BERT源码分析

一、整体

整个代码文件如下：

二、tensorflow基础

1.tf.expand_dims

用法：给定张量“ input”，此操作将在“ input”形状的尺寸索引“ axis”处插入尺寸为1的尺寸。 尺寸索引“轴”从零开始； 如果为“ axis”指定负数，则从末尾开始算起。
　　如果要将批次尺寸添加到单个元素，此操作很有用。 例如，如果您有一个形状为[[height，width，channels]`的图像，则可以将其与具有`expand_dims（image，0）`的1张图像一起批处理，这将使形状为[[1，height ，width，channels]。

# 't' is a tensor of shape [2]
tf.shape(tf.expand_dims(t, 0))  # [1, 2]
tf.shape(tf.expand_dims(t, 1))  # [2, 1]
tf.shape(tf.expand_dims(t, -1))  # [2, 1]

# 't2' is a tensor of shape [2, 3, 5]
tf.shape(tf.expand_dims(t2, 0))  # [1, 2, 3, 5]
tf.shape(tf.expand_dims(t2, 2))  # [2, 3, 1, 5]
tf.shape(tf.expand_dims(t2, 3))  # [2, 3, 5, 1]
```

This operation requires that:

`-1-input.dims() <= dim <= input.dims()`

This operation is related to `squeeze()`, which removes dimensions of
size 1.

Args:
  input: A `Tensor`.
  axis: 0-D (scalar). Specifies the dimension index at which to
    expand the shape of `input`. Must be in the range
    `[-rank(input) - 1, rank(input)]`.
  name: The name of the output `Tensor`.
  dim: 0-D (scalar). Equivalent to `axis`, to be deprecated.

Returns:
  A `Tensor` with the same data as `input`, but its shape has an additional
  dimension of size 1 added.

Raises:
  ValueError: if both `dim` and `axis` are specified.

bert中源码：

# 该函数默认输入的形状为【batch_size, seq_length, input_num】
# 如果输入为2D的【batch_size, seq_length】，则扩展到【batch_size, seq_length, 1】
if input_ids.shape.ndims == 2:
  input_ids = tf.expand_dims(input_ids, axis=[-1])

2.tf.reshape

reshape(tensor, shape, name=None)

作用：重塑张量。给定张量，此操作将返回与形状为shape的张量具有相同值的张量。 如果“形状”的一个分量为特殊值-1，则将计算该尺寸的大小，以使总大小保持恒定。 具体来说，[-1]的“形状”会展平为一维。 “形状”的至多一个分量可以为-1。 如果“ shape”为一维或更高，则该操作将返回一个形状为“ shape”的张量，其中填充了“ tensor”的值。 在这种情况下，“形状”所隐含的元素数量必须与“张量”中的元素数量相同。 

 举例：

For example:
  ```
  # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
  # tensor 't' has shape [9]
  reshape(t, [3, 3]) ==> [[1, 2, 3],
                          [4, 5, 6],
                          [7, 8, 9]]

  # tensor 't' is [[[1, 1], [2, 2]],
  #                [[3, 3], [4, 4]]]
  # tensor 't' has shape [2, 2, 2]
  reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
                          [3, 3, 4, 4]]

  # tensor 't' is [[[1, 1, 1],
  #                 [2, 2, 2]],
  #                [[3, 3, 3],
  #                 [4, 4, 4]],
  #                [[5, 5, 5],
  #                 [6, 6, 6]]]
  # tensor 't' has shape [3, 2, 3]
  # pass '[-1]' to flatten 't'
  reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]

  # -1 can also be used to infer the shape
  # -1 is inferred to be 9:
  reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                           [4, 4, 4, 5, 5, 5, 6, 6, 6]]

  # -1 is inferred to be 2:
  reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                           [4, 4, 4, 5, 5, 5, 6, 6, 6]]

  # -1 is inferred to be 3:
  reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
                                [2, 2, 2],
                                [3, 3, 3]],
                               [[4, 4, 4],
                                [5, 5, 5],
                                [6, 6, 6]]]

  # tensor 't' is [7]
  # shape `[]` reshapes to a scalar
  reshape(t, []) ==> 7
  ```
  Args:
    tensor: A `Tensor`.
    shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
      Defines the shape of the output tensor.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `tensor`.
  """

bert中源码：　　

# If the input is a 2D tensor of shape [batch_size, seq_length], we
# reshape to [batch_size, seq_length, 1].
if input_ids.shape.ndims == 2:
  input_ids = tf.expand_dims(input_ids, axis=[-1])

embedding_table = tf.get_variable(
      name=word_embedding_name,
      shape=[vocab_size, embedding_size],
      initializer=create_initializer(initializer_range))

flat_input_ids = tf.reshape(input_ids, [-1])

　　

参考文献：

【1】BERT实战（源码分析+踩坑）