我目前正在使用神经网络训练我的数据，并使用fit函数。
history=model.fit(X, encoded_Y, batch_size=50, nb_epoch=500, validation_split = 0.2, verbose=1)
现在我已经将validation_split设为20%。我理解的是我的训练数据将占80%，测试数据将占20%。我不清楚后台是如何处理这些数据的。是像取前80%的样本用于训练，下面20%用于测试，还是随机从中间选择？如果我想提供单独的训练和测试数据，我该怎么用fit()实现？
此外，我的第二个问题是如何检查数据是否很好地适合于模型？我可以从结果中看出，训练准确率约为90%，而验证准确率约为55%。这是过拟合还是欠拟合的情况？
我的最后一个问题是evaluate返回什么？文档说它返回损失，但我在每个epoch（作为fit()中history的返回）中已经得到了损失和准确率。evaluate返回的准确率和分数是什么意思？如果evaluate返回的准确率为90％，那么我是否可以说我的数据很好地适合，而不考虑每个epoch的各自准确度和损失率？
以下是我的代码:
import numpy
import pandas
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from keras.utils import np_utils
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix
import itertools
seed = 7
numpy.random.seed(seed)
dataframe = pandas.read_csv("INPUTFILE.csv", skiprows=range(0, 0))
dataset = dataframe.values
X = dataset[:,0:50].astype(float) # number of cols-1
Y = dataset[:,50]
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
encoded_Y = np_utils.to_categorical(encoded_Y)
print("encoded_Y=", encoded_Y)
# baseline model
def create_baseline():
# create model
model = Sequential()
model.add(Dense(5, input_dim=5, kernel_initializer='normal', activation='relu'))
model.add(Dense(5, kernel_initializer='normal', activation='relu'))
#model.add(Dense(2, kernel_initializer='normal', activation='sigmoid'))
model.add(Dense(2, kernel_initializer='normal', activation='softmax'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # for binayr classification
#model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # for multi class
return model
model=create_baseline();
history=model.fit(X, encoded_Y, batch_size=50, nb_epoch=500, validation_split = 0.2, verbose=1)
print(history.history.keys())
# list all data in history
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
pre_cls=model.predict_classes(X)
cm1 = confusion_matrix(encoder.transform(Y),pre_cls)
print('Confusion Matrix : \n')
print(cm1)
score, acc = model.evaluate(X,encoded_Y)
print('Test score:', score)
print('Test accuracy:', acc)