Define team lookup
Shared layers allow a model to use the same weight matrix for multiple steps. In this exercise, you will build a "team strength" layer that represents each team by a single number. You will use this number for both teams in the model. The model will learn a number for each team that works well both when the team is team_1
and when the team is team_2
in the input data.
The games_season
DataFrame is available in your workspace.
This exercise is part of the course
Advanced Deep Learning with Keras
Exercise instructions
- Count the number of unique teams.
- Create an embedding layer that maps each team ID to a single number representing that team's strength.
- The output shape should be 1 dimension (as we want to represent the teams by a single number).
- The input length should be 1 dimension (as each team is represented by exactly one id).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Imports
from tensorflow.keras.layers import Embedding
from numpy import unique
# Count the unique number of teams
n_teams = ____(games_season[____]).shape[0]
# Create an embedding layer
team_lookup = Embedding(input_dim=n_teams,
output_dim=____,
input_length=____,
name='Team-Strength')