python tensorflow

数据

Python代码

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense, Activation

def load_data(file_name, sequence_length=10, split=0.883):
    df = pd.read_csv(file_name, sep=',')
    datedata = pd.DataFrame(df['date'])
    #timedata = pd.DataFrame(df['time'])
    uvdata = pd.DataFrame(df['uv'])
    
    # 处理uv,float -> scaler
    uvdata_arr = np.array(uvdata).astype(float)
    #timedata_arr = np.array(timedata).astype(int)
    scaler = MinMaxScaler()
    uvdata_scaler = scaler.fit_transform(uvdata_arr)
    
    uv_tmp = []
    for i in range(len(uvdata_scaler) - sequence_length):
        uv_tmp.append(uvdata_scaler[i: i + sequence_length + 1])

    reshaped_uvdata = np.array(uv_tmp).astype('float64')
    #np.random.shuffle(reshaped_uvdata)  # 随机打乱顺序

    x = reshaped_uvdata[:, :-1] # 1-10
    y = reshaped_uvdata[:, -1]  # 11
    time = datedata[sequence_length:] # 11对应的日期，用于画图
    # 切分数据
    split_boundary = int(reshaped_uvdata.shape[0] * split)
    train_x = x[: split_boundary]
    test_x = x[split_boundary:]
    train_y = y[: split_boundary]
    test_y = y[split_boundary:]
    test_time = time[split_boundary:]
    
    return  df, x, y, train_x, train_y, test_x, test_y, test_time, scaler

def build_model():
    # input_dim是输入的train_x的最后一个维度，train_x的维度为(n_samples, time_steps, input_dim)
    model = Sequential()
    model.add(LSTM(input_dim=1, output_dim=50, return_sequences=True))
    print(model.layers)
    model.add(LSTM(100, return_sequences=False))
    model.add(Dense(output_dim=1))
    model.add(Activation('linear'))

    model.compile(loss='mse', optimizer='rmsprop')
    return model

def train_model(train_x, train_y, test_x, test_y):
    model = build_model()

    try:
        model.fit(train_x, train_y, batch_size=256, nb_epoch=300, validation_split=0.1)
        predict = model.predict(test_x)
        predict = np.reshape(predict, (predict.size, ))
    except KeyboardInterrupt:
        print(predict)
        print(test_y)
    #print(predict)
    #print(test_y)
    try:
        fig = plt.figure(1)
        plt.figure(figsize=(20, 10))
        plt.plot(predict, 'r:')
        plt.plot(test_y, 'g-')
        plt.xlabel('date')
        plt.ylabel('uv_scaler')
        plt.legend(['predict', 'true'])
    except Exception as e:
        print(e)
    return predict, test_y

if __name__ == '__main__':
    df, x, y, train_x, train_y, test_x, test_y, test_time, scaler = load_data('data/uvdata18192.csv')
    train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 1))
    test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 1))
    predict_y, test_y = train_model(train_x, train_y, test_x, test_y)
    predict_y = scaler.inverse_transform([[i] for i in predict_y])
    test_y = scaler.inverse_transform(test_y)
    
    n = len(test_time)
    x = range(1,n+1,4)
    
    fig = plt.figure(2)
    plt.figure(figsize=(20, 10))
    plt.plot(predict_y, 'r:')
    plt.plot(test_y, 'g-')
    plt.xticks(x,test_time['date'][::4],rotation=60)
    plt.xlabel('date')
    plt.ylabel('uv')
    plt.legend(['predict', 'true'])

Train on 1135 samples, validate on 127 samples
Epoch 1/300
1135/1135 [==============================] - 19s 17ms/step - loss: 0.0209 - val_loss: 0.0311
Epoch 2/300
1135/1135 [==============================] - 0s 371us/step - loss: 0.0144 - val_loss: 0.0297
Epoch 3/300
1135/1135 [==============================] - 0s 373us/step - loss: 0.0135 - val_loss: 0.0292
Epoch 4/300
1135/1135 [==============================] - 0s 376us/step - loss: 0.0133 - val_loss: 0.0252
Epoch 5/300
1135/1135 [==============================] - 0s 381us/step - loss: 0.0115 - val_loss: 0.0226
······
Epoch 300/300
1135/1135 [==============================] - 0s 396us/step - loss: 0.0034 - val_loss: 0.0344

预测结果

image.png