Few things to keep in mind:
- Not all points shared in the other topic have been incorporated. Here are a few:
a. Number of units of hidden layers is not a power of 2.
b. Model complexity can be increased when underfitting happens. Since the embedding layer configuration is specified by the staff, I recommend you play with layers other than theEmbedding
layer and the final output layer. - Do not hardcode values for the
Embedding
layer. Please rely on constants defined at the top of the notebook likeEMBEDDING_DIM
. Do follow this fix across the notebook where applicable. Rely on function parameters and then constants when defined.
True labels are the actual labels that are part of the dataset you want the model to train on. Labels generated by the model are called as predictions. When all predicted labels match the actual labels, we have 100% accuracy. Please read the section on Machine learning and statistics
here to learn about one-hot encoding.
Please complete the deep learning specialization
before this one to get a good understanding of model analysis.