Keras + LSTM 做回归demo 2

接上回, 这次做了一个多元回归

这里贴一下代码

import numpy as np
np.random.seed(1337)
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Activation
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import Dense
import pandas as pd

　　

datan = 1000
# 真实参数
ori_weights = [5, -4, 3, -2, 1]
colsn = len(ori_weights)
bias = -1
ori = np.zeros((1, colsn))
ori[0] = np.asarray(ori_weights)

# 列信息
cols_name = [chr(65+i) for i in range(colsn)]
X = np.zeros((colsn, datan))

for i in range(colsn):
    X[i] = np.random.normal(1, 0.1, datan)
# 真实Y
Y = np.matmul(ori, X) + bias + np.random.normal(-0.1, 0.1, (datan, ))

# 数据预览
df = pd.DataFrame(X.T, columns=cols_name)
df['Y'] = df.apply(lambda row: np.matmul(ori, [row[k] for k in df.columns] )[0]+bias, axis=1)
df['target'] = Y[0]
df.head()

　　

X_train, X_test, Y_train, Y_test = train_test_split(X.T, Y.T, test_size=0.33, random_state=42)

　　

neurons = 128
activation_function = 'tanh'
loss = 'mse'
optimizer="adam"
dropout = 0.01
batch_size = 12
epochs = 200

　　

model = Sequential()

model.add(LSTM(neurons, return_sequences=True, input_shape=(1, colsn), activation=activation_function))
model.add(Dropout(dropout))
model.add(LSTM(neurons, return_sequences=True, activation=activation_function))
model.add(Dropout(dropout))
model.add(LSTM(neurons, activation=activation_function))
model.add(Dropout(dropout))
model.add(Dense(output_dim=1, input_dim=1))

　　

model.compile(loss=loss, optimizer=optimizer)

　　

epochs = 2001
for step in range(epochs):
    cost = model.train_on_batch(X_train[:, np.newaxis], Y_train)
    if step % 30 == 0:
        print(f'{step} train cost: ', cost)

　　

# test
print('Testing ------------')
cost = model.evaluate(X_test[:, np.newaxis], Y_test, batch_size=40)
print('test cost:', cost)

　　

# plotting the prediction
Y_pred = model.predict(X_test[:, np.newaxis])
#
sdf = pd.DataFrame({'test':list(Y_test.T[0]), 'pred':list(Y_pred.T[0])})
sdf.sort_values(by='test', inplace=True)
#
plt.scatter(range(len(Y_test)), list(sdf.test))
plt.plot(range(len(Y_test)), list(sdf.pred), 'r--')
plt.show()