Mastering TensorFlow 1.x

skip-gram model with Keras

The flow of the embedding model with Keras remains the same as TensorFlow.

• Create the network architecture in the Keras functional or sequential model
• Feed the true and false pairs of the target and context words to the network
• Look up the word vector for target and context words
• Perform a dot product of the word vectors to get the similarity score
• Pass the similarity score through a sigmoid layer to get the output as the true or false pair

Now let's implement these steps using the Keras functional API:

1. Import the required libraries:

from keras.models import Modelfrom keras.layers.embeddings import Embeddingfrom keras.preprocessing import sequencefrom keras.preprocessing.sequence import skipgramsfrom keras.layers ...