def create_model():
model = keras.Sequential()
model.add(layers.Dense(16, activation="relu", input_shape=(4,)))
model.add(layers.Dense(3, activation="softmax"))
model.compile(
optimizer=keras.optimizers.Adam(lr=0.001, epsilon=1e-07),
loss="categorical_crossentropy",
metrics=["acc"],
)
return model
def build(self, hp):
model = keras.Sequential()
model.add(
Conv2D(
filters=16,
kernel_size=3,
activation='relu',
input_shape=self.input_shape
)
)
model.add(
Conv2D(
filters=16,
activation='relu',
kernel_size=3
)
)
model.add(MaxPooling2D(pool_size=2))
model.add(
Dropout(rate=hp.Float(
'dropout_1',
min_value=0.0,
max_value=0.5,
default=0.25,
step=0.05,
))
)
model.add(
Conv2D(
filters=32,
kernel_size=3,
activation='relu'
)
)
model.add(
Conv2D(
filters=hp.Choice(
'num_filters',
values=[32, 64],
default=64,
),
activation='relu',
kernel_size=3
)
)
model.add(MaxPooling2D(pool_size=2))
model.add(
Dropout(rate=hp.Float(
'dropout_2',
min_value=0.0,
max_value=0.5,
default=0.25,
step=0.05,
))
)
model.add(Flatten())
model.add(
Dense(
units=hp.Int(
'units',
min_value=32,
max_value=512,
step=32,
default=128
),
activation=hp.Choice(
'dense_activation',
values=['relu', 'tanh', 'sigmoid'],
default='relu'
)
)
)
model.add(
Dropout(
rate=hp.Float(
'dropout_3',
min_value=0.0,
max_value=0.5,
default=0.25,
step=0.05
)
)
)
model.add(Dense(self.num_classes, activation='softmax'))
model.compile(
optimizer=keras.optimizers.Adam(
hp.Float(
'learning_rate',
min_value=1e-4,
max_value=1e-2,
sampling='LOG',
default=1e-3
)
),
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
mlflow.keras.autolog()
return model