深度学习框架Tensorflow2系列

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这个系列主要和大家分享深度学习框架Tensorflow2的各种api，从基础开始。
#博学谷IT学习技术支持#

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前言

通过时间序列预测任务实战案例，学习Tensorflow2中一些API

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一、时间序列预测任务实战

数据集：气温数据，多种指标
任务目标：预测未来某一时间点的气温/未来某一时间片段的气温

二、数据集介绍

import warnings
warnings.filterwarnings("ignore")
# from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import os
import pandas as pd
mpl.rcParams['figure.figsize'] = (8, 6)
mpl.rcParams['axes.grid'] = False

df = pd.read_csv('jena_climate_2009_2016.csv')
df.head()

三、实战代码

1.单特征构建序列数据

def univariate_data(dataset, start_index, end_index, history_size, target_size):
    data = []
    labels = []

    start_index = start_index + history_size
    if end_index is None:
        end_index = len(dataset) - target_size

    for i in range(start_index, end_index):
        indices = range(i-history_size, i)
        # Reshape data from (history_size,) to (history_size, 1)
        data.append(np.reshape(dataset[indices], (history_size, 1)))
        labels.append(dataset[i+target_size])
    return np.array(data), np.array(labels)
TRAIN_SPLIT = 300000
tf.random.set_seed(13)
# 只选一个温度特征
uni_data = df['T (degC)']
uni_data.index = df['Date Time']
uni_data = uni_data.values
# 数据标准化
uni_train_mean = uni_data[:TRAIN_SPLIT].mean()
uni_train_std = uni_data[:TRAIN_SPLIT].std()
uni_data = (uni_data-uni_train_mean)/uni_train_std
univariate_past_history = 20
univariate_future_target = 0

x_train_uni, y_train_uni = univariate_data(uni_data, 0, TRAIN_SPLIT,
                                           univariate_past_history,
                                           univariate_future_target)
x_val_uni, y_val_uni = univariate_data(uni_data, TRAIN_SPLIT, None,
                                       univariate_past_history,
                                       univariate_future_target)
print ('Single window of past history')
print (x_train_uni[0])
print ('\n Target temperature to predict')
print (y_train_uni[0])

只用’T (degC)'的前20个数据，预测第21的值。

把数据画图展示一下

def create_time_steps(length):
    time_steps = []
    for i in range(-length, 0, 1):
        time_steps.append(i)
    return time_steps

def show_plot(plot_data, delta, title):
    labels = ['History', 'True Future', 'Model Prediction']
    marker = ['.-', 'rx', 'go']
    time_steps = create_time_steps(plot_data[0].shape[0])
    if delta:
        future = delta
    else:
        future = 0

    plt.title(title)
    for i, x in enumerate(plot_data):
        if i:
            plt.plot(future, plot_data[i], marker[i], markersize=10,label=labels[i])
        else:
            plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i])
    plt.legend()
    plt.xlim([time_steps[0], (future+5)*2])
    plt.xlabel('Time-Step')
    return plt
show_plot([x_train_uni[0], y_train_uni[0]], 0, 'Sample Example')

2.构建LSTM模型进行预测

BATCH_SIZE = 256
BUFFER_SIZE = 10000

train_univariate = tf.data.Dataset.from_tensor_slices((x_train_uni, y_train_uni))
train_univariate = train_univariate.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

val_univariate = tf.data.Dataset.from_tensor_slices((x_val_uni, y_val_uni))
val_univariate = val_univariate.batch(BATCH_SIZE)

simple_lstm_model = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(8, input_shape=x_train_uni.shape[-2:]),
    tf.keras.layers.Dense(1)
])

simple_lstm_model.compile(optimizer='adam', loss='mae')
EPOCHS = 10
simple_lstm_model.fit(train_univariate, epochs=EPOCHS,
                      validation_data=val_univariate, validation_steps=50)

3.用训练好的模型预测结果

for x, y in val_univariate.take(1):
    plot = show_plot([x[0].numpy(), y[0].numpy(),
                    simple_lstm_model.predict(x)[0]], 0, 'Simple LSTM model')
    plot.show()

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总结

通过时间序列预测任务实战案例，学习Tensorflow2中一些API