Build the model first by calling build()

[badfish2019]

this is my code, it copy from the getting_started https://keras.io/guides/keras_tuner/getting_started/
but I got an error
ValueError: This model has not yet been built. Build the model first by calling build() or by calling the model on a batch of data.
i use the TensorFlow 2.11 and keras 2.xx

这是我的 代码 import keras
import numpy as np
import keras
from keras import layers
import keras_tuner

定义模型

def build_model(hp):
model = keras.Sequential()
model.add(layers.Flatten())
# Tune the number of layers.
for i in range(hp.Int("num_layers", 1, 3)):
model.add(
layers.Dense(
# Tune number of units separately.
units=hp.Int(f"units_{i}", min_value=32, max_value=512, step=32),
activation=hp.Choice("activation", ["relu", "tanh"]),
)
)
if hp.Boolean("dropout"):
model.add(layers.Dropout(rate=0.25))
model.add(layers.Dense(10, activation="softmax"))
learning_rate = hp.Float("lr", min_value=1e-4, max_value=1e-2, sampling="log")
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=learning_rate),
loss="categorical_crossentropy",
metrics=["accuracy"],
)

return model

build_model(keras_tuner.HyperParameters())

定义调参

tuner = keras_tuner.RandomSearch(
hypermodel=build_model,
objective="val_accuracy",
max_trials=3,
executions_per_trial=2,
overwrite=True,
directory="my_dir",
project_name="helloworld",
)

#搜索空间摘要
tuner.search_space_summary()

(x, y), (x_test, y_test) = keras.datasets.mnist.load_data()

x_train = x[:-10000]
x_val = x[-10000:]
y_train = y[:-10000]
y_val = y[-10000:]

x_train = np.expand_dims(x_train, -1).astype("float32") / 255.0
x_val = np.expand_dims(x_val, -1).astype("float32") / 255.0
x_test = np.expand_dims(x_test, -1).astype("float32") / 255.0

num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_val = keras.utils.to_categorical(y_val, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

训练

tuner.search(x_train, y_train, epochs=2, validation_data=(x_val, y_val))

查询结果

models = tuner.get_best_models(num_models=2)
best_model = models[0]
best_model.summary()
best_model
使用keras_tuner优化