags = transition_params.get_shape()[0]
  num_transitions = array_ops.shape(tag_indices)[1] - 1

  # Truncate by one on each side of the sequence to get the start and end
  # indices of each transition.
  start_tag_indices = array_ops.slice(tag_indices, [0, 0],
                                      [-1, num_transitions])
  end_tag_indices = array_ops.slice(tag_indices, [0, 1], [-1, num_transitions])

  # Encode the indices in a flattened representation.
  flattened_transition_indices = start_tag_indices * num_tags + end_tag_indices
  flattened_transition_params = array_ops.reshape(transition_params, [-1])

  # Get the binary scores based on the flattened representation.
  binary_scores = array_ops.gather(flattened_transition_params,
                                   flattened_transition_indices)

  masks = _lengths_to_masks(sequence_lengths, array_ops.shape(tag_indices)[1])
  truncated_masks = array_ops.slice(masks, [0, 1], [-1, -1])
  binary_scores = math_ops.reduce_sum(binary_scores * truncated_masks, 1)
  return binary_scores

def unit_norm(inputs, dim, epsilon=1e-7, scope=None):
  """Normalizes the given input across the specified dimension to unit length.

  Note that the rank of `input` must be known.

  Args:
    inputs: A `Tensor` of arbitrary size.
    dim: The dimension along which the input is normalized.
    epsilon: A small value to add to the inputs to avoid dividing by zero.
    scope: Optional scope for variable_scope.

  Returns:
    The normalized `Tensor`.

  Raises:
    ValueError: If dim is smaller than the number of dimensions in 'inputs'.
  """
  with variable_scope.variable_scope(scope, 'UnitNorm', [inputs]):
    if not inputs.get_shape():
      raise ValueError('The input rank must be known.')
    input_rank = len(inputs.get_shape().as_list())
    if dim < 0 or dim >= input_rank:
      raise ValueError(
          'dim must be positive but smaller than the input rank.')

    lengths = math_ops.sqrt(epsilon + math_ops.reduce_sum(
        math_ops.square(inputs), dim, True))
    multiples = []
    if dim > 0:
      multiples.append(array_ops.ones([dim], dtypes.int32))
    multiples.append(array_ops.slice(array_ops.shape(inputs), [dim], [1]))
    if dim < (input_rank - 1):
      multiples.append(array_ops.ones([input_rank - 1 - dim], dtypes.int32))
    multiples = array_ops.concat(0, multiples)
    return math_ops.div(inputs, array_ops.tile(lengths, multiples))

def get_stats(self, session):
    num_nodes = self.variables.end_of_tree.eval(session=session) - 1
    num_leaves = array_ops.where(
        math_ops.equal(array_ops.squeeze(array_ops.slice(
            self.variables.tree, [0, 0], [-1, 1])), constants.LEAF_NODE)
        ).eval(session=session).shape[0]
    return TreeStats(num_nodes, num_leaves)

def inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      path_probability, path = self.training_ops.hard_routing_function(
          data,
          self.tree_parameters,
          self.tree_thresholds,
          max_nodes=self.params.num_nodes,
          tree_depth=self.params.hybrid_tree_depth)

      output = array_ops.slice(
          self.training_ops.unpack_path(path, path_probability),
          [0, self.params.num_nodes - self.params.num_leaves - 1],
          [-1, self.params.num_leaves])

      return output

def inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      path_probability, path = self.training_ops.hard_routing_function(
          data,
          self.tree_parameters,
          self.tree_thresholds,
          max_nodes=self.params.num_nodes,
          tree_depth=self.params.hybrid_tree_depth)

      output = array_ops.slice(
          self.training_ops.unpack_path(path, path_probability),
          [0, self.params.num_nodes - self.params.num_leaves - 1],
          [-1, self.params.num_leaves])

      return output

def inference_graph(self, data):
    with ops.device(self.device_assigner.get_device(self.layer_num)):
      routes = self.training_ops.routing_function(
          data,
          self.tree_parameters,
          self.tree_thresholds,
          max_nodes=self.params.num_nodes)

      leaf_routes = array_ops.slice(
          routes, [0, self.params.num_nodes - self.params.num_leaves - 1],
          [-1, self.params.num_leaves])

      return leaf_routes

def get_shape(self, x, name="get_shape"):
    """Returns `Tensor`'s shape partitioned into `sample`, `batch`, `event`.

    Args:
      x: `Tensor`.
      name: `String`. The name to give this op.

