在用tensorflow做一維的卷積神經網絡的時候會遇到tf.nn.conv1d和layers.conv1d這兩個函數，但是這兩個函數有什么區別呢，通過計算得到一些規律。

1.關于tf.nn.conv1d的解釋，以下是Tensor Flow中關于tf.nn.conv1d的API注解：

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] whereoutwidthisafunctionofthestrideandpaddingasinconv2dwhereoutwidthisafunctionofthestrideandpaddingasinconv2d 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.

什么意思呢？就是說conv1d的參數含義：(以NHWC格式為例，即，通道維在最后)

1、value：在注釋中，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數組。這樣的話可以方便理解。

2、filters：在注釋中，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，這里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。