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026-天气预测.py
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026-天气预测.py
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"""
@file : 018-天气预测.py
@author : xiaolu
@time : 2019-06-17
"""
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.layers import Dense, Dropout
from keras.layers import Flatten, GRU, LSTM, TimeDistributed
def load_data(path):
with open(path, 'r', encoding='utf8') as f:
data = []
lines = f.readlines()
for line in lines:
temp = line.replace('\n', '').split(',')
data.append(temp)
float_data = np.zeros((len(data), len(data[0]) - 1))
for i, temp in enumerate(data[1:]):
for j, d in enumerate(temp[1:]):
float_data[i][j] = float(d)
return float_data
def plot_image(float_data):
# 画前九个属性的变化趋势
for i in range(1, 10):
plt.subplot("33{}".format(i))
plt.plot(float_data[:, i])
plt.show()
def generator_data(data, lookback, delay, min_index, max_index, shuffle=False, batch_size=128, step=6):
# 生成批数据
if max_index is None:
# 若没有指定max_index 这里将其赋值为最后索引减delay
max_index = len(data) - delay - 1
i = min_index + lookback
while True:
if shuffle:
# np.random.randint(a, b, size=c) 指的是在a到b索引之间随机取值 可能会取到重复值
rows = np.random.randint(min_index + lookback, max_index, size=batch_size)
else:
# 数据若不打乱
if i + batch_size >= max_index:
i = min_index + lookback
rows = np.arange(i, min(i+batch_size, max_index))
i += len(rows)
# 若是shuffle为True 从数据中随机选取batch个点， 这个点之前的lookback为观测数据，这个数据之后24小的数据为预测数据
samples = np.zeros((len(rows), lookback // step, data.shape[-1]))
targets = np.zeros((len(rows),))
for j, row in enumerate(rows):
indices = range(rows[j] - lookback, rows[j], step)
samples[j] = data[indices]
targets[j] = data[rows[j] + delay][1] # 取出温度那一列
yield samples, targets
def build_model1():
# 密集型模型
model = Sequential()
model.add(Flatten(input_shape=(lookback // step, float_data.shape[-1])))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=RMSprop(), loss='mae')
history = model.fit_generator(train_gen, steps_per_epoch=500, # steps_per_epoch指的是多少时间步算一个批次
epochs=20, validation_data=val_gen,
validation_steps=val_steps)
# 画损失
plt.figure(figsize=(20, 8))
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Vaildation loss')
plt.legend()
plt.show()
def build_model2():
# GRU网络
model = Sequential()
model.add(GRU(32, dropout=0.2, recurrent_dropout=0.2, input_shape=(None, float_data.shape[-1])))
model.add(Dense(1))
model.compile(optimizer=RMSprop(), loss='mae')
history = model.fit_generator(train_gen, steps_per_epoch=500, epochs=40,
validation_data=val_gen, validation_steps=val_steps)
# 画损失
plt.figure(figsize=(20, 8))
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Vaildation loss')
plt.legend()
plt.show()
def build_model3():
# 堆叠多层GRU
model = Sequential()
model.add(GRU(12, dropout=0.1, recurrent_dropout=0.2,
return_sequences=True, input_shape=(None, float_data.shape[-1])))
model.add(GRU(64, activation='relu', dropout=0.1, recurrent_dropout=0.3))
model.add(Dense(1))
model.compile(optimizer=RMSprop(), loss='mae')
history = model.fit_generator(train_gen, steps_per_epoch=500, epochs=40,
validation_data=val_gen, validation_steps=val_steps)
# 画损失
plt.figure(figsize=(20, 8))
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Vaildation loss')
plt.legend()
plt.show()
if __name__ == '__main__':
# 初始的数据是每隔10分钟测一次
path = './data/jena_climate_2009_2016.csv'
float_data = load_data(path)
# 画画几列数据的走向
# plot_image(float_data)
# 给定过去lookback多时间观测数据，预测未来delay步的数据 每隔step进行采样 知道10天预测11天
lookback = 1440 # 10天
step = 6 # 每个一小时采样依次
delay = 144 # 目标是接下来的24小时天气
batch_size = 128
# 数据标准化
mean = float_data[:200000].mean(axis=0)
float_data -= mean
std = float_data[:200000].std(axis=0)
float_data /= std
# 生成批数据
train_gen = generator_data(float_data, lookback=lookback,
delay=delay, min_index=0,
max_index=200000, shuffle=True,
step=step, batch_size=batch_size)
val_gen = generator_data(float_data, lookback=lookback,
delay=delay, min_index=200001,
max_index=300000, step=step,
batch_size=batch_size)
test_gen = generator_data(float_data, lookback=lookback,
delay=delay, min_index=300001,
max_index=None, step=step,
batch_size=batch_size)
val_steps = (300000 - 200001 - lookback) // batch_size
test_steps = (len(float_data) - 300001 - lookback) // batch_size
# 定义密集连接模型
# build_model1()
# 定义GRU
build_model2()
# 定义DGRU
# build_model3()