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Convolutional Layers

Conv1D

keras.layers.Conv1D(filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

1D convolution layer (e.g. temporal convolution).

This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide an input_shape argument (tuple of integers or None, does not include the batch axis), e.g. input_shape=(10, 128) for time series sequences of 10 time steps with 128 features per step in data_format="channels_last", or (None, 128) for variable-length sequences with 128 features per step.

Arguments

  • filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
  • kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
  • strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
  • data_format: A string, one of "channels_last" (default) or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, steps, channels) (default format for temporal data in Keras) while "channels_first" corresponds to inputs with shape (batch, channels, steps).

NOTE: 关于**filters**和**kernel_size**，参见下面这篇文章：Keras conv1d layer parameters: filters and kernel_size

NOTE : 关于channel-last和channel-first，参见这篇文章：A Gentle Introduction to Channels-First and Channels-Last Image Formats

Input shape

3D tensor with shape: (batch, steps, channels)

NOTE: 要想理解这段话中steps、channels的含义，首先需要仔细阅读上面的第三段，其中已经给出了一个example；这里我再补充一个例子：

如果以Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks中的sentence为例，那么steps则表示的sentence的长度，即sentence中word的个数；channels则表示word embedding+position embedding的长度；

Output shape

3D tensor with shape: (batch, new_steps, filters) steps value might have changed due to padding or strides.

NOTE: 上述output shape和Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks中描述的不同；

NOTE: 一般在讲解model的原理时候都是不会涉及到batch_size的，而是仅仅一一条输入数据为例来进行说明，但是实现库中则必须要涉及到batch_size，这里便是这样；其实我觉得应该这样来理解：Conv1D肯定会对输入的batch_size条记录中的每一条都执行系统的卷积过程；