导入数据生成二层神经网络参数调优超参数优化并输出accuracy的代码TensorFlow

首先，感谢您的提问。以下是您所需的代码：

import tensorflow as tf
import numpy as np

# 导入数据
x_train = ...
y_train = ...
x_test = ...
y_test = ...

# 定义模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 参数调优
grid_search = tf.keras.wrappers.scikit_learn.KerasClassifier(build_fn=model, verbose=0)
batch_size = [16, 32, 64]
epochs = [10, 20, 30]
param_grid = dict(batch_size=batch_size, epochs=epochs)
grid = GridSearchCV(estimator=grid_search, param_grid=param_grid, n_jobs=-1, cv=3)
grid_result = grid.fit(x_train, y_train)

# 输出accuracy
print(grid_result.best_score_)

希望这段代码能够满足您的需求。如果您有任何问题，请随时联系我。