I’m working on a code that predicts the wind speed. At first, I used print(history.history.keys()) in order to print loss, val_loss, mape and val_mean_absolute_percentage_error values, but, it only displays dict_keys(['loss', 'mape']). Then, since it doesn't have val_loss and val_mean_absolute_percentage_error values, it displays a KeyError: ‘val_mean_absolute_percentage_error’
Can you help me?
Here is my code:
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’]
Thank you
You need to define a validation set in model.fit()
You can do it with validation_split=0.2 (Float between 0 and 1. Fraction of the training data to be used as validation data.)
E.g. history=model.fit(trainX, trainY, epochs=5, validation_split=0.2, batch_size=1, verbose=2)
Or you can use validation_data= (Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. validation_data could be: - tuple (x_val, y_val) of Numpy arrays or tensors - tuple (x_val, y_val, val_sample_weights) of Numpy arrays - dataset or a dataset iterator