Apache Spark Deep Learning Cookbook

How to do it...

The steps are as follows:

1. Squash the dimensionality of the 300-dimensional word vectors by using the following command:

 tsne = sklearn.manifold.TSNE(n_components=2, random_state=0)

2. Put all the word vectors into one giant matrix (named all_word_vectors_matrix), and view it using the following commands:

 all_word_vectors_matrix = got2vec.wv.syn0 print (all_word_vectors_matrix)

3. Use the tsne technique to fit all the learned representations into a two- dimensional space using the following command:

 all_word_vectors_matrix_2d =  tsne.fit_transform(all_word_vectors_matrix)

4. Gather all the word vectors, as well as their associated words, using the following code:

 points = pd.DataFrame(     [            (word, coords[0], coords[1]) for word, ...