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Improving the Model

Summary​

  • When creating the model
    • By increasing the number of layers
    • By increasing the number of units per layer
  • When compiling the model
    • By changing the optimization function
  • When fitting the model
    • By changing the epochs
    • By increasing the number of training data

Content​

# number of layers and units per layers has increased
model = tf.keras.Sequential([
    tf.keras.layers.Input(1),
    tf.keras.layers.Dense(100),
    tf.keras.layers.Dense(100),
    tf.keras.layers.Dense(1)
])

# using a better optimizer for the job
model.compile(
    loss=tf.keras.losses.mae,
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
    metrics=[ tf.keras.losses.mae ]
)

# increase the number of iterations
model.fit(X, y, epochs=100)

model.predict([7])
# array([[15.082852]], dtype=float32)
# pretty close hah?