https://yaju3d.hatenablog.jp/entry/2019/12/30/202503

代码

# tensorflow_version 2.0
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import math
import random

steps_per_cycle = 80
number_of_cycles = 50

# 在“t”列存放1、2、3……50*80+1
df = pd.DataFrame(np.arange(steps_per_cycle * number_of_cycles + 1), columns=["t"])
# 在“sin_t”列存放sin(t+噪声)
df["sin_t"] = df.t.apply(lambda x: math.sin(x * (2 * math.pi / steps_per_cycle)+ random.uniform(-1.0, +1.0) * 0.05))
# 以“t”为横轴，“sin_t”为纵轴绘图
# df[["sin_t"]].head(steps_per_cycle * 2).plot()
# plt.show()

def load_data(data, n_prev = 100): 
	docX, docY = [], []
	for i in range(len(data)-n_prev):
		docX.append(data.iloc[i:i+n_prev].values)
		docY.append(data.iloc[i+n_prev].values)
	alsX = np.array(docX)
	alsY = np.array(docY)
	return alsX, alsY

# 划分出90%的数据用于训练
def train_test_split(df, test_size = 0.1, n_prev = 100):
	ntrn = round(len(df) * (1 - test_size))
	ntrn = int(ntrn)
	X_train, y_train = load_data(df.iloc[0:ntrn], n_prev)
	X_test, y_test = load_data(df.iloc[ntrn:], n_prev)
	return (X_train, y_train), (X_test, y_test)

length_of_sequences = 100
(X_train, y_train), (X_test, y_test) = train_test_split(df[["sin_t"]], n_prev = length_of_sequences)

# 建立神经网络
in_out_neurons = 1
hidden_neurons = 300

model = Sequential()  
model.add(tf.keras.layers.LSTM(hidden_neurons, batch_input_shape=(None, length_of_sequences, in_out_neurons), return_sequences=False))  
model.add(Dense(in_out_neurons))  
model.add(Activation("linear"))  
model.compile(loss="mean_squared_error", optimizer="rmsprop")
model.fit(X_train, y_train, batch_size=600, epochs=15, validation_split=0.05)

# 预测
predicted = model.predict(X_test)

dataf = pd.DataFrame(predicted[:200])
dataf.columns = ["predict"]
dataf["input"] = y_test[:200]
dataf.plot(figsize=(15, 5))
plt.show()

运行