在LSTM训练模型中使用n_jobs=-1 python代码

在LSTM训练模型中使用n_jobs=-1的Python代码如下：

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

# Load the data
data = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                 [2, 4, 6, 8, 10, 12, 14, 16, 18, 20],
                 [3, 6, 9, 12, 15, 18, 21, 24, 27, 30],
                 [4, 8, 12, 16, 20, 24, 28, 32, 36, 40]])

# Normalize the data
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# Split data into input and output
X, y = data[:, :-1], data[:, -1]

# Reshape input data into [samples, timesteps, features]
X = X.reshape((X.shape[0], X.shape[1], 1))

# Define the model
model = Sequential()
model.add(LSTM(50, input_shape=(X.shape[1], X.shape[2])))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam')

# Define the grid search parameters
param_grid = {'batch_size': [1, 2, 3], 'epochs': [50, 100, 150]}

# Create a grid search object
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)

# Fit the grid search object to the data
grid_result = grid.fit(X, y)

# Print the best parameters and score
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

在这个例子中，我们使用了GridSearchCV来搜索最佳的batch_size和epochs参数配置。我们将n_jobs参数设置为-1，以使用所有可用的CPU核心进行并行处理。这可以加快搜索过程的速度。