Full disclosure this question is based on following this tutorial: https://tinyurl.com/vmyj8rf8

I am trying to fully understand embedded layers in Keras. Imagine having a network to try and understand basic sentiment analysis as a binary classifier (1 positive sentiment and 0 negative sentiment). The toy dataset for this is as follows:

# Define 10 restaurant reviews
reviews =[
          'Never coming back!',
          'horrible service',
          'rude waitress',
          'cold food',
          'horrible food!',
          'awesome services!',
          'poor work',
          'couldn\'t have done better'
]#Define labels
labels = array([1,1,1,1,1,0,0,0,0,0])

This data can be used to train a really simple network as follows:

Vocab_size = 50
model = Sequential()
embedding_layer = Embedding(input_dim=Vocab_size,output_dim=8,input_length=max_length)

In order to feed this data into he network, we can one hot encode it using Keras one_hot as follows:

encoded_reviews = [one_hot(d,Vocab_size) for d in reviews]
print(f'encoded reviews: {encoded_reviews}')

We get the following output:

encoded reviews: [[14, 45, 43], [8, 2], [6, 43], [24, 1], [8, 1], [11], [11, 21], [16], [34, 40], [2, 25, 36, 15]]

I understand that the purpose of setting Vocab_size = 50, even though there are only around 20 unique words in the corpus is to give a large enough hashing space for the hashing algorithm behind one_hot to avoid collisions when the text is encoded.

If I train the model on these words (assume fixed length input and padding) and then get the weights of the embedded layer:


(50, 8)

We can see this it is an array of 50 vectors that look like this as an example:

[ 0.17051394 0.13659576 -0.05245572 -0.12567708 0.06743167 0.05893507 -0.14506021 0.06448647]

My understanding is that each of these vectors corresponds to a word embedding for each word in the corpus. But if there are only 20 unique words in the corpus and Vocab_size is set larger than this then that can't be completely true? If Vocab_size > corpus_vocab_size, then what do these embeddings represent? Any help would be appreciated.


1 Answer 1


tf.keras.layers.Embedding(..., embeddings_initializer="uniform"*,..., *kwargs)

  • All the weights are initialized with the init strategy
  • All learn the optimum values with the backprop
  • Weights for which there is no input will have zero output every time, hence no learning.
  • Hence these extra weights will remain at their initialization value

You may check these extra weights before and after.

weight = model.layers[0].get_weights() # Save before training
history = model.fit(x, y)

# These two should be same
weight[0][-1]  # Last weight - Before
model.layers[0].get_weights()[0][-1] # Last weight - After
  • $\begingroup$ Amazing, thanks for clearing that up for me! $\endgroup$
    – Sandy Lee
    May 17, 2021 at 8:41
  • $\begingroup$ What is the advantage of using extra weights ? $\endgroup$ Nov 15, 2021 at 8:49

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