    Returns:
      sample_shape: `Tensor` (1D, `int32`).
      batch_shape: `Tensor` (1D, `int32`).
      event_shape: `Tensor` (1D, `int32`).
    """
    with self._name_scope(name, values=[x]):
      x = ops.convert_to_tensor(x, name="x")
      def slice_shape(start_sum, size, name):
        """Closure to slice out shape."""
        start_sum = start_sum if start_sum else (
            array_ops.zeros((), dtype=dtypes.int32, name="zero"),)
        if (x.get_shape().ndims is not None and
            self._is_all_constant_helper(size, *start_sum)):
          start = sum([tensor_util.constant_value(s) for s in start_sum])
          stop = start + tensor_util.constant_value(size)
          slice_ = x.get_shape()[start:stop].as_list()
          if all(s is not None for s in slice_):
            return ops.convert_to_tensor(slice_, dtype=dtypes.int32, name=name)
          # Fall-through intended.
        return array_ops.slice(array_ops.shape(x), (sum(start_sum),), (size,))
      sample_ndims = self.get_sample_ndims(x, name=name)
      return (slice_shape((), sample_ndims,
                          name="sample_shape"),
              slice_shape((sample_ndims,), self.batch_ndims,
                          name="batch_shape"),
              slice_shape((sample_ndims, self.batch_ndims), self.event_ndims,
                          name="event_shape"))

def _event_shape(self):
    s = self.scale_operator_pd.shape()
    return array_ops.slice(s, array_ops.shape(s) - 2, [2])

def _flip_vector_to_matrix_dynamic(vec, batch_shape):
  """flip_vector_to_matrix with dynamic shapes."""
  # Shapes associated with batch_shape
  batch_rank = array_ops.size(batch_shape)

  # Shapes associated with vec.
  vec = ops.convert_to_tensor(vec, name="vec")
  vec_shape = array_ops.shape(vec)
  vec_rank = array_ops.rank(vec)
  vec_batch_rank = vec_rank - 1

  m = vec_batch_rank - batch_rank
  # vec_shape_left = [M1,...,Mm] or [].
  vec_shape_left = array_ops.slice(vec_shape, [0], [m])
  # If vec_shape_left = [], then condensed_shape = [1] since reduce_prod([]) = 1
  # If vec_shape_left = [M1,...,Mm], condensed_shape = [M1*...*Mm]
  condensed_shape = [math_ops.reduce_prod(vec_shape_left)]
  k = array_ops.gather(vec_shape, vec_rank - 1)
  new_shape = array_ops.concat(0, (batch_shape, [k], condensed_shape))

  def _flip_front_dims_to_back():
    # Permutation corresponding to [N1,...,Nn] + [k, M1,...,Mm]
    perm = array_ops.concat(
        0, (math_ops.range(m, vec_rank), math_ops.range(0, m)))
    return array_ops.transpose(vec, perm=perm)

  x_flipped = control_flow_ops.cond(
      math_ops.less(0, m),
      _flip_front_dims_to_back,
      lambda: array_ops.expand_dims(vec, -1))

  return array_ops.reshape(x_flipped, new_shape)

def _check_shapes_dynamic(self, operator, v, diag):
    """Return (v, diag) with Assert dependencies, which check shape."""
    checks = []
    with ops.name_scope("check_shapes", values=[operator, v, diag]):
      s_v = array_ops.shape(v)
      r_op = operator.rank()
      r_v = array_ops.rank(v)
      if diag is not None:
        s_d = array_ops.shape(diag)
        r_d = array_ops.rank(diag)

      # Check tensor rank.
      checks.append(check_ops.assert_rank(v, r_op))
      if diag is not None:
        checks.append(check_ops.assert_rank(diag, r_op - 1))

      # Check batch shape
      checks.append(check_ops.assert_equal(
          operator.batch_shape(), array_ops.slice(s_v, [0], [r_v - 2])))
      if diag is not None:
        checks.append(check_ops.assert_equal(
            operator.batch_shape(), array_ops.slice(s_d, [0], [r_d - 1])))

      # Check event shape
      checks.append(check_ops.assert_equal(
          operator.vector_space_dimension(), array_ops.gather(s_v, r_v - 2)))
      if diag is not None:
        checks.append(check_ops.assert_equal(
            array_ops.gather(s_v, r_v - 1), array_ops.gather(s_d, r_d - 1)))

      v = control_flow_ops.with_dependencies(checks, v)
      if diag is not None:
        diag = control_flow_ops.with_dependencies(checks, diag)
      return v, diag