Number of parameters comparison

You saw that the one-hot representation is not a good representation of words because it is very sparse. Using the Embedding layer creates a dense representation of the vectors, but also demands a lot of parameters to be learned.

In this exercise you will compare the number of parameters of two models using embeddings and one-hot encoding to see the difference.

The model model_onehot is already loaded in the environment, as well as the Sequential, Dense and GRU from keras. Finally, the parameters vocabulary_size=80000 and sentence_len=200 are also loaded.

Import the Embedding layer from keras.layers.
On the embedding layer, use vocabulary size plus one as input dimension and sentence size as input length.
Compile the model.
Print the summary of the model with embedding.

Exercise

Number of parameters comparison

Instructions