Keras LSTM准确度太高

我试图让一个LSTM在Keras工作，但即使在第一个时代之后，准确性似乎太高（90％）而且我担心没有正确训练，我从这篇文章中得到了一些想法：

https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/

这是我的代码：

import numpy
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.preprocessing.sequence import pad_sequences
from pandas import read_csv
import simplejson

numpy.random.seed(7)

dataset = read_csv("mydataset.csv", delimiter=",", quotechar='"').values

char_to_int = dict((c, i) for i, c in enumerate(dataset[:,1]))
int_to_char = dict((i, c) for i, c in enumerate(dataset[:,1]))

f = open('char_to_int_v2.txt', 'w')
simplejson.dump(char_to_int, f)
f.close()

f = open('int_to_char_v2.txt', 'w')
simplejson.dump(int_to_char, f)
f.close()

seq_length = 1

max_len = 5

dataX = []
dataY = []

for i in range(0, len(dataset) - seq_length, 1):
    start = numpy.random.randint(len(dataset)-2)
    end = numpy.random.randint(start, min(start+max_len,len(dataset)-1))
    sequence_in = dataset[start:end+1]
    sequence_out = dataset[end + 1]
    dataX.append([[char[0], char_to_int[char[1]], char[2]] for char in sequence_in])
    dataY.append([sequence_out[0], char_to_int[sequence_out[1]], sequence_out[2]])

X = pad_sequences(dataX, maxlen=max_len, dtype='float32')
X = numpy.reshape(X, (X.shape[0], max_len, 3))

y = numpy.reshape(dataY, (X.shape[0], 3))

batch_size = 1

model = Sequential()
model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

n_epoch = 1

for i in range(n_epoch):
    model.fit(X, y, epochs=1, batch_size=batch_size, verbose=1, shuffle=False)
    model.reset_states()

model.save_weights("weights.h5")
model.save('model.h5')
with open('model-params.json', 'w') as f:
    f.write(model.to_json())

scores = model.evaluate(X, y, verbose=0)
print("Model Accuracy: %.2f%%" % (scores[1]*100))

这是我的数据集的样子：

"time_date","name","user_id"
1402,"Sugar",3012
1402,"Milk",3012
1802,"Tomatoes",3012
1802,"Cucumber",3012
etc...

根据我的理解，我的dataX将具有（n_samples，5,3）的形状，因为我在我的序列的左边填充零，所以如果我将前3个结果构建为某些东西（第二列是基于char_to_int函数，所以我把一个随机数作为例子）：

[[0, 0, 0], [0, 0, 0], [0, 0, 0], [1402, 5323, 3012], [1402, 5324, 3012]]

我的数据Y将是：

[[1802, 3212, 3012]]

那是对的吗？如果是这样，那么其他东西肯定是错的，因为这是1个纪元后的输出：

9700/9700 [==============================] - 31s - loss: 10405.0951 - acc: 0.8544
Model Accuracy: 87.49%

我觉得我差不多有这个型号，但我遗漏了一些重要的东西，我不知道它是什么，我将不胜感激 . 谢谢 .