pred = pred.reshape(-1, 85) ValueError: cannot reshape array of size 151200 into shape (85)
时间: 2023-06-10 07:03:30 浏览: 72
这个错误提示意味着你试图将一个大小为151200的数据矩阵转化为一个大小为85的矩阵,但是这两个数量级不兼容。如果你想要reshape数据矩阵并使其包含85个元素,你需要确保原始数据矩阵实际上是由85个元素构成的。可能需要重新检查你的数据或者代码中的bug。
相关问题
import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM import matplotlib.pyplot as plt # 读取CSV文件 data = pd.read_csv('77.csv', header=None) # 将数据集划分为训练集和测试集 train_size = int(len(data) * 0.7) train_data = data.iloc[:train_size, 1:2].values.reshape(-1,1) test_data = data.iloc[train_size:, 1:2].values.reshape(-1,1) # 对数据进行归一化处理 scaler = MinMaxScaler(feature_range=(0, 1)) train_data = scaler.fit_transform(train_data) test_data = scaler.transform(test_data) # 构建训练集和测试集 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back): X.append(dataset[i:(i+look_back), 0]) Y.append(dataset[i+look_back, 0]) return np.array(X), np.array(Y) look_back = 3 X_train, Y_train = create_dataset(train_data, look_back) X_test, Y_test = create_dataset(test_data, look_back) # 转换为LSTM所需的输入格式 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(optimizer='adam', loss='mean_squared_error') model.fit(X_train, Y_train, epochs=100, batch_size=32) # 预测测试集并进行反归一化处理 Y_pred = model.predict(X_test) Y_pred = scaler.inverse_transform(Y_pred) Y_test = scaler.inverse_transform(Y_test) # 输出RMSE指标 rmse = np.sqrt(np.mean((Y_pred - Y_test)**2)) print('RMSE:', rmse) # 绘制训练集真实值和预测值图表 train_predict = model.predict(X_train) train_predict = scaler.inverse_transform(train_predict) train_actual = scaler.inverse_transform(Y_train.reshape(-1, 1)) plt.plot(train_actual, label='Actual') plt.plot(train_predict, label='Predicted') plt.title('Training Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show() # 绘制测试集真实值和预测值图表 plt.plot(Y_test, label='Actual') plt.plot(Y_pred, label='Predicted') plt.title('Testing Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show()以上代码运行时报错,错误为ValueError: Expected 2D array, got 1D array instead: array=[-0.04967795 0.09031832 0.07590125]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.如何进行修改
可以将以下代码:
```
X_train, Y_train = create_dataset(train_data, look_back)
X_test, Y_test = create_dataset(test_data, look_back)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
```
修改为:
```
X_train, Y_train = create_dataset(train_data, look_back)
X_test, Y_test = create_dataset(test_data, look_back)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
Y_train = np.reshape(Y_train, (Y_train.shape[0], 1))
Y_test = np.reshape(Y_test, (Y_test.shape[0], 1))
```
这样就可以解决报错问题。
training model = CNNmodel() print(model) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) epochs = 50 model.train() start_time = time.time() for epoch in range(epochs): for seq, y_train in train_norm: # The gradient is zeroed and initialized before each parameter update optimizer.zero_grad() # reshape # convert to conv1d input size(batch size, channel, series length) y_pred = model(seq.reshape(1,1,-1)) loss = criterion(y_pred, y_train) loss.backward() optimizer.step() print(f'Epoch: {epoch+1:2} Loss: {loss.item():10.8f}') print(f'\nDuration: {time.time() - start_time:.0f} seconds')报错ValueError: not enough values to unpack (expected 2, got 1)
这个错误通常是由于train_norm中的元素不是一个tuple,而是一个单独的tensor引起的。可以在代码中添加一些断点来检查train_norm中每个元素的形状和类型是否正确。如果发现有问题,可以更改train_norm的格式以确保每个元素都是一个tuple,第一个元素是输入序列,第二个元素是目标输出。
另外,您可能需要使用enumerate函数来遍历train_norm中的元素并获取它们的索引,如下所示:
```python
for i, (seq, y_train) in enumerate(train_norm):
# The gradient is zeroed and initialized before each parameter update
optimizer.zero_grad()
# reshape
# convert to conv1d input size(batch size, channel, series length)
y_pred = model(seq.reshape(1,1,-1))
loss = criterion(y_pred, y_train)
loss.backward()
optimizer.step()
print(f'Epoch: {epoch+1:2} Batch: {i+1:3} Loss: {loss.item():10.8f}')
```
这样做可以更好地跟踪模型的训练进度,并且如果出现错误,也可以更轻松地找到问题所在。