Keras conv1d layer parameters: filters and kernel_size : <p>I am very confused by these two parameters in the conv1d layer from keras:
<a href="https://keras.io/layers/convolutional/#conv1d" rel="noreferrer">https://keras.io/layers/convolutional/#conv1d</a></p>

<p>the documentation says:</p>

<pre><code>filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
</code></pre>

<p>But that does not seem to relate to the standard terminologies I see on many tutorials such as <a href="https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner" rel="noreferrer">https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner</a>'s-Guide-To-Understanding-Convolutional-Neural-Networks/ and <a href="https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/" rel="noreferrer">https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/</a></p>

<p>Using the second tutorial link which uses Keras, I'd imagine that in fact 'kernel_size' is relevant to the conventional 'filter' concept which defines the sliding window on the input feature space. But what about the 'filter' parameter in conv1d? What does it do? </p>

<p>For example, in the following code snippet:</p>

<pre><code>model.add(embedding_layer)
model.add(Dropout(0.2))
model.add(Conv1D(filters=100, kernel_size=4, padding='same', activation='relu'))
</code></pre>

<p>suppose the embedding layer outputs a matrix of dimension 50 (rows, each row is a word in a sentence) x 300 (columns, the word vector dimension), how does the conv1d layer transforms that matrix?</p>

<p>Many thanks</p>
