【发布时间】：2020-02-23 17:58:51
【问题描述】：
我正在尝试使用 LSTM 通过每 62 个点创建一个单位脉冲信号来捕获长期依赖关系。
想法是返回62个时间步并复制下一个时间步的值，以便预测脉冲，但lstm没有这样做......
import sys
import os
import numpy as np
import math
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from keras.models import Sequential, load_model
from keras.layers import LSTM, Dense, Flatten, Dropout
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras import backend as K
import tensorflow as tf
from tensorflow.python.client import device_lib
K.clear_session() #pulire eventuali sessioni precedenti (cosi i nomi dei layer ripartono da 0)
print(K.tensorflow_backend._get_available_gpus())
print(device_lib.list_local_devices())
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
config = tf.ConfigProto( device_count = {'GPU': 1 , 'CPU': 4} )
sess = tf.Session(config=config)
K.set_session(sess)
# hyper-parametri
params = {
"batch_size": 20,
"epochs": 1000,
"time_steps": 70,
}
OUTPUT_PATH = "/home/..."
TIME_STEPS = params["time_steps"]
BATCH_SIZE = params["batch_size"]
def generate_impulse(dim):
arr = np.zeros(dim)
frequency = 62
for i in range(0, len(arr)):
if i % frequency == 0:
arr[i] = 1
return arr
y = generate_impulse(1300)
plt.figure(figsize=(20,5))
plt.plot(y)
plt.title('unit impulse')
plt.ylabel('y')
plt.xlabel('x')
plt.show()
def create_timeseries(arr):
# Costruzione time series univariata, predict di un single-step.
# Prende i primi TIME_STEPS valori come input e calcola il sin del valore TIME_STEPS+1
dim_0 = len(arr) - TIME_STEPS
x = np.zeros((dim_0, TIME_STEPS))
y = np.zeros((dim_0,))
for i in range(dim_0):
x[i] = arr[i:TIME_STEPS+i] #TIME_STEPS+i non compreso
y[i] = arr[TIME_STEPS+i]
#print(x[i], y[i])
print("length of time-series i/o",x.shape,y.shape)
return x, y
x_ts, y_ts = create_timeseries(y)
len_train = int(len(x_ts)*80/100)
len_val = int(len(x_ts)*10/100)
#DATASET DI TRAINING: 80%
x_train = x_ts[0:len_train]
y_train = y_ts[0:len_train]
#DATASET DI VALIDATION: 10%
x_val = x_ts[len_train:len_train+len_val]
y_val = y_ts[len_train:len_train+len_val]
#DATASET DI TEST 10%
x_test = x_ts[len_train+len_val:]
y_test = y_ts[len_train+len_val:]
x_train =x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_val =x_val.reshape((x_val.shape[0], x_val.shape[1], 1))
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1)
def create_model():
model = Sequential()
model.add(LSTM(1, input_shape=(TIME_STEPS, 1)))
model.compile(optimizer='adam', loss='mse')
return model
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,
patience=50, min_delta=0.0001)
model = create_model()
history = model.fit(x_train, y_train, epochs=params["epochs"], verbose=2, batch_size=BATCH_SIZE, shuffle=False,
validation_data=(x_val, y_val), callbacks=[es])
plt.figure()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('MSE LOSS')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
y_pred = model.predict(x_test, batch_size=BATCH_SIZE)
y_pred = y_pred.flatten()
error = mean_squared_error(y_test, y_pred)
plt.figure(figsize=(20,5))
plt.plot(y_pred)
plt.plot(y_test)
plt.title('PREDICTION ON TEST SET')
plt.ylabel('sin(x)')
plt.xlabel('x')
plt.legend(['Prediction', 'Real'], loc='upper left')
plt.show()
训练集给了我相同的结果（这是相同的信号..）。我尝试了其他具有更多神经元的 LSTM 模型，但它无论如何都不起作用。
【问题讨论】：
标签： machine-learning keras neural-network time-series lstm