下面的代码与kera-tuner网站上的Hello-World示例相同，但使用Hyperband而不是RandomSearch。
from tensorflow import keras
from tensorflow.keras import layers
from kerastuner.tuners import RandomSearch, Hyperband
from kerastuner.engine.hypermodel import HyperModel
from kerastuner.engine.hyperparameters import HyperParameters
(x, y), (val_x, val_y) = keras.datasets.mnist.load_data()
x = x.astype('float32') / 255.
val_x = val_x.astype('float32') / 255.
x = x[:10000]
y = y[:10000]
def build_model(hp):
model = keras.Sequential()
model.add(layers.Flatten(input_shape=(28, 28)))
for i in range(hp.Int('num_layers', 2, 20)):
model.add(layers.Dense(units=hp.Int('units_' + str(i), 32, 512, 32),
activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(
optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
tuner = Hyperband(
build_model,
max_epochs=50,
objective='val_accuracy',
seed=20,
executions_per_trial=1,
directory='test_dir',
project_name='daninhas_hyperband'
)
# tuner.search_space_summary()
tuner.search(x=x,
y=y,
epochs=50,
validation_data=(val_x, val_y))
tuner.results_summary()
即使设置了max_epochs=50和epoch=50，模型训练仍然只运行了2个epochs。
(...)
2020-06-03 12:55:23.245993: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-03 12:55:23.246022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]
Epoch 1/2
313/313 [==============================] - 4s 12ms/step - loss: 2.3247 - accuracy: 0.1109 - val_loss: 2.3025 - val_accuracy: 0.1135
Epoch 2/2
313/313 [==============================] - 3s 9ms/step - loss: 2.3020 - accuracy: 0.1081 - val_loss: 2.3033 - val_accuracy: 0.1135
[Trial complete]
[Trial summary]
|-Trial ID: 32396974f43cade5b6c3ef511b5548f3
|-Score: 0.11349999904632568
|-Best step: 0
> Hyperparameters:
|-learning_rate: 0.01
|-num_layers: 19
|-tuner/bracket: 3
|-tuner/epochs: 2
|-tuner/initial_epoch: 0
|-tuner/round: 0
|-units_0: 448
|-units_1: 384
|-units_10: 416
|-units_11: 160
|-units_12: 384
|-units_13: 480
|-units_14: 288
|-units_15: 64
|-units_16: 288
|-units_17: 64
|-units_18: 32
|-units_2: 96
|-units_3: 160
|-units_4: 480
|-units_5: 416
|-units_6: 256
|-units_7: 32
|-units_8: 160
|-units_9: 448
Epoch 1/2
313/313 [==============================] - 4s 11ms/step - loss: 2.3109 - accuracy: 0.1081 - val_loss: 2.3028 - val_accuracy: 0.1135
Epoch 2/2
313/313 [==============================] - 3s 10ms/step - loss: 2.3022 - accuracy: 0.1067 - val_loss: 2.3019 - val_accuracy: 0.1135
[Trial complete]
[Trial summary]
|-Trial ID: 98376f698826a2068c3412301a7aece4
|-Score: 0.11349999904632568
|-Best step: 0
> Hyperparameters:
|-learning_rate: 0.01
|-num_layers: 19
|-tuner/bracket: 3
|-tuner/epochs: 2
|-tuner/initial_epoch: 0
|-tuner/round: 0
|-units_0: 480
|-units_1: 320
|-units_10: 320
|-units_11: 64
|-units_12: 128
|-units_13: 32
|-units_14: 416
|-units_15: 288
|-units_16: 320
|-units_17: 480
|-units_18: 256
|-units_2: 480
|-units_3: 320
|-units_4: 288
|-units_5: 192
|-units_6: 224
|-units_7: 256
|-units_8: 256
|-units_9: 352
(...)
是否有某些配置可以强制只进行2个时期的训练？ 或者这可能是某个错误吗？ 还是我漏掉了什么？
如何让模型进行更多时期的训练？