报错：ValueError: cannot reshape array of size 10 into shape (2,6,1)

非常抱歉，我之前的回答有误导。根据您的数据形状，模型的输入应该是 `(2, 5, 1)` 而不是 `(2, 6, 1)`。请将代码中的 `X_train.reshape(2, 5, 1)` 和 `y_train.reshape(2, 5, 1)` 改为 `X_train.reshape(2, 3, 1)` 和 `y_train.reshape(2, 3, 1)`。

以下是修改后的代码：

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU, Dense
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import matplotlib.pyplot as plt

# 输入数据
data = np.array([[1, 4, 6, 7, 9, 13],
                 [4, 7, 5, 8, 19, 26],
                 [1, 5, 7, 245, 145, 11]])

# 将数据分为输入和输出序列
X_train = data[:2, :-1]
y_train = data[:2, 1:]
X_val = data[2:3, :-1]
y_val = data[2:3, 1:]

# 构建GRU模型
model = Sequential()
model.add(GRU(32, input_shape=(3, 1)))  # 输入序列长度为3
model.add(Dense(3))

# 编译并训练模型
model.compile(optimizer='adam', loss='mse')
model.fit(X_train.reshape(2, 3, 1), y_train.reshape(2, 3, 1), epochs=100)

# 预测验证集数据
predictions = model.predict(X_val.reshape(1, 3, 1))

# 计算指标
mse = mean_squared_error(y_val.reshape(-1), predictions.reshape(-1))
mae = mean_absolute_error(y_val.reshape(-1), predictions.reshape(-1))
rmse = np.sqrt(mse)
r2 = r2_score(y_val.reshape(-1), predictions.reshape(-1))

# 绘制验证集和预测值
plt.plot(np.arange(2, 8), y_val.reshape(-1), label='Validation')
plt.plot(np.arange(3, 9), predictions.reshape(-1), label='Prediction')
plt.xlabel('Time Step')
plt.ylabel('Value')
plt.legend()
plt.show()

print("MSE:", mse)
print("MAE:", mae)
print("RMSE:", rmse)
print("R2:", r2)

再次非常抱歉给您带来的困扰，希望这次能够顺利运行。如果还有其他问题，请随时提问。