python - 无法绘制 MAPE 和 MSE 的训练和测试值？

Question

我正在编写一个预测风速的代码。起初，我使用 print(history.history.keys()) 来打印 loss、val_loss、mape 和 val_mean_absolute_percentage_error 值，但是，它只显示 dict_keys(['loss', 'mape']).然后，由于它没有 val_loss 和 val_mean_absolute_percentage_error 值，因此它显示 KeyError: ‘val_mean_absolute_percentage_error’

你能帮我吗？

Dataset

这是我的代码:

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

# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)

# fix random seed for reproducibility
np.random.seed(7)

# load the dataset
dataframe = read_csv(‘OND_Q4.csv’, usecols=[7], engine=’python’, header=3) 
dataset = dataframe.values
print(dataframe.head)
dataset = dataset.astype(‘float32′) 

# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# split into train and test sets
train_size = int(len(dataset) * 0.7) # Use 70% of data to train
test_size = len(dataset) – train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# reshape input to be [samples, time steps, features]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))

#compile model
model.compile(loss=’mean_squared_error’, optimizer=’adam’,metrics=[‘mape’])
history=model.fit(trainX, trainY, epochs=5, batch_size=1, verbose=2)

# list all data in history
print(history.history.keys())
train_MAPE = history.history[‘mape’]
valid_MAPE = history.history[‘val_mean_absolute_percentage_error’]
train_MSE = history.history[‘loss’]
valid_MSE = history.history[‘val_loss’]

谢谢

Answer 1

您需要在model.fit()中定义验证集

您可以使用 validation_split=0.2 (在 0 和 1 之间 float 。用作验证数据的训练数据的分数。)

例如history=model.fit(trainX、trainY、epochs=5、validation_split=0.2、batch_size=1、verbose=2)

或者您可以使用 validation_data= (用于评估每个时期结束时的损失和任何模型指标的数据。模型不会根据此数据进行训练。validation_data 将覆盖validation_split。 validation_data 可以是: - Numpy 数组或张量的元组 (x_val, y_val) - Numpy 数组的元组 (x_val, y_val, val_sample_weights) - 数据集或数据集迭代器