一、conv1d
  在NLP領域，甚至影象處理的時候，我們可能會用到一維卷積（conv1d）。所謂的一維卷積可以看作是二維卷積（conv2d）的簡化，二維卷積是將一個特徵圖在width和height兩個方向上進行滑窗操作，對應位置進行相乘並求和；而一維卷積則是隻在width或者說height方向上進行滑窗並相乘求和。
  以下是Tensor Flow中關於tf.nn.conv1d的API註解：

Computes a 1-D convolution given 3-D input and filter tensors.

Given an input tensor of shape
  [batch, in_width, in_channels]
if
 
 data_format is "NHWC", or
  [batch, in_channels, in_width]
if data_format is "NCHW",
and a filter / kernel tensor of shape
[filter_width, in_channels, out_channels], this op reshapes
the arguments to pass them to conv2d to perform the equivalent
convolution operation.

Internally, this op reshapes the
 
 input tensors and invokes `tf.nn.conv2d`.
For example, if `data_format` does not start with "NC", a tensor of shape
  [batch, in_width, in_channels]
is reshaped to
  [batch, 1, in_width, in_channels],
and the filter is reshaped to
  [1, filter_width, in_channels, out_channels].
The result is then reshaped back
 
 to
  [batch, out_width, out_channels]
\(where out_width is a function of the stride and padding as in conv2d\) and
returned to the caller.

Args:
  value: A 3D `Tensor`.  Must be of type `float32` or `float64`.
  filters: A 3D `Tensor`.  Must have the same type as `input`.
  stride: An `integer`.  The number of entries by which
    the filter is moved right at each step.
  padding: 'SAME' or 'VALID'
  use_cudnn_on_gpu: An optional `bool`.  Defaults to `True`.
  data_format: An optional `string` from `"NHWC", "NCHW"`.  Defaults
    to `"NHWC"`, the data is stored in the order of
    [batch, in_width, in_channels].  The `"NCHW"` format stores
    data as [batch, in_channels, in_width].
  name: A name for the operation (optional).

Returns:
  A `Tensor`.  Has the same type as input.

Raises:
  ValueError: if `data_format` is invalid.

二、詳解
  conv1d的引數含義：(以NHWC格式為例，即，通道維在最後)

1、value：在註釋中，value的格式為：[batch, in_width, in_channels]，batch為樣本維，表示多少個樣本，in_width為寬度維，表示樣本的寬度，in_channels維通道維，表示樣本有多少個通道。
  事實上，也可以把格式看作如下:[batch, 行數, 列數]，把每一個樣本看作一個平鋪開的二維陣列。這樣的話可以方便理解。

2、filters：在註釋中，filters的格式為：[filter_width, in_channels, out_channels]。按照value的第二種看法，filter_width可以看作每次與value進行卷積的行數，in_channels表示value一共有多少列（與value中的in_channels相對應）。out_channels表示輸出通道，可以理解為一共有多少個卷積核，即卷積核的數目。

3、stride：一個整數，表示步長，每次（向下）移動的距離（TensorFlow中解釋是向右移動的距離，這裡可以看作向下移動的距離）。

4、padding：同conv2d，value是否需要在下方填補0。

5、name：名稱。可省略。

三、程式碼示例:
  conv1d簡單使用的程式碼如下：

import tensorflow as tf
import numpy as np

# 定義一個矩陣a，表示需要被卷積的矩陣。
a = np.array(np.arange(1, 1 + 20).reshape([1, 10, 2]), dtype=np.float32)

# 卷積核，此處卷積核的數目為1
kernel = np.array(np.arange(1, 1 + 4), dtype=np.float32).reshape([2, 2, 1])

# 進行conv1d卷積
conv1d = tf.nn.conv1d(a, kernel, 1, 'VALID')

with tf.Session() as sess:
    # 初始化
    tf.global_variables_initializer().run()
    # 輸出卷積值
    print(sess.run(conv1d))

結果如下：

[[[ 30.]
  [ 50.]
  [ 70.]
  [ 90.]
  [110.]
  [130.]
  [150.]
  [170.]
  [190.]]]