一、整体

整个代码文件如下：

 

分类之后如下： 

 

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])

　　

 

 

 

 

 

 

 

参考文献：

【1】BERT实战（源码分析+踩坑）