How does Keras 1d convolution layer work with word embeddings - text classification problem? (Filters, kernel size, and all hyperparameter)

model = Sequential() model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length)) model.add(Conv1D(filters=32, kernel_size=2, padding='same', activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(labels_count, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary())

I would try to explain how 1D-Convolution is applied on a sequence data. I just use the example of a sentence consisting of words but obviously it is not specific to text data and it is the same with other sequence data and timeseries.

Suppose we have a sentence consisting of m words where each word has been represented using word embeddings:

Now we would like to apply a 1D convolution layer consisting of n different filters with kernel size of k on this data. To do so, sliding windows of length k are extracted from the data and then each filter is applied on each of those extracted windows. Here is an illustration of what happens (here I have assumed k=3 and removed the bias parameter of each filter for simplicity):

As you can see in the figure above, the response of each filter is equivalent to the result of its convolution (i.e. element-wise multiplication and then summing all the results) with the extracted window of length k (i.e. i-th to (i+k-1)-th words in the given sentence). Further, note that each filter has the same number of channels as the number of features (i.e. word-embeddings dimension) of the training sample (hence performing convolution, i.e. element-wise multiplication, is possible). Essentially, each filter is detecting the presence of a particular feature of pattern in a local window of training data (e.g. whether a couple of specific words exist in this window or not). After all the filters have been applied on all the windows of length k we would have an output of like this which is the result of convolution:

As you can see, there are m-k+1 windows in the figure since we have assumed that the padding='valid' and stride=1 (default behavior of Conv1D layer in Keras). The stride argument determines how much the window should slide (i.e. shift) to extract the next window (e.g. in our example above, a stride of 2 would extract windows of words: (1,2,3), (3,4,5), (5,6,7), ... instead). The padding argument determines whether the window should entirely consists of the words in training sample or there should be paddings at the beginning and at the end; this way, the convolution response may have the same length (i.e. m and not m-k+1) as the training sample (e.g. in our example above, padding='same' would extract windows of words: (PAD,1,2), (1,2,3), (2,3,4), ..., (m-2,m-1,m), (m-1,m, PAD)).

You can verify some of the things I mentioned using Keras:

from keras import models
from keras import layers

n = 32  # number of filters
m = 20  # number of words in a sentence
k = 3   # kernel size of filters
emb_dim = 100  # embedding dimension

model = models.Sequential()
model.add(layers.Conv1D(n, k, input_shape=(m, emb_dim)))

model.summary()

Model summary:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1d_2 (Conv1D)            (None, 18, 32)            9632      
=================================================================
Total params: 9,632
Trainable params: 9,632
Non-trainable params: 0
_________________________________________________________________

As you can see the output of convolution layer has a shape of (m-k+1,n) = (18, 32) and the number of parameters (i.e. filters weights) in the convolution layer is equal to: num_filters * (kernel_size * n_features) + one_bias_per_filter = n * (k * emb_dim) + n = 32 * (3 * 100) + 32 = 9632.

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