November 2019
Intermediate to advanced
296 pages
7h 52m
English
Content preview from Hands-On Machine Learning with TensorFlow.js
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Embedding the model
The embedding model is constructed using the Layers API. tf.layers.embedding allows us to train the mapping in order to embed words into fixed-length vectors. The size of the input dimension is the vocabulary size, and each output should have an embeddingSize-length vector. Please note that inputLength is the maximum length of the sentence in the dataset, which is different from inputDim. Since the target label is a positive/negative flag, the sigmoid activation function is used after the output of the embedding is flattened. One interesting thing about this is that this embedding can take labels into consideration. These are set in terms of the application's purpose. This indicates the flexibility in customizing the embedded ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.