网格搜索优化lstm超参数的python代码

以下是一个简单的网格搜索优化LSTM超参数的Python代码示例：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error

# 准备数据
X_train = np.random.rand(100, 10, 1)
y_train = np.random.rand(100, 1)

# 定义模型
def create_model(lstm_units=50, optimizer='adam'):
    model = Sequential()
    model.add(LSTM(lstm_units, input_shape=(10, 1)))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer=optimizer)
    return model

# 定义要搜索的参数
lstm_units = [50, 100, 150]
optimizer = ['adam', 'rmsprop']

# 创建模型
model = KerasRegressor(build_fn=create_model, verbose=0)

# 定义网格参数
param_grid = dict(lstm_units=lstm_units, optimizer=optimizer)

# 进行网格搜索
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)
grid_result = grid.fit(X_train, y_train)

# 输出最好的结果
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

# 输出所有结果
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, std, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, std, param))

以上代码使用了Keras和Scikit-learn库进行超参数优化，使用LSTM模型对一个随机生成的10维时间序列进行预测。在这个例子中，我们需要搜索2个超参数：LSTM单元数量和优化器类型。这个例子演示了如何使用网格搜索来找到最佳超参数组合。