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()
# 给出迭代次数,并且加入 Session执行
n_samples = xs.shape[0]
init = tf.global_variables_initializer()
with tf.Session() as sess:
# 首先初始化所有的变量
sess.run(init)
# 写入日记,使用tensorboard观察
writer = tf.summary.FileWriter('./graphs', sess.graph)
result_loss = []
# 训练模型
for i in range(50):
total_loss = 0
for x, y in zip(xs, ys):
# 通过feed_dic把数据灌进去
_, l = sess.run([optimizer, loss], feed_dict={X: x, Y: y})
total_loss += l
if i % 5 == 0:
print('Epoch {0}: {1}'.format(i, total_loss / n_samples))
# 关闭writer
writer.close()
w, b = sess.run([w, b])
print(w, b)
print("W:" + str(w[0]))
print("b:" + str(b[0]))
plt.plot(xs, ys, "bo", label="Real data")
plt.plot(xs, xs * w + b, 'r', label="Predicted data")
plt.legend()
plt.show()
# 将数据转换为3D输入，timesteps=3，3条数据预测1条 [samples, timesteps, features] train_X = train_X.reshape((train_X.shape[0], n_hours, n_features)) test_X = test_X.reshape((test_X.shape[0], n_hours, n_features)) print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) # 设计网络 model = Sequential() model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') # 拟合网络 history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False) # plot history pyplot.plot(history.history['loss'], label='train') pyplot.plot(history.history['val_loss'], label='test') pyplot.legend() pyplot.show() # 执行预测 yhat = model.predict(test_X) test_X = test_X.reshape((test_X.shape[0], n_hours*n_features)) # 将预测列据和后7列数据拼接，列数有要有 inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1) # 对拼接好的数据进行逆缩放 inv_yhat = scaler.inverse_transform(inv_yhat) inv_yhat = inv_yhat[:,0] test_y = test_y.reshape((len(test_y), 1)) # 将真实列据和后7列数据拼接，列数有要有 inv_y = concatenate((test_y, test_X[:, -7:]), axis=1) # 对拼接好的数据进行逆缩放
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()