Issue
I'm reading an article about tuning hyperparameters in keras tuner. It includes code to build a model that has this code:
def build_model(hp):
"""
Builds model and sets up hyperparameter space to search.
Parameters
----------
hp : HyperParameter object
Configures hyperparameters to tune.
Returns
-------
model : keras model
Compiled model with hyperparameters to tune.
"""
# Initialize sequential API and start building model.
model = keras.Sequential()
model.add(keras.layers.Flatten(input_shape=(28,28)))
# Tune the number of hidden layers and units in each.
# Number of hidden layers: 1 - 5
# Number of Units: 32 - 512 with stepsize of 32
for i in range(1, hp.Int("num_layers", 2, 6)):
model.add(
keras.layers.Dense(
units=hp.Int("units_" + str(i), min_value=32, max_value=512, step=32),
activation="relu")
)
# Tune dropout layer with values from 0 - 0.3 with stepsize of 0.1.
model.add(keras.layers.Dropout(hp.Float("dropout_" + str(i), 0, 0.3, step=0.1)))
# Add output layer.
model.add(keras.layers.Dense(units=10, activation="softmax"))
# Tune learning rate for Adam optimizer with values from 0.01, 0.001, or 0.0001
hp_learning_rate = hp.Choice("learning_rate", values=[1e-2, 1e-3, 1e-4])
# Define optimizer, loss, and metrics
model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=["accuracy"])
return model
I'm confused about what the numbers, 1,2 and 6 mean in the range function for the num_layers
line.
Solution
If you see the docs, the 2 and 6 are referring to the min and max values respectively. Also note:
[...] max_value is included in the possible values this parameter can take on
So this line:
for i in range(1, hp.Int("num_layers", 2, 6)):
basically means: generate a x
number of Dense
layers, where x
is between 1 and 5 and the range from 2 to 6 is the hyperparameter you are working on / tuning.
Answered By - AloneTogether
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