def drowPicture(History):
pyplot.plot(History.history['loss'])
pyplot.plot(History.history['val_loss'])
pyplot.plot(History.history['acc'])
pyplot.plot(History.history['val_acc'])
pyplot.title('model train vs validation loss')
pyplot.ylabel('loss')
pyplot.xlabel('epoch')
pyplot.legend(['train', 'validation'], loc='upper right')
pyplot.show()
def plot_results(xlog, ulog):
t = [0.25 * x for x in range(xlog.shape[1])]
plt.figure()
plt.subplot(311)
plt.plot(t, xlog[3, :], label="altitude", color="b")
plt.ylabel('altitude')
plt.subplot(312)
plt.plot(t, xlog[1, :], label="pitch angle", color="r")
plt.ylabel('pitch angle')
plt.subplot(313)
plt.plot(t, ulog[0, :], label="elevator angle", color="g")
plt.ylabel('elevator angle')
plt.show()
return X, y
# define model
model = Sequential()
model.add(LSTM(10, input_shape=(1, 1)))
# activation是激活函数的选择，linear是线性函数
model.add(Dense(1, activation='linear'))
# compile model， loss是损失函数的取值方式，mse是mean_squared_error，代表均方误差，
# optimizer是优化控制器的选择，AdamOptimizer通过使用动量（参数的移动平均数）来改善传统梯度下降
model.compile(loss='mse', optimizer='adam')
# fit model
X, y = get_train()
valX, valY = get_val()
# validation_data是要验证的测试集，shuffle代表是否混淆打乱数据
# 不合格的原因是epochs=100,训练周期不足
history = model.fit(X,
y,
epochs=100,
validation_data=(valX, valY),
shuffle=False)
# plot train and validation loss
# loss是训练集的损失函数值，val_loss是验证数据集的损失值
pyplot.plot(history.history['loss'])
pyplot.plot(history.history['val_loss'])
pyplot.title('model train vs validation loss')
pyplot.ylabel('loss')
pyplot.xlabel('epoch')
pyplot.legend(['train', 'validation'], loc='upper right')
pyplot.show()