Question

由于我的示例代码如下所示，我正在使用LSTM神经网络使用找到的in this link的CSV格式数据集开发预测模型。

import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import math
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error


np.random.seed(7)

# Load data
#df = pd.read_csv('test32_C_data.csv')
df = pd.DataFrame(np.random.randint(0,100, size=(100,3)), columns = ['time', 'X', 'Y'])
n_features = 100


def create_sequences(data, window=15, step=1, prediction_distance=15):
    x = []
    y = []

    for i in range(0, len(data) - window - prediction_distance, step):
        x.append(data[i:i + window])
        y.append(data[i + window + prediction_distance][1])

    x, y = np.asarray(x), np.asarray(y)

    return x, y


# Scaling prior to splitting
scaler_x = MinMaxScaler(feature_range=(0.01, 0.99))
scaler_y = MinMaxScaler(feature_range=(0.01, 0.99))

scaled_x = scaler_x.fit_transform(df.loc[:, "X"].reshape([-1,1]))
scaled_y = scaler_y.fit_transform(df.loc[:, "Y"].reshape([-1,1]))

scaled_data = np.column_stack((scaled_x, scaled_y))

# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)

test_len = int(len(x_sequence) * 0.90)
valid_len = int(len(x_sequence) * 0.90)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]

# Initialising the RNN
model = Sequential()

# Adding the input layerand the LSTM layer
model.add(LSTM(15, input_shape=(15, 2)))

# Adding the output layer
model.add(Dense(1))

# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')

# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)

# Getting the predicted values
y_pred = model.predict(x_test)

# invert the predictions
y_pred = scaler_y.inverse_transform(y_pred)
y_test = scaler_y.inverse_transform(y_test)

最后，我想从我的预测模型中计算出均方根误差（RMSE），如下所示

rmse_out = math.sqrt(mean_squared_error(y_test[0], y_pred[:,0]))

但是，它抛出了这个错误：TypeError: Singleton array 225.0 cannot be considered a valid collection.我们如何修复此错误？

Answer 1

删除数组上的切片，因为您获取整个数组的mse。请参阅此处的文档：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html

old: rmse_out = math.sqrt(mean_squared_error(y_test[0], y_pred[:,0]))

new: rmse_out = math.sqrt(mean_squared_error(y_test, y_pred))

Python：TypeError - Singleton Array

1 个答案